Emilie Finch, Adam J Kucharski, Shuzhen Sim, Lee Ching Ng, Rachel Lowe
{"title":"Climate variation and serotype competition drive dengue outbreak dynamics in Singapore","authors":"Emilie Finch, Adam J Kucharski, Shuzhen Sim, Lee Ching Ng, Rachel Lowe","doi":"10.1101/2024.09.17.24313793","DOIUrl":null,"url":null,"abstract":"Dengue poses a rapidly increasing threat to global health, with Southeast Asia as one of the worst affected regions. Climate-informed early warning systems can help to mitigate the impact of outbreaks; however, prediction of large outbreaks with sufficient lead time to guide interventions remains a challenge. In this work, we quantify the role of climatic variation and serotype competition in shaping dengue risk in Singapore using over 20 years of weekly case data. We integrated these findings into an early warning system framework able to predict dengue outbreaks up to 2 months ahead. While a climate-informed model improved predictive power by 54% compared to a seasonal baseline, including additional serotype information increased predictive performance to 60%, helping to explain interannual variation. By incorporating serotype competition as a proxy for population immunity, this work advances the field of dengue prediction and demonstrates the value of long-term virus surveillance.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Epidemiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.09.17.24313793","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Dengue poses a rapidly increasing threat to global health, with Southeast Asia as one of the worst affected regions. Climate-informed early warning systems can help to mitigate the impact of outbreaks; however, prediction of large outbreaks with sufficient lead time to guide interventions remains a challenge. In this work, we quantify the role of climatic variation and serotype competition in shaping dengue risk in Singapore using over 20 years of weekly case data. We integrated these findings into an early warning system framework able to predict dengue outbreaks up to 2 months ahead. While a climate-informed model improved predictive power by 54% compared to a seasonal baseline, including additional serotype information increased predictive performance to 60%, helping to explain interannual variation. By incorporating serotype competition as a proxy for population immunity, this work advances the field of dengue prediction and demonstrates the value of long-term virus surveillance.