Abstract
BACKGROUND: Since 1950, there have been increasingly atypical climatological patterns in the Amazon, some caused by El Niño-Southern Oscillation (ENSO) events (El Niño or La Niña). In 2023-2024, the region faced the most severe droughts in recorded history. These weather patterns are major drivers of malaria. Deforestation has exacerbated these impacts. This study estimates the impact of weather and ENSO events on malaria transmission in the Brazilian Amazon from 2003 to 2022. METHODS: We used daily individual-level data on reported malaria cases from the Brazilian Malaria Epidemiological Surveillance Information System (Sivep-Malaria). A case-crossover approach was used to analyze the effects of lagged weather variables and ENSO events on malaria transmission at the Amazon-wide and state levels. Generalized additive quasi-Poisson models were used to assess the influence of ENSO events on malaria cases. FINDINGS: From 2003 to 2022, 5,381,105 malaria cases were recorded in the Brazilian Amazon. Temperatures between 25.64 and 30.85 °C and precipitation >4.46 cm in the week prior to infection increased malaria infection risk up to 9% (95% CI: 8-10%) and 86% (95% CI: 35-155%), respectively. Two- and three-week lagged temperatures >25.64 °C and diurnal variation >6.75 °C reduced malaria infection risk by a maximum of 27% (95% CI: 19-33%) and 59% (95% CI: 52-69%). State-specific variations in relationships were notable. ENSO events significantly influenced weather conditions and malaria transmission, with El Niño and La Niña associated with a net reduction in malaria cases of 2179 (95% CI: 1837, 2520) and 37,258 (95% CI: 37,171, 37,345), respectively, with marked spatiotemporal heterogeneity in effect. INTERPRETATION: This study clarifies the short- and long-term influences of weather and ENSO events on malaria transmission in the Brazilian Amazon. The results underscore the high degree of heterogeneity in the effects of weather on malaria transmission in the region, and the need for proactive and fine-scale malaria control based on weather forecasting and the development of early warning systems to achieve malaria elimination. FUNDING: This research was supported by the Division of Intramural Research at the National Institute of Allergy and Infectious Diseases (Award Number: 2U19AI089681-08) and the Foundation for the National Institutes of Health (Award Number: NIH/T32 AI007535).