Author: Ralf Engbert; Maximilian M. Rabe; Reinhold Kliegl; Sebastian Reich
Title: Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics Document date: 2020_4_17
ID: 855am0mv_3
Snippet: Since we are interested in short-term modeling (weeks to months), we neglect birth and death processes as a first-order approximation for the dynamics of the model. Disease-related model parameters are the rate parameters a = 1/Z (with average latency period Z) and g = 1/D (with mean infectious period D), which can be estimated independently from analysis of infected cases [14, 16] . Therefore, the time-dependent contact parameter β is the most .....
Document: Since we are interested in short-term modeling (weeks to months), we neglect birth and death processes as a first-order approximation for the dynamics of the model. Disease-related model parameters are the rate parameters a = 1/Z (with average latency period Z) and g = 1/D (with mean infectious period D), which can be estimated independently from analysis of infected cases [14, 16] . Therefore, the time-dependent contact parameter β is the most critical parameter that needs to be determined via data assimilation [20] . The contact parameter β is directly related to the basic reproductive number R 0 in a SEIR-type model (see SEIR model and basic reproductive number). Therefore, non-pharmaceutical interventions that aim at R 0 < 1 translate into the relation β < g in the model. In the following, we will use a combination of sequential data assimilation and stochastic modeling on the regional level to estimate spatial heterogeneity in epidemics spread and show how to use such a combined approach for epidemics prediction and uncertainty quantification.
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