team_name: Universitaet Leipzig IMISE/GenStat
model_name: SECIR
model_abbr: LeipzigIMISE-SECIR
model_contributors:
  - name: Yuri Kheifetz
    email: Yuri.Kheifetz@imise.uni-leipzig.de
  - name: Holger Kirsten
    email: holger.kirsten@imise.uni-leipzig.de
  - name: Markus Scholz
    email: Markus.Scholz@imise.uni-leipzig.de
    twitter: GenStatLeipzig
website_url: https://github.com/holgerman/covid19-forecast-hub-europe
repo_url: https://github.com/holgerman/covid19-forecast-hub-europe
license: mit
team_model_designation: primary
data_inputs: RKI and DIVI, ECDC, age distributions for cases and deaths from RKI as
  described in methods, case & ICU  & death counts
methods: SECIR type model
methods_long: We integrate an adapted mechanistic epidemiologic model of the SECIR
  type into Input-Output Non-Linear Dynamical Systems (IO-NLDS) serving as hidden
  layers, i.e. the true dynamics cannot directly be observed. Thereby, we include
  an asymptomatic compartment, a compartment of patients requiring intensive care,
  and subdivide most of the compartments into three sub-compartments to model time
  delays.  Changing factors of the system due to non-pharmaceutical interventions,
  changing age-structure of infected population, and changes in testing policy are
  imposed as inputs to the system. We then estimate parameters by a knowledge synthesis
  process considering parameter ranges derived from external studies and public data.
  Specifically, we use Bayesian inference for the parameters’ estimation, which can
  also be time-dependent. Public data is translated to model outputs not identical
  but related to hidden states of the model. The model is fitted to data by a full
  information approach.