Model-based ensembles: Lessons learned from retrospective analysis of COVID-19 infection forecasts across 10 countries.

ABSTRACT: Mathematical models of different types and data intensities are highly used by researchers, epidemiologists, and national authorities to explore the inherently unpredictable progression of COVID-19, including the effects of different non-pharmaceutical interventions. Regardless of model complexity, forecasts of future COVID-19 infections, deaths and hospitalization are associated with large uncertainties, and critically depend on the quality of the training data, and in particular how well the recorded national or regional numbers of infections, deaths and recoveries reflect the the actual situation. In turn, this depends on, e.g., local test and abatement strategies, treatment capacities and available technologies. Other influencing factors including temperature and humidity, which are su

SUBMITTER: Drews M 

PROVIDER: S-EPMC8479318 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature