{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Preamble" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "libraries = c(\"dplyr\",\"magrittr\",\"tidyr\",\"ggplot2\",\"readxl\",\"glue\",\n", " \"grid\",\"gridExtra\",\"zoo\",\"RColorBrewer\",\"scales\",\"gganimate\",\n", " \"kableExtra\",\"formattable\",\"IRdisplay\")\n", "for(x in libraries) { library(x,character.only=TRUE,warn.conflicts=FALSE) }\n", "\n", "# to show the plots as svg-graphics in Jupyter\n", "# options(jupyter.plot_mimetypes = \"image/svg+xml\")\n", "options(jupyter.plot_mimetypes = \"image/png\") \n", "\n", "if (Sys.info()[['sysname']]=='Windows') {\n", " windowsFonts(Times = windowsFont(\"Times New Roman\"))\n", " theme_set(theme_bw(base_size=11,base_family='Times')) \n", "} else { theme_set(theme_bw(base_size=11)) }\n", "\n", "'%&%' = function(x,y)paste0(x,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (Fig 1) Epicurve" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
onsetconfirmedprefecture
2018-04-292018-05-12Aichi
2018-05-092018-05-12Aichi
2018-05-122018-05-13Aichi
2018-05-122018-05-18Aichi
2018-05-062018-05-08Tokyo
2018-04-192018-05-02Kanagawa
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " onset & confirmed & prefecture\\\\\n", "\\hline\n", "\t 2018-04-29 & 2018-05-12 & Aichi \\\\\n", "\t 2018-05-09 & 2018-05-12 & Aichi \\\\\n", "\t 2018-05-12 & 2018-05-13 & Aichi \\\\\n", "\t 2018-05-12 & 2018-05-18 & Aichi \\\\\n", "\t 2018-05-06 & 2018-05-08 & Tokyo \\\\\n", "\t 2018-04-19 & 2018-05-02 & Kanagawa \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "onset | confirmed | prefecture | \n", "|---|---|---|---|---|---|\n", "| 2018-04-29 | 2018-05-12 | Aichi | \n", "| 2018-05-09 | 2018-05-12 | Aichi | \n", "| 2018-05-12 | 2018-05-13 | Aichi | \n", "| 2018-05-12 | 2018-05-18 | Aichi | \n", "| 2018-05-06 | 2018-05-08 | Tokyo | \n", "| 2018-04-19 | 2018-05-02 | Kanagawa | \n", "\n", "\n" ], "text/plain": [ " onset confirmed prefecture\n", "1 2018-04-29 2018-05-12 Aichi \n", "2 2018-05-09 2018-05-12 Aichi \n", "3 2018-05-12 2018-05-13 Aichi \n", "4 2018-05-12 2018-05-18 Aichi \n", "5 2018-05-06 2018-05-08 Tokyo \n", "6 2018-04-19 2018-05-02 Kanagawa " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filename = \"data.xlsx\"\n", "\n", "read_excel(filename, sheet = \"Okinawa\", range = cell_cols(\"G:H\"), col_types = rep(\"date\",2), col_names = TRUE) -> df_epicurve\n", "colnames(df_epicurve) = c(\"onset\",\"confirmed\")\n", "df_epicurve %<>% mutate(prefecture=\"Okinawa\")\n", "\n", "read_excel(filename, sheet = \"Aichi\", range = cell_cols(\"G:H\"), col_types = rep(\"date\",2), col_names = TRUE) -> df_epicurve_\n", "colnames(df_epicurve_) = c(\"onset\",\"confirmed\")\n", "df_epicurve_ %<>% mutate(prefecture=\"Aichi\")\n", "df_epicurve %<>% rbind(df_epicurve_)\n", "\n", "read_excel(filename, sheet = \"Tokyo\", range = cell_cols(\"G:H\"), col_types = rep(\"date\",2), col_names = TRUE) -> df_epicurve_\n", "colnames(df_epicurve_) = c(\"onset\",\"confirmed\")\n", "df_epicurve_ %<>% mutate(prefecture=\"Tokyo\")\n", "df_epicurve %<>% rbind(df_epicurve_)\n", "\n", "read_excel(filename, sheet = \"Kanagawa\", range = cell_cols(\"G:H\"), col_types = rep(\"date\",2), col_names = TRUE) -> df_epicurve_\n", "colnames(df_epicurve_) = c(\"onset\",\"confirmed\")\n", "df_epicurve_ %<>% mutate(prefecture=\"Kanagawa\")\n", "df_epicurve %<>% rbind(df_epicurve_)\n", "\n", "df_epicurve %<>% \n", " mutate(onset=as.Date(onset), confirmed=as.Date(confirmed))\n", "\n", "df_epicurve %>% tail" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
dateAichiKanagawaOkinawaTokyo
2018-03-140 0 0 0
2018-03-150 0 0 0
2018-03-160 0 0 0
2018-03-170 0 0 0
2018-03-180 0 0 0
2018-03-190 0 0 0
2018-03-200 0 1 0
2018-03-210 0 0 0
2018-03-220 0 0 0
2018-03-230 0 0 0
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " date & Aichi & Kanagawa & Okinawa & Tokyo\\\\\n", "\\hline\n", "\t 2018-03-14 & 0 & 0 & 0 & 0 \\\\\n", "\t 2018-03-15 & 0 & 0 & 0 & 0 \\\\\n", "\t 2018-03-16 & 0 & 0 & 0 & 0 \\\\\n", "\t 2018-03-17 & 0 & 0 & 0 & 0 \\\\\n", "\t 2018-03-18 & 0 & 0 & 0 & 0 \\\\\n", "\t 2018-03-19 & 0 & 0 & 0 & 0 \\\\\n", "\t 2018-03-20 & 0 & 0 & 1 & 0 \\\\\n", "\t 2018-03-21 & 0 & 0 & 0 & 0 \\\\\n", "\t 2018-03-22 & 0 & 0 & 0 & 0 \\\\\n", "\t 2018-03-23 & 0 & 0 & 0 & 0 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "date | Aichi | Kanagawa | Okinawa | Tokyo | \n", "|---|---|---|---|---|---|---|---|---|---|\n", "| 2018-03-14 | 0 | 0 | 0 | 0 | \n", "| 2018-03-15 | 0 | 0 | 0 | 0 | \n", "| 2018-03-16 | 0 | 0 | 0 | 0 | \n", "| 2018-03-17 | 0 | 0 | 0 | 0 | \n", "| 2018-03-18 | 0 | 0 | 0 | 0 | \n", "| 2018-03-19 | 0 | 0 | 0 | 0 | \n", "| 2018-03-20 | 0 | 0 | 1 | 0 | \n", "| 2018-03-21 | 0 | 0 | 0 | 0 | \n", "| 2018-03-22 | 0 | 0 | 0 | 0 | \n", "| 2018-03-23 | 0 | 0 | 0 | 0 | \n", "\n", "\n" ], "text/plain": [ " date Aichi Kanagawa Okinawa Tokyo\n", "1 2018-03-14 0 0 0 0 \n", "2 2018-03-15 0 0 0 0 \n", "3 2018-03-16 0 0 0 0 \n", "4 2018-03-17 0 0 0 0 \n", "5 2018-03-18 0 0 0 0 \n", "6 2018-03-19 0 0 0 0 \n", "7 2018-03-20 0 0 1 0 \n", "8 2018-03-21 0 0 0 0 \n", "9 2018-03-22 0 0 0 0 \n", "10 2018-03-23 0 0 0 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Df_epicurve = data.frame(date=seq(as.Date('2018-03-14'),as.Date('2018-05-27'),by='1 day'))\n", "\n", "df_epicurve %>% \n", " group_by(prefecture,onset) %>%\n", " count %>%\n", " rename(i=n,date=onset) %>%\n", " ungroup %>%\n", " mutate(date=as.Date(date)) %>%\n", " spread(prefecture,i) %>%\n", " right_join(Df_epicurve,by=\"date\") %>%\n", " mutate_if(is.numeric, funs(ifelse(is.na(.), 0, .))) -> df_onset\n", "\n", "df_epicurve %>% \n", " group_by(prefecture,confirmed) %>%\n", " count %>%\n", " rename(i=n,date=confirmed) %>%\n", " ungroup %>%\n", " mutate(date=as.Date(date)) %>%\n", " spread(prefecture,i) %>%\n", " right_join(Df_epicurve,by=\"date\") %>%\n", " mutate_if(is.numeric, funs(ifelse(is.na(.), 0, .))) -> df_confirmed\n", "\n", "df_confirmed %>% head(10)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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jA2LynzBurt7X3huRf7vH3mmcd8x623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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width=7,repr.plot.height=7)\n", "\n", "cols <- c(\"Okinawa\" = \"#377eb8\", \"Aichi\" = \"#e41a1c\", \"Kanagawa\" = \"#4daf4a\", \"Tokyo\" = \"#984ea3\")\n", "\n", "df_confirmed %>%\n", " gather(Prefecture,c,-date) %>%\n", " mutate(Prefecture=factor(Prefecture, levels=c(\"Okinawa\", \"Aichi\", \"Kanagawa\", \"Tokyo\") %>% rev)) %>%\n", " filter(c>0) %>%\n", " ggplot(aes(date,c,fill=Prefecture)) +\n", " geom_bar(stat = \"identity\",color=\"white\",size=.25) +\n", " scale_fill_manual(values = cols) +\n", " coord_cartesian(xlim=c(as.Date(\"2018-03-14\"),as.Date(\"2018-05-18\"))) +\n", " scale_x_date(labels = date_format(\"%d-%b\")) +\n", " labs(x=\"Date of laboratory confirmation (day-month)\",y=\"Number of cases\") +\n", " guides(fill = guide_legend(reverse=T)) +\n", " theme(legend.key.size = unit(.5, \"cm\"),\n", " axis.text.x = element_text(angle = 45, hjust = .5, vjust=0.5),\n", " strip.text.x = element_blank(),\n", " panel.grid.minor.x = element_blank(),\n", " plot.title = element_text(size=11, hjust = 0.5, face=\"bold\"),\n", " strip.background = element_rect(colour=\"white\", fill=\"white\"),\n", " plot.margin = unit(c(.5,1,1,.5),\"lines\")) -> p_confirmed\n", "\n", "p_confirmed = arrangeGrob(p_confirmed, top = textGrob(\"B\", x = unit(0, \"npc\"), y = unit(.5, \"npc\"), just=c(\"left\",\"top\"),\n", " gp=gpar(col=\"black\", fontsize=16, fontface=\"bold\", fontfamily=\"Times\")))\n", "\n", "df_onset %>%\n", " gather(Prefecture,i,-date) %>%\n", " mutate(Prefecture=factor(Prefecture, levels=c(\"Okinawa\", \"Aichi\", \"Kanagawa\", \"Tokyo\")%>% rev)) %>%\n", " filter(i>0) %>%\n", " ggplot(aes(date,i,fill=Prefecture)) +\n", " geom_bar(stat = \"identity\",color=\"white\",size=.25) +\n", " scale_fill_manual(values = cols) +\n", " coord_cartesian(xlim=c(as.Date(\"2018-03-14\"),as.Date(\"2018-05-18\"))) +\n", " scale_x_date(labels = date_format(\"%d-%b\")) +\n", " labs(x=\"Date of illness onset (day-month)\",y=\"Number of cases\") +\n", " guides(fill = guide_legend(reverse=T)) +\n", " theme(legend.key.size = unit(.5, \"cm\"),\n", " axis.text.x = element_text(angle = 45, hjust = .5, vjust=0.5),\n", " strip.text.x = element_blank(),\n", " panel.grid.minor.x = element_blank(),\n", " plot.title = element_text(size=11, hjust = 0.5, face=\"bold\"),\n", " strip.background = element_rect(colour=\"white\", fill=\"white\"),\n", " plot.margin = unit(c(1,1,.5,.5),\"lines\")) -> p_onset\n", "\n", "p_onset = arrangeGrob(p_onset, top = textGrob(\"A\", x = unit(0, \"npc\"), y = unit(.25, \"npc\"), just=c(\"left\",\"top\"),\n", " gp=gpar(col=\"black\", fontsize=16, fontface=\"bold\", fontfamily=\"Times\")))\n", "\n", "grid.arrange(p_onset, p_confirmed, widths=c(1), heights=c(1,1), nrow=2, ncol=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Delay function is assumed to be constant \n", "\n", "For exploration of the case of a time-varying delay, please see another accompanying notebook. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
onsetconfirmed
2018-03-142018-03-20
2018-03-272018-03-29
2018-03-272018-03-29
2018-03-262018-03-31
2018-03-252018-03-31
2018-03-272018-03-31
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " onset & confirmed\\\\\n", "\\hline\n", "\t 2018-03-14 & 2018-03-20\\\\\n", "\t 2018-03-27 & 2018-03-29\\\\\n", "\t 2018-03-27 & 2018-03-29\\\\\n", "\t 2018-03-26 & 2018-03-31\\\\\n", "\t 2018-03-25 & 2018-03-31\\\\\n", "\t 2018-03-27 & 2018-03-31\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "onset | confirmed | \n", "|---|---|---|---|---|---|\n", "| 2018-03-14 | 2018-03-20 | \n", "| 2018-03-27 | 2018-03-29 | \n", "| 2018-03-27 | 2018-03-29 | \n", "| 2018-03-26 | 2018-03-31 | \n", "| 2018-03-25 | 2018-03-31 | \n", "| 2018-03-27 | 2018-03-31 | \n", "\n", "\n" ], "text/plain": [ " onset confirmed \n", "1 2018-03-14 2018-03-20\n", "2 2018-03-27 2018-03-29\n", "3 2018-03-27 2018-03-29\n", "4 2018-03-26 2018-03-31\n", "5 2018-03-25 2018-03-31\n", "6 2018-03-27 2018-03-31" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_epicurve %>% \n", " select(-prefecture) -> df_delay\n", "\n", "df_delay %>% head" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
onsetconfirmeddifference
2018-03-172018-03-203
2018-03-272018-03-292
2018-03-272018-03-292
2018-03-262018-03-315
2018-03-252018-03-316
2018-03-272018-03-314
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " onset & confirmed & difference\\\\\n", "\\hline\n", "\t 2018-03-17 & 2018-03-20 & 3 \\\\\n", "\t 2018-03-27 & 2018-03-29 & 2 \\\\\n", "\t 2018-03-27 & 2018-03-29 & 2 \\\\\n", "\t 2018-03-26 & 2018-03-31 & 5 \\\\\n", "\t 2018-03-25 & 2018-03-31 & 6 \\\\\n", "\t 2018-03-27 & 2018-03-31 & 4 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "onset | confirmed | difference | \n", "|---|---|---|---|---|---|\n", "| 2018-03-17 | 2018-03-20 | 3 | \n", "| 2018-03-27 | 2018-03-29 | 2 | \n", "| 2018-03-27 | 2018-03-29 | 2 | \n", "| 2018-03-26 | 2018-03-31 | 5 | \n", "| 2018-03-25 | 2018-03-31 | 6 | \n", "| 2018-03-27 | 2018-03-31 | 4 | \n", "\n", "\n" ], "text/plain": [ " onset confirmed difference\n", "1 2018-03-17 2018-03-20 3 \n", "2 2018-03-27 2018-03-29 2 \n", "3 2018-03-27 2018-03-29 2 \n", "4 2018-03-26 2018-03-31 5 \n", "5 2018-03-25 2018-03-31 6 \n", "6 2018-03-27 2018-03-31 4 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# we shift the onset data of the index case to the date of first exposure \n", "df_delay[which(df_delay$onset=='2018-03-14'),'onset'] = as.Date('2018-03-17')\n", "\n", "df_delay %<>%\n", " mutate(difference=confirmed-onset,\n", " # if the onset date is unknown, it is assummed to be 5 days prior the confirmation\n", " difference=ifelse(is.na(onset),5,difference), \n", " onset=if_else(is.na(as.numeric(onset)),confirmed-difference,onset)) %>%\n", " mutate(onset = as.Date(onset)) %>%\n", " na.omit\n", "\n", "df_delay %>% head" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Delay distribution function" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "h = function(t,parms) {\n", " pweibull(t,parms[1],parms[2])-pweibull(t-1,parms[1],parms[2]) }\n", "\n", "# Cumulative distribution function for delay distribution\n", "H = function(t,parms) { pweibull(t,parms[1],parms[2]) }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Main analysis \n", "\n", "## Loading the data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Joining, by = c(\"epicurve\", \"number\")\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
epicurveonsetconfirmed
May27 2018-04-292018-05-12
May27 2018-05-092018-05-12
May27 2018-05-122018-05-13
May27 2018-05-082018-05-14
May27 2018-05-102018-05-15
May27 2018-05-122018-05-18
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " epicurve & onset & confirmed\\\\\n", "\\hline\n", "\t May27 & 2018-04-29 & 2018-05-12\\\\\n", "\t May27 & 2018-05-09 & 2018-05-12\\\\\n", "\t May27 & 2018-05-12 & 2018-05-13\\\\\n", "\t May27 & 2018-05-08 & 2018-05-14\\\\\n", "\t May27 & 2018-05-10 & 2018-05-15\\\\\n", "\t May27 & 2018-05-12 & 2018-05-18\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "epicurve | onset | confirmed | \n", "|---|---|---|---|---|---|\n", "| May27 | 2018-04-29 | 2018-05-12 | \n", "| May27 | 2018-05-09 | 2018-05-12 | \n", "| May27 | 2018-05-12 | 2018-05-13 | \n", "| May27 | 2018-05-08 | 2018-05-14 | \n", "| May27 | 2018-05-10 | 2018-05-15 | \n", "| May27 | 2018-05-12 | 2018-05-18 | \n", "\n", "\n" ], "text/plain": [ " epicurve onset confirmed \n", "1 May27 2018-04-29 2018-05-12\n", "2 May27 2018-05-09 2018-05-12\n", "3 May27 2018-05-12 2018-05-13\n", "4 May27 2018-05-08 2018-05-14\n", "5 May27 2018-05-10 2018-05-15\n", "6 May27 2018-05-12 2018-05-18" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filename = \"data.xlsx\"\n", "\n", "options(warn=-1)\n", "read_excel(filename, sheet = \"raw_onset\") %>% ncol -> nclmns\n", "read_excel(filename, sheet = \"raw_onset\", col_types = rep(\"date\",nclmns)) %>%\n", " gather(epicurve,onset) %>% \n", " mutate(number=1:n()) -> df\n", "if (read_excel(filename, sheet = \"raw_confirm\") %>% ncol!=nclmns)\n", " message(\"Something wrong with number of columns in Excel file!\")\n", "read_excel(filename, sheet = \"raw_confirm\", col_types = rep(\"date\",nclmns)) %>%\n", " gather(epicurve,confirmed) %>%\n", " mutate(number=1:n()) %>%\n", " left_join(df) %>%\n", " select(epicurve,onset,confirmed) %>%\n", " mutate(onset=as.Date(onset), confirmed=as.Date(confirmed)) -> df\n", "options(warn=0)\n", "df %>% tail" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
epicurveonsetconfirmeddifference
Apr01 2018-03-172018-03-206
Apr01 2018-03-272018-03-292
Apr01 2018-03-272018-03-292
Apr01 2018-03-252018-03-316
Apr01 2018-03-252018-03-316
Apr01 2018-03-262018-03-315
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " epicurve & onset & confirmed & difference\\\\\n", "\\hline\n", "\t Apr01 & 2018-03-17 & 2018-03-20 & 6 \\\\\n", "\t Apr01 & 2018-03-27 & 2018-03-29 & 2 \\\\\n", "\t Apr01 & 2018-03-27 & 2018-03-29 & 2 \\\\\n", "\t Apr01 & 2018-03-25 & 2018-03-31 & 6 \\\\\n", "\t Apr01 & 2018-03-25 & 2018-03-31 & 6 \\\\\n", "\t Apr01 & 2018-03-26 & 2018-03-31 & 5 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "epicurve | onset | confirmed | difference | \n", "|---|---|---|---|---|---|\n", "| Apr01 | 2018-03-17 | 2018-03-20 | 6 | \n", "| Apr01 | 2018-03-27 | 2018-03-29 | 2 | \n", "| Apr01 | 2018-03-27 | 2018-03-29 | 2 | \n", "| Apr01 | 2018-03-25 | 2018-03-31 | 6 | \n", "| Apr01 | 2018-03-25 | 2018-03-31 | 6 | \n", "| Apr01 | 2018-03-26 | 2018-03-31 | 5 | \n", "\n", "\n" ], "text/plain": [ " epicurve onset confirmed difference\n", "1 Apr01 2018-03-17 2018-03-20 6 \n", "2 Apr01 2018-03-27 2018-03-29 2 \n", "3 Apr01 2018-03-27 2018-03-29 2 \n", "4 Apr01 2018-03-25 2018-03-31 6 \n", "5 Apr01 2018-03-25 2018-03-31 6 \n", "6 Apr01 2018-03-26 2018-03-31 5 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df %<>%\n", " mutate(difference=confirmed-onset,\n", " # if the onset date is unknown, it is assummed to be 5 days prior the confirmation\n", " difference=ifelse(is.na(onset),5,difference), \n", " onset=if_else(is.na(as.numeric(onset)),confirmed-difference,onset)) %>%\n", " na.omit\n", "\n", "# we shift the onset data of the index case to the date of first exposure \n", "df[which(df$onset=='2018-03-14'),'onset'] = as.Date('2018-03-17')\n", "\n", "df %>% head" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "2066" ], "text/latex": [ "2066" ], "text/markdown": [ "2066" ], "text/plain": [ "[1] 2066" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# total number of records\n", "df %>% nrow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use Gamma distribution to define generation time distribution $g_t$, Weibull distribution for delay distribution $h_t$ between symptoms onset and lab confirmation" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Gamma distribution for incubation period\n", "g = function(time) { \n", " g_mean = 11.7; g_var = 9.0 # from (Klinkenberg and Nishiura 2011)\n", " scl = g_var/g_mean\n", " pgamma(time,shape=g_mean/scl,scale=scl)-pgamma(time-1,shape=g_mean/scl,scale=scl) }" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/latex": [ "2018-03-17" ], "text/markdown": [ "2018-03-17" ], "text/plain": [ "[1] \"2018-03-17\"" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/latex": [ "2018-05-18" ], "text/markdown": [ "2018-05-18" ], "text/plain": [ "[1] \"2018-05-18\"" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
epicurveonsetconfirmeddifferenceday_onsetday_confirmation
Apr01 2018-03-172018-03-206 0 3
Apr01 2018-03-272018-03-292 10 12
Apr01 2018-03-272018-03-292 10 12
Apr01 2018-03-252018-03-316 8 14
Apr01 2018-03-252018-03-316 8 14
Apr01 2018-03-262018-03-315 9 14
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " epicurve & onset & confirmed & difference & day\\_onset & day\\_confirmation\\\\\n", "\\hline\n", "\t Apr01 & 2018-03-17 & 2018-03-20 & 6 & 0 & 3 \\\\\n", "\t Apr01 & 2018-03-27 & 2018-03-29 & 2 & 10 & 12 \\\\\n", "\t Apr01 & 2018-03-27 & 2018-03-29 & 2 & 10 & 12 \\\\\n", "\t Apr01 & 2018-03-25 & 2018-03-31 & 6 & 8 & 14 \\\\\n", "\t Apr01 & 2018-03-25 & 2018-03-31 & 6 & 8 & 14 \\\\\n", "\t Apr01 & 2018-03-26 & 2018-03-31 & 5 & 9 & 14 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "epicurve | onset | confirmed | difference | day_onset | day_confirmation | \n", "|---|---|---|---|---|---|\n", "| Apr01 | 2018-03-17 | 2018-03-20 | 6 | 0 | 3 | \n", "| Apr01 | 2018-03-27 | 2018-03-29 | 2 | 10 | 12 | \n", "| Apr01 | 2018-03-27 | 2018-03-29 | 2 | 10 | 12 | \n", "| Apr01 | 2018-03-25 | 2018-03-31 | 6 | 8 | 14 | \n", "| Apr01 | 2018-03-25 | 2018-03-31 | 6 | 8 | 14 | \n", "| Apr01 | 2018-03-26 | 2018-03-31 | 5 | 9 | 14 | \n", "\n", "\n" ], "text/plain": [ " epicurve onset confirmed difference day_onset day_confirmation\n", "1 Apr01 2018-03-17 2018-03-20 6 0 3 \n", "2 Apr01 2018-03-27 2018-03-29 2 10 12 \n", "3 Apr01 2018-03-27 2018-03-29 2 10 12 \n", "4 Apr01 2018-03-25 2018-03-31 6 8 14 \n", "5 Apr01 2018-03-25 2018-03-31 6 8 14 \n", "6 Apr01 2018-03-26 2018-03-31 5 9 14 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# using days since index case instead of dates\n", "(mindate = min(df$onset))\n", "(maxdate = max(df$confirmed))\n", "df %<>% \n", " mutate(day_onset = unclass(onset)-unclass(mindate),\n", " day_confirmation = unclass(confirmed)-unclass(mindate))\n", "df %>% head" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Apr01'\n", "\\item 'Apr05'\n", "\\item 'Apr09'\n", "\\item 'Apr13'\n", "\\item 'Apr17'\n", "\\item 'Apr21'\n", "\\item 'Apr25'\n", "\\item 'Apr29'\n", "\\item 'May01'\n", "\\item 'May03'\n", "\\item 'May05'\n", "\\item 'May07'\n", "\\item 'May09'\n", "\\item 'May11'\n", "\\item 'May13'\n", "\\item 'May15'\n", "\\item 'May17'\n", "\\item 'May19'\n", "\\item 'May21'\n", "\\item 'May23'\n", "\\item 'May25'\n", "\\item 'May27'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Apr01'\n", "2. 'Apr05'\n", "3. 'Apr09'\n", "4. 'Apr13'\n", "5. 'Apr17'\n", "6. 'Apr21'\n", "7. 'Apr25'\n", "8. 'Apr29'\n", "9. 'May01'\n", "10. 'May03'\n", "11. 'May05'\n", "12. 'May07'\n", "13. 'May09'\n", "14. 'May11'\n", "15. 'May13'\n", "16. 'May15'\n", "17. 'May17'\n", "18. 'May19'\n", "19. 'May21'\n", "20. 'May23'\n", "21. 'May25'\n", "22. 'May27'\n", "\n", "\n" ], "text/plain": [ " [1] \"Apr01\" \"Apr05\" \"Apr09\" \"Apr13\" \"Apr17\" \"Apr21\" \"Apr25\" \"Apr29\" \"May01\"\n", "[10] \"May03\" \"May05\" \"May07\" \"May09\" \"May11\" \"May13\" \"May15\" \"May17\" \"May19\"\n", "[19] \"May21\" \"May23\" \"May25\" \"May27\"" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
    \n", "\t
  1. 'Apr01'
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  2. 'Apr09'
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  3. 'Apr17'
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  4. 'Apr25'
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  5. 'May03'
    6. \n", "\t
  6. 'May11'
    7. \n", "\t
  7. 'May17'
    8. \n", "\t
  8. 'May25'
    9. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Apr01'\n", "\\item 'Apr09'\n", "\\item 'Apr17'\n", "\\item 'Apr25'\n", "\\item 'May03'\n", "\\item 'May11'\n", "\\item 'May17'\n", "\\item 'May25'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Apr01'\n", "2. 'Apr09'\n", "3. 'Apr17'\n", "4. 'Apr25'\n", "5. 'May03'\n", "6. 'May11'\n", "7. 'May17'\n", "8. 'May25'\n", "\n", "\n" ], "text/plain": [ "[1] \"Apr01\" \"Apr09\" \"Apr17\" \"Apr25\" \"May03\" \"May11\" \"May17\" \"May25\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Available epicurves\n", "(all_epicurves = unique(df$epicurve))\n", "# we restrict ourselves to the following epicurves \n", "# starting from the first one with the time step of eight days\n", "(all_epicurves = c(\"Apr01\",\"Apr09\",\"Apr17\",\"Apr25\",\"May03\",\"May11\",\"May17\",\"May25\"))\n", "df = filter(df,epicurve %in% all_epicurves)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# As an example, we analyse first only one particular epicurve" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "'Apr25'" ], "text/latex": [ "'Apr25'" ], "text/markdown": [ "'Apr25'" ], "text/plain": [ "[1] \"Apr25\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(current_epicurve = all_epicurves %>% rev %>% .[5])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "83" ], "text/latex": [ "83" ], "text/markdown": [ "83" ], "text/plain": [ "[1] 83" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(maxDay = max(df$day_confirmation)+21)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
dayic
343 2
350 5
360 0
370 2
381 4
390 3
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " day & i & c\\\\\n", "\\hline\n", "\t 34 & 3 & 2 \\\\\n", "\t 35 & 0 & 5 \\\\\n", "\t 36 & 0 & 0 \\\\\n", "\t 37 & 0 & 2 \\\\\n", "\t 38 & 1 & 4 \\\\\n", "\t 39 & 0 & 3 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "day | i | c | \n", "|---|---|---|---|---|---|\n", "| 34 | 3 | 2 | \n", "| 35 | 0 | 5 | \n", "| 36 | 0 | 0 | \n", "| 37 | 0 | 2 | \n", "| 38 | 1 | 4 | \n", "| 39 | 0 | 3 | \n", "\n", "\n" ], "text/plain": [ " day i c\n", "1 34 3 2\n", "2 35 0 5\n", "3 36 0 0\n", "4 37 0 2\n", "5 38 1 4\n", "6 39 0 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df %>% \n", " filter(epicurve==current_epicurve) %>% \n", " select(-epicurve) -> df_current\n", "\n", "Df = data.frame(day=0:(unclass(as.Date('2018'%&%current_epicurve,\"%Y%b%d\"))-unclass(as.Date('2018-03-17'))))\n", "\n", "df_current %>% \n", " filter(day_onset>0) %>% #removing index case from fitting\n", " group_by(day_onset) %>%\n", " count %>%\n", " rename(day=day_onset) %>%\n", " right_join(Df,by=\"day\") %>%\n", " mutate(n=ifelse(is.na(n),0,n)) %>%\n", " rename(i=n) -> Df\n", "\n", "df_current %>%\n", " filter(day_onset>0) %>% #removing index case from fitting\n", " group_by(day_confirmation) %>%\n", " count %>%\n", " rename(day=day_confirmation) %>%\n", " right_join(Df,by=\"day\") %>%\n", " mutate(n=ifelse(is.na(n),0,n)) %>%\n", " rename(c=n) %>%\n", " select(day,i,c) %>%\n", " arrange(day) -> Df\n", "\n", "Df %>% tail" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# if the parameter \"prediction\" is FALSE, the result of the function is log-likelihood,\n", "# otherwise it give the resulting data.frame with all entities\n", "calculate_six_generations = function(prms,prediction=FALSE,ndays=maxDay) {\n", " K = prms[3]; R2 = prms[4]; R3 = prms[5]; R4 = prms[6]; R5 = prms[7]\n", " \n", " data.frame(day = 0:ndays) %>%\n", " mutate(gt=g(day),ht=h(day,prms[1:2])) -> df_\n", "\n", " #calculating first convolution\n", " conv = c()\n", " for (x in 1:nrow(df_)) {\n", " conv = c(conv,sum(df_$gt[1:x]*df_$gt[x:1])) }\n", " df_ %<>% do(cbind(.,conv1_gt=conv))\n", " \n", " #calculating second convolution\n", " conv = c()\n", " for (x in 1:nrow(df_)) {\n", " conv = c(conv,sum(df_$conv1_gt[1:x]*df_$gt[x:1])) }\n", " df_ %<>% do(cbind(.,conv2_gt=conv))\n", " \n", " #calculating third convolution\n", " conv = c()\n", " for (x in 1:nrow(df_)) {\n", " conv = c(conv,sum(df_$conv2_gt[1:x]*df_$gt[x:1])) }\n", " df_ %<>% do(cbind(.,conv3_gt=conv))\n", " \n", " #calculating fourth convolution\n", " conv = c()\n", " for (x in 1:nrow(df_)) {\n", " conv = c(conv,sum(df_$conv3_gt[1:x]*df_$gt[x:1])) }\n", " df_ %<>% do(cbind(.,conv4_gt=conv))\n", "\n", "\n", " df_ %<>% \n", " mutate(ft = (gt+R2*conv1_gt+R2*R3*conv2_gt+R2*R3*R4*conv3_gt+R2*R3*R4*R5*conv3_gt)/(1+R2+R2*R3+R2*R3*R4+R2*R3*R4*R5)) \n", " \n", " #calculating convolution with h\n", " conv = c()\n", " for (x in 1:nrow(df_)) {\n", " conv = c(conv,sum(df_$ft[1:x]*df_$ht[x:1])) }\n", " df_ %<>% do(cbind(.,conv_ht=conv)) \n", " \n", " if (prediction) {\n", " df_ %>% \n", " left_join(Df,by=\"day\") %>%\n", " mutate(lambda_i = K*ft, lambda_c = K*conv_ht) %>%\n", " left_join(select(Df,-i,-c),by=\"day\") %>% return\n", " \n", " } else {\n", " df_ %<>% right_join(Df,by=\"day\")\n", " \n", " maxday = max(df_$day)\n", " \n", " df_ %>%\n", " filter(ft>0 & day% \n", " summarize(loglk = sum(i*log(H(maxday-day,prms[1:2])*K*ft)-H(maxday-day,prms[1:2])*K*ft-lfactorial(i))) %>% \n", " .$loglk -> loglk_onset\n", " \n", " df_current %>% \n", " group_by(difference) %>%\n", " count %>%\n", " ungroup %>%\n", " summarize(loglk = sum(n*log(h(difference,prms[1:2])))) %>% \n", " .$loglk -> loglk_delay\n", " \n", " return(loglk_delay+loglk_onset)\n", " }\n", "}\n", "\n", "calculate_five_generations = function(x,prediction=FALSE,ndays=maxDay) { calculate_six_generations(c(x[1:6],0),prediction,ndays) }\n", "\n", "calculate_four_generations = function(x,prediction=FALSE,ndays=maxDay) { calculate_six_generations(c(x[1:5],0,0),prediction,ndays) }\n", "\n", "calculate_three_generations = function(x,prediction=FALSE,ndays=maxDay) { calculate_six_generations(c(x[1:4],0,0,0),prediction,ndays) }\n", "\n", "calculate_two_generations = function(x,prediction=FALSE,ndays=maxDay) { calculate_six_generations(c(x[1:3],0,0,0,0),prediction,ndays) }\n", "\n", "getDelay = function(prms) {\n", " df_current %>% \n", " mutate(delta = day_confirmation-day_onset) %>%\n", " group_by(delta) %>%\n", " count %>%\n", " ungroup -> df_\n", "\n", " df_ %>% \n", " right_join(data.frame(delta=1:max(df_$delta)+1),by=\"delta\") %>%\n", " mutate(n = ifelse(is.na(n),0,n), ht = h(delta,prms)) %>%\n", " mutate(freq = n/sum(n)) %>%\n", " return\n", "}" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "-234.360271666443" ], "text/latex": [ "-234.360271666443" ], "text/markdown": [ "-234.360271666443" ], "text/plain": [ "[1] -234.3603" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# test\n", "init = c(2,4,100,1.2)\n", "calculate_three_generations(init)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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daygthtconv1_gtconv2_gtconv3_gtconv4_gtftconv_hticlambda_ilambda_c
7574 0 0 3.420300e-157.139891e-096.381711e-050.00953193122.197041e-154.024195e-13NA NA 1.798468e-133.294151e-11
7675 0 0 1.391898e-153.591380e-093.977577e-050.00737912058.940899e-161.715407e-13NA NA 7.318898e-141.404209e-11
7776 0 0 5.633177e-161.791567e-092.451426e-050.00563174913.618489e-167.264719e-14NA NA 2.962046e-145.946800e-12
7877 0 0 2.267441e-168.865478e-101.494404e-050.00423899121.456498e-163.057120e-14NA NA 1.192269e-142.502517e-12
7978 0 0 9.077752e-174.352722e-109.013465e-060.00314790245.831123e-171.278565e-14NA NA 4.773278e-151.046616e-12
8079 0 0 3.614878e-172.120791e-105.380346e-060.00230713142.322028e-175.315207e-15NA NA 1.900781e-154.350957e-13
8180 0 0 1.431809e-171.025647e-103.179356e-060.00166940889.197272e-182.196693e-15NA NA 7.528762e-161.798183e-13
8281 0 0 5.640885e-184.924284e-111.860330e-060.00119298163.623440e-189.026705e-16NA NA 2.966099e-167.389138e-14
8382 0 0 2.210376e-182.347535e-111.078127e-060.00084221231.419842e-183.688523e-16NA NA 1.162263e-163.019375e-14
8483 0 0 8.614324e-191.111426e-116.189882e-070.00058756675.533438e-191.498940e-16NA NA 4.529597e-171.227012e-14
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllll}\n", " & day & gt & ht & conv1\\_gt & conv2\\_gt & conv3\\_gt & conv4\\_gt & ft & conv\\_ht & i & c & lambda\\_i & lambda\\_c\\\\\n", "\\hline\n", "\t75 & 74 & 0 & 0 & 3.420300e-15 & 7.139891e-09 & 6.381711e-05 & 0.0095319312 & 2.197041e-15 & 4.024195e-13 & NA & NA & 1.798468e-13 & 3.294151e-11\\\\\n", "\t76 & 75 & 0 & 0 & 1.391898e-15 & 3.591380e-09 & 3.977577e-05 & 0.0073791205 & 8.940899e-16 & 1.715407e-13 & NA & NA & 7.318898e-14 & 1.404209e-11\\\\\n", "\t77 & 76 & 0 & 0 & 5.633177e-16 & 1.791567e-09 & 2.451426e-05 & 0.0056317491 & 3.618489e-16 & 7.264719e-14 & NA & NA & 2.962046e-14 & 5.946800e-12\\\\\n", "\t78 & 77 & 0 & 0 & 2.267441e-16 & 8.865478e-10 & 1.494404e-05 & 0.0042389912 & 1.456498e-16 & 3.057120e-14 & NA & NA & 1.192269e-14 & 2.502517e-12\\\\\n", "\t79 & 78 & 0 & 0 & 9.077752e-17 & 4.352722e-10 & 9.013465e-06 & 0.0031479024 & 5.831123e-17 & 1.278565e-14 & NA & NA & 4.773278e-15 & 1.046616e-12\\\\\n", "\t80 & 79 & 0 & 0 & 3.614878e-17 & 2.120791e-10 & 5.380346e-06 & 0.0023071314 & 2.322028e-17 & 5.315207e-15 & NA & NA & 1.900781e-15 & 4.350957e-13\\\\\n", "\t81 & 80 & 0 & 0 & 1.431809e-17 & 1.025647e-10 & 3.179356e-06 & 0.0016694088 & 9.197272e-18 & 2.196693e-15 & NA & NA & 7.528762e-16 & 1.798183e-13\\\\\n", "\t82 & 81 & 0 & 0 & 5.640885e-18 & 4.924284e-11 & 1.860330e-06 & 0.0011929816 & 3.623440e-18 & 9.026705e-16 & NA & NA & 2.966099e-16 & 7.389138e-14\\\\\n", "\t83 & 82 & 0 & 0 & 2.210376e-18 & 2.347535e-11 & 1.078127e-06 & 0.0008422123 & 1.419842e-18 & 3.688523e-16 & NA & NA & 1.162263e-16 & 3.019375e-14\\\\\n", "\t84 & 83 & 0 & 0 & 8.614324e-19 & 1.111426e-11 & 6.189882e-07 & 0.0005875667 & 5.533438e-19 & 1.498940e-16 & NA & NA & 4.529597e-17 & 1.227012e-14\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | day | gt | ht | conv1_gt | conv2_gt | conv3_gt | conv4_gt | ft | conv_ht | i | c | lambda_i | lambda_c | \n", "|---|---|---|---|---|---|---|---|---|---|\n", "| 75 | 74 | 0 | 0 | 3.420300e-15 | 7.139891e-09 | 6.381711e-05 | 0.0095319312 | 2.197041e-15 | 4.024195e-13 | NA | NA | 1.798468e-13 | 3.294151e-11 | \n", "| 76 | 75 | 0 | 0 | 1.391898e-15 | 3.591380e-09 | 3.977577e-05 | 0.0073791205 | 8.940899e-16 | 1.715407e-13 | NA | NA | 7.318898e-14 | 1.404209e-11 | \n", "| 77 | 76 | 0 | 0 | 5.633177e-16 | 1.791567e-09 | 2.451426e-05 | 0.0056317491 | 3.618489e-16 | 7.264719e-14 | NA | NA | 2.962046e-14 | 5.946800e-12 | \n", "| 78 | 77 | 0 | 0 | 2.267441e-16 | 8.865478e-10 | 1.494404e-05 | 0.0042389912 | 1.456498e-16 | 3.057120e-14 | NA | NA | 1.192269e-14 | 2.502517e-12 | \n", "| 79 | 78 | 0 | 0 | 9.077752e-17 | 4.352722e-10 | 9.013465e-06 | 0.0031479024 | 5.831123e-17 | 1.278565e-14 | NA | NA | 4.773278e-15 | 1.046616e-12 | \n", "| 80 | 79 | 0 | 0 | 3.614878e-17 | 2.120791e-10 | 5.380346e-06 | 0.0023071314 | 2.322028e-17 | 5.315207e-15 | NA | NA | 1.900781e-15 | 4.350957e-13 | \n", "| 81 | 80 | 0 | 0 | 1.431809e-17 | 1.025647e-10 | 3.179356e-06 | 0.0016694088 | 9.197272e-18 | 2.196693e-15 | NA | NA | 7.528762e-16 | 1.798183e-13 | \n", "| 82 | 81 | 0 | 0 | 5.640885e-18 | 4.924284e-11 | 1.860330e-06 | 0.0011929816 | 3.623440e-18 | 9.026705e-16 | NA | NA | 2.966099e-16 | 7.389138e-14 | \n", "| 83 | 82 | 0 | 0 | 2.210376e-18 | 2.347535e-11 | 1.078127e-06 | 0.0008422123 | 1.419842e-18 | 3.688523e-16 | NA | NA | 1.162263e-16 | 3.019375e-14 | \n", "| 84 | 83 | 0 | 0 | 8.614324e-19 | 1.111426e-11 | 6.189882e-07 | 0.0005875667 | 5.533438e-19 | 1.498940e-16 | NA | NA | 4.529597e-17 | 1.227012e-14 | \n", "\n", "\n" ], "text/plain": [ " day gt ht conv1_gt conv2_gt conv3_gt conv4_gt ft \n", "75 74 0 0 3.420300e-15 7.139891e-09 6.381711e-05 0.0095319312 2.197041e-15\n", "76 75 0 0 1.391898e-15 3.591380e-09 3.977577e-05 0.0073791205 8.940899e-16\n", "77 76 0 0 5.633177e-16 1.791567e-09 2.451426e-05 0.0056317491 3.618489e-16\n", "78 77 0 0 2.267441e-16 8.865478e-10 1.494404e-05 0.0042389912 1.456498e-16\n", "79 78 0 0 9.077752e-17 4.352722e-10 9.013465e-06 0.0031479024 5.831123e-17\n", "80 79 0 0 3.614878e-17 2.120791e-10 5.380346e-06 0.0023071314 2.322028e-17\n", "81 80 0 0 1.431809e-17 1.025647e-10 3.179356e-06 0.0016694088 9.197272e-18\n", "82 81 0 0 5.640885e-18 4.924284e-11 1.860330e-06 0.0011929816 3.623440e-18\n", "83 82 0 0 2.210376e-18 2.347535e-11 1.078127e-06 0.0008422123 1.419842e-18\n", "84 83 0 0 8.614324e-19 1.111426e-11 6.189882e-07 0.0005875667 5.533438e-19\n", " conv_ht i c lambda_i lambda_c \n", "75 4.024195e-13 NA NA 1.798468e-13 3.294151e-11\n", "76 1.715407e-13 NA NA 7.318898e-14 1.404209e-11\n", "77 7.264719e-14 NA NA 2.962046e-14 5.946800e-12\n", "78 3.057120e-14 NA NA 1.192269e-14 2.502517e-12\n", "79 1.278565e-14 NA NA 4.773278e-15 1.046616e-12\n", "80 5.315207e-15 NA NA 1.900781e-15 4.350957e-13\n", "81 2.196693e-15 NA NA 7.528762e-16 1.798183e-13\n", "82 9.026705e-16 NA NA 2.966099e-16 7.389138e-14\n", "83 3.688523e-16 NA NA 1.162263e-16 3.019375e-14\n", "84 1.498940e-16 NA NA 4.529597e-17 1.227012e-14" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "calculate_three_generations(pars,prediction=TRUE,ndays=maxDay) -> dfMLE \n", "dfMLE %>% tail(10)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"Removed 44 rows containing missing values (geom_bar).\"Warning message:\n", "\"Removed 44 rows containing missing values (geom_bar).\"" ] }, { "data": { "image/png": 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XZlKsNu9LSRFj9s3d32O/GqN27/jar2b3S4h3/65te/4/b39x8/fiyE+PjHP07r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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width=9,repr.plot.height=7.5)\n", "xmax = max(dfMLE$day)\n", "\n", "dfMLE %>%\n", " select(one_of(\"day\",\"lambda_i\",\"i\")) %>%\n", " rename(pred=lambda_i,obs=i) %>%\n", " gather(Category,Count,-day) %>%\n", " mutate(Category=factor(Category,levels=c(\"pred\",\"obs\") %>% rev)) %>%\n", " ggplot(aes(x=day)) +\n", " geom_bar(aes(y=Count,fill=Category),stat=\"identity\",position=position_dodge(1)) +\n", " coord_cartesian(xlim=c(0,xmax)) +\n", " guides(fill=F) +\n", " labs(y=\"incidence by onset of symptoms\",x=\"days since index case\") +\n", " theme(axis.text.x = element_text(angle = 45, hjust = .5, vjust=0.5),\n", " strip.text.x = element_blank(),\n", " panel.grid.minor.x = element_blank(),\n", " strip.background = element_rect(colour=\"white\", fill=\"white\"),\n", " legend.position=c(.2,.87),\n", " plot.margin = unit(c(.5,1,.5,.5),\"lines\"),\n", " legend.direction=\"horizontal\") -> plt_onset\n", "\n", "dfMLE %>%\n", " select(one_of(\"day\",\"lambda_c\",\"c\")) %>%\n", " rename(pred=lambda_c,obs=c) %>%\n", " gather(Category,Count,-day) %>%\n", " mutate(Category=factor(Category,levels=c(\"pred\",\"obs\") %>% rev)) %>%\n", " ggplot(aes(x=day)) +\n", " geom_bar(aes(y=Count,fill=Category),stat=\"identity\",position=position_dodge(1)) +\n", " coord_cartesian(xlim=c(0,xmax)) +\n", " guides(fill=F) +\n", " labs(y=\"incidence by day of confirmation\",x=\"days since index case\") +\n", " theme(axis.text.x = element_text(angle = 45, hjust = .5, vjust=0.5),\n", " strip.text.x = element_blank(),\n", " panel.grid.minor.x = element_blank(),\n", " strip.background = element_rect(colour=\"white\", fill=\"white\"),\n", " legend.position=c(.2,.87),\n", " plot.margin = unit(c(.5,1,.5,.5),\"lines\"),\n", " legend.direction=\"horizontal\") -> plt_confirmation\n", "\n", "getDelay(pars[1:2]) %>%\n", " select(delta,ht,freq) %>%\n", " rename(predicted=ht,observed=freq) %>%\n", " gather(Category,Count,-delta) %>%\n", " mutate(Category=factor(Category,levels=c(\"predicted\",\"observed\") %>% rev)) %>%\n", " ggplot(aes(x=delta)) +\n", " geom_bar(aes(y=Count,fill=Category),stat=\"identity\",position=position_dodge(1)) +\n", " guides(fill=guide_legend(title.hjust = 0.5)) +\n", " labs(y=\"delay distribution\",x=\"difference in days\") +\n", " theme(axis.text.x = element_text(angle = 45, hjust = .5, vjust=0.5),\n", " strip.text.x = element_blank(),\n", " panel.grid.minor.x = element_blank(),\n", " strip.background = element_rect(colour=\"white\", fill=\"white\"),\n", " legend.position=c(1.25,.9),\n", " plot.margin = unit(c(.5,4,.5,.5),\"lines\")\n", " ) -> plt_delay\n", "\n", "grid.arrange(ggplotGrob(plt_onset), ggplotGrob(plt_confirmation), ggplotGrob(plt_delay),\n", " widths=c(1,1), heights=c(1,1), nrow=2, ncol=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constructing confidence intervals\n", "\n", "Note that all parameters are sqrts of the real parameters" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
0.05441722820.0158539376 -0.00276646 -0.0001090684
0.01585393760.0427808944 0.01659264 0.0006544021
-0.00276646040.0165926403 82.74034592 0.0347772366
-0.00010906840.0006544021 0.03477724 0.1994432375
\n" ], "text/latex": [ "\\begin{tabular}{llll}\n", "\t 0.0544172282 & 0.0158539376 & -0.00276646 & -0.0001090684\\\\\n", "\t 0.0158539376 & 0.0427808944 & 0.01659264 & 0.0006544021\\\\\n", "\t -0.0027664604 & 0.0165926403 & 82.74034592 & 0.0347772366\\\\\n", "\t -0.0001090684 & 0.0006544021 & 0.03477724 & 0.1994432375\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| 0.0544172282 | 0.0158539376 | -0.00276646 | -0.0001090684 | \n", "| 0.0158539376 | 0.0427808944 | 0.01659264 | 0.0006544021 | \n", "| -0.0027664604 | 0.0165926403 | 82.74034592 | 0.0347772366 | \n", "| -0.0001090684 | 0.0006544021 | 0.03477724 | 0.1994432375 | \n", "\n", "\n" ], "text/plain": [ " [,1] [,2] [,3] [,4] \n", "[1,] 0.0544172282 0.0158539376 -0.00276646 -0.0001090684\n", "[2,] 0.0158539376 0.0427808944 0.01659264 0.0006544021\n", "[3,] -0.0027664604 0.0165926403 82.74034592 0.0347772366\n", "[4,] -0.0001090684 0.0006544021 0.03477724 0.1994432375" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
    \n", "\t
  1. 0.233275005599109
    2. \n", "\t
  2. 0.206835428253923
    3. \n", "\t
  3. 9.09617204778135
    4. \n", "\t
  4. 0.446590682300467
    5. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 0.233275005599109\n", "\\item 0.206835428253923\n", "\\item 9.09617204778135\n", "\\item 0.446590682300467\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 0.233275005599109\n", "2. 0.206835428253923\n", "3. 9.09617204778135\n", "4. 0.446590682300467\n", "\n", "\n" ], "text/plain": [ "[1] 0.2332750 0.2068354 9.0961720 0.4465907" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(vc = solve(-sol$hessian))\n", "(sd = sqrt(diag(vc)))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
h1h2KR2
2.4129844.38855088.140532.742298
2.2999174.42265494.604662.177294
2.4205934.79891288.238512.128072
2.8804784.84875190.768461.901418
2.7303924.46234281.044832.303271
2.5054174.32942684.420631.911473
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " h1 & h2 & K & R2\\\\\n", "\\hline\n", "\t 2.412984 & 4.388550 & 88.14053 & 2.742298\\\\\n", "\t 2.299917 & 4.422654 & 94.60466 & 2.177294\\\\\n", "\t 2.420593 & 4.798912 & 88.23851 & 2.128072\\\\\n", "\t 2.880478 & 4.848751 & 90.76846 & 1.901418\\\\\n", "\t 2.730392 & 4.462342 & 81.04483 & 2.303271\\\\\n", "\t 2.505417 & 4.329426 & 84.42063 & 1.911473\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "h1 | h2 | K | R2 | \n", "|---|---|---|---|---|---|\n", "| 2.412984 | 4.388550 | 88.14053 | 2.742298 | \n", "| 2.299917 | 4.422654 | 94.60466 | 2.177294 | \n", "| 2.420593 | 4.798912 | 88.23851 | 2.128072 | \n", "| 2.880478 | 4.848751 | 90.76846 | 1.901418 | \n", "| 2.730392 | 4.462342 | 81.04483 | 2.303271 | \n", "| 2.505417 | 4.329426 | 84.42063 | 1.911473 | \n", "\n", "\n" ], "text/plain": [ " h1 h2 K R2 \n", "1 2.412984 4.388550 88.14053 2.742298\n", "2 2.299917 4.422654 94.60466 2.177294\n", "3 2.420593 4.798912 88.23851 2.128072\n", "4 2.880478 4.848751 90.76846 1.901418\n", "5 2.730392 4.462342 81.04483 2.303271\n", "6 2.505417 4.329426 84.42063 1.911473" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# sampling\n", "sample = 1e3\n", "hess_sam = MASS::mvrnorm(n=sample, mu=sol$par, \n", " Sigma=solve(-sol$hessian),\n", " tol=1e-06, empirical=FALSE, EISPACK=FALSE)\n", "hess_sam %>% as.data.frame -> df_hess\n", "\n", "colnames(df_hess) = c(\"h1\",\"h2\",\"K\",\"R2\")\n", "df_hess %>% head" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
h1h2KR2
2.4129844.38855088.140532.177294
2.2999174.42265494.604662.128072
2.4205934.79891288.238511.901418
2.8804784.84875190.768462.303271
2.7303924.46234281.044831.911473
2.5054174.32942684.420631.989761
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " h1 & h2 & K & R2\\\\\n", "\\hline\n", "\t 2.412984 & 4.388550 & 88.14053 & 2.177294\\\\\n", "\t 2.299917 & 4.422654 & 94.60466 & 2.128072\\\\\n", "\t 2.420593 & 4.798912 & 88.23851 & 1.901418\\\\\n", "\t 2.880478 & 4.848751 & 90.76846 & 2.303271\\\\\n", "\t 2.730392 & 4.462342 & 81.04483 & 1.911473\\\\\n", "\t 2.505417 & 4.329426 & 84.42063 & 1.989761\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "h1 | h2 | K | R2 | \n", "|---|---|---|---|---|---|\n", "| 2.412984 | 4.388550 | 88.14053 | 2.177294 | \n", "| 2.299917 | 4.422654 | 94.60466 | 2.128072 | \n", "| 2.420593 | 4.798912 | 88.23851 | 1.901418 | \n", "| 2.880478 | 4.848751 | 90.76846 | 2.303271 | \n", "| 2.730392 | 4.462342 | 81.04483 | 1.911473 | \n", "| 2.505417 | 4.329426 | 84.42063 | 1.989761 | \n", "\n", "\n" ], "text/plain": [ " h1 h2 K R2 \n", "1 2.412984 4.388550 88.14053 2.177294\n", "2 2.299917 4.422654 94.60466 2.128072\n", "3 2.420593 4.798912 88.23851 1.901418\n", "4 2.880478 4.848751 90.76846 2.303271\n", "5 2.730392 4.462342 81.04483 1.911473\n", "6 2.505417 4.329426 84.42063 1.989761" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# selecting 95% middle range\n", "data.frame(\n", " h1=filter(df_hess,h1 > quantile(h1, 0.025) & h1 < quantile(h1, 0.975))$h1,\n", " h2=filter(df_hess,h2 > quantile(h2, 0.025) & h2 < quantile(h2, 0.975))$h2,\n", " K=filter(df_hess,K > quantile(K, 0.025) & K < quantile(K, 0.975))$K,\n", " R2=filter(df_hess,R2 > quantile(R2, 0.025) & R2 < quantile(R2, 0.975))$R2\n", ") -> df_perturb\n", "\n", "df_perturb[df_perturb<0] = 0\n", "\n", "df_perturb %>% head" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# prediction\n", "res = NULL\n", "for (i in 1:nrow(df_perturb)) {\n", " res = rbind(res,calculate_three_generations(as.numeric(df_perturb[i,]),prediction=TRUE) %>% \n", " select(day,contains(\"lambda\"))) }" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "res %>% \n", " group_by(day) %>%\n", " summarize(c025_i=quantile(lambda_i,0.025),median_i=median(lambda_i),c975_i=quantile(lambda_i,0.975),\n", " c025_c=quantile(lambda_c,0.025),median_c=median(lambda_c),c975_c=quantile(lambda_c,0.975)) -> result" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"Removed 44 rows containing missing values (geom_bar).\"Warning message:\n", "\"Removed 44 rows containing missing values (geom_bar).\"" ] }, { "data": { "image/png": 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HhoMtBIBcuP7uzQeH0UBywcAHQnLIVjfX0ds67agcH5f6IkRKM0NdWuZegJ6Hvx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\nH320FEOqIq+++urY2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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width=8,repr.plot.height=4)\n", "xmax = max(dfMLE$day)\n", "\n", "dfMLE %>%\n", " ggplot(aes(x=day)) +\n", " geom_bar(aes(y=i,fill=\"red\"),stat=\"identity\",position=position_dodge(1)) +\n", " geom_ribbon(data=result,aes(ymin=c025_i,ymax=c975_i),fill=\"blue\",alpha=.5) +\n", " geom_line(data=result,aes(y=median_i),size=.7,color=\"blue\") +\n", " coord_cartesian(xlim=c(0,maxDay)) +\n", " guides(fill=F) +\n", " labs(y=\"incidence by onset of symptoms\", x=\"days since index case\") +\n", " theme_bw() +\n", " theme(axis.text.x = element_text(angle = 45, hjust = .5, vjust=0.5),\n", " strip.text.x = element_blank(),\n", " panel.grid.minor.x = element_blank(),\n", " strip.background = element_rect(colour=\"white\", fill=\"white\"),\n", " legend.position=c(.2,.87),\n", " plot.margin = unit(c(.5,1,.5,.5),\"lines\"),\n", " legend.direction=\"horizontal\") -> plt_onset\n", "\n", "dfMLE %>%\n", " ggplot(aes(x=day,fill=\"red\")) +\n", " geom_bar(aes(y=c),stat=\"identity\",position=position_dodge(1)) +\n", " geom_ribbon(data=result,aes(ymin=c025_c,ymax=c975_c),fill=\"blue\",alpha=.5) +\n", " geom_line(data=result,aes(y=median_c),size=.7,color=\"blue\") +\n", " coord_cartesian(xlim=c(0,maxDay)) +\n", " guides(fill=F) +\n", " labs(y=\"incidence by day of confirmation\",x=\"days since index case\") +\n", " theme_bw() +\n", " theme(axis.text.x = element_text(angle = 45, hjust = .5, vjust=0.5),\n", " strip.text.x = element_blank(),\n", " panel.grid.minor.x = element_blank(),\n", " strip.background = element_rect(colour=\"white\", fill=\"white\"),\n", " legend.position=c(.2,.87),\n", " plot.margin = unit(c(.5,1,.5,.5),\"lines\"),\n", " legend.direction=\"horizontal\") -> plt_confirmation\n", "\n", "p = grid.arrange(ggplotGrob(plt_onset), ggplotGrob(plt_confirmation), \n", " widths=c(1,1), heights=c(1), nrow=1, ncol=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generating a table with varied number of generations\n", "\n", "## Two generations" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Apr01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K loglk AIC rmse_i\n", "1 4.044729 3.93481 2.147092 4.567165 28.42619 -37.19753 80.39506 1.665254\n", " rmse_c\n", "1 0.8761112\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Apr09\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K loglk AIC rmse_i\n", "1 3.912038 3.872446 2.087287 4.416723 35.3252 -100.4916 206.9832 1.085741\n", " rmse_c\n", "1 1.375038\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Apr17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K loglk AIC rmse_i rmse_c\n", "1 3.92412 2.91365 2.454593 4.424606 63.0124 -273.625 553.25 2.808329 2.719264\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Apr25\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K loglk AIC rmse_i\n", "1 3.95583 2.91829 2.474558 4.459547 80.99987 -483.1372 972.2744 3.343216\n", " rmse_c\n", "1 3.175626\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May03\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K loglk AIC rmse_i\n", "1 4.181956 4.216275 2.144271 4.722104 103.9999 -902.1055 1810.211 4.088817\n", " rmse_c\n", "1 3.783279\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May11\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K loglk AIC rmse_i\n", "1 4.411256 5.55761 1.951456 4.974953 117.0001 -1234.551 2475.101 4.316133\n", " rmse_c\n", "1 3.843505\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K loglk AIC rmse_i rmse_c\n", "1 4.481232 6.066929 1.891583 5.049322 121 -1353.2 2712.4 4.241854 3.736616\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May25\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K loglk AIC rmse_i rmse_c\n", "1 4.505738 6.104282 1.896587 5.077376 121 -1354.903 2715.805 3.997477 3.51448\n" ] } ], "source": [ "nsmpl = 1e3\n", "\n", "final_two_generations = NULL\n", "pars_final_two_generations = NULL\n", "# initial parameter values used in optim function\n", "pars = c(5,4,30)\n", "options(warn=-1)\n", "for (current_epicurve in unique(df$epicurve)) { \n", " message(current_epicurve)\n", " \n", " df %>% \n", " filter(epicurve==current_epicurve) %>% \n", " select(-epicurve) -> df_current\n", "\n", " Df = data.frame(day=0:(unclass(as.Date('2018'%&%current_epicurve,\"%Y%b%d\"))-unclass(as.Date('2018-03-17'))))\n", "\n", " df_current %>% \n", " filter(day_onset>0) %>% #removing index case\n", " group_by(day_onset) %>%\n", " count %>%\n", " rename(day=day_onset) %>%\n", " right_join(Df,by=\"day\") %>%\n", " mutate(n=ifelse(is.na(n),0,n)) %>%\n", " rename(i=n) -> Df\n", "\n", " df_current %>%\n", " filter(day_onset>0) %>% #removing index case\n", " group_by(day_confirmation) %>%\n", " count %>%\n", " rename(day=day_confirmation) %>%\n", " right_join(Df,by=\"day\") %>%\n", " mutate(n=ifelse(is.na(n),0,n)) %>%\n", " rename(c=n) %>%\n", " select(day,i,c) %>%\n", " arrange(day) -> Df\n", "\n", " sol = optim(c(pars[1:2],pars[3]+10),calculate_two_generations,method=\"L-BFGS-B\",control=list(fnscale=-1),lower=rep(0,length(pars)),hessian=TRUE)\n", " pars = sol$par\n", " \n", " # Weibull distribution mean and variance\n", " pars_h = c(pars[2]*gamma(1+1/pars[1]),pars[2]^2*(gamma(1+2/pars[1])-(gamma(1+1/pars[1]))^2))\n", " \n", " # Maximum likelihood estimates (MLE)\n", " dfMLE = calculate_two_generations(sol$par,prediction=TRUE) %>% rename(`MLE_i`=`lambda_i`,`MLE_c`=`lambda_c`)\n", " \n", " # RMSE\n", " calculate_two_generations(sol$par,prediction=TRUE) %>%\n", " na.omit %>%\n", " mutate(epsilon_i = (lambda_i-i), epsilon_c = (lambda_c-c)) %>%\n", " summarize(rmse_i = sqrt(sum(epsilon_i^2)/n()),rmse_c = sqrt(sum(epsilon_c^2)/n())) %>% as.numeric -> rmse\n", " \n", " npars = length(pars)\n", " output = data.frame(t(c(pars_h,pars,sol$value,2*(npars-sol$value),rmse)))\n", " colnames(output) = c(\"mean_h\",\"var_h\",\"par1_h\",\"par2_h\",\"K\",\"loglk\",\"AIC\",\"rmse_i\",\"rmse_c\")\n", "\n", " print(output) \n", " \n", " tryCatch({\n", " hess_sam = MASS::mvrnorm(n=nsmpl, mu=sol$par, \n", " Sigma=solve(-sol$hessian),\n", " tol=1e-06, empirical=FALSE, EISPACK=FALSE)\n", " hess_sam %>% as.data.frame -> df_hess\n", " colnames(df_hess) = c(\"h1\",\"h2\",\"K\")\n", "\n", " data.frame(\n", " h1=filter(df_hess,h1 > quantile(h1, 0.025) & h1 < quantile(h1, 0.975))$h1,\n", " h2=filter(df_hess,h2 > quantile(h2, 0.025) & h2 < quantile(h2, 0.975))$h2,\n", " K=filter(df_hess,K > quantile(K, 0.025) & K < quantile(K, 0.975))$K\n", " ) -> df_perturb\n", " \n", " df_perturb[df_perturb<0] = 0 # if some parameters from CIs become negative, we truncate them to zero\n", " \n", " df_perturb %<>% \n", " mutate(mean_h=h2*gamma(1+1/h1),var_h=h2^2*(gamma(1+2/h1)-(gamma(1+1/h1))^2))\n", " \n", " output2 = data.frame(t(c(df_perturb %>% summarize_all(min) %>% as.numeric, \n", " df_perturb %>% summarize_all(max) %>% as.numeric)))\n", " colnames(output2) = c(\"h1_CIlower\",\"h2_CIlower\",\"K_CIlower\",\"mean_h_CIlower\",\"var_h_CIlower\",\n", " \"h1_CIupper\",\"h2_CIupper\",\"K_CIupper\",\"mean_h_CIupper\",\"var_h_CIupper\")\n", " output %<>% cbind(output2)\n", " \n", " df_perturb %<>% select(-mean_h,-var_h)\n", " \n", " output %>%\n", " gather(parameter,estimate) %>%\n", " mutate(epicurve=current_epicurve) %>%\n", " rbind(pars_final_two_generations) -> pars_final_two_generations\n", "\n", " res = NULL\n", " for (i in 1:nrow(df_perturb)) {\n", " res = rbind(res,\n", " calculate_two_generations(as.numeric(df_perturb[i,]),prediction=TRUE) %>% \n", " select(day,contains(\"lambda\")))\n", " }\n", "\n", " res %>% \n", " group_by(day) %>%\n", " summarize(c025_i=quantile(lambda_i,0.025),median_i=median(lambda_i),c975_i=quantile(lambda_i,0.975),\n", " c025_c=quantile(lambda_c,0.025),median_c=median(lambda_c),c975_c=quantile(lambda_c,0.975)) -> result\n", "\n", " result %>% \n", " mutate(epicurve=current_epicurve) %>%\n", " left_join(Df,by=\"day\") %>%\n", " left_join(dfMLE %>% select(one_of(\"day\",\"MLE_i\",\"MLE_c\")),by=\"day\") %>%\n", " rbind(final_two_generations) -> final_two_generations},\n", " error=function(cond){print(cond)})\n", "}\n", "options(warn=0) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Three generations" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Apr01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 loglk AIC\n", "1 4.043375 3.929088 2.148032 4.56564 55.00361 0.942021 -37.18387 82.36774\n", " rmse_i rmse_c\n", "1 1.678754 0.8764463\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Apr09\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 loglk AIC\n", "1 4.061809 3.803687 2.198327 4.586387 68.10914 1.206308 -98.06433 204.1287\n", " rmse_i rmse_c\n", "1 1.575942 1.365755\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Apr17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 loglk AIC\n", "1 4.0686 3.037541 2.497002 4.585693 75.93085 1.562446 -168.2095 344.4191\n", " rmse_i rmse_c\n", "1 1.423323 1.574114\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Apr25\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 loglk AIC\n", "1 3.96996 2.944498 2.472058 4.47558 81.85764 1.796067 -227.8017 463.6033\n", " rmse_i rmse_c\n", "1 1.395616 1.759488\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May03\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 loglk AIC\n", "1 4.182729 4.219104 2.143912 4.722976 104.0288 2.680026 -367.6309 743.2619\n", " rmse_i rmse_c\n", "1 2.092078 2.423855\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May11\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 loglk AIC\n", "1 4.411276 5.557732 1.951442 4.974974 117.0004 3.206211 -503.2963 1014.593\n", " rmse_i rmse_c\n", "1 2.38226 2.577793\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 loglk AIC\n", "1 4.468838 6.048349 1.888994 5.035126 122.0004 3.410735 -569.8415 1147.683\n", " rmse_i rmse_c\n", "1 2.390018 2.55106\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May25\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 loglk AIC\n", "1 4.505307 6.100825 1.896981 5.076924 122.9987 3.451793 -585.4792 1178.958\n", " rmse_i rmse_c\n", "1 2.279852 2.419238\n" ] } ], "source": [ "final_three_generations = NULL\n", "pars_final_three_generations = NULL\n", "pars = c(2,4,50,1)\n", "options(warn=-1)\n", "for (current_epicurve in unique(df$epicurve)) { \n", " message(current_epicurve)\n", " # initial parameter values used in optim function\n", " \n", " df %>% \n", " filter(epicurve==current_epicurve) %>% \n", " select(-epicurve) -> df_current\n", "\n", " Df = data.frame(day=0:(unclass(as.Date('2018'%&%current_epicurve,\"%Y%b%d\"))-unclass(as.Date('2018-03-17'))))\n", "\n", " df_current %>% \n", " filter(day_onset>0) %>% #removing index case\n", " group_by(day_onset) %>%\n", " count %>%\n", " rename(day=day_onset) %>%\n", " right_join(Df,by=\"day\") %>%\n", " mutate(n=ifelse(is.na(n),0,n)) %>%\n", " rename(i=n) -> Df\n", "\n", " df_current %>%\n", " filter(day_onset>0) %>% #removing index case\n", " group_by(day_confirmation) %>%\n", " count %>%\n", " rename(day=day_confirmation) %>%\n", " right_join(Df,by=\"day\") %>%\n", " mutate(n=ifelse(is.na(n),0,n)) %>%\n", " rename(c=n) %>%\n", " select(day,i,c) %>%\n", " arrange(day) -> Df\n", " \n", "\n", " sol = optim(c(pars[1:2],pars[3]+5,pars[4]),calculate_three_generations,method=\"L-BFGS-B\",control=list(fnscale=-1),lower=rep(0,length(pars)),hessian=TRUE)\n", " pars = sol$par\n", " \n", " # Weibull distribution mean and variance\n", " pars_h = c(pars[2]*gamma(1+1/pars[1]),pars[2]^2*(gamma(1+2/pars[1])-(gamma(1+1/pars[1]))^2))\n", " \n", " # Maximum likelihood estimates (MLE)\n", " dfMLE = calculate_three_generations(sol$par,prediction=TRUE) %>% rename(`MLE_i`=`lambda_i`,`MLE_c`=`lambda_c`)\n", " \n", " # RMSE\n", " calculate_three_generations(sol$par,prediction=TRUE) %>%\n", " na.omit %>%\n", " mutate(epsilon_i = (lambda_i-i), epsilon_c = (lambda_c-c)) %>%\n", " summarize(rmse_i = sqrt(sum(epsilon_i^2)/n()),rmse_c = sqrt(sum(epsilon_c^2)/n())) %>% as.numeric -> rmse\n", " \n", " npars = length(pars)\n", " output = data.frame(t(c(pars_h,pars,sol$value,2*(npars-sol$value),rmse)))\n", " colnames(output) = c(\"mean_h\",\"var_h\",\"par1_h\",\"par2_h\",\"K\",\"R2\",\"loglk\",\"AIC\",\"rmse_i\",\"rmse_c\")\n", "\n", " print(output) \n", " \n", " tryCatch({\n", " hess_sam = MASS::mvrnorm(n=nsmpl, mu=sol$par, \n", " Sigma=solve(-sol$hessian),\n", " tol=1e-06, empirical=FALSE, EISPACK=FALSE)\n", " hess_sam %>% as.data.frame -> df_hess\n", " colnames(df_hess) = c(\"h1\",\"h2\",\"K\",\"R2\")\n", "\n", " data.frame(\n", " h1=filter(df_hess,h1 > quantile(h1, 0.025) & h1 < quantile(h1, 0.975))$h1,\n", " h2=filter(df_hess,h2 > quantile(h2, 0.025) & h2 < quantile(h2, 0.975))$h2,\n", " K=filter(df_hess,K > quantile(K, 0.025) & K < quantile(K, 0.975))$K,\n", " R2=filter(df_hess,R2 > quantile(R2, 0.025) & R2 < quantile(R2, 0.975))$R2\n", " ) -> df_perturb\n", " \n", " df_perturb[df_perturb<0] = 0 # if some parameters from CIs become negative, we truncate them to zero\n", " \n", " df_perturb %<>% \n", " mutate(mean_h=h2*gamma(1+1/h1),var_h=h2^2*(gamma(1+2/h1)-(gamma(1+1/h1))^2))\n", " \n", " output2 = data.frame(t(c(df_perturb %>% summarize_all(min) %>% as.numeric, \n", " df_perturb %>% summarize_all(max) %>% as.numeric)))\n", " colnames(output2) = c(\"h1_CIlower\",\"h2_CIlower\",\"K_CIlower\",\"R2_CIlower\",\"mean_h_CIlower\",\"var_h_CIlower\",\n", " \"h1_CIupper\",\"h2_CIupper\",\"K_CIupper\",\"R2_CIupper\",\"mean_h_CIupper\",\"var_h_CIupper\")\n", " output %<>% cbind(output2)\n", " \n", " df_perturb %<>% select(-mean_h,-var_h)\n", " \n", " output %>%\n", " gather(parameter,estimate) %>%\n", " mutate(epicurve=current_epicurve) %>%\n", " rbind(pars_final_three_generations) -> pars_final_three_generations\n", "\n", " res = NULL\n", " for (i in 1:nrow(df_perturb)) {\n", " res = rbind(res,\n", " calculate_three_generations(as.numeric(df_perturb[i,]),prediction=TRUE) %>% \n", " select(day,contains(\"lambda\")))\n", " }\n", "\n", " res %>% \n", " group_by(day) %>%\n", " summarize(c025_i=quantile(lambda_i,0.025),median_i=median(lambda_i),c975_i=quantile(lambda_i,0.975),\n", " c025_c=quantile(lambda_c,0.025),median_c=median(lambda_c),c975_c=quantile(lambda_c,0.975)) -> result\n", "\n", " result %>% \n", " mutate(epicurve=current_epicurve) %>%\n", " left_join(Df,by=\"day\") %>%\n", " left_join(dfMLE %>% select(one_of(\"day\",\"MLE_i\",\"MLE_c\")),by=\"day\") %>%\n", " rbind(final_three_generations) -> final_three_generations},\n", " error=function(cond){print(cond)})\n", "}\n", "options(warn=0) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Four generations" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "May25\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 loglk\n", "1 4.505385 6.100896 1.897005 5.077014 123.0019 1.231521 1.492512 -385.3901\n", " AIC rmse_i rmse_c\n", "1 780.7802 1.362435 1.737148\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 loglk\n", "1 4.470651 6.056689 1.888401 5.037115 122.036 1.235419 1.459959 -379.2837\n", " AIC rmse_i rmse_c\n", "1 768.5674 1.421695 1.83589\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May11\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 loglk\n", "1 4.424447 5.602962 1.949123 4.989681 117.5911 1.255956 1.306539 -353.1258\n", " AIC rmse_i rmse_c\n", "1 716.2517 1.450414 1.87779\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May03\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 loglk\n", "1 4.244364 4.325074 2.149232 4.792594 109.5891 1.298885 1.033695 -294.6049\n", " AIC rmse_i rmse_c\n", "1 599.2098 1.36427 1.837281\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Apr25\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 loglk\n", "1 4.038411 2.96489 2.510024 4.551081 98.94443 1.341174 0.7126048 -219.0389\n", " AIC rmse_i rmse_c\n", "1 448.0779 1.42144 1.607982\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Apr17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 loglk AIC\n", "1 4.068598 3.037498 2.49702 4.58569 75.93068 1.562429 0 -168.2095 346.4191\n", " rmse_i rmse_c\n", "1 1.423319 1.574113\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Apr09\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 loglk\n", "1 4.062064 3.803468 2.198552 4.586675 85.9197 1.20725 0.4770969 -98.06528\n", " AIC rmse_i rmse_c\n", "1 206.1306 1.58002 1.365827\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Apr01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 loglk AIC\n", "1 4.043177 3.921509 2.150226 4.565423 95.93348 2.396707 0 -37.16367 84.32734\n", " rmse_i rmse_c\n", "1 1.705506 0.876105\n", "\n" ] } ], "source": [ "final_four_generations = NULL\n", "pars_final_four_generations = NULL\n", "pars = c(2,4,50,1,.5) # initial parameter values used in optim function\n", "options(warn=-1)\n", "for (current_epicurve in rev(unique(df$epicurve))) { \n", " message(current_epicurve)\n", " \n", " df %>% \n", " filter(epicurve==current_epicurve) %>% \n", " select(-epicurve) -> df_current\n", "\n", " Df = data.frame(day=0:(unclass(as.Date('2018'%&%current_epicurve,\"%Y%b%d\"))-unclass(as.Date('2018-03-17'))))\n", "\n", " df_current %>% \n", " filter(day_onset>0) %>% #removing index case\n", " group_by(day_onset) %>%\n", " count %>%\n", " rename(day=day_onset) %>%\n", " right_join(Df,by=\"day\") %>%\n", " mutate(n=ifelse(is.na(n),0,n)) %>%\n", " rename(i=n) -> Df\n", "\n", " df_current %>%\n", " filter(day_onset>0) %>% #removing index case\n", " group_by(day_confirmation) %>%\n", " count %>%\n", " rename(day=day_confirmation) %>%\n", " right_join(Df,by=\"day\") %>%\n", " mutate(n=ifelse(is.na(n),0,n)) %>%\n", " rename(c=n) %>%\n", " select(day,i,c) %>%\n", " arrange(day) -> Df\n", " \n", "\n", " sol = optim(c(pars[1:2],pars[3]+10,pars[4:5]),calculate_four_generations,method=\"L-BFGS-B\",control=list(fnscale=-1),lower=rep(0,3),hessian=TRUE)\n", " pars = sol$par\n", " \n", " # Weibull distribution mean and variance\n", " pars_h = c(pars[2]*gamma(1+1/pars[1]),pars[2]^2*(gamma(1+2/pars[1])-(gamma(1+1/pars[1]))^2))\n", " \n", " # Maximum likelihood estimates (MLE)\n", " dfMLE = calculate_four_generations(sol$par,prediction=TRUE) %>% rename(`MLE_i`=`lambda_i`,`MLE_c`=`lambda_c`)\n", " \n", " # RMSE\n", " calculate_four_generations(sol$par,prediction=TRUE) %>%\n", " na.omit %>%\n", " mutate(epsilon_i = (lambda_i-i), epsilon_c = (lambda_c-c)) %>%\n", " summarize(rmse_i = sqrt(sum(epsilon_i^2)/n()),rmse_c = sqrt(sum(epsilon_c^2)/n())) %>% as.numeric -> rmse\n", " \n", " npars = length(pars)\n", " output = data.frame(t(c(pars_h,pars,sol$value,2*(npars-sol$value),rmse)))\n", " colnames(output) = c(\"mean_h\",\"var_h\",\"par1_h\",\"par2_h\",\"K\",\"R2\",\"R3\",\"loglk\",\"AIC\",\"rmse_i\",\"rmse_c\")\n", "\n", " print(output) \n", " \n", " tryCatch({\n", " hess_sam = MASS::mvrnorm(n=nsmpl, mu=sol$par, \n", " Sigma=solve(-sol$hessian),\n", " tol=1e-06, empirical=FALSE, EISPACK=FALSE)\n", " hess_sam %>% as.data.frame -> df_hess\n", " colnames(df_hess) = c(\"h1\",\"h2\",\"K\",\"R2\",\"R3\")\n", "\n", " data.frame(\n", " h1=filter(df_hess,h1 > quantile(h1, 0.025) & h1 < quantile(h1, 0.975))$h1,\n", " h2=filter(df_hess,h2 > quantile(h2, 0.025) & h2 < quantile(h2, 0.975))$h2,\n", " K=filter(df_hess,K > quantile(K, 0.025) & K < quantile(K, 0.975))$K,\n", " R2=filter(df_hess,R2 > quantile(R2, 0.025) & R2 < quantile(R2, 0.975))$R2,\n", " R3=filter(df_hess,R3 > quantile(R3, 0.025) & R3 < quantile(R3, 0.975))$R3\n", " ) -> df_perturb\n", " \n", " df_perturb[df_perturb<0] = 0 # if some parameters from CIs become negative, we truncate them to zero\n", " \n", " df_perturb %<>% \n", " mutate(mean_h=h2*gamma(1+1/h1),var_h=h2^2*(gamma(1+2/h1)-(gamma(1+1/h1))^2))\n", " \n", " output2 = data.frame(t(c(df_perturb %>% summarize_all(min) %>% as.numeric, \n", " df_perturb %>% summarize_all(max) %>% as.numeric)))\n", " colnames(output2) = c(\"h1_CIlower\",\"h2_CIlower\",\"K_CIlower\",\"R2_CIlower\",\"R3_CIlower\",\"mean_h_CIlower\",\"var_h_CIlower\",\n", " \"h1_CIupper\",\"h2_CIupper\",\"K_CIupper\",\"R2_CIupper\",\"R3_CIupper\",\"mean_h_CIupper\",\"var_h_CIupper\")\n", " output %<>% cbind(output2)\n", " \n", " df_perturb %<>% select(-mean_h,-var_h)\n", " \n", " output %>%\n", " gather(parameter,estimate) %>%\n", " mutate(epicurve=current_epicurve) %>%\n", " rbind(pars_final_four_generations) -> pars_final_four_generations\n", " \n", " res = NULL\n", " for (i in 1:nrow(df_perturb)) {\n", " res = rbind(res,\n", " calculate_four_generations(as.numeric(df_perturb[i,]),prediction=TRUE) %>% \n", " select(day,contains(\"lambda\")))\n", " }\n", "\n", " res %>% \n", " group_by(day) %>%\n", " summarize(c025_i=quantile(lambda_i,0.025),median_i=median(lambda_i),c975_i=quantile(lambda_i,0.975),\n", " c025_c=quantile(lambda_c,0.025),median_c=median(lambda_c),c975_c=quantile(lambda_c,0.975)) -> result\n", "\n", " result %>% \n", " mutate(epicurve=current_epicurve) %>%\n", " left_join(Df,by=\"day\") %>%\n", " left_join(dfMLE %>% select(one_of(\"day\",\"MLE_i\",\"MLE_c\")),by=\"day\") %>%\n", " rbind(final_four_generations) -> final_four_generations}, \n", " error=function(cond){print(cond)})\n", "}\n", "options(warn=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Five generations" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "May25\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 R4\n", "1 4.510033 6.117942 1.89624 5.082185 123.2087 1.320717 0.9124833 0.4764253\n", " loglk AIC rmse_i rmse_c\n", "1 -368.9547 749.9095 1.204524 1.608375\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 R4\n", "1 4.500801 6.134764 1.889065 5.071145 124.3386 1.325099 0.8917298 0.5351187\n", " loglk AIC rmse_i rmse_c\n", "1 -364.4321 740.8643 1.26653 1.702766\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May11\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 R4\n", "1 4.480769 5.690116 1.959821 5.053868 125.5775 1.33297 0.8529986 0.6264135\n", " loglk AIC rmse_i rmse_c\n", "1 -345.5786 703.1571 1.372707 1.777549\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May03\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 R4\n", "1 4.293687 4.32566 2.176918 4.848307 142.1119 1.351254 0.7622644 1.317261\n", " loglk AIC rmse_i rmse_c\n", "1 -293.3547 598.7095 1.572422 1.771923\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Apr25\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 R4\n", "1 4.040469 2.957629 2.514895 4.553177 152.0903 1.35062 0.6565181 2.078147\n", " loglk AIC rmse_i rmse_c\n", "1 -218.909 449.8179 1.487563 1.60544\n" ] } ], "source": [ "final_five_generations = NULL\n", "pars_final_five_generations = NULL\n", "# initial parameter values used in optim function\n", "pars = c(2,4,50,1,.5,.5)\n", "options(warn=-1)\n", "for (current_epicurve in rev(unique(df$epicurve))[1:5]) { \n", " message(current_epicurve)\n", " \n", " df %>% \n", " filter(epicurve==current_epicurve) %>% \n", " select(-epicurve) -> df_current\n", "\n", " Df = data.frame(day=0:(unclass(as.Date('2018'%&%current_epicurve,\"%Y%b%d\"))-unclass(as.Date('2018-03-17'))))\n", "\n", " df_current %>% \n", " filter(day_onset>0) %>% #removing index case\n", " group_by(day_onset) %>%\n", " count %>%\n", " rename(day=day_onset) %>%\n", " right_join(Df,by=\"day\") %>%\n", " mutate(n=ifelse(is.na(n),0,n)) %>%\n", " rename(i=n) -> Df\n", "\n", " df_current %>%\n", " filter(day_onset>0) %>% #removing index case\n", " group_by(day_confirmation) %>%\n", " count %>%\n", " rename(day=day_confirmation) %>%\n", " right_join(Df,by=\"day\") %>%\n", " mutate(n=ifelse(is.na(n),0,n)) %>%\n", " rename(c=n) %>%\n", " select(day,i,c) %>%\n", " arrange(day) -> Df\n", " \n", "\n", " sol = optim(c(pars[1:2],pars[3]+10,pars[4:6]),calculate_five_generations,method=\"L-BFGS-B\",control=list(fnscale=-1),lower=rep(0,length(pars)),hessian=TRUE)\n", " pars = sol$par \n", " \n", " # Weibull distribution mean and variance\n", " pars_h = c(pars[2]*gamma(1+1/pars[1]),pars[2]^2*(gamma(1+2/pars[1])-(gamma(1+1/pars[1]))^2))\n", " \n", " # Maximum likelihood estimates (MLE)\n", " dfMLE = calculate_five_generations(sol$par,prediction=TRUE) %>% rename(`MLE_i`=`lambda_i`,`MLE_c`=`lambda_c`)\n", " \n", " # RMSE\n", " calculate_five_generations(sol$par,prediction=TRUE) %>%\n", " na.omit %>%\n", " mutate(epsilon_i = (lambda_i-i), epsilon_c = (lambda_c-c)) %>%\n", " summarize(rmse_i = sqrt(sum(epsilon_i^2)/n()),rmse_c = sqrt(sum(epsilon_c^2)/n())) %>% as.numeric -> rmse\n", " \n", " npars = length(pars)\n", " output = data.frame(t(c(pars_h,pars,sol$value,2*(npars-sol$value),rmse)))\n", " colnames(output) = c(\"mean_h\",\"var_h\",\"par1_h\",\"par2_h\",\"K\",\"R2\",\"R3\",\"R4\",\"loglk\",\"AIC\",\"rmse_i\",\"rmse_c\")\n", "\n", " print(output) \n", " \n", " tryCatch({\n", " hess_sam = MASS::mvrnorm(n=nsmpl, mu=sol$par, \n", " Sigma=solve(-sol$hessian),\n", " tol=1e-06, empirical=FALSE, EISPACK=FALSE)\n", " hess_sam %>% as.data.frame -> df_hess\n", " colnames(df_hess) = c(\"h1\",\"h2\",\"K\",\"R2\",\"R3\",\"R4\")\n", "\n", " data.frame(\n", " h1=filter(df_hess,h1 > quantile(h1, 0.025) & h1 < quantile(h1, 0.975))$h1,\n", " h2=filter(df_hess,h2 > quantile(h2, 0.025) & h2 < quantile(h2, 0.975))$h2,\n", " K=filter(df_hess,K > quantile(K, 0.025) & K < quantile(K, 0.975))$K,\n", " R2=filter(df_hess,R2 > quantile(R2, 0.025) & R2 < quantile(R2, 0.975))$R2,\n", " R3=filter(df_hess,R3 > quantile(R3, 0.025) & R3 < quantile(R3, 0.975))$R3,\n", " R4=filter(df_hess,R4 > quantile(R4, 0.025) & R4 < quantile(R4, 0.975))$R4\n", " ) -> df_perturb\n", " \n", " df_perturb[df_perturb<0] = 0 # if some parameters from CIs become negative, we truncate them to zero\n", " \n", " df_perturb %<>% \n", " mutate(mean_h=h2*gamma(1+1/h1),var_h=h2^2*(gamma(1+2/h1)-(gamma(1+1/h1))^2))\n", " \n", " output2 = data.frame(t(c(df_perturb %>% summarize_all(min) %>% as.numeric, \n", " df_perturb %>% summarize_all(max) %>% as.numeric)))\n", " colnames(output2) = c(\"h1_CIlower\",\"h2_CIlower\",\"K_CIlower\",\"R2_CIlower\",\"R3_CIlower\",\"R4_CIlower\",\"mean_h_CIlower\",\"var_h_CIlower\",\n", " \"h1_CIupper\",\"h2_CIupper\",\"K_CIupper\",\"R2_CIupper\",\"R3_CIupper\",\"R4_CIupper\",\"mean_h_CIupper\",\"var_h_CIupper\")\n", " output %<>% cbind(output2)\n", " \n", " df_perturb %<>% select(-mean_h,-var_h)\n", " \n", " output %>%\n", " gather(parameter,estimate) %>%\n", " mutate(epicurve=current_epicurve) %>%\n", " rbind(pars_final_five_generations) -> pars_final_five_generations\n", "\n", " res = NULL\n", " for (i in 1:nrow(df_perturb)) {\n", " res = rbind(res,\n", " calculate_five_generations(as.numeric(df_perturb[i,]),prediction=TRUE) %>% \n", " select(day,contains(\"lambda\")))\n", " }\n", "\n", " res %>% \n", " group_by(day) %>%\n", " summarize(c025_i=quantile(lambda_i,0.025),median_i=median(lambda_i),c975_i=quantile(lambda_i,0.975),\n", " c025_c=quantile(lambda_c,0.025),median_c=median(lambda_c),c975_c=quantile(lambda_c,0.975)) -> result\n", "\n", " result %>% \n", " mutate(epicurve=current_epicurve) %>%\n", " left_join(Df,by=\"day\") %>%\n", " left_join(dfMLE %>% select(one_of(\"day\",\"MLE_i\",\"MLE_c\")),by=\"day\") %>%\n", " rbind(final_five_generations) -> final_five_generations},\n", " error=function(cond){print(cond)})\n", "}\n", "options(warn=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Six generations" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "May25\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 R4\n", "1 4.509962 6.11754 1.896276 5.082108 123.2098 1.320998 0.9123912 0.3351625\n", " R5 loglk AIC rmse_i rmse_c\n", "1 0.4215619 -368.9547 751.9095 1.204519 1.608389\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 R4\n", "1 4.500689 6.133833 1.889172 5.071029 124.3417 1.325124 0.8917507 0.3813006\n", " R5 loglk AIC rmse_i rmse_c\n", "1 0.4033538 -364.4321 742.8643 1.266531 1.702765\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May11\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 R4\n", "1 4.48078 5.690438 1.959765 5.053877 125.5748 1.332983 0.8531439 0.4551575\n", " R5 loglk AIC rmse_i rmse_c\n", "1 0.3761764 -345.5786 705.1571 1.372709 1.77756\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "May03\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 R4\n", "1 4.293689 4.325764 2.17689 4.848309 142.095 1.351341 0.7623126 0.862254\n", " R5 loglk AIC rmse_i rmse_c\n", "1 0.5270132 -293.3547 600.7095 1.572263 1.771947\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Apr25\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " mean_h var_h par1_h par2_h K R2 R3 R4\n", "1 4.040527 2.957701 2.514901 4.553243 152.0894 1.35066 0.6565404 1.200561\n", " R5 loglk AIC rmse_i rmse_c\n", "1 0.7309249 -218.909 451.8179 1.487571 1.605442\n" ] } ], "source": [ "final_six_generations = NULL\n", "pars_final_six_generations = NULL\n", "pars = c(2,4,50,1,.5,.5,.25)\n", "options(warn=-1)\n", "for (current_epicurve in rev(unique(df$epicurve))[1:5]) { \n", " message(current_epicurve)\n", " \n", " df %>% \n", " filter(epicurve==current_epicurve) %>% \n", " select(-epicurve) -> df_current\n", "\n", " Df = data.frame(day=0:(unclass(as.Date('2018'%&%current_epicurve,\"%Y%b%d\"))-unclass(as.Date('2018-03-17'))))\n", "\n", " df_current %>% \n", " filter(day_onset>0) %>% #removing index case\n", " group_by(day_onset) %>%\n", " count %>%\n", " rename(day=day_onset) %>%\n", " right_join(Df,by=\"day\") %>%\n", " mutate(n=ifelse(is.na(n),0,n)) %>%\n", " rename(i=n) -> Df\n", "\n", " df_current %>%\n", " filter(day_onset>0) %>% #removing index case\n", " group_by(day_confirmation) %>%\n", " count %>%\n", " rename(day=day_confirmation) %>%\n", " right_join(Df,by=\"day\") %>%\n", " mutate(n=ifelse(is.na(n),0,n)) %>%\n", " rename(c=n) %>%\n", " select(day,i,c) %>%\n", " arrange(day) -> Df\n", "\n", " sol = optim(c(pars[1:2],pars[3]+10,pars[4:7]),calculate_six_generations,method=\"L-BFGS-B\",control=list(fnscale=-1),lower=rep(0,length(pars)),hessian=TRUE)\n", " pars = sol$par \n", " \n", " # Weibull distribution mean and variance\n", " pars_h = c(pars[2]*gamma(1+1/pars[1]),pars[2]^2*(gamma(1+2/pars[1])-(gamma(1+1/pars[1]))^2))\n", " \n", " # Maximum likelihood estimates (MLE)\n", " dfMLE = calculate_six_generations(sol$par,prediction=TRUE) %>% rename(`MLE_i`=`lambda_i`,`MLE_c`=`lambda_c`)\n", " \n", " # RMSE\n", " calculate_six_generations(sol$par,prediction=TRUE) %>%\n", " na.omit %>%\n", " mutate(epsilon_i = (lambda_i-i), epsilon_c = (lambda_c-c)) %>%\n", " summarize(rmse_i = sqrt(sum(epsilon_i^2)/n()),rmse_c = sqrt(sum(epsilon_c^2)/n())) %>% as.numeric -> rmse\n", " \n", " npars = length(pars)\n", " output = data.frame(t(c(pars_h,pars,sol$value,2*(npars-sol$value),rmse)))\n", " colnames(output) = c(\"mean_h\",\"var_h\",\"par1_h\",\"par2_h\",\"K\",\"R2\",\"R3\",\"R4\",\"R5\",\"loglk\",\"AIC\",\"rmse_i\",\"rmse_c\")\n", "\n", " print(output) \n", " \n", " tryCatch({\n", " hess_sam = MASS::mvrnorm(n=nsmpl, mu=sol$par, \n", " Sigma=solve(-sol$hessian),\n", " tol=1e-06, empirical=FALSE, EISPACK=FALSE)\n", " hess_sam %>% as.data.frame -> df_hess\n", " colnames(df_hess) = c(\"h1\",\"h2\",\"K\",\"R2\",\"R3\",\"R4\",\"R5\")\n", "\n", " data.frame(\n", " h1=filter(df_hess,h1 > quantile(h1, 0.025) & h1 < quantile(h1, 0.975))$h1,\n", " h2=filter(df_hess,h2 > quantile(h2, 0.025) & h2 < quantile(h2, 0.975))$h2,\n", " K=filter(df_hess,K > quantile(K, 0.025) & K < quantile(K, 0.975))$K,\n", " R2=filter(df_hess,R2 > quantile(R2, 0.025) & R2 < quantile(R2, 0.975))$R2,\n", " R3=filter(df_hess,R3 > quantile(R3, 0.025) & R3 < quantile(R3, 0.975))$R3,\n", " R4=filter(df_hess,R4 > quantile(R4, 0.025) & R4 < quantile(R4, 0.975))$R4,\n", " R5=filter(df_hess,R5 > quantile(R5, 0.025) & R5 < quantile(R5, 0.975))$R5\n", " ) -> df_perturb\n", " \n", " df_perturb[df_perturb<0] = 0 # if some parameters from CIs become negative, we truncate them to zero\n", " \n", " df_perturb %<>% \n", " mutate(mean_h=h2*gamma(1+1/h1),var_h=h2^2*(gamma(1+2/h1)-(gamma(1+1/h1))^2))\n", " \n", " output2 = data.frame(t(c(df_perturb %>% summarize_all(min) %>% as.numeric, \n", " df_perturb %>% summarize_all(max) %>% as.numeric)))\n", " colnames(output2) = c(\"h1_CIlower\",\"h2_CIlower\",\"K_CIlower\",\"R2_CIlower\",\"R3_CIlower\",\"R4_CIlower\",\"R5_CIlower\",\"mean_h_CIlower\",\"var_h_CIlower\",\n", " \"h1_CIupper\",\"h2_CIupper\",\"K_CIupper\",\"R2_CIupper\",\"R3_CIupper\",\"R4_CIupper\",\"R5_CIupper\",\"mean_h_CIupper\",\"var_h_CIupper\")\n", " output %<>% cbind(output2)\n", " \n", " df_perturb %<>% select(-mean_h,-var_h)\n", " \n", " output %>%\n", " gather(parameter,estimate) %>%\n", " mutate(epicurve=current_epicurve) %>%\n", " rbind(pars_final_six_generations) -> pars_final_six_generations\n", "\n", " res = NULL\n", " for (i in 1:nrow(df_perturb)) {\n", " res = rbind(res,\n", " calculate_six_generations(as.numeric(df_perturb[i,]),prediction=TRUE) %>% \n", " select(day,contains(\"lambda\")))\n", " }\n", "\n", " res %>% \n", " group_by(day) %>%\n", " summarize(c025_i=quantile(lambda_i,0.025),median_i=median(lambda_i),c975_i=quantile(lambda_i,0.975),\n", " c025_c=quantile(lambda_c,0.025),median_c=median(lambda_c),c975_c=quantile(lambda_c,0.975)) -> result\n", "\n", " result %>% \n", " mutate(epicurve=current_epicurve) %>%\n", " left_join(Df,by=\"day\") %>%\n", " left_join(dfMLE %>% select(one_of(\"day\",\"MLE_i\",\"MLE_c\")),by=\"day\") %>%\n", " rbind(final_six_generations) -> final_six_generations},\n", " error=function(cond){print(cond)})\n", "}\n", "options(warn=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aggregation of the results" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
generationsdayc025_imedian_ic975_ic025_cmedian_cc975_cepicurveicMLE_iMLE_c
2 0 0.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+00May25 0 0 0.000000e+000.000000e+00
2 1 7.048828e-108.286777e-109.569254e-100.000000e+000.000000e+000.000000e+00May25 0 0 8.323354e-100.000000e+00
2 2 7.963221e-069.361760e-061.081060e-052.460082e-113.763182e-115.641193e-11May25 0 0 9.403082e-063.733066e-11
2 3 1.132356e-031.331226e-031.537249e-032.779970e-074.252271e-076.374123e-07May25 0 0 1.337102e-034.218264e-07
2 4 2.631172e-023.093271e-023.571992e-024.037793e-056.149440e-059.191020e-05May25 0 0 3.106925e-026.102488e-05
2 5 2.226063e-012.617015e-013.022029e-011.040708e-031.554836e-032.290330e-03May25 0 0 2.628567e-011.544922e-03
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllll}\n", " generations & day & c025\\_i & median\\_i & c975\\_i & c025\\_c & median\\_c & c975\\_c & epicurve & i & c & MLE\\_i & MLE\\_c\\\\\n", "\\hline\n", "\t 2 & 0 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & May25 & 0 & 0 & 0.000000e+00 & 0.000000e+00\\\\\n", "\t 2 & 1 & 7.048828e-10 & 8.286777e-10 & 9.569254e-10 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & May25 & 0 & 0 & 8.323354e-10 & 0.000000e+00\\\\\n", "\t 2 & 2 & 7.963221e-06 & 9.361760e-06 & 1.081060e-05 & 2.460082e-11 & 3.763182e-11 & 5.641193e-11 & May25 & 0 & 0 & 9.403082e-06 & 3.733066e-11\\\\\n", "\t 2 & 3 & 1.132356e-03 & 1.331226e-03 & 1.537249e-03 & 2.779970e-07 & 4.252271e-07 & 6.374123e-07 & May25 & 0 & 0 & 1.337102e-03 & 4.218264e-07\\\\\n", "\t 2 & 4 & 2.631172e-02 & 3.093271e-02 & 3.571992e-02 & 4.037793e-05 & 6.149440e-05 & 9.191020e-05 & May25 & 0 & 0 & 3.106925e-02 & 6.102488e-05\\\\\n", "\t 2 & 5 & 2.226063e-01 & 2.617015e-01 & 3.022029e-01 & 1.040708e-03 & 1.554836e-03 & 2.290330e-03 & May25 & 0 & 0 & 2.628567e-01 & 1.544922e-03\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "generations | day | c025_i | median_i | c975_i | c025_c | median_c | c975_c | epicurve | i | c | MLE_i | MLE_c | \n", "|---|---|---|---|---|---|\n", "| 2 | 0 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | May25 | 0 | 0 | 0.000000e+00 | 0.000000e+00 | \n", "| 2 | 1 | 7.048828e-10 | 8.286777e-10 | 9.569254e-10 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | May25 | 0 | 0 | 8.323354e-10 | 0.000000e+00 | \n", "| 2 | 2 | 7.963221e-06 | 9.361760e-06 | 1.081060e-05 | 2.460082e-11 | 3.763182e-11 | 5.641193e-11 | May25 | 0 | 0 | 9.403082e-06 | 3.733066e-11 | \n", "| 2 | 3 | 1.132356e-03 | 1.331226e-03 | 1.537249e-03 | 2.779970e-07 | 4.252271e-07 | 6.374123e-07 | May25 | 0 | 0 | 1.337102e-03 | 4.218264e-07 | \n", "| 2 | 4 | 2.631172e-02 | 3.093271e-02 | 3.571992e-02 | 4.037793e-05 | 6.149440e-05 | 9.191020e-05 | May25 | 0 | 0 | 3.106925e-02 | 6.102488e-05 | \n", "| 2 | 5 | 2.226063e-01 | 2.617015e-01 | 3.022029e-01 | 1.040708e-03 | 1.554836e-03 | 2.290330e-03 | May25 | 0 | 0 | 2.628567e-01 | 1.544922e-03 | \n", "\n", "\n" ], "text/plain": [ " generations day c025_i median_i c975_i c025_c \n", "1 2 0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00\n", "2 2 1 7.048828e-10 8.286777e-10 9.569254e-10 0.000000e+00\n", "3 2 2 7.963221e-06 9.361760e-06 1.081060e-05 2.460082e-11\n", "4 2 3 1.132356e-03 1.331226e-03 1.537249e-03 2.779970e-07\n", "5 2 4 2.631172e-02 3.093271e-02 3.571992e-02 4.037793e-05\n", "6 2 5 2.226063e-01 2.617015e-01 3.022029e-01 1.040708e-03\n", " median_c c975_c epicurve i c MLE_i MLE_c \n", "1 0.000000e+00 0.000000e+00 May25 0 0 0.000000e+00 0.000000e+00\n", "2 0.000000e+00 0.000000e+00 May25 0 0 8.323354e-10 0.000000e+00\n", "3 3.763182e-11 5.641193e-11 May25 0 0 9.403082e-06 3.733066e-11\n", "4 4.252271e-07 6.374123e-07 May25 0 0 1.337102e-03 4.218264e-07\n", "5 6.149440e-05 9.191020e-05 May25 0 0 3.106925e-02 6.102488e-05\n", "6 1.554836e-03 2.290330e-03 May25 0 0 2.628567e-01 1.544922e-03" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rbind(\n", " final_two_generations %>% mutate(generations=2),\n", " final_three_generations %>% mutate(generations=3),\n", " final_four_generations %>% mutate(generations=4),\n", " final_five_generations %>% mutate(generations=5),\n", " final_six_generations %>% mutate(generations=6)) %>%\n", "select(generations,everything()) -> final\n", "\n", "final %>% head" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
generationsparameterestimateepicurve
2 mean_h 4.505738May25
2 var_h 6.104282May25
2 par1_h 1.896587May25
2 par2_h 5.077376May25
2 K 121.000022May25
2 loglk -1354.902665May25
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " generations & parameter & estimate & epicurve\\\\\n", "\\hline\n", "\t 2 & mean\\_h & 4.505738 & May25 \\\\\n", "\t 2 & var\\_h & 6.104282 & May25 \\\\\n", "\t 2 & par1\\_h & 1.896587 & May25 \\\\\n", "\t 2 & par2\\_h & 5.077376 & May25 \\\\\n", "\t 2 & K & 121.000022 & May25 \\\\\n", "\t 2 & loglk & -1354.902665 & May25 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "generations | parameter | estimate | epicurve | \n", "|---|---|---|---|---|---|\n", "| 2 | mean_h | 4.505738 | May25 | \n", "| 2 | var_h | 6.104282 | May25 | \n", "| 2 | par1_h | 1.896587 | May25 | \n", "| 2 | par2_h | 5.077376 | May25 | \n", "| 2 | K | 121.000022 | May25 | \n", "| 2 | loglk | -1354.902665 | May25 | \n", "\n", "\n" ], "text/plain": [ " generations parameter estimate epicurve\n", "1 2 mean_h 4.505738 May25 \n", "2 2 var_h 6.104282 May25 \n", "3 2 par1_h 1.896587 May25 \n", "4 2 par2_h 5.077376 May25 \n", "5 2 K 121.000022 May25 \n", "6 2 loglk -1354.902665 May25 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rbind(\n", " pars_final_two_generations %>% mutate(generations=2),\n", " pars_final_three_generations %>% mutate(generations=3),\n", " pars_final_four_generations %>% mutate(generations=4),\n", " pars_final_five_generations %>% mutate(generations=5),\n", " pars_final_six_generations %>% mutate(generations=6)) %>%\n", "select(generations,everything()) -> pars_final\n", "\n", "pars_final %>% head" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Output that is further used to generate Table 1" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
generationsepicurvemean_hmean_h_CIlowermean_h_CIuppervar_hvar_h_CIlowervar_h_CIupperKK_CIlower...R4R4_CIlowerR4_CIupperR5R5_CIlowerR5_CIupperrmse_crmse_iloglkAIC
2 Apr01 4.044729 3.047817 5.237805 3.934810 1.295805 19.730880 28.42619 10.60133 ... NA NA NA NA NA NA 0.8761112 1.665254 -37.19753 80.39506
2 Apr09 3.912038 3.308452 4.654906 3.872446 1.913601 9.861646 35.32520 23.14232 ... NA NA NA NA NA NA 1.3750378 1.085741 -100.49162 206.98325
2 Apr17 3.924120 3.510476 4.323322 2.913650 1.891486 4.823307 63.01240 48.56934 ... NA NA NA NA NA NA 2.7192636 2.808329 -273.62498 553.24995
2 Apr25 3.955830 3.618954 4.305033 2.918290 1.944458 4.716382 80.99987 63.37236 ... NA NA NA NA NA NA 3.1756256 3.343216 -483.13721 972.27441
2 May03 4.181956 3.824818 4.570541 4.216275 2.818678 6.397058 103.99988 83.33127 ... NA NA NA NA NA NA 3.7832788 4.088817 -902.105491810.21099
2 May11 4.411256 4.000980 4.855401 5.557610 3.821100 8.264751 117.00015 96.85182 ... NA NA NA NA NA NA 3.8435051 4.316133 -1234.550702475.10140
2 May17 4.481232 4.061954 4.967866 6.066929 4.191562 9.533892 120.99995 100.19952 ... NA NA NA NA NA NA 3.7366163 4.241854 -1353.200012712.40002
2 May25 4.505738 4.033883 4.950997 6.104282 4.005697 9.242184 121.00002 100.37428 ... NA NA NA NA NA NA 3.5144798 3.997477 -1354.902672715.80533
3 Apr01 4.043375 3.053632 5.214922 3.929088 1.270452 18.496125 55.00361 0.00000 ... NA NA NA NA NA NA 0.8764463 1.678754 -37.18387 82.36774
3 Apr09 4.061809 3.440430 4.725307 3.803687 1.990015 8.543030 68.10914 18.66919 ... NA NA NA NA NA NA 1.3657549 1.575942 -98.06433 204.12866
3 Apr17 4.068600 3.611844 4.501637 3.037541 1.891029 5.363905 75.93085 55.72670 ... NA NA NA NA NA NA 1.5741142 1.423323 -168.20953 344.41905
3 Apr25 3.969960 3.636268 4.345385 2.944498 1.973076 4.853180 81.85764 63.75061 ... NA NA NA NA NA NA 1.7594877 1.395616 -227.80167 463.60335
3 May03 4.182729 3.808638 4.563331 4.219104 2.895567 6.632516 104.02880 82.54255 ... NA NA NA NA NA NA 2.4238553 2.092078 -367.63095 743.26190
3 May11 4.411276 3.957471 4.868999 5.557732 3.723858 8.659680 117.00042 95.24907 ... NA NA NA NA NA NA 2.5777931 2.382260 -503.296311014.59263
3 May17 4.468838 4.034637 4.917804 6.048349 4.112431 9.210711 122.00041 100.11790 ... NA NA NA NA NA NA 2.5510602 2.390018 -569.841531147.68305
3 May25 4.505307 4.038734 4.977383 6.100825 4.118169 8.870843 122.99871 102.02639 ... NA NA NA NA NA NA 2.4192381 2.279852 -585.479181178.95836
4 Apr09 4.062064 3.411006 4.772888 3.803468 1.677444 9.930992 85.91970 0.00000 ... NA NA NA NA NA NA 1.3658265 1.580020 -98.06528 206.13056
4 Apr17 4.068598 3.643193 4.514057 3.037498 1.908809 5.392237 75.93068 0.00000 ... NA NA NA NA NA NA 1.5741128 1.423319 -168.20953 346.41905
4 Apr25 4.038411 3.670469 4.411734 2.964890 1.856786 4.604190 98.94443 75.76882 ... NA NA NA NA NA NA 1.6079817 1.421440 -219.03894 448.07788
4 May03 4.244364 3.850831 4.642122 4.325074 2.926149 6.600806 109.58913 88.26649 ... NA NA NA NA NA NA 1.8372812 1.364270 -294.60491 599.20981
4 May11 4.424447 4.004363 4.831435 5.602962 3.635898 8.321790 117.59112 96.98185 ... NA NA NA NA NA NA 1.8777904 1.450414 -353.12583 716.25167
4 May17 4.470651 4.046526 4.950723 6.056689 4.111191 9.112338 122.03604 100.98551 ... NA NA NA NA NA NA 1.8358896 1.421695 -379.28371 768.56743
4 May25 4.505385 4.064096 4.935798 6.100896 4.091914 9.306961 123.00189 100.84561 ... NA NA NA NA NA NA 1.7371481 1.362435 -385.39011 780.78022
5 Apr25 4.040469 3.689150 4.415264 2.957629 1.969486 4.790315 152.09032 0.00000 ... 2.0781475 0.00000000 21.5097955 NA NA NA 1.6054400 1.487563 -218.90896 449.81792
5 May03 4.293687 3.910633 4.706519 4.325660 2.965573 6.726438 142.11194 88.75172 ... 1.3172612 0.00000000 3.7754680 NA NA NA 1.7719233 1.572422 -293.35475 598.70950
5 May11 4.480769 4.040822 4.923334 5.690116 3.967054 8.155874 125.57746 102.69976 ... 0.6264135 0.00000000 1.2789728 NA NA NA 1.7775491 1.372707 -345.57857 703.15713
5 May17 4.500801 4.075607 5.009461 6.134764 4.338513 9.794049 124.33858 103.25265 ... 0.5351187 0.09444607 0.9813031 NA NA NA 1.7027658 1.266530 -364.43213 740.86426
5 May25 4.510033 4.037324 4.957114 6.117942 4.283100 8.973657 123.20867 102.12007 ... 0.4764253 0.10044330 0.8695922 NA NA NA 1.6083754 1.204524 -368.95473 749.90946
6 Apr25 4.040527 3.661042 4.420901 2.957701 1.819198 4.716017 152.08937 0.00000 ... 1.2005613 0.00000000 150.66671720.7309249 0 208.96405 1.6054423 1.487571 -218.90896 451.81793
6 May03 4.293689 3.920496 4.728484 4.325764 2.994388 6.524427 142.09498 86.26841 ... 0.8622540 0.00000000 109.23730610.5270132 0 195.18488 1.7719466 1.572263 -293.35475 600.70950
6 May17 4.500689 4.076800 4.943803 6.133833 4.031760 9.577383 124.34165 102.11552 ... 0.3813006 0.00000000 26.00901170.4033538 0 88.51121 1.7027648 1.266531 -364.43213 742.86426
6 May25 4.509962 4.054777 4.994422 6.117540 4.230975 9.121565 123.20980 101.33508 ... 0.3351625 0.00000000 26.25331870.4215619 0 105.26123 1.6083890 1.204519 -368.95473 751.90946
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllllllllllllllllll}\n", " generations & epicurve & mean\\_h & mean\\_h\\_CIlower & mean\\_h\\_CIupper & var\\_h & var\\_h\\_CIlower & var\\_h\\_CIupper & K & K\\_CIlower & ... & R4 & R4\\_CIlower & R4\\_CIupper & R5 & R5\\_CIlower & R5\\_CIupper & rmse\\_c & rmse\\_i & loglk & AIC\\\\\n", "\\hline\n", "\t 2 & Apr01 & 4.044729 & 3.047817 & 5.237805 & 3.934810 & 1.295805 & 19.730880 & 28.42619 & 10.60133 & ... & NA & NA & NA & NA & NA & NA & 0.8761112 & 1.665254 & -37.19753 & 80.39506 \\\\\n", "\t 2 & Apr09 & 3.912038 & 3.308452 & 4.654906 & 3.872446 & 1.913601 & 9.861646 & 35.32520 & 23.14232 & ... & NA & NA & NA & NA & NA & NA & 1.3750378 & 1.085741 & -100.49162 & 206.98325 \\\\\n", "\t 2 & Apr17 & 3.924120 & 3.510476 & 4.323322 & 2.913650 & 1.891486 & 4.823307 & 63.01240 & 48.56934 & ... & NA & NA & NA & NA & NA & NA & 2.7192636 & 2.808329 & -273.62498 & 553.24995 \\\\\n", "\t 2 & Apr25 & 3.955830 & 3.618954 & 4.305033 & 2.918290 & 1.944458 & 4.716382 & 80.99987 & 63.37236 & ... & NA & NA & NA & NA & NA & NA & 3.1756256 & 3.343216 & -483.13721 & 972.27441 \\\\\n", "\t 2 & May03 & 4.181956 & 3.824818 & 4.570541 & 4.216275 & 2.818678 & 6.397058 & 103.99988 & 83.33127 & ... & NA & NA & NA & NA & NA & NA & 3.7832788 & 4.088817 & -902.10549 & 1810.21099 \\\\\n", "\t 2 & May11 & 4.411256 & 4.000980 & 4.855401 & 5.557610 & 3.821100 & 8.264751 & 117.00015 & 96.85182 & ... & NA & NA & NA & NA & NA & NA & 3.8435051 & 4.316133 & -1234.55070 & 2475.10140 \\\\\n", "\t 2 & May17 & 4.481232 & 4.061954 & 4.967866 & 6.066929 & 4.191562 & 9.533892 & 120.99995 & 100.19952 & ... & NA & NA & NA & NA & NA & NA & 3.7366163 & 4.241854 & -1353.20001 & 2712.40002 \\\\\n", "\t 2 & May25 & 4.505738 & 4.033883 & 4.950997 & 6.104282 & 4.005697 & 9.242184 & 121.00002 & 100.37428 & ... & NA & NA & NA & NA & NA & NA & 3.5144798 & 3.997477 & -1354.90267 & 2715.80533 \\\\\n", "\t 3 & Apr01 & 4.043375 & 3.053632 & 5.214922 & 3.929088 & 1.270452 & 18.496125 & 55.00361 & 0.00000 & ... & NA & NA & NA & NA & NA & NA & 0.8764463 & 1.678754 & -37.18387 & 82.36774 \\\\\n", "\t 3 & Apr09 & 4.061809 & 3.440430 & 4.725307 & 3.803687 & 1.990015 & 8.543030 & 68.10914 & 18.66919 & ... & NA & NA & NA & NA & NA & NA & 1.3657549 & 1.575942 & -98.06433 & 204.12866 \\\\\n", "\t 3 & Apr17 & 4.068600 & 3.611844 & 4.501637 & 3.037541 & 1.891029 & 5.363905 & 75.93085 & 55.72670 & ... & NA & NA & NA & NA & NA & NA & 1.5741142 & 1.423323 & -168.20953 & 344.41905 \\\\\n", "\t 3 & Apr25 & 3.969960 & 3.636268 & 4.345385 & 2.944498 & 1.973076 & 4.853180 & 81.85764 & 63.75061 & ... & NA & NA & NA & NA & NA & NA & 1.7594877 & 1.395616 & -227.80167 & 463.60335 \\\\\n", "\t 3 & May03 & 4.182729 & 3.808638 & 4.563331 & 4.219104 & 2.895567 & 6.632516 & 104.02880 & 82.54255 & ... & NA & NA & NA & NA & NA & NA & 2.4238553 & 2.092078 & -367.63095 & 743.26190 \\\\\n", "\t 3 & May11 & 4.411276 & 3.957471 & 4.868999 & 5.557732 & 3.723858 & 8.659680 & 117.00042 & 95.24907 & ... & NA & NA & NA & NA & NA & NA & 2.5777931 & 2.382260 & -503.29631 & 1014.59263 \\\\\n", "\t 3 & May17 & 4.468838 & 4.034637 & 4.917804 & 6.048349 & 4.112431 & 9.210711 & 122.00041 & 100.11790 & ... & NA & NA & NA & NA & NA & NA & 2.5510602 & 2.390018 & -569.84153 & 1147.68305 \\\\\n", "\t 3 & May25 & 4.505307 & 4.038734 & 4.977383 & 6.100825 & 4.118169 & 8.870843 & 122.99871 & 102.02639 & ... & NA & NA & NA & NA & NA & NA & 2.4192381 & 2.279852 & -585.47918 & 1178.95836 \\\\\n", "\t 4 & Apr09 & 4.062064 & 3.411006 & 4.772888 & 3.803468 & 1.677444 & 9.930992 & 85.91970 & 0.00000 & ... & NA & NA & NA & NA & NA & NA & 1.3658265 & 1.580020 & -98.06528 & 206.13056 \\\\\n", "\t 4 & Apr17 & 4.068598 & 3.643193 & 4.514057 & 3.037498 & 1.908809 & 5.392237 & 75.93068 & 0.00000 & ... & NA & NA & NA & NA & NA & NA & 1.5741128 & 1.423319 & -168.20953 & 346.41905 \\\\\n", "\t 4 & Apr25 & 4.038411 & 3.670469 & 4.411734 & 2.964890 & 1.856786 & 4.604190 & 98.94443 & 75.76882 & ... & NA & NA & NA & NA & NA & NA & 1.6079817 & 1.421440 & -219.03894 & 448.07788 \\\\\n", "\t 4 & May03 & 4.244364 & 3.850831 & 4.642122 & 4.325074 & 2.926149 & 6.600806 & 109.58913 & 88.26649 & ... & NA & NA & NA & NA & NA & NA & 1.8372812 & 1.364270 & -294.60491 & 599.20981 \\\\\n", "\t 4 & May11 & 4.424447 & 4.004363 & 4.831435 & 5.602962 & 3.635898 & 8.321790 & 117.59112 & 96.98185 & ... & NA & NA & NA & NA & NA & NA & 1.8777904 & 1.450414 & -353.12583 & 716.25167 \\\\\n", "\t 4 & May17 & 4.470651 & 4.046526 & 4.950723 & 6.056689 & 4.111191 & 9.112338 & 122.03604 & 100.98551 & ... & NA & NA & NA & NA & NA & NA & 1.8358896 & 1.421695 & -379.28371 & 768.56743 \\\\\n", "\t 4 & May25 & 4.505385 & 4.064096 & 4.935798 & 6.100896 & 4.091914 & 9.306961 & 123.00189 & 100.84561 & ... & NA & NA & NA & NA & NA & NA & 1.7371481 & 1.362435 & -385.39011 & 780.78022 \\\\\n", "\t 5 & Apr25 & 4.040469 & 3.689150 & 4.415264 & 2.957629 & 1.969486 & 4.790315 & 152.09032 & 0.00000 & ... & 2.0781475 & 0.00000000 & 21.5097955 & NA & NA & NA & 1.6054400 & 1.487563 & -218.90896 & 449.81792 \\\\\n", "\t 5 & May03 & 4.293687 & 3.910633 & 4.706519 & 4.325660 & 2.965573 & 6.726438 & 142.11194 & 88.75172 & ... & 1.3172612 & 0.00000000 & 3.7754680 & NA & NA & NA & 1.7719233 & 1.572422 & -293.35475 & 598.70950 \\\\\n", "\t 5 & May11 & 4.480769 & 4.040822 & 4.923334 & 5.690116 & 3.967054 & 8.155874 & 125.57746 & 102.69976 & ... & 0.6264135 & 0.00000000 & 1.2789728 & NA & NA & NA & 1.7775491 & 1.372707 & -345.57857 & 703.15713 \\\\\n", "\t 5 & May17 & 4.500801 & 4.075607 & 5.009461 & 6.134764 & 4.338513 & 9.794049 & 124.33858 & 103.25265 & ... & 0.5351187 & 0.09444607 & 0.9813031 & NA & NA & NA & 1.7027658 & 1.266530 & -364.43213 & 740.86426 \\\\\n", "\t 5 & May25 & 4.510033 & 4.037324 & 4.957114 & 6.117942 & 4.283100 & 8.973657 & 123.20867 & 102.12007 & ... & 0.4764253 & 0.10044330 & 0.8695922 & NA & NA & NA & 1.6083754 & 1.204524 & -368.95473 & 749.90946 \\\\\n", "\t 6 & Apr25 & 4.040527 & 3.661042 & 4.420901 & 2.957701 & 1.819198 & 4.716017 & 152.08937 & 0.00000 & ... & 1.2005613 & 0.00000000 & 150.6667172 & 0.7309249 & 0 & 208.96405 & 1.6054423 & 1.487571 & -218.90896 & 451.81793 \\\\\n", "\t 6 & May03 & 4.293689 & 3.920496 & 4.728484 & 4.325764 & 2.994388 & 6.524427 & 142.09498 & 86.26841 & ... & 0.8622540 & 0.00000000 & 109.2373061 & 0.5270132 & 0 & 195.18488 & 1.7719466 & 1.572263 & -293.35475 & 600.70950 \\\\\n", "\t 6 & May17 & 4.500689 & 4.076800 & 4.943803 & 6.133833 & 4.031760 & 9.577383 & 124.34165 & 102.11552 & ... & 0.3813006 & 0.00000000 & 26.0090117 & 0.4033538 & 0 & 88.51121 & 1.7027648 & 1.266531 & -364.43213 & 742.86426 \\\\\n", "\t 6 & May25 & 4.509962 & 4.054777 & 4.994422 & 6.117540 & 4.230975 & 9.121565 & 123.20980 & 101.33508 & ... & 0.3351625 & 0.00000000 & 26.2533187 & 0.4215619 & 0 & 105.26123 & 1.6083890 & 1.204519 & -368.95473 & 751.90946 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "generations | epicurve | mean_h | mean_h_CIlower | mean_h_CIupper | var_h | var_h_CIlower | var_h_CIupper | K | K_CIlower | ... | R4 | R4_CIlower | R4_CIupper | R5 | R5_CIlower | R5_CIupper | rmse_c | rmse_i | loglk | AIC | \n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 2 | Apr01 | 4.044729 | 3.047817 | 5.237805 | 3.934810 | 1.295805 | 19.730880 | 28.42619 | 10.60133 | ... | NA | NA | NA | NA | NA | NA | 0.8761112 | 1.665254 | -37.19753 | 80.39506 | \n", "| 2 | Apr09 | 3.912038 | 3.308452 | 4.654906 | 3.872446 | 1.913601 | 9.861646 | 35.32520 | 23.14232 | ... | NA | NA | NA | NA | NA | NA | 1.3750378 | 1.085741 | -100.49162 | 206.98325 | \n", "| 2 | Apr17 | 3.924120 | 3.510476 | 4.323322 | 2.913650 | 1.891486 | 4.823307 | 63.01240 | 48.56934 | ... | NA | NA | NA | NA | NA | NA | 2.7192636 | 2.808329 | -273.62498 | 553.24995 | \n", "| 2 | Apr25 | 3.955830 | 3.618954 | 4.305033 | 2.918290 | 1.944458 | 4.716382 | 80.99987 | 63.37236 | ... | NA | NA | NA | NA | NA | NA | 3.1756256 | 3.343216 | -483.13721 | 972.27441 | \n", "| 2 | May03 | 4.181956 | 3.824818 | 4.570541 | 4.216275 | 2.818678 | 6.397058 | 103.99988 | 83.33127 | ... | NA | NA | NA | NA | NA | NA | 3.7832788 | 4.088817 | -902.10549 | 1810.21099 | \n", "| 2 | May11 | 4.411256 | 4.000980 | 4.855401 | 5.557610 | 3.821100 | 8.264751 | 117.00015 | 96.85182 | ... | NA | NA | NA | NA | NA | NA | 3.8435051 | 4.316133 | -1234.55070 | 2475.10140 | \n", "| 2 | May17 | 4.481232 | 4.061954 | 4.967866 | 6.066929 | 4.191562 | 9.533892 | 120.99995 | 100.19952 | ... | NA | NA | NA | NA | NA | NA | 3.7366163 | 4.241854 | -1353.20001 | 2712.40002 | \n", "| 2 | May25 | 4.505738 | 4.033883 | 4.950997 | 6.104282 | 4.005697 | 9.242184 | 121.00002 | 100.37428 | ... | NA | NA | NA | NA | NA | NA | 3.5144798 | 3.997477 | -1354.90267 | 2715.80533 | \n", "| 3 | Apr01 | 4.043375 | 3.053632 | 5.214922 | 3.929088 | 1.270452 | 18.496125 | 55.00361 | 0.00000 | ... | NA | NA | NA | NA | NA | NA | 0.8764463 | 1.678754 | -37.18387 | 82.36774 | \n", "| 3 | Apr09 | 4.061809 | 3.440430 | 4.725307 | 3.803687 | 1.990015 | 8.543030 | 68.10914 | 18.66919 | ... | NA | NA | NA | NA | NA | NA | 1.3657549 | 1.575942 | -98.06433 | 204.12866 | \n", "| 3 | Apr17 | 4.068600 | 3.611844 | 4.501637 | 3.037541 | 1.891029 | 5.363905 | 75.93085 | 55.72670 | ... | NA | NA | NA | NA | NA | NA | 1.5741142 | 1.423323 | -168.20953 | 344.41905 | \n", "| 3 | Apr25 | 3.969960 | 3.636268 | 4.345385 | 2.944498 | 1.973076 | 4.853180 | 81.85764 | 63.75061 | ... | NA | NA | NA | NA | NA | NA | 1.7594877 | 1.395616 | -227.80167 | 463.60335 | \n", "| 3 | May03 | 4.182729 | 3.808638 | 4.563331 | 4.219104 | 2.895567 | 6.632516 | 104.02880 | 82.54255 | ... | NA | NA | NA | NA | NA | NA | 2.4238553 | 2.092078 | -367.63095 | 743.26190 | \n", "| 3 | May11 | 4.411276 | 3.957471 | 4.868999 | 5.557732 | 3.723858 | 8.659680 | 117.00042 | 95.24907 | ... | NA | NA | NA | NA | NA | NA | 2.5777931 | 2.382260 | -503.29631 | 1014.59263 | \n", "| 3 | May17 | 4.468838 | 4.034637 | 4.917804 | 6.048349 | 4.112431 | 9.210711 | 122.00041 | 100.11790 | ... | NA | NA | NA | NA | NA | NA | 2.5510602 | 2.390018 | -569.84153 | 1147.68305 | \n", "| 3 | May25 | 4.505307 | 4.038734 | 4.977383 | 6.100825 | 4.118169 | 8.870843 | 122.99871 | 102.02639 | ... | NA | NA | NA | NA | NA | NA | 2.4192381 | 2.279852 | -585.47918 | 1178.95836 | \n", "| 4 | Apr09 | 4.062064 | 3.411006 | 4.772888 | 3.803468 | 1.677444 | 9.930992 | 85.91970 | 0.00000 | ... | NA | NA | NA | NA | NA | NA | 1.3658265 | 1.580020 | -98.06528 | 206.13056 | \n", "| 4 | Apr17 | 4.068598 | 3.643193 | 4.514057 | 3.037498 | 1.908809 | 5.392237 | 75.93068 | 0.00000 | ... | NA | NA | NA | NA | NA | NA | 1.5741128 | 1.423319 | -168.20953 | 346.41905 | \n", "| 4 | Apr25 | 4.038411 | 3.670469 | 4.411734 | 2.964890 | 1.856786 | 4.604190 | 98.94443 | 75.76882 | ... | NA | NA | NA | NA | NA | NA | 1.6079817 | 1.421440 | -219.03894 | 448.07788 | \n", "| 4 | May03 | 4.244364 | 3.850831 | 4.642122 | 4.325074 | 2.926149 | 6.600806 | 109.58913 | 88.26649 | ... | NA | NA | NA | NA | NA | NA | 1.8372812 | 1.364270 | -294.60491 | 599.20981 | \n", "| 4 | May11 | 4.424447 | 4.004363 | 4.831435 | 5.602962 | 3.635898 | 8.321790 | 117.59112 | 96.98185 | ... | NA | NA | NA | NA | NA | NA | 1.8777904 | 1.450414 | -353.12583 | 716.25167 | \n", "| 4 | May17 | 4.470651 | 4.046526 | 4.950723 | 6.056689 | 4.111191 | 9.112338 | 122.03604 | 100.98551 | ... | NA | NA | NA | NA | NA | NA | 1.8358896 | 1.421695 | -379.28371 | 768.56743 | \n", "| 4 | May25 | 4.505385 | 4.064096 | 4.935798 | 6.100896 | 4.091914 | 9.306961 | 123.00189 | 100.84561 | ... | NA | NA | NA | NA | NA | NA | 1.7371481 | 1.362435 | -385.39011 | 780.78022 | \n", "| 5 | Apr25 | 4.040469 | 3.689150 | 4.415264 | 2.957629 | 1.969486 | 4.790315 | 152.09032 | 0.00000 | ... | 2.0781475 | 0.00000000 | 21.5097955 | NA | NA | NA | 1.6054400 | 1.487563 | -218.90896 | 449.81792 | \n", "| 5 | May03 | 4.293687 | 3.910633 | 4.706519 | 4.325660 | 2.965573 | 6.726438 | 142.11194 | 88.75172 | ... | 1.3172612 | 0.00000000 | 3.7754680 | NA | NA | NA | 1.7719233 | 1.572422 | -293.35475 | 598.70950 | \n", "| 5 | May11 | 4.480769 | 4.040822 | 4.923334 | 5.690116 | 3.967054 | 8.155874 | 125.57746 | 102.69976 | ... | 0.6264135 | 0.00000000 | 1.2789728 | NA | NA | NA | 1.7775491 | 1.372707 | -345.57857 | 703.15713 | \n", "| 5 | May17 | 4.500801 | 4.075607 | 5.009461 | 6.134764 | 4.338513 | 9.794049 | 124.33858 | 103.25265 | ... | 0.5351187 | 0.09444607 | 0.9813031 | NA | NA | NA | 1.7027658 | 1.266530 | -364.43213 | 740.86426 | \n", "| 5 | May25 | 4.510033 | 4.037324 | 4.957114 | 6.117942 | 4.283100 | 8.973657 | 123.20867 | 102.12007 | ... | 0.4764253 | 0.10044330 | 0.8695922 | NA | NA | NA | 1.6083754 | 1.204524 | -368.95473 | 749.90946 | \n", "| 6 | Apr25 | 4.040527 | 3.661042 | 4.420901 | 2.957701 | 1.819198 | 4.716017 | 152.08937 | 0.00000 | ... | 1.2005613 | 0.00000000 | 150.6667172 | 0.7309249 | 0 | 208.96405 | 1.6054423 | 1.487571 | -218.90896 | 451.81793 | \n", "| 6 | May03 | 4.293689 | 3.920496 | 4.728484 | 4.325764 | 2.994388 | 6.524427 | 142.09498 | 86.26841 | ... | 0.8622540 | 0.00000000 | 109.2373061 | 0.5270132 | 0 | 195.18488 | 1.7719466 | 1.572263 | -293.35475 | 600.70950 | \n", "| 6 | May17 | 4.500689 | 4.076800 | 4.943803 | 6.133833 | 4.031760 | 9.577383 | 124.34165 | 102.11552 | ... | 0.3813006 | 0.00000000 | 26.0090117 | 0.4033538 | 0 | 88.51121 | 1.7027648 | 1.266531 | -364.43213 | 742.86426 | \n", "| 6 | May25 | 4.509962 | 4.054777 | 4.994422 | 6.117540 | 4.230975 | 9.121565 | 123.20980 | 101.33508 | ... | 0.3351625 | 0.00000000 | 26.2533187 | 0.4215619 | 0 | 105.26123 | 1.6083890 | 1.204519 | -368.95473 | 751.90946 | \n", "\n", "\n" ], "text/plain": [ " generations epicurve mean_h mean_h_CIlower mean_h_CIupper var_h \n", "1 2 Apr01 4.044729 3.047817 5.237805 3.934810\n", "2 2 Apr09 3.912038 3.308452 4.654906 3.872446\n", "3 2 Apr17 3.924120 3.510476 4.323322 2.913650\n", "4 2 Apr25 3.955830 3.618954 4.305033 2.918290\n", "5 2 May03 4.181956 3.824818 4.570541 4.216275\n", "6 2 May11 4.411256 4.000980 4.855401 5.557610\n", "7 2 May17 4.481232 4.061954 4.967866 6.066929\n", "8 2 May25 4.505738 4.033883 4.950997 6.104282\n", "9 3 Apr01 4.043375 3.053632 5.214922 3.929088\n", "10 3 Apr09 4.061809 3.440430 4.725307 3.803687\n", "11 3 Apr17 4.068600 3.611844 4.501637 3.037541\n", "12 3 Apr25 3.969960 3.636268 4.345385 2.944498\n", "13 3 May03 4.182729 3.808638 4.563331 4.219104\n", "14 3 May11 4.411276 3.957471 4.868999 5.557732\n", "15 3 May17 4.468838 4.034637 4.917804 6.048349\n", "16 3 May25 4.505307 4.038734 4.977383 6.100825\n", "17 4 Apr09 4.062064 3.411006 4.772888 3.803468\n", "18 4 Apr17 4.068598 3.643193 4.514057 3.037498\n", "19 4 Apr25 4.038411 3.670469 4.411734 2.964890\n", "20 4 May03 4.244364 3.850831 4.642122 4.325074\n", "21 4 May11 4.424447 4.004363 4.831435 5.602962\n", "22 4 May17 4.470651 4.046526 4.950723 6.056689\n", "23 4 May25 4.505385 4.064096 4.935798 6.100896\n", "24 5 Apr25 4.040469 3.689150 4.415264 2.957629\n", "25 5 May03 4.293687 3.910633 4.706519 4.325660\n", "26 5 May11 4.480769 4.040822 4.923334 5.690116\n", "27 5 May17 4.500801 4.075607 5.009461 6.134764\n", "28 5 May25 4.510033 4.037324 4.957114 6.117942\n", "29 6 Apr25 4.040527 3.661042 4.420901 2.957701\n", "30 6 May03 4.293689 3.920496 4.728484 4.325764\n", "31 6 May17 4.500689 4.076800 4.943803 6.133833\n", "32 6 May25 4.509962 4.054777 4.994422 6.117540\n", " var_h_CIlower var_h_CIupper K K_CIlower ... R4 R4_CIlower\n", "1 1.295805 19.730880 28.42619 10.60133 ... NA NA\n", "2 1.913601 9.861646 35.32520 23.14232 ... NA NA\n", "3 1.891486 4.823307 63.01240 48.56934 ... NA NA\n", "4 1.944458 4.716382 80.99987 63.37236 ... NA NA\n", "5 2.818678 6.397058 103.99988 83.33127 ... NA NA\n", "6 3.821100 8.264751 117.00015 96.85182 ... NA NA\n", "7 4.191562 9.533892 120.99995 100.19952 ... NA NA\n", "8 4.005697 9.242184 121.00002 100.37428 ... NA NA\n", "9 1.270452 18.496125 55.00361 0.00000 ... NA NA\n", "10 1.990015 8.543030 68.10914 18.66919 ... NA NA\n", "11 1.891029 5.363905 75.93085 55.72670 ... NA NA\n", "12 1.973076 4.853180 81.85764 63.75061 ... NA NA\n", "13 2.895567 6.632516 104.02880 82.54255 ... NA NA\n", "14 3.723858 8.659680 117.00042 95.24907 ... NA NA\n", "15 4.112431 9.210711 122.00041 100.11790 ... NA NA\n", "16 4.118169 8.870843 122.99871 102.02639 ... NA NA\n", "17 1.677444 9.930992 85.91970 0.00000 ... NA NA\n", "18 1.908809 5.392237 75.93068 0.00000 ... NA NA\n", "19 1.856786 4.604190 98.94443 75.76882 ... NA NA\n", "20 2.926149 6.600806 109.58913 88.26649 ... NA NA\n", "21 3.635898 8.321790 117.59112 96.98185 ... NA NA\n", "22 4.111191 9.112338 122.03604 100.98551 ... NA NA\n", "23 4.091914 9.306961 123.00189 100.84561 ... NA NA\n", "24 1.969486 4.790315 152.09032 0.00000 ... 2.0781475 0.00000000\n", "25 2.965573 6.726438 142.11194 88.75172 ... 1.3172612 0.00000000\n", "26 3.967054 8.155874 125.57746 102.69976 ... 0.6264135 0.00000000\n", "27 4.338513 9.794049 124.33858 103.25265 ... 0.5351187 0.09444607\n", "28 4.283100 8.973657 123.20867 102.12007 ... 0.4764253 0.10044330\n", "29 1.819198 4.716017 152.08937 0.00000 ... 1.2005613 0.00000000\n", "30 2.994388 6.524427 142.09498 86.26841 ... 0.8622540 0.00000000\n", "31 4.031760 9.577383 124.34165 102.11552 ... 0.3813006 0.00000000\n", "32 4.230975 9.121565 123.20980 101.33508 ... 0.3351625 0.00000000\n", " R4_CIupper R5 R5_CIlower R5_CIupper rmse_c rmse_i loglk \n", "1 NA NA NA NA 0.8761112 1.665254 -37.19753\n", "2 NA NA NA NA 1.3750378 1.085741 -100.49162\n", "3 NA NA NA NA 2.7192636 2.808329 -273.62498\n", "4 NA NA NA NA 3.1756256 3.343216 -483.13721\n", "5 NA NA NA NA 3.7832788 4.088817 -902.10549\n", "6 NA NA NA NA 3.8435051 4.316133 -1234.55070\n", "7 NA NA NA NA 3.7366163 4.241854 -1353.20001\n", "8 NA NA NA NA 3.5144798 3.997477 -1354.90267\n", "9 NA NA NA NA 0.8764463 1.678754 -37.18387\n", "10 NA NA NA NA 1.3657549 1.575942 -98.06433\n", "11 NA NA NA NA 1.5741142 1.423323 -168.20953\n", "12 NA NA NA NA 1.7594877 1.395616 -227.80167\n", "13 NA NA NA NA 2.4238553 2.092078 -367.63095\n", "14 NA NA NA NA 2.5777931 2.382260 -503.29631\n", "15 NA NA NA NA 2.5510602 2.390018 -569.84153\n", "16 NA NA NA NA 2.4192381 2.279852 -585.47918\n", "17 NA NA NA NA 1.3658265 1.580020 -98.06528\n", "18 NA NA NA NA 1.5741128 1.423319 -168.20953\n", "19 NA NA NA NA 1.6079817 1.421440 -219.03894\n", "20 NA NA NA NA 1.8372812 1.364270 -294.60491\n", "21 NA NA NA NA 1.8777904 1.450414 -353.12583\n", "22 NA NA NA NA 1.8358896 1.421695 -379.28371\n", "23 NA NA NA NA 1.7371481 1.362435 -385.39011\n", "24 21.5097955 NA NA NA 1.6054400 1.487563 -218.90896\n", "25 3.7754680 NA NA NA 1.7719233 1.572422 -293.35475\n", "26 1.2789728 NA NA NA 1.7775491 1.372707 -345.57857\n", "27 0.9813031 NA NA NA 1.7027658 1.266530 -364.43213\n", "28 0.8695922 NA NA NA 1.6083754 1.204524 -368.95473\n", "29 150.6667172 0.7309249 0 208.96405 1.6054423 1.487571 -218.90896\n", "30 109.2373061 0.5270132 0 195.18488 1.7719466 1.572263 -293.35475\n", "31 26.0090117 0.4033538 0 88.51121 1.7027648 1.266531 -364.43213\n", "32 26.2533187 0.4215619 0 105.26123 1.6083890 1.204519 -368.95473\n", " AIC \n", "1 80.39506\n", "2 206.98325\n", "3 553.24995\n", "4 972.27441\n", "5 1810.21099\n", "6 2475.10140\n", "7 2712.40002\n", "8 2715.80533\n", "9 82.36774\n", "10 204.12866\n", "11 344.41905\n", "12 463.60335\n", "13 743.26190\n", "14 1014.59263\n", "15 1147.68305\n", "16 1178.95836\n", "17 206.13056\n", "18 346.41905\n", "19 448.07788\n", "20 599.20981\n", "21 716.25167\n", "22 768.56743\n", "23 780.78022\n", "24 449.81792\n", "25 598.70950\n", "26 703.15713\n", "27 740.86426\n", "28 749.90946\n", "29 451.81793\n", "30 600.70950\n", "31 742.86426\n", "32 751.90946" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# raw output\n", "pars_final %<>% \n", " spread(parameter,estimate) %>% \n", " select(generations,epicurve,mean_h,mean_h_CIlower,mean_h_CIupper,\n", " var_h,var_h_CIlower,var_h_CIupper,K,K_CIlower,K_CIupper,starts_with(\"R\"),loglk,AIC,rmse_i,rmse_c)\n", "\n", "pars_final %>% write.csv(file=\"pars_final.csv\")\n", "\n", "pars_final" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", 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epicurve generations mean of ht variance of ht K R2 R3 R4 R5 negloglk AIC rmse_i rmse_c
Apr01 2 4.04 (3.05-5.24) 3.93 (1.30-19.73) 28.4 (10.6-46.0) 37.2 80.4 1.67 0.88
Apr01 3 4.04 3.93 55.0 0.94 37.2 82.4 1.68 0.88
Apr09 2 3.91 3.87 35.3 100.5 207.0 1.09 1.38
Apr09 3 4.06 (3.44-4.73) 3.80 (1.99-8.54) 68.1 (18.7-116.2) 1.21 (0.00-3.01) 98.1 204.1 1.58 1.37
Apr09 4 4.06 3.80 85.9 1.21 0.48 98.1 206.1 1.58 1.37
Apr17 2 3.92 2.91 63.0 273.6 553.2 2.81 2.72
Apr17 3 4.07 (3.61-4.50) 3.04 (1.89-5.36) 75.9 (55.7-95.5) 1.56 (0.71-2.44) 168.2 344.4 1.42 1.57
Apr17 4 4.07 3.04 75.9 1.56 0.00 168.2 346.4 1.42 1.57
Apr25 2 3.96 2.92 81.0 483.1 972.3 3.34 3.18
Apr25 3 3.97 2.94 81.9 1.80 227.8 463.6 1.40 1.76
Apr25 4 4.04 (3.67-4.41) 2.96 (1.86-4.60) 98.9 (75.8-124.2) 1.34 (0.67-2.05) 0.71 (0.04-1.33) 219.0 448.1 1.42 1.61
Apr25 5 4.04 2.96 152.1 1.35 0.66 2.08 218.9 449.8 1.49 1.61
Apr25 6 4.04 2.96 152.1 1.35 0.66 1.20 0.73 218.9 451.8 1.49 1.61
May03 2 4.18 4.22 104.0 902.1 1810.2 4.09 3.78
May03 3 4.18 4.22 104.0 2.68 367.6 743.3 2.09 2.42
May03 4 4.24 4.33 109.6 1.30 1.03 294.6 599.2 1.36 1.84
May03 5 4.29 (3.91-4.71) 4.33 (2.97-6.73) 142.1 (88.8-200.4) 1.35 (0.59-2.03) 0.76 (0.17-1.33) 1.32 (0.00-3.78) 293.4 598.7 1.57 1.77
May03 6 4.29 4.33 142.1 1.35 0.76 0.86 0.53 293.4 600.7 1.57 1.77
May11 2 4.41 5.56 117.0 1234.6 2475.1 4.32 3.84
May11 3 4.41 5.56 117.0 3.21 503.3 1014.6 2.38 2.58
May11 4 4.42 5.60 117.6 1.26 1.31 353.1 716.3 1.45 1.88
May11 5 4.48 (4.04-4.92) 5.69 (3.97-8.16) 125.6 (102.7-149.2) 1.33 (0.63-2.06) 0.85 (0.34-1.35) 0.63 (0.00-1.28) 345.6 703.2 1.37 1.78
May17 2 4.48 6.07 121.0 1353.2 2712.4 4.24 3.74
May17 3 4.47 6.05 122.0 3.41 569.8 1147.7 2.39 2.55
May17 4 4.47 6.06 122.0 1.24 1.46 379.3 768.6 1.42 1.84
May17 5 4.50 (4.08-5.01) 6.13 (4.34-9.79) 124.3 (103.3-146.5) 1.33 (0.61-2.02) 0.89 (0.36-1.40) 0.54 (0.09-0.98) 364.4 740.9 1.27 1.70
May17 6 4.50 6.13 124.3 1.33 0.89 0.38 0.40 364.4 742.9 1.27 1.70
May25 2 4.51 6.10 121.0 1354.9 2715.8 4.00 3.51
May25 3 4.51 6.10 123.0 3.45 585.5 1179.0 2.28 2.42
May25 4 4.51 6.10 123.0 1.23 1.49 385.4 780.8 1.36 1.74
May25 5 4.51 (4.04-4.96) 6.12 (4.28-8.97) 123.2 (102.1-145.4) 1.32 (0.58-2.00) 0.91 (0.39-1.45) 0.48 (0.10-0.87) 369.0 749.9 1.20 1.61
May25 6 4.51 6.12 123.2 1.32 0.91 0.34 0.42 369.0 751.9 1.20 1.61
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# a bit nicer form\n", "options(warn=-1)\n", "pars_final %>% \n", " arrange(epicurve,generations) %>%\n", " group_by(epicurve) %>%\n", " mutate(AICmin = ifelse(AIC==min(AIC),TRUE,FALSE),\n", " `mean of ht`=ifelse(AIC==min(AIC),\n", " glue('{sprintf(\"%0.2f\",mean_h)} ({sprintf(\"%0.2f\",mean_h_CIlower)}-{sprintf(\"%0.2f\",mean_h_CIupper)})'),\n", " sprintf(\"%0.2f\",mean_h)),\n", " `variance of ht`=ifelse(AIC==min(AIC),\n", " glue('{sprintf(\"%0.2f\",var_h)} ({sprintf(\"%0.2f\",var_h_CIlower)}-{sprintf(\"%0.2f\",var_h_CIupper)})'),\n", " sprintf(\"%0.2f\",var_h)),\n", " K=ifelse(AIC==min(AIC),\n", " glue('{sprintf(\"%0.1f\",K)} ({sprintf(\"%0.1f\",K_CIlower)}-{sprintf(\"%0.1f\",K_CIupper)})'),\n", " sprintf(\"%0.1f\",K)),\n", " R2=ifelse(!is.na(R2),\n", " ifelse(AIC==min(AIC),\n", " glue('{sprintf(\"%0.2f\",R2)} ({sprintf(\"%0.2f\",R2_CIlower)}-{sprintf(\"%0.2f\",R2_CIupper)})'),\n", " sprintf(\"%0.2f\",R2)),\n", " ''),\n", " R3=ifelse(!is.na(R3),\n", " ifelse(AIC==min(AIC),\n", " glue('{sprintf(\"%0.2f\",R3)} ({sprintf(\"%0.2f\",R3_CIlower)}-{sprintf(\"%0.2f\",R3_CIupper)})'),\n", " sprintf(\"%0.2f\",R3)),\n", " ''),\n", " R4=ifelse(!is.na(R4),\n", " ifelse(AIC==min(AIC),\n", " glue('{sprintf(\"%0.2f\",R4)} ({sprintf(\"%0.2f\",R4_CIlower)}-{sprintf(\"%0.2f\",R4_CIupper)})'),\n", " sprintf(\"%0.2f\",R4)),\n", " ''),\n", " R5=ifelse(!is.na(R5),\n", " ifelse(AIC==min(AIC),\n", " glue('{sprintf(\"%0.2f\",R5)} ({sprintf(\"%0.2f\",R5_CIlower)}-{sprintf(\"%0.2f\",R5_CIupper)})'),\n", " sprintf(\"%0.2f\",R5)),\n", " ''),\n", " negloglk=sprintf(\"%.1f\",-loglk),\n", " AIC=sprintf(\"%.1f\",AIC),\n", " rmse_i=sprintf(\"%.2f\",rmse_i),\n", " rmse_c=sprintf(\"%.2f\",rmse_c)\n", " ) %>%\n", " select(epicurve,generations,`mean of ht`,`variance of ht`,K,R2,R3,R4,R5,negloglk,AIC,rmse_i,rmse_c,AICmin) -> pars_final_kabble\n", "\n", "pars_final_kabble %>%\n", " select(-AICmin) %>%\n", " kable(\"html\", escape = F) %>%\n", " kable_styling(\"hover\", full_width = F) %>%\n", " row_spec(which(pars_final_kabble$AICmin), bold = T, color = \"brown\") %>%\n", " as.character() %>%\n", " display_html()\n", "options(warn=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Selecting the ones with minimal AIC" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
generationsepicurvemean_hmean_h_CIlowermean_h_CIuppervar_hvar_h_CIlowervar_h_CIupperKK_CIlower...R4R4_CIlowerR4_CIupperR5R5_CIlowerR5_CIupperrmse_crmse_iloglkAIC
2 Apr01 4.044729 2.965966 5.430933 3.934810 1.166348 20.957400 28.42619 9.875538... NA NA NA NA NA NA 0.8761112 1.665254 -37.19753 80.39506
3 Apr09 4.061809 3.416039 4.721406 3.803687 1.777842 8.968100 68.10914 18.520947... NA NA NA NA NA NA 1.3657549 1.575942 -98.06433204.12866
3 Apr17 4.068600 3.645938 4.505535 3.037541 1.808486 5.487110 75.93085 56.659730... NA NA NA NA NA NA 1.5741142 1.423323 -168.20953344.41905
4 Apr25 4.038411 3.673708 4.422226 2.964890 1.838493 4.916997 98.94443 74.485966... NA NA NA NA NA NA 1.6079817 1.421440 -219.03894448.07788
5 May03 4.293687 3.891017 4.702551 4.325660 2.815386 6.827528 142.11194 85.886326... 1.3172612 0.000000003.7062045 NA NA NA 1.7719233 1.572422 -293.35475598.70950
5 May11 4.480769 4.039215 4.926651 5.690116 3.733014 8.839956 125.57746 101.747339... 0.6264135 0.014390651.2624419 NA NA NA 1.7775491 1.372707 -345.57857703.15713
5 May17 4.500801 4.047643 4.963235 6.134764 3.966473 9.605248 124.33858 102.121114... 0.5351187 0.079928100.9812402 NA NA NA 1.7027658 1.266530 -364.43213740.86426
5 May25 4.510033 4.058967 4.985647 6.117942 4.035311 9.715661 123.20867 101.677758... 0.4764253 0.079386520.8734229 NA NA NA 1.6083754 1.204524 -368.95473749.90946
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllllllllllllllllll}\n", " generations & epicurve & mean\\_h & mean\\_h\\_CIlower & mean\\_h\\_CIupper & var\\_h & var\\_h\\_CIlower & var\\_h\\_CIupper & K & K\\_CIlower & ... & R4 & R4\\_CIlower & R4\\_CIupper & R5 & R5\\_CIlower & R5\\_CIupper & rmse\\_c & rmse\\_i & loglk & AIC\\\\\n", "\\hline\n", "\t 2 & Apr01 & 4.044729 & 2.965966 & 5.430933 & 3.934810 & 1.166348 & 20.957400 & 28.42619 & 9.875538 & ... & NA & NA & NA & NA & NA & NA & 0.8761112 & 1.665254 & -37.19753 & 80.39506 \\\\\n", "\t 3 & Apr09 & 4.061809 & 3.416039 & 4.721406 & 3.803687 & 1.777842 & 8.968100 & 68.10914 & 18.520947 & ... & NA & NA & NA & NA & NA & NA & 1.3657549 & 1.575942 & -98.06433 & 204.12866 \\\\\n", "\t 3 & Apr17 & 4.068600 & 3.645938 & 4.505535 & 3.037541 & 1.808486 & 5.487110 & 75.93085 & 56.659730 & ... & NA & NA & NA & NA & NA & NA & 1.5741142 & 1.423323 & -168.20953 & 344.41905 \\\\\n", "\t 4 & Apr25 & 4.038411 & 3.673708 & 4.422226 & 2.964890 & 1.838493 & 4.916997 & 98.94443 & 74.485966 & ... & NA & NA & NA & NA & NA & NA & 1.6079817 & 1.421440 & -219.03894 & 448.07788 \\\\\n", "\t 5 & May03 & 4.293687 & 3.891017 & 4.702551 & 4.325660 & 2.815386 & 6.827528 & 142.11194 & 85.886326 & ... & 1.3172612 & 0.00000000 & 3.7062045 & NA & NA & NA & 1.7719233 & 1.572422 & -293.35475 & 598.70950 \\\\\n", "\t 5 & May11 & 4.480769 & 4.039215 & 4.926651 & 5.690116 & 3.733014 & 8.839956 & 125.57746 & 101.747339 & ... & 0.6264135 & 0.01439065 & 1.2624419 & NA & NA & NA & 1.7775491 & 1.372707 & -345.57857 & 703.15713 \\\\\n", "\t 5 & May17 & 4.500801 & 4.047643 & 4.963235 & 6.134764 & 3.966473 & 9.605248 & 124.33858 & 102.121114 & ... & 0.5351187 & 0.07992810 & 0.9812402 & NA & NA & NA & 1.7027658 & 1.266530 & -364.43213 & 740.86426 \\\\\n", "\t 5 & May25 & 4.510033 & 4.058967 & 4.985647 & 6.117942 & 4.035311 & 9.715661 & 123.20867 & 101.677758 & ... & 0.4764253 & 0.07938652 & 0.8734229 & NA & NA & NA & 1.6083754 & 1.204524 & -368.95473 & 749.90946 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "generations | epicurve | mean_h | mean_h_CIlower | mean_h_CIupper | var_h | var_h_CIlower | var_h_CIupper | K | K_CIlower | ... | R4 | R4_CIlower | R4_CIupper | R5 | R5_CIlower | R5_CIupper | rmse_c | rmse_i | loglk | AIC | \n", "|---|---|---|---|---|---|---|---|\n", "| 2 | Apr01 | 4.044729 | 2.965966 | 5.430933 | 3.934810 | 1.166348 | 20.957400 | 28.42619 | 9.875538 | ... | NA | NA | NA | NA | NA | NA | 0.8761112 | 1.665254 | -37.19753 | 80.39506 | \n", "| 3 | Apr09 | 4.061809 | 3.416039 | 4.721406 | 3.803687 | 1.777842 | 8.968100 | 68.10914 | 18.520947 | ... | NA | NA | NA | NA | NA | NA | 1.3657549 | 1.575942 | -98.06433 | 204.12866 | \n", "| 3 | Apr17 | 4.068600 | 3.645938 | 4.505535 | 3.037541 | 1.808486 | 5.487110 | 75.93085 | 56.659730 | ... | NA | NA | NA | NA | NA | NA | 1.5741142 | 1.423323 | -168.20953 | 344.41905 | \n", "| 4 | Apr25 | 4.038411 | 3.673708 | 4.422226 | 2.964890 | 1.838493 | 4.916997 | 98.94443 | 74.485966 | ... | NA | NA | NA | NA | NA | NA | 1.6079817 | 1.421440 | -219.03894 | 448.07788 | \n", "| 5 | May03 | 4.293687 | 3.891017 | 4.702551 | 4.325660 | 2.815386 | 6.827528 | 142.11194 | 85.886326 | ... | 1.3172612 | 0.00000000 | 3.7062045 | NA | NA | NA | 1.7719233 | 1.572422 | -293.35475 | 598.70950 | \n", "| 5 | May11 | 4.480769 | 4.039215 | 4.926651 | 5.690116 | 3.733014 | 8.839956 | 125.57746 | 101.747339 | ... | 0.6264135 | 0.01439065 | 1.2624419 | NA | NA | NA | 1.7775491 | 1.372707 | -345.57857 | 703.15713 | \n", "| 5 | May17 | 4.500801 | 4.047643 | 4.963235 | 6.134764 | 3.966473 | 9.605248 | 124.33858 | 102.121114 | ... | 0.5351187 | 0.07992810 | 0.9812402 | NA | NA | NA | 1.7027658 | 1.266530 | -364.43213 | 740.86426 | \n", "| 5 | May25 | 4.510033 | 4.058967 | 4.985647 | 6.117942 | 4.035311 | 9.715661 | 123.20867 | 101.677758 | ... | 0.4764253 | 0.07938652 | 0.8734229 | NA | NA | NA | 1.6083754 | 1.204524 | -368.95473 | 749.90946 | \n", "\n", "\n" ], "text/plain": [ " generations epicurve mean_h mean_h_CIlower mean_h_CIupper var_h \n", "1 2 Apr01 4.044729 2.965966 5.430933 3.934810\n", "2 3 Apr09 4.061809 3.416039 4.721406 3.803687\n", "3 3 Apr17 4.068600 3.645938 4.505535 3.037541\n", "4 4 Apr25 4.038411 3.673708 4.422226 2.964890\n", "5 5 May03 4.293687 3.891017 4.702551 4.325660\n", "6 5 May11 4.480769 4.039215 4.926651 5.690116\n", "7 5 May17 4.500801 4.047643 4.963235 6.134764\n", "8 5 May25 4.510033 4.058967 4.985647 6.117942\n", " var_h_CIlower var_h_CIupper K K_CIlower ... R4 R4_CIlower\n", "1 1.166348 20.957400 28.42619 9.875538 ... NA NA\n", "2 1.777842 8.968100 68.10914 18.520947 ... NA NA\n", "3 1.808486 5.487110 75.93085 56.659730 ... NA NA\n", "4 1.838493 4.916997 98.94443 74.485966 ... NA NA\n", "5 2.815386 6.827528 142.11194 85.886326 ... 1.3172612 0.00000000\n", "6 3.733014 8.839956 125.57746 101.747339 ... 0.6264135 0.01439065\n", "7 3.966473 9.605248 124.33858 102.121114 ... 0.5351187 0.07992810\n", "8 4.035311 9.715661 123.20867 101.677758 ... 0.4764253 0.07938652\n", " R4_CIupper R5 R5_CIlower R5_CIupper rmse_c rmse_i loglk AIC \n", "1 NA NA NA NA 0.8761112 1.665254 -37.19753 80.39506\n", "2 NA NA NA NA 1.3657549 1.575942 -98.06433 204.12866\n", "3 NA NA NA NA 1.5741142 1.423323 -168.20953 344.41905\n", "4 NA NA NA NA 1.6079817 1.421440 -219.03894 448.07788\n", "5 3.7062045 NA NA NA 1.7719233 1.572422 -293.35475 598.70950\n", "6 1.2624419 NA NA NA 1.7775491 1.372707 -345.57857 703.15713\n", "7 0.9812402 NA NA NA 1.7027658 1.266530 -364.43213 740.86426\n", "8 0.8734229 NA NA NA 1.6083754 1.204524 -368.95473 749.90946" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pars_final %>%\n", " group_by(epicurve) %>%\n", " filter(AIC==min(AIC)) %>%\n", " ungroup -> pars_final_minAIC\n", "\n", "pars_final_minAIC" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Joining, by = c(\"generations\", \"epicurve\")\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
generationsepicurvedayc025_imedian_ic975_ic025_cmedian_cc975_cicMLE_iMLE_c
2 Apr01 0 0.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000 0 0.000000e+000.000000e+00
2 Apr01 1 8.767959e-111.953046e-103.037796e-100.000000e+000.000000e+000.000000e+000 0 1.955382e-100.000000e+00
2 Apr01 2 9.905362e-072.206400e-063.431867e-061.448319e-127.358796e-123.001963e-110 0 2.209039e-067.355458e-12
2 Apr01 3 1.408525e-043.137463e-044.880054e-041.636888e-088.315140e-083.391746e-070 0 3.141215e-048.311946e-08
2 Apr01 4 3.272886e-037.290290e-031.133942e-022.426965e-061.207808e-054.874847e-050 0 7.299009e-031.207812e-05
2 Apr01 5 2.768975e-026.167839e-029.593545e-026.822064e-053.118900e-041.186102e-030 0 6.175216e-023.122019e-04
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllll}\n", " generations & epicurve & day & c025\\_i & median\\_i & c975\\_i & c025\\_c & median\\_c & c975\\_c & i & c & MLE\\_i & MLE\\_c\\\\\n", "\\hline\n", "\t 2 & Apr01 & 0 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0 & 0 & 0.000000e+00 & 0.000000e+00\\\\\n", "\t 2 & Apr01 & 1 & 8.767959e-11 & 1.953046e-10 & 3.037796e-10 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0 & 0 & 1.955382e-10 & 0.000000e+00\\\\\n", "\t 2 & Apr01 & 2 & 9.905362e-07 & 2.206400e-06 & 3.431867e-06 & 1.448319e-12 & 7.358796e-12 & 3.001963e-11 & 0 & 0 & 2.209039e-06 & 7.355458e-12\\\\\n", "\t 2 & Apr01 & 3 & 1.408525e-04 & 3.137463e-04 & 4.880054e-04 & 1.636888e-08 & 8.315140e-08 & 3.391746e-07 & 0 & 0 & 3.141215e-04 & 8.311946e-08\\\\\n", "\t 2 & Apr01 & 4 & 3.272886e-03 & 7.290290e-03 & 1.133942e-02 & 2.426965e-06 & 1.207808e-05 & 4.874847e-05 & 0 & 0 & 7.299009e-03 & 1.207812e-05\\\\\n", "\t 2 & Apr01 & 5 & 2.768975e-02 & 6.167839e-02 & 9.593545e-02 & 6.822064e-05 & 3.118900e-04 & 1.186102e-03 & 0 & 0 & 6.175216e-02 & 3.122019e-04\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "generations | epicurve | day | c025_i | median_i | c975_i | c025_c | median_c | c975_c | i | c | MLE_i | MLE_c | \n", "|---|---|---|---|---|---|\n", "| 2 | Apr01 | 0 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0 | 0 | 0.000000e+00 | 0.000000e+00 | \n", "| 2 | Apr01 | 1 | 8.767959e-11 | 1.953046e-10 | 3.037796e-10 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0 | 0 | 1.955382e-10 | 0.000000e+00 | \n", "| 2 | Apr01 | 2 | 9.905362e-07 | 2.206400e-06 | 3.431867e-06 | 1.448319e-12 | 7.358796e-12 | 3.001963e-11 | 0 | 0 | 2.209039e-06 | 7.355458e-12 | \n", "| 2 | Apr01 | 3 | 1.408525e-04 | 3.137463e-04 | 4.880054e-04 | 1.636888e-08 | 8.315140e-08 | 3.391746e-07 | 0 | 0 | 3.141215e-04 | 8.311946e-08 | \n", "| 2 | Apr01 | 4 | 3.272886e-03 | 7.290290e-03 | 1.133942e-02 | 2.426965e-06 | 1.207808e-05 | 4.874847e-05 | 0 | 0 | 7.299009e-03 | 1.207812e-05 | \n", "| 2 | Apr01 | 5 | 2.768975e-02 | 6.167839e-02 | 9.593545e-02 | 6.822064e-05 | 3.118900e-04 | 1.186102e-03 | 0 | 0 | 6.175216e-02 | 3.122019e-04 | \n", "\n", "\n" ], "text/plain": [ " generations epicurve day c025_i median_i c975_i c025_c \n", "1 2 Apr01 0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00\n", "2 2 Apr01 1 8.767959e-11 1.953046e-10 3.037796e-10 0.000000e+00\n", "3 2 Apr01 2 9.905362e-07 2.206400e-06 3.431867e-06 1.448319e-12\n", "4 2 Apr01 3 1.408525e-04 3.137463e-04 4.880054e-04 1.636888e-08\n", "5 2 Apr01 4 3.272886e-03 7.290290e-03 1.133942e-02 2.426965e-06\n", "6 2 Apr01 5 2.768975e-02 6.167839e-02 9.593545e-02 6.822064e-05\n", " median_c c975_c i c MLE_i MLE_c \n", "1 0.000000e+00 0.000000e+00 0 0 0.000000e+00 0.000000e+00\n", "2 0.000000e+00 0.000000e+00 0 0 1.955382e-10 0.000000e+00\n", "3 7.358796e-12 3.001963e-11 0 0 2.209039e-06 7.355458e-12\n", "4 8.315140e-08 3.391746e-07 0 0 3.141215e-04 8.311946e-08\n", "5 1.207808e-05 4.874847e-05 0 0 7.299009e-03 1.207812e-05\n", "6 3.118900e-04 1.186102e-03 0 0 6.175216e-02 3.122019e-04" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pars_final_minAIC %>%\n", " select(generations,epicurve) %>%\n", " left_join(final) -> final_minAIC\n", "\n", "final_minAIC %>% head" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generating Figure 2" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "'May25'" ], "text/latex": [ "'May25'" ], "text/markdown": [ "'May25'" ], "text/plain": [ "[1] \"May25\"" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"Removed 14 rows containing missing values (geom_bar).\"Warning message:\n", "\"Removed 14 rows containing missing values (geom_bar).\"" ] }, { "data": { "image/png": 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q1tZ0nuD/J2++ZgyD3++qstFGWQPBQM+z/r/+2b7/xCIdmNCgAotzvT3oVAdJMX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WRuCYb5jMf/u/qdpuCdRXc2NjIw97FSoq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\nmGQdu896Pmh2Frtk79skbW1t8dciXu4vxxOFQvT5bzA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Gg6sy2hecsCTZZHnOJi2m65qm6RijwNh0iWBYyHkZaamIG8JYnYiqDgAQiR58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\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bJswptITFIxak+DzvRMe6pSCqUrFCDJ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\n+uXOjeZif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ARj3stJ8BAEmSrl178slX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WzLJutWfdaiKi9lu3T/26kduz/QusUm5J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\nXuFZrmx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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width=7,repr.plot.height=7.5)\n", "final_minAIC %<>% mutate(date = as.Date('2018-03-17')+day, epidate = as.Date('2018'%&%epicurve,\"%Y%b%d\"))\n", "\n", "(background_epicurve = rev(all_epicurves)[1])\n", "\n", "ymax=7\n", "\n", "final_minAIC %>%\n", " ggplot(aes(x=date,group=epicurve,color=epicurve,fill=epicurve)) +\n", " geom_bar(data=filter(final_minAIC,epicurve==background_epicurve),\n", " aes(y=c),fill=\"black\",color=\"black\",stat=\"identity\",\n", " position=position_dodge(1),alpha=.25,size=.15) +\n", " geom_ribbon(aes(ymin=c025_c,ymax=c975_c),color=\"black\",alpha=.55,size=.05) +\n", " geom_line(data=final_minAIC %>% filter(date<=epidate),aes(y=MLE_c),size=.8) +\n", " geom_line(data=final_minAIC %>% filter(date>epidate-1),aes(y=MLE_c),size=.6,linetype=\"dashed\") +\n", " scale_color_brewer(palette=\"Spectral\",direction=-1) +\n", " scale_fill_brewer(palette=\"Spectral\",direction=-1) +\n", " scale_x_date(labels = date_format(\"%d-%b\")) +\n", " labs(x=\"Date of laboratory confirmation (day-month)\",\n", " y=\"Number of cases\",\n", " color=\"Epicurve\", \n", " fill=\"Epicurve\") +\n", " coord_cartesian(ylim=c(0,ymax)) +\n", " theme(axis.text.x = element_text(angle = 45, hjust = .5, vjust=0.5),\n", " strip.text.x = element_blank(),\n", " panel.grid.minor.x = element_blank(),\n", " plot.title = element_text(size=11, hjust = 0.5, face=\"bold\"),\n", " strip.background = element_rect(colour=\"white\", fill=\"white\"),\n", " plot.margin = unit(c(.5,1,1,.5),\"lines\")) -> plt_confirmation\n", "\n", "plt_confirmation %<>% arrangeGrob(top = textGrob(\"B\", x = unit(0, \"npc\"), y = unit(.5, \"npc\"), just=c(\"left\",\"top\"),\n", " gp=gpar(col=\"black\", fontsize=16, fontface=\"bold\", fontfamily=\"Times\")))\n", "\n", "final_minAIC %>%\n", " ggplot(aes(x=date,group=epicurve,color=epicurve,fill=epicurve)) +\n", " geom_bar(data=filter(final_minAIC,epicurve==background_epicurve),\n", " aes(y=i),fill=\"black\",color=\"black\",stat=\"identity\",\n", " position=position_dodge(1),alpha=.25,size=.15) +\n", " geom_ribbon(aes(ymin=c025_i,ymax=c975_i),color=\"black\",alpha=.55,size=.05) +\n", " geom_line(data=final_minAIC %>% filter(date<=epidate),aes(y=MLE_i),size=.8) +\n", " geom_line(data=final_minAIC %>% filter(date>epidate-1),aes(y=MLE_i),size=.6,linetype=\"dashed\") +\n", " coord_cartesian(ylim=c(0,ymax)) +\n", " scale_color_brewer(palette=\"Spectral\",direction=-1) +\n", " scale_fill_brewer(palette=\"Spectral\",direction=-1) +\n", " labs(x=\"Date of illness onset (day-month)\",\n", " y=\"Number of cases\",\n", " color=\"Epicurve\", \n", " fill=\"Epicurve\") +\n", " scale_x_date(labels = date_format(\"%d-%b\")) +\n", " theme(\n", " axis.text.x = element_text(angle = 45, hjust = .5, vjust=0.5),\n", " strip.text.x = element_blank(),\n", " panel.grid.minor.x = element_blank(),\n", " plot.title = element_text(size=11, hjust = 0.5, face=\"bold\"),\n", " strip.background = element_rect(colour=\"white\", fill=\"white\"),\n", " plot.margin = unit(c(1,1,.5,.5),\"lines\")) -> plt_onset\n", "\n", "plt_onset %<>% arrangeGrob(top = textGrob(\"A\", x = unit(0, \"npc\"), y = unit(.25, \"npc\"), just=c(\"left\",\"top\"),\n", " gp=gpar(col=\"black\", fontsize=16, fontface=\"bold\", fontfamily=\"Times\")))\n", "\n", "grid.arrange(plt_onset, plt_confirmation, widths=c(1), heights=c(1,1), nrow=2, ncol=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Creating a small animation" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"Unknown or uninitialised column: 'date'.\"Warning message in max(.$date):\n", "\"no non-missing arguments to max; returning -Inf\"Warning message:\n", "\"Removed 14 rows containing missing values (geom_bar).\"Warning message:\n", "\"Removed 60 rows containing missing values (geom_bar).\"" ] }, { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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9U0V1Qkl3zZWonADgAArEFg13zUrA+WSa2ktrmi7Lt+WCIiYr8sgAYAAF6TNLBD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\nqfxX752dDE13s45f46c6+Ym2b6wvM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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width=7,repr.plot.height=4)\n", "\n", "current_epicurve = \"Apr09\"\n", "\n", "final_minAIC %>%\n", " ggplot(aes(x=date,group=epicurve,color=epicurve,fill=epicurve)) +\n", " geom_bar(data=filter(final_minAIC,epicurve==background_epicurve),\n", " aes(y=c),fill=\"white\",color=\"black\",stat=\"identity\",\n", " position=position_dodge(1),alpha=.25,size=.2) +\n", " geom_bar(data=filter(final_minAIC,epicurve==current_epicurve),\n", " aes(y=c),fill=\"black\",color=\"black\",stat=\"identity\",\n", " position=position_dodge(1),alpha=.5,size=.15) +\n", " geom_ribbon(data=filter(final_minAIC,epicurve==current_epicurve),\n", " aes(ymin=c025_c,ymax=c975_c),alpha=.5,size=0) +\n", " geom_line(data=filter(final_minAIC,epicurve==current_epicurve & date<=epidate),aes(y=MLE_c),size=.8) +\n", " geom_line(data=filter(final_minAIC,epicurve==current_epicurve & date>epidate-1),aes(y=MLE_c),size=.6,linetype=\"dashed\") +\n", " geom_vline(xintercept=filter(final,epicurve==background_epicurve) %>% na.omit %>% { max(.$date) },\n", " size=.45, linetype=\"dashed\") +\n", " scale_color_brewer(palette=\"Spectral\",direction=-1) +\n", " scale_fill_brewer(palette=\"Spectral\",direction=-1) +\n", " scale_x_date(labels = date_format(\"%d-%b\")) +\n", " labs(y=\"Count\", x=\"Date (day-month)\",\n", " title=\"Number of cases by date of laboratory confirmation\",\n", " color=\"Epicurve\", fill=\"Epicurve\") +\n", " coord_cartesian(ylim=c(0,ymax)) +\n", " theme_bw() + \n", " theme(axis.text.x = element_text(angle = 45, hjust = .5, vjust=0.5),\n", " strip.text.x = element_blank(),\n", " panel.grid.minor.x = element_blank(),\n", " plot.title = element_text(size=11, hjust = 0.5, face=\"bold\"),\n", " strip.background = element_rect(colour=\"white\", fill=\"white\"),\n", " plot.margin = unit(c(.5,1,.5,.5),\"lines\"))" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"Unknown or uninitialised column: 'date'.\"Warning message in max(.$date):\n", "\"no non-missing arguments to max; returning -Inf\"Warning message:\n", "\"Removed 68 rows containing missing values (geom_bar).\"Warning message:\n", "Rendering [=====>----------------------------------------] at 4 fps ~ eta: 2sWarning message:\n", "Rendering [==========>---------------------------------] at 3.7 fps ~ eta: 2sWarning message:\n", "Rendering [===============>----------------------------] at 3.6 fps ~ eta: 1sWarning message:\n", "Rendering [=====================>----------------------] at 3.7 fps ~ eta: 1sWarning message:\n", "Rendering [===========================>----------------] at 3.7 fps ~ eta: 1sWarning message:\n", "Rendering [================================>-----------] at 3.6 fps ~ eta: 1sWarning message:\n", "Rendering [=====================================>------] at 3.6 fps ~ eta: 0sWarning message:\n", " \r" ] }, { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ " format width height colorspace matte filesize density\n", "1 gif 650 450 sRGB FALSE 0 39x39\n", "2 gif 650 450 sRGB TRUE 0 39x39\n", "3 gif 650 450 sRGB TRUE 0 39x39\n", "4 gif 650 450 sRGB TRUE 0 39x39\n", "5 gif 650 450 sRGB TRUE 0 39x39\n", "6 gif 650 450 sRGB TRUE 0 39x39\n", "7 gif 650 450 sRGB TRUE 0 39x39\n", "8 gif 650 450 sRGB TRUE 0 39x39" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width=8,repr.plot.height=4)\n", "\n", "p = final_minAIC %>%\n", " ggplot(aes(x=date,group=epicurve,color=epicurve,fill=epicurve)) +\n", " geom_bar(aes(y=c),fill=\"black\",color=\"black\",stat=\"identity\",\n", " position=position_dodge(1),alpha=.5,size=.15) +\n", " geom_ribbon(data=filter(final_minAIC,epicurve==epicurve),\n", " aes(ymin=c025_c,ymax=c975_c),alpha=.5,size=0) +\n", " geom_line(data=filter(final_minAIC,date<=epidate),aes(y=MLE_c),size=.8) +\n", " geom_line(data=filter(final_minAIC,date>epidate-1),aes(y=MLE_c),size=.6,linetype=\"dashed\") +\n", " geom_vline(xintercept=filter(final,epicurve==background_epicurve) %>% na.omit %>% { max(.$date) },\n", " size=.45, linetype=\"dashed\") +\n", " scale_color_brewer(palette=\"Spectral\",direction=-1) +\n", " scale_fill_brewer(palette=\"Spectral\",direction=-1) +\n", " scale_x_date(labels = date_format(\"%d-%b\")) +\n", " labs(y=\"Count\", x=\"Date (day-month)\",\n", " title=\"Number of cases by date of laboratory confirmation\",\n", " color=\"Epicurve\", fill=\"Epicurve\") +\n", " coord_cartesian(ylim=c(0,ymax)) +\n", " theme_bw() + \n", " theme(axis.text.x = element_text(angle = 45, hjust = .5, vjust=0.5),\n", " strip.text.x = element_blank(),\n", " panel.grid.minor.x = element_blank(),\n", " plot.title = element_text(size=11, hjust = 0.5, face=\"bold\"),\n", " strip.background = element_rect(colour=\"white\", fill=\"white\"),\n", " plot.margin = unit(c(.5,1,.5,.5),\"lines\")) +\n", " transition_manual(epicurve)\n", "\n", "animate(p, nframes=length(unique(final_minAIC$epicurve)), fps=2, width=650, height=450, res=100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 2 }