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.