#' ---
#' title: "Simulation of stochastic dynamic models"
#' author: "Aaron A. King and Edward L. Ionides"
#' output:
#'   html_document:
#'     toc: yes
#'     toc_depth: 4
#' bibliography: ../course.bib
#' csl: ../ecology.csl
#' nocite: >
#'   @Keeling2007
#' 
#' ---
#' 
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#' 
#' --------------------------
#' 
#' [Licensed under the Creative Commons Attribution-NonCommercial license](http://creativecommons.org/licenses/by-nc/4.0/).
#' Please share and remix noncommercially, mentioning its origin.  
#' ![CC-BY_NC](../graphics/cc-by-nc.png)
#' This document has its origins in the [SISMID short course on Simulation-based Inference](https://kingaa.github.io/sbied/stochsim/stochsim.html) given by Aaron King and Edward Ionides.
#' 
#' Produced with **R** version `r getRversion()` and **pomp** version `r packageVersion("pomp")`.
#' 
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#' <div class="nb"> 
#' **Important Note:**
#' These materials have been updated for use with version `r packageVersion("pomp")`.
#' As of version 2, **pomp** syntax has changed substantially.
#' These changes [are documented](http://kingaa.github.io/pomp/vignettes/upgrade_guide.html) on the **pomp** website.
#' </div>
#' 
#' --------------------------
#' 

#' 
## ----prelims,echo=F,cache=F---------------------------------------------------
library(plyr)
library(reshape2)
library(pomp)
library(ggplot2)
theme_set(theme_bw())
options(stringsAsFactors=FALSE)
stopifnot(packageVersion("pomp")>="2.8")
set.seed(594709947L)

#' 
#' ## Objectives
#' 
#' This tutorial develops some classes of dynamic models of relevance in biological systems, especially epidemiology.
#' We have the following goals:
#' 
#' 1. Dynamic systems can often be represented in terms of _flows_ between _compartments_.
#' We will develop the concept of a _compartment model_ for which we specify _rates_ for the flows between compartments.
#' 1. We show how deterministic and stochastic versions of a compartment model are derived and related.
#' 1. We introduce Euler's method to simulate from dynamic models, and we apply it to both deterministic and stochastic compartment models.
#' 
#' ## Introduction
#' 
#' Compartmental models are of great utility in many disciplines and very much so in epidemiology.
#' Let us derive deterministic and stochastic versions of the susceptible-infected-recovered (SIR) model of disease transmission dynamics in a closed population.
#' In so doing, we will use notation that generalizes to more complex systems [[@breto09]](http://dx.doi.org/10.1214/08-AOAS201).
#' 

#' 
#' - Let $S$, $I$, and $R$ represent, respectively, the number of susceptible hosts, the number of infected (and, by assumption, infectious) hosts, and the number of recovered or removed hosts. 
#' - We suppose that each arrow has an associated *per capita* rate, so here there is a rate $\mu_{SI}$ at which individuals in $S$ transition to $I$, and $\mu_{IR}$ at which individuals in $I$ transition to $R$. 
#' - To account for demography (birth/death/migration) we allow the possibility of a source and sink compartment, which is not represented on the flow diagram above.
#'     - We write $\mu_{{\small\bullet} S}$ for a rate of births into $S$.
#'     - Mortality rates are denoted by $\mu_{S{\small\bullet}}$, $\mu_{I{\small\bullet}}$, $\mu_{R{\small\bullet}}$.
#' - The rates may be either constant or varying. In particular, for a simple SIR model, the recovery rate $\mu_{IR}$ is a constant but the infection rate has the time-varying form $$\mu_{SI}(t)=\beta\,\frac{I(t)}{N(t)},$$ with $\beta$ being the _contact rate_ and $N$ the total size of the host population.
#'   In the present case, since the population is closed, we set 
#'   $$\mu_{{\small\bullet} S}=\mu_{S{\small\bullet}}=\mu_{I{\small\bullet}}=\mu_{R{\small\bullet}}=0.$$
#' - In general, it turns out to be convenient to keep track of the flows between compartments as well as the number of individuals in each compartment. 
#'   Let $N_{SI}(t)$ count the number of individuals who have transitioned from $S$ to $I$ by time $t$. 
#'   We say that $N_{SI}(t)$ is a _counting process_. 
#'   A similarly constructed process $N_{IR}(t)$ counts individuals transitioning from $I$ to $R$.
#'   To include demography, we could keep track of birth and death events by the counting processes $N_{{\small\bullet} S}(t)$, $N_{S{\small\bullet}}(t)$, $N_{I{\small\bullet}}(t)$, $N_{R{\small\bullet}}(t)$.
#'     - For discrete population compartment models, the flow counting processes are non-decreasing and integer valued.
#'     - For continuous population compartment models, the flow counting processes are non-decreasing and real valued.
#' - The number of hosts in each compartment can be computed via these counting processes.
#'   Ignoring demography, we have:
#' $$\begin{aligned} 
#' S(t) &= S(0) - N_{SI}(t)\\
#' I(t) &= I(0) + N_{SI}(t) - N_{IR}(t)\\
#' R(t) &= R(0) + N_{IR}(t)
#' \end{aligned}$$
#'   These equations represent a kind of conservation law. 
#' - Over any finite time interval $[t,t+\delta)$, we have
#' $$\begin{aligned} 
#' \dlta{S} &= -\dlta{N}_{SI}\\
#' \dlta{I} &= \dlta{N}_{SI}-\dlta{N}_{IR}\\
#' \dlta{R} &= \dlta{N}_{IR},
#' \end{aligned}$$
#' where the $\Delta$ notation indicates the increment in the corresponding process.
#' Thus, for example $\dlta{N}_{SI}(t) = N_{SI}(t+\delta)-N_{SI}(t)$.
#' 
#' ## Compartmental models in theory
#' 
#' ### The deterministic version of the SIR model
#' 
#' Together with initial conditions specifying $S(0)$, $I(0)$ and $R(0)$, we just need to write down ordinary differential equations (ODE) for the flow counting processes.
#' These are,
#' $$\begin{gathered}
#' \frac{dN_{SI}}{dt} = \mu_{SI}(t)\,S(t), \qquad
#' \frac{dN_{IR}}{dt} = \mu_{IR}\,I(t).
#' \end{gathered}$$
#' 
#' ### The simple continuous-time Markov chain version of the SIR model
#' 
#' - Continuous-time Markov chains are the basic tool for building discrete population epidemic models.
#' - Recall that a _Markov chain_ is a discrete-valued stochastic process with the _Markov property_:
#'   the future evolution of the process depends only on the current state.
#' - Surprisingly many models have this Markov property. 
#'   If all important variables are included in the state of the system, then the Markov property appears automatically.
#' - The Markov property lets us specify a model by giving the transition probabilities on small intervals together with initial conditions. 
#'   For the SIR model in a closed population, we have
#' $$\begin{aligned}
#' &\prob{N_{SI}(t+\delta)=N_{SI}(t)+1} &=& &\mu_{SI}(t)\,S(t)\,\delta + o(\delta)\\
#' &\prob{N_{SI}(t+\delta)=N_{SI}(t)} &=& &1-\mu_{SI}(t)\,S(t)\,\delta + o(\delta)\\
#' &\prob{N_{IR}(t+\delta)=N_{IR}(t)+1} &=& &\mu_{IR}(t)\,I(t)\,\delta + o(\delta)\\
#' &\prob{N_{IR}(t+\delta)=N_{IR}(t)} &=& &1-\mu_{IR}(t)\,I(t)\,\delta + o(\delta)\\
#' \end{aligned}$$
#' - A *simple* counting process is one for which no more than one event can occur at a time ([Wikipedia: point process](https://en.wikipedia.org/wiki/Point_process)). 
#'   Thus, in a technical sense, the SIR Markov chain model we have written is simple. 
#'   One may want to model the extra randomness resulting from multiple simultaneous events:
#'   someone sneezing in a crowded bus, large gatherings at football matches, etc. 
#'   This extra randomness may even be critical to match the variability in data. 
#' - We will see later, in the [measles case study](../measles/measles.html), a situation where this extra randomness plays an important role. 
#'   The representation of the model in terms of counting processes turns out to be useful for this.
#' 
#' <!-- ### Demographic vs environmental stochasticity -->
#' 
#' --------------------------
#' 
#' ##### Exercise: From Markov chain to ODE 
#' Find the expected value of $N_{SI}(t+\delta)-N_{SI}(t)$ and $N_{IR}(t+\delta)-N_{IR}(t)$ given the current state, $S(t)$, $I(t)$ and $R(t)$.
#' Take the limit as $\delta\to 0$ and show that this gives the ODE model.
#' 
#' --------------------------
#' 
#' ### Euler's method for ODE
#' 
#' - [Euler](https://en.wikipedia.org/wiki/Leonhard_Euler) took the following approach to numeric solution of an ODE:
#'     + He wanted to investigate an ODE $$\frac{dx}{dt}=h(x,t)$$
#'       with an initial condition $x(0)$. 
#'       He supposed this ODE has some true solution $x(t)$ which could not be worked out analytically. 
#'       He therefore wished to approximate $x(t)$ numerically.
#'     + He initialized the numerical solution at the known starting value, $$\tilde x(0)=x(0).$$
#'       Then, for $k=1,2,\dots$, he supposed that the gradient $dx/dt$ is approximately constant over the small time interval $k\delta\le t\le (k+1)\delta$. 
#'       Therefore, he defined $$\tilde{x}\big((k+1)\delta\big) = \tilde{x}(k\delta) + \delta\,h\big(\tilde{x}(k\delta),k\delta\big).$$
#'    + This defines $\tilde x(t)$ when only for those $t$ that are multiples of $\delta$, but let's suppose $\tilde x(t)$ is constant between these discrete times.
#' - We now have a numerical scheme, stepping forwards in time increments of size $\delta$, that can be readily evaluated by computer.
#' - [Mathematical analysis of Euler's method](https://en.wikipedia.org/wiki/Euler_method) says that, as long as the function $h(x)$ is not too exotic, then $x(t)$ is well approximated by $\tilde x(t)$  when the discretization time-step, $\delta$, is sufficiently small.
#' - Euler's method is not the only numerical scheme to solve ODEs. 
#'   More advanced schemes have better convergence properties, meaning that the numerical approximation is closer to $x(t)$.
#'   However, there are 3 reasons we choose to lean heavily on Euler's method:
#'     1. Euler's method is the simplest (the KISS principle).
#'     2. Euler's method extends naturally to stochastic models, both continuous-time Markov chains models and stochastic differential equation (SDE) models.
#'     3. In the context of data analysis, close approximation of the numerical solutions to a continuous-time model is less important than may be supposed, a topic worth further discussion....
#' 
#' ### Some comments on using continuous-time models and discretized approximations
#' 
#' - In some physical situations, a system follows an ODE model closely. 
#'   For example, Newton's laws provide a very good approximation to the motions of celestial bodies. 
#' - In many biological situations, ODE models become good approximations to reality only at relatively large scales. 
#'   On small temporal scales, models cannot usually capture the full scope of biological variation and biological complexity. 
#' - If we are going to expect substantial error in using $x(t)$ to model a biological system, maybe the numerical solution $\tilde x(t)$ represents the system being modeled as well as $x(t)$  does.
#' - If our model fitting, model investigation, and final conclusions are all based on our numerical solution  $\tilde x(t)$ (e.g., we are sticking entirely to simulation-based methods) then we are most immediately concerned with how well $\tilde x(t)$ describes the system of interest.  
#'   $\tilde x(t)$ becomes more important than the original model, $x(t)$.
#' - When following this perspective, it is important that one fully describe the numerical model $\tilde x(t)$. 
#'   From this point of view, then, the main advantage of the continuous-time model $x(t)$ is then that it gives a succinct way to describe how $\tilde x(t)$ was constructed.
#' - All numerical methods are, ultimately, discretizations. 
#'   Epidemiologically, setting $\delta$ to be a day, or an hour, can be quite different from setting $\delta$ to be two weeks or a month. 
#'   For continuous-time modeling, we still require that $\delta$ is small compared to the timescale of the process being modeled, and the choice of $\delta$ does not play an explicit role in the interpretation of the model.
#' - Putting more emphasis on the scientific role of the numerical solution itself reminds you that the numerical solution has to do more than approximate a target model in some asymptotic sense: 
#'   the numerical solution should be a sensible model in its own right. 
#' 
#' ### Euler's method for a discrete SIR model
#' 
#' - Recall the simple continuous-time Markov chain interpretation of the SIR model without demography:
#' $$\begin{aligned}
#' \prob{N_{SI}(t+\delta)=N_{SI}(t)+1} &= \mu_{SI}(t)\,S(t)\,\delta + o(\delta),\\
#' \prob{N_{IR}(t+\delta)=N_{IR}(t)+1} &= \mu_{IR}\,I(t)\,\delta + o(\delta).
#' \end{aligned}$$
#' 
#' - We look for a numerical solution with state variables $\tilde S(k\delta)$, $\tilde I(k\delta)$, $\tilde R(k\delta)$. 
#' 
#' - The counting processes for the flows between compartments are $\tilde N_{SI}(t)$ and $\tilde N_{IR}(t)$. The counting processes are related to the numbers of individuals in the compartments by the same flow equations we had before:
#' $$\begin{aligned} 
#' \dlta{\tilde S} &= -\dlta{\tilde N}_{SI}\\
#' \dlta{\tilde I} &= \dlta{\tilde N}_{SI}-\dlta{\tilde N}_{IR}\\
#' \dlta{\tilde R} &= \dlta{\tilde N}_{IR},
#' \end{aligned}$$
#' 
#' - Let's focus $N_{SI}(t)$;
#'   the same methods can also be applied to $N_{IR}(t)$.
#' 
#' - Here are three stochastic Euler schemes for $N_{SI}$:
#'     1. Poisson increments:
#'     $$\dlta{\tilde N}_{SI}\;\sim\;\dist{Poisson}{\tilde \mu_{SI}(t)\,\tilde S(t)\,\delta},$$ where $\dist{Poisson}{\mu}$ is the Poisson distribution with mean $\mu$ and $$\tilde\mu_{SI}(t)=\beta\,\frac{\tilde I(t)}{N}.$$
#'     1. Binomial increments with linear probability:
#'     $$\dlta{\tilde N}_{SI}\;\sim\;\dist{Binomial}{\tilde{S}(t),\tilde\mu_{SI}(t)\,\delta},$$ where $\dist{Binomial}{n,p}$ is the binomial distribution with mean $n\,p$ and variance $n\,p\,(1-p)$.
#'     1. $\dlta{\tilde{N}}_{SI}\;\sim\;\dist{Binomial}{\tilde{S}(t),1-e^{-\tilde{\mu}_{SI}(t)\,\delta}}$.
#' - Note that these schemes agree as $\delta\to 0$.
#' - What are the advantages and disadvantages of these different schemes?
#'   Conceptually, it is simplest to think of (1) or (2). 
#'   Numerically, it is usually preferable to implement (3). 
#' 
#' ### Compartmental models via stochastic differential equations (SDE)
#' 
#' The Euler method extends naturally to stochastic differential equations. A natural way to add stochastic variation to an ODE $dx/dt=h(x)$ is
#' $$\frac{dX}{dt}=h(X)+\sigma\,\frac{dB}{dt}$$
#' where $B(t)$ is Brownian motion and so $dB/dt$ is Gaussian white noise.
#' The so-called Euler-Maruyama approximation $\tilde X$ is generated by 
#' $$\tilde X\big(\,(k+1)\delta\,\big) = \tilde X( k\delta) + \delta\, h\big(\, \tilde X(k\delta)\,\big) + \sigma \sqrt{\delta} \, Z_k$$
#' where $Z_1,Z_2,\dots$ is a sequence of independent standard normal random variables, i.e., $Z_k\sim\dist{Normal}{0,1}$. 
#' Although SDEs are often considered an advanced topic in probability, the Euler approximation doesn't demand much more than familiarity with the normal distribution.
#' 
#' --------------------------
#' 
#' ##### Exercise: SDE version of the SIR model
#' 
#' Write down the Euler-Maruyama method for an SDE representation of the closed-population SIR model. 
#' Consider some difficulties that might arise with non-negativity constraints, and propose some practical way one might deal with that issue.
#' 
#' --------------------------
#' 
#' - A useful method to deal with positivity constraints is to use Gamma noise rather than Brownian noise [@bhadra11,@He2010,@laneri10].
#' SDEs driven by Gamma noise can be investigated by Euler solutions simply by replacing the Gaussian noise by an appropriate Gamma distribution.
#' 
#' ### Euler's method vs.&nbsp;Gillspie's algorithm
#' 
#' - A widely used, exact simulation method for continuous time Markov chains is [Gillspie's algorithm](https://en.wikipedia.org/wiki/Gillespie_algorithm) [@Gillespie1977a].
#'   We do not put much emphasis on Gillespie's algorithm here. 
#'   Why?
#'   When would you prefer an implementation of Gillespie's algorithm to an Euler solution?
#' - Numerically, Gillespie's algorithm is often approximated using so-called [tau-leaping](https://en.wikipedia.org/wiki/Tau-leaping) methods [@Gillespie2001]. 
#'   These are closely related to Euler's approach.
#'   Is it reasonable to call a suitable Euler approach a tau-leaping method?
#' 
#' ## Compartmental models in **pomp**.
#' 
#' ### The boarding-school flu outbreak
#' 
#' As an example that we can probe in some depth, let's look at an isolated outbreak of influenza that occurred in a boarding school for boys in England [@Anonymous1978].
#' <!--- 763 boys were at risk, and ultimately 512 spent time away from class (either confined to bed or in convalescence. --->
#' Download the data and examine it:
## ----flu-data1----------------------------------------------------------------
read.table("http://kingaa.github.io/short-course/stochsim/bsflu_data.txt") -> bsflu
head(bsflu)

#' The variable `B` refers to boys confined to bed and `C` to boys in convalescence.
#' Let's restrict our attention for the moment to the `B` variable.
## ----flu-data2,echo=F---------------------------------------------------------
ggplot(data=bsflu,aes(x=day,y=B))+geom_line()+geom_point()

#' 
#' ### A first POMP model
#' 
#' Let's assume that $B$ indicates the number of boys confined to bed the preceding day and that the disease follows the simple SIR model.
#' Our tasks will be, first, to estimate the parameters of the SIR and, second, to decide whether or not the SIR model is an adequate description of these data.
#' 
#' Below is a diagram of the SIR model.
#' The host population is divided into three classes according to their infection status: 
#' S, susceptible hosts; 
#' I, infected (and infectious) hosts; 
#' R, recovered and immune hosts. 
#' The rate at which individuals move from S to I is the force of infection, $\lambda=\mu_{SI}=\beta\,I/N$, while that at which individuals move into the R class is $\mu_{IR}=\gamma$.
#' 

#' 
#' Let's look at how we can view the SIR as a POMP model.
#' The unobserved state variables, in this case, are the numbers of individuals, $S$, $I$, $R$ in the S, I, and R compartments, respectively.
#' It's reasonable in this case to view the population size $N=S+I+R$, as fixed.
#' The numbers that actually move from one compartment to another over any particular time interval are modeled as stochastic processes.
#' In this case, we'll assume that the stochasticity is purely demographic, i.e., that each individual in a compartment at any given time faces the same risk of exiting the compartment.
#' 
#' ### Implementing the model
#' 
#' To implement the model in **pomp**, the first thing we need is a stochastic simulator for the unobserved state process.
#' We've seen that there are several ways of approximating the process just described for numerical purposes.
#' An attractive option here is to model the number moving from one compartment to the next over a very short time interval as a binomial random variable.
#' In particular, we model the number, $\dlta{N_{SI}}$, moving from S to I over interval $\dlta{t}$ as $$\dlta{N_{SI}} \sim \dist{Binomial}{S,1-e^{-\lambda\dlta{t}}},$$ and the number moving from I to R as $$\dlta{N_{IR}} \sim \dist{Binomial}{I,1-e^{-\gamma\dlta{t}}}.$$
#' 
#' A `Csnippet` that encodes such a simulator is as follows:
## ----rproc1-------------------------------------------------------------------
sir_step <- Csnippet("
  double dN_SI = rbinom(S,1-exp(-Beta*I/N*dt));
  double dN_IR = rbinom(I,1-exp(-gamma*dt));
  S -= dN_SI;
  I += dN_SI - dN_IR;
  R += dN_IR;
")

#' At day zero, we'll assume that $I=1$ and $R=0$, but we don't know how big the school is, so we treat $N$ as a parameter to be estimated and let $S(0)=N-1$.
#' Thus an initializer `Csnippet` is
## ----init1--------------------------------------------------------------------
sir_init <- Csnippet("
  S = N-1;
  I = 1;
  R = 0;
")

#' We fold these `Csnippets`, with the data, into a `pomp` object thus:
## ----rproc1-pomp--------------------------------------------------------------
pomp(bsflu,time="day",t0=0,rprocess=euler(sir_step,delta.t=1/6),
     rinit=sir_init,paramnames=c("N","Beta","gamma"),
     statenames=c("S","I","R")) -> sir

#' 
#' Now let's assume that the case reports, $B$, result from a process by which new infections result in confinement with probability $\rho$, which we can think of as the probability that an infection is severe enough to be noticed by the school authorities.
#' Since confined cases have, presumably, a much lower transmission rate, let's treat $B$ as being a count of the number of boys who have moved from I to R over the course of the past day.
#' We need a variable to track this.
#' Let's modify our `Csnippet` above, adding a variable $H$ to track the incidence.
#' We'll then replace the `rprocess` with the new one.
#' 
## ----rproc2-------------------------------------------------------------------
sir_step <- Csnippet("
  double dN_SI = rbinom(S,1-exp(-Beta*I/N*dt));
  double dN_IR = rbinom(I,1-exp(-gamma*dt));
  S -= dN_SI;
  I += dN_SI - dN_IR;
  R += dN_IR;
  H += dN_IR;
")

sir_init <- Csnippet("
  S = N-1;
  I = 1;
  R = 0;
  H = 0;
")

pomp(sir,rprocess=euler(sir_step,delta.t=1/6),rinit=sir_init,
     paramnames=c("Beta","gamma","N"),statenames=c("S","I","R","H")) -> sir

#' 
#' Now, we'll model the data, $B$, as a binomial process,
#' $$B_t \sim \dist{Binomial}{H(t)-H(t-1),\rho}.$$
#' But we have a problem, since at time $t$, the variable `H` we've defined will contain $H(t)$, not $H(t)-H(t-1)$.
#' We can overcome this by telling `pomp` that we want `H` to be set to zero immediately following each observation.
#' We do this by setting the `accumvars` argument to `pomp`:
## ----zero1--------------------------------------------------------------------
pomp(sir,accumvars="H") -> sir

#' 
#' Now, to include the observations in the model, we must write an `rmeasure` component:
## ----meas-model---------------------------------------------------------------
rmeas <- Csnippet("B = rbinom(H,rho);")

#' and put these into our `pomp` object:
## ----add-meas-model-----------------------------------------------------------
sir <- pomp(sir,rmeasure=rmeas,statenames="H",paramnames="rho")

#' 
#' ### Testing the model: simulations
#' 
#' Let's perform some simulations, just to verify that our codes are working as intended.
#' To do so, we'll need some parameters.
#' A little thought will get us some ballpark estimates.
#' In the data, it looks like there were a total of `r sum(bsflu$B)` infections, so the population size, $N$, must be somewhat in excess of this number.
#' In fact, we can use the final-size equation
#' $$R_0 = -\frac{\log{(1-f)}}{f},$$
#' where $f=R(\infty)/N$ is the final size of the epidemic, together with the idea that $R_0$ for influenza is typically thought to be around 1.5, to estimate that $f\approx 0.6$, whence $N\approx 2600$.
#' If the infectious period is roughly 1&nbsp;da, then $1/\gamma \approx 1~\text{da}$ and $\beta = \gamma\,R_0 \approx 1.5~\text{da}^{-1}$.
#' Let's simulate the model at these parameters.
#' 
## ----sir_sim1-----------------------------------------------------------------
sims <- simulate(sir,params=c(Beta=1.5,gamma=1,rho=0.9,N=2600),
                 nsim=20,format="data.frame",include.data=TRUE)

ggplot(sims,mapping=aes(x=day,y=B,group=.id,color=.id=="data"))+
  geom_line()+guides(color=FALSE)

#' 
#' --------------------------
#' 
#' ##### Exercise: Explore the SIR model
#' 
#' Fiddle with the parameters to see if you can't find parameters for which the data are a more plausible realization.
#' 
#' --------------------------
#' 
#' ##### Exercise: The SEIR model
#' 
#' Below is a diagram of the so-called SEIR model.
#' This differs from the SIR model in that infected individuals must pass a period of latency before becoming infectious.
#' 

#' 
#' Modify the codes above to construct a `pomp` object containing the flu data and an SEIR model.
#' Perform simulations as above and adjust parameters to get a sense of whether improvement is possible by including a latent period.
#' 
#' --------------------------
#' 
#' ##### Exercise: Rethinking the boarding-school flu data
#' 
#' In the preceding, we've been assuming that $B_t$ represents the number of boys *sent* to bed on day $t$.
#' Actually, this isn't correct at all.
#' As described in the report [@Anonymous1978], $B_t$ represents the total number of boys *in* bed on day $t$.
#' Since boys were potentially confined for more than one day, the data count each infection multiple times.
#' On the other hand, we have information about the total number of boys at risk and the total number who were infected.
#' In fact, we know that $N=763$ boys were at risk and 512 boys in total spent between 3 and 7 days away from class (either in bed or convalescent).
#' Moreover, we have data on the number of boys, $C_t$, convalescent at day $t$.
#' Since $1540~\text{boy-da}/512~\text{boy} \approx 3~\text{da}$, we know that the average duration spent in bed was 3&nbsp;da and, since $\sum_t\!C_t=`r sum(bsflu$C)`$, we can infer that the average time spent convalescing was $`r sum(bsflu$C)`~\text{boy-da}/512~\text{boy} \approx `r signif(sum(bsflu$C)/512,2)`~\text{da}$.
#' 

#' 
#' Formulate a model with both confinement and convalescent stages.
#' Implement it in **pomp** using a compartmental model like that diagrammed below.
#' 

#' 
#' You will have to give some thought to just how to model the relationship between the data ($B$ and $C$) and the state variables.
#' How many parameters can reasonably be fixed?  How many must be estimated?
#' Obtain some ballpark estimates of the parameters and simulate to see if you can plausibly explain the data as a realization of this model.
#' 
#' --------------------------
#' 
#' ## [Back to course homepage](http://kingaa.github.io/short-course)
#' ## [**R** codes for this document](http://raw.githubusercontent.com/kingaa/short-course/master/stochsim/stochsim.R)
#' 
#' --------------------------
#' 
#' ## References
#'