Comparing the Performance of Three Models Incorporating Weather Data to Forecast Dengue Epidemics in Reunion Island, 2018–2019

The Journal of Infectious Diseases

Alessio Andronico, Luce Menudier, Henrik Salje, Muriel Vincent, Juliette Paireau, Henriette de Valk, Pierre Gallian, Boris Pastorino, Oliver Brady, Xavier de Lamballerie, Clément Lazarus, Marie-Claire Paty, Pascal Vilain, Harold Noel, and Simon Cauchemez

Summary

We developed mathematical models to analyze a large dengue virus (DENV) epidemic in Reunion Island in 2018–2019. Our models captured major drivers of uncertainty including the complex relationship between climate and DENV transmission, temperature trends, and underreporting. Early assessment correctly concluded that persistence of DENV transmission during the austral winter 2018 was likely and that the second epidemic wave would be larger than the first one. From November 2018, the detection probability was estimated at 10%–20% and, for this range of values, our projections were found to be remarkably accurate. Overall, we estimated that 8% and 18% of the population were infected during the first and second wave, respectively. Out of the 3 models considered, the best-fitting one was calibrated to laboratory entomological data, and accounted for temperature but not precipitation. This study showcases the contribution of modeling to strengthen risk assessments and planning of national and local authorities.

Keywords. dengue; climate; epidemic forecasting; modelling.

More information at DOI: https://doi.org/10.1093/infdis/jiad468