Improving early epidemiological assessment of emerging Aedes-transmitted epidemics using historical data

Julien Riou; Chiara Poletto; Pierre-Yves Boëlle

Predicting the future course of epidemics from an early point is increasingly seen as a problem of interest [46, 9, 8] , and forecasting challenges have been set up for inuenza [47] , for Ebola [45] and for chikungunya [48] . Comparing and systematically evaluating models' forecasting performances is still at the beginning. As of now, comparisons targeted the merits of dierent models including exponential growth models, sigmoid models, or mechanistic epidemic models [45] . Our work provides a complementary approach where information from past epidemics is combined using hierarchical models to inform on parameter ranges, thus increasing the reliability of early forecasts. It was applied here to a dynamic discrete-time SIR model that for its parsimony is well-adapted to real-time forecasting. Complex mechanistic models can provide a more realistic description of the epidemic, accounting, for instance, for heterogenous spatial distribution of individuals and mobility coupling a relevant ingredient for describing epidemics in more extended spatial areas , or vector population dynamics and its mixing with humans. Our framework could in principle be adapted to these more sophisticated models.