Numerous prediction models of SARS-CoV-2 pandemic were proposed in the past. Unknown parameters of these models are often estimated based on observational data. However, lag
in case-reporting, changing testing policy or incompleteness of data lead to biased estimates. Moreover, parametrization is time-dependent due to changing age-structures, emerging virus variants,
non-pharmaceutical interventions, and vaccination programs. To cover these aspects, we propose a
principled approach to parametrize a SIR-type epidemiologic model by embedding it as a hidden
layer into an input-output non-linear dynamical system (IO-NLDS). Observable data are coupled to
hidden states of the model by appropriate data models considering possible biases of the data. This
includes data issues such as known delays or biases in reporting. 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 specific
SIR-type model and data of Germany and Saxony demonstrating good prediction performances. Our
approach can estimate and compare the relative effectiveness of non-pharmaceutical interventions
and provide scenarios of the future course of the epidemic under specified conditions. It can be
translated to other data sets, i.e., other countries and other SIR-type models.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:87760 |
| Date | 27 October 2023 |
| Creators | Kheifetz, Yuri, Kirsten, Holger, Scholz, Markus |
| Publisher | MDPI |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 1468 |
Page generated in 0.0022 seconds