Transactions of the Royal Society of Tropical Medicine and Hygiene · 2025
Projecting high-level dengue increases during low-incidence months in a large endemic urban area.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Costa Alexandre Cunha, da Costa José Micael Ferreira, Moreira Rafaella Pessoa, Cavalcante Tahissa Frota, de Araújo Júnior Luiz Martins, Fernandes Thamara Kely de Sousa, Ferreira Glauciano de Oliveira, Silva Jéssica, Silveira Cleiton da Silva, Cavalcanti Luciano Pamplona de Góes
Аннотация
BACKGROUND: Climate change projections impact dengue transmission in tropical regions, creating diverse and complex public health challenges. The aim of this study was to project future seasonal dengue incidence in a large tropical urban area of Brazil, where dengue is endemic, by systematically analyzing climatic and dengue case time series.
METHODS: An epidemiological dengue model was developed using time series analysis techniques and forced with statistical downscaling from various Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models based on Shared Socioeconomic Pathway (SSP) scenarios for the late twenty-first century (2071-2100). The ecological time-series investigation analyzed 193 618 dengue cases from January 2008 to December 2018. Statistical downscaling transformed low-resolution Global Climate Models' outputs into high-resolution data, based on a 30-y climate baseline.
RESULTS: The results indicated significant climate changes in seasonal temperature and precipitation by the end of the century. Under both SSP2-4.5 and SSP5-8.5 scenarios, substantial increases in dengue cases are projected during current low-incidence months, resulting in a reduced seasonality of dengue.
CONCLUSIONS: The findings represent considerable challenges for future public health systems, which may face sustained high levels of dengue incidence throughout the year.
METHODS: An epidemiological dengue model was developed using time series analysis techniques and forced with statistical downscaling from various Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models based on Shared Socioeconomic Pathway (SSP) scenarios for the late twenty-first century (2071-2100). The ecological time-series investigation analyzed 193 618 dengue cases from January 2008 to December 2018. Statistical downscaling transformed low-resolution Global Climate Models' outputs into high-resolution data, based on a 30-y climate baseline.
RESULTS: The results indicated significant climate changes in seasonal temperature and precipitation by the end of the century. Under both SSP2-4.5 and SSP5-8.5 scenarios, substantial increases in dengue cases are projected during current low-incidence months, resulting in a reduced seasonality of dengue.
CONCLUSIONS: The findings represent considerable challenges for future public health systems, which may face sustained high levels of dengue incidence throughout the year.