Integrating information from historical data into mechanistic models for influenza forecasting.

ABSTRACT: Seasonal influenza causes significant annual morbidity and mortality worldwide. In France, it is estimated that, on average, 2 million individuals consult their GP for influenza-like-illness (ILI) every year. Traditionally, mathematical models used for epidemic forecasting can either include parameters capturing the infection process (mechanistic or compartmental models) or rely on time series analysis approaches that do not make mechanistic assumptions (statistical or phenomenological models). While the latter make extensive use of past epidemic data, mechanistic models are usually independently initialized in each season. As a result, forecasts from such models can contain trajectories that are vastly different from past epidemics. We developed a mechanistic model that takes into account

SUBMITTER: Andronico A 

PROVIDER: S-EPMC11524484 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature