A principled approach to parametrize SIR-type epidemiologic models of different complexities by embedding the model structure as a hidden layer into a general Input-Output Non-Linear Dynamical System (IO-NLDS). Non-explicitly modelled impacts on the system are imposed as inputs of the system. Observable data are coupled to hidden states of the model by appropriate data models considering possible biases of the data. We estimate model parameters including their time-dependence by a Bayesian knowledge synthesis process considering parameter ranges derived from external studies as prior information. We applied this approach on a SIR-type model and data of Germany and Saxony demonstrating good prediction performances.
By our approach, we can estimate and compare for example the relative effectiveness of non-pharmaceutical interventions and can provide predictions regarding the further course of the epidemic under specified scenarios. Our method of parameter estimation can be translated to other data sets, i.e. other countries and other SIR-type models even for other disease contexts.
LHA ID: 88Q2HXKTJF-00 items (and an image) are associated with this Model:
Human Disease: Covid-19
Model type: Ordinary differential equations (ODE)
Model format: R package
Execution or visualisation environment: Not specified
Model image: (Click on the image to zoom) (Original)
Created: 18th Sep 2021 at 09:41
Last updated: 18th Sep 2021 at 09:41
Last used: 20th Jan 2022 at 15:34