During infectious disease outbreaks such as the current coronavirus disease (COVID-19) pandemic, modern surveillance systems continuously produce detailed data on reported disease incidence. Typically, these data are available at various geographic resolutions and stratified by age and sex, leading to high-dimensional count time series. Statistical modelling approaches which can handle the heterogeneities and interdependencies in such data are a valuable tool to inform public health decision makers about disease dynamics, to evaluate the effect of intervention measures, and to provide probabilistic forecasts of disease spread. Important factors which need to be taken into account are social contact patterns, mechanisms of geographic spread, and possible underreporting, all of which can vary across regions, age groups, and time. The endemic-epidemic (in the following: EE) framework is an established flexible modelling framework for multivariate infectious disease surveillance counts. A robust, free, and easy-to-use implementation is provided in several R packages. To our knowledge, this is the only readily available implementation of a sophisticated and general model framework for age-stratified spatio-temporal surveillance data. In the past, the EE framework has mainly been used for seasonal diseases, but there is a clear need for general and well-implemented multivariate modelling tools also for acute outbreak situations like the current COVID-19 pandemic. The goal of this project is to extend the EE framework to further improve its applicability in such contexts. Specifically the extensions aim to better address the following aspects:
This project will provide evidence to improve public health response and aid in decisions on optimal social control strategies, particularly when to initiate travel restrictions and social distancing measures, and improve situational awareness.
For further information please visit the project page.