ArticleThe Journal of infectious diseases2024
Comparing the Performance of Three Models Incorporating Weather Data to Forecast Dengue Epidemics in Reunion Island, 2018-2019.
Article in The Journal of infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Spatiotemporal dynamics of Aedes aegypti and Culex quinquefasciatus populations in Miami-Dade County, Florida.PloS one · 2026Article
- Modeling the impact of vaccine campaigns on the epidemic transmission dynamics of chikungunya virus outbreaks.Nature medicine · 2025Article
- Why the growth of arboviral diseases necessitates a new generation of global risk maps and future projections.PLoS computational biology · 2025Article
- Modeling zoonotic and vector-borne viruses.Current opinion in virology · 2024Review
- Assessing the impact of vaccination and medical resource allocation on infectious disease outbreak management: a case study of COVID-19 in Taiyuan City.Frontiers in public health · 2024Article
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15 authors.
Funding
Abstract
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.
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