Model: Department of Mathematics and Statistics Masaryk University Team - MUNI_DMS-SEIAR

    Name: SEIAR
    Abbreviation: MUNI_DMS-SEIAR
    Owner: epiforecasts
    Team name: Department of Mathematics and Statistics Masaryk University Team
    Description: 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)
    Contributors: Veronika Hajnova, Lenka Pribylova
    License: Creative Commons Attribution 4.0
    Notes: primary
    Citation: Missing
    Methods: 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 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 b.1.1.7 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 b.1.1.7 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 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 SEIAR model.
    Home: https://share.uzis.cz/s/cmFHjc4jbqPBAER/6_Metodicka_dokumentace_model_ZSEIAR.pdf
    Auxiliary data: Missing

    Forecasts (63)

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