team_name: Department of Mathematics and Statistics Masaryk University Team
model_name: SEIAR
model_abbr: MUNI_DMS-SEIAR
model_contributors:
  - name: Veronika Eclerova
  - name: Lenka Pribylova
website_url: https://webstudio.shinyapps.io/MAMES/
license: cc-by-4.0
team_model_designation: primary
methods: SEIAR model with A compartment of absent unobserved infected estimated from
  hospital data with incorporated mobility data dependence; optimized to the compartment
  of all exposed (unobserved included)
team_funding: MUNI/11/02202001/2020
data_inputs: UZIS data for predictive models (anonymized set modely_05_datumy.csv
  of confirmed subjects https://onemocneni-aktualne.mzcr.cz/api/account/dokumentace),
  google mobility reports (https://github.com/ActiveConclusion/COVID19_mobility/tree/master/google_reports)
methods_long: This is a model based on a mechanistic compartmental approach, where
  some parameters are taken from literature, some parameters are estimated from an
  anonymized dataset of confirmed subjects. It estimates moving ascertainment rate
  using data of hospitalized subjects (using a proportion of cases not caught before
  admission to hospital), so it estimates not only the observed part of the epidemic
  (compartment I) but also the undetected absent infected (compartment A). The model
  incorporates transmission rate estimate dependence on mobility data and immunization
  after vaccination and optimizes affected clusters' size to estimate all exposed
  individuals using the moving ascertainment rate estimate. To model deaths, we incorporated
  fixed time delay from positivity report to death estimated from data. Currently,
  we assume around 0.44% IFR, a simple division of the age structure 0-20/20-65/65+
  is used. The model estimates continuously from the first outbreak (spring 2020),
  the prevalence corresponds to the Czech prevalence study from May 2020 as well as
  to December community test screenings. We incorporated dominance of alpha variant
  at the beginning of the year 2021 by multiplying the transmission rate by 1.5 (the
  estimate is based on data that reveal evidence on its dependence on the number of
  risk contacts - study PAQ research https://zivotbehempandemie.cz/kontakty or mobility
  data). With the dominance of alpha variant we increased the probability of hospitalization
  by factor 1.3 and the conditional probability of death in case of hospitalization
  by factor 1.1. There is a significant decline of death rate of 65+ cohort in hospitals
  from the second half of March.  We started to adjust its value to estimated 0.36%
  daily decrease based on fit to data of hospitalized positive subjects (starting
  form submission 2021-05-10). Thanks to time series of vaccinated we could compare
  two different estimates of the probability of hospitalization and we deduced that
  we underestimated the prob. of hospitalization and overestimated the size of the
  affected clusters, and that 1/100 (starting from 2021-03-15) now seems more appropriate
  than 1/160 (starting from submission 2021-06-07). We incorporated dominance of delta
  variant. We assume the transmisibility of the delta varinant is 1.4 times higher
  than the alpha variant (submissions 2021-07-12, 2021-07-19). Starting from submission
  2021-07-26 during the summer time we work again without incresed transmissibility
  of the delta variant. Starting from 2021-09-25 we increased the transmissibility
  of the delta variant to 1.8 times higher value than the alpha variant value. Starting
  from 2021-12-20 we gradually increased the virus's transmissibility according to
  the ratio of the omicron variant in discriminated PCRs with a multiplicative coefficient
  2.61 according to delta variant. We started to decrease the probability of hospitalization
  and death proportionally. We changed the model from SEIAR to SEIARS.  We submit
  a single calibration to forecast deaths and cases while the prediction intervals
  are estimated using time series decomposition. Especially, we subtract a deterministic
  trend given by the SEIARS model.