Unraveling regional variability in Dengue outbreaks in Brazil: leveraging the Moving Epidemics Method (MEM) and climate data to optimize vector control strategies

A country with continental dimensions like Brazil, characterized by heterogeneity of climates, biomes, natural resources, population density, socioeconomic conditions, and regional challenges, also exhibits significant spatial variation in dengue outbreaks. This study aimed to characterize Brazilian territory based on epidemiological and climate data to determine the optimal time to guide preventive and control strategies. To achieve this, the Moving Epidemics Method (MEM) was employed to analyze dengue historical patterns using 14-year disease data (2010–2023) aggregated by the 120 Brazilian Health Macro-Regions (HMR). Statistical outputs from MEM included the mean outbreak onset, duration, and variation of these measurements, pre- and post-epidemic thresholds, and the high-intensity level of cases. Environmental data used includes mean annual precipitation, temperature, and altitude, as well as the Köppen Climate Classification of each area. A multivariate cluster analysis using the k-means algorithm was applied to MEM outputs and climate data. Four clusters/regions were identified, with the mean temperature, mean precipitation, mean outbreak onset, high-intensity level of cases, and mean altitude explaining 80% of the centroid variation among the clusters. Region 1 (North-Northwest) encompasses areas with the highest temperatures, precipitation, and early outbreak onset, in February. Region 2a (Northeast) has the lowest precipitation and a later onset, in March. Region 3 (Southeast) presents higher altitude, and early outbreak onset in February; while Region 4 (South) has a lower temperature, with onset in March. To better adjust the results, the unique Roraima state HMR state was manually classified as Region 2b (Roraima) because of its outbreak onset in July and the highest precipitation volume. The results suggested preventive and control measures should be implemented first in Regions North-Northwest and Southeast, followed by Regions Northeast, South, and Roraima, highlighting the importance of regional vector control measures based on historical and climatic patterns. Integrating these findings with monitoring systems and fostering cross-sector collaboration can enhance surveillance and mitigate future outbreaks. The proposed methodology also holds potential for application in controlling other mosquito-transmitted viral diseases, expanding its public health impact.Author summary: Dengue fever, a mosquito-transmitted viral disease, represents a significant public health challenge in tropical countries like Brazil. Transmission patterns vary widely across the country, shaped by diverse geography, climate, and local conditions. This study analyzed 14-year dengue data (2010–2023) from 120 Health Macro-Regions (HMR) in Brazil, integrating epidemiological and climate data to understand regional variations and optimize the timing of vector control strategies. This study applied the Moving Epidemics Method (MEM) output metrics, such as intensity and outbreak onset, along with environmental data (e.g., temperature and precipitation) and altitude in a multivariate cluster analysis to identify similar transmission seasons potentially driven by comparable climatic conditions. As a result, five regions emerged with similar outbreak patterns, emphasizing the need for tailored interventions. For example, some regions require vector control measures before February (~10 weeks of uncertainty), while others can delay until June (~13 weeks of uncertainty), highlighting the importance of locally adapted strategies. By integrating environmental data with traditional epidemiological approaches, this research offers valuable insights for enhancing disease control efforts. This methodology provides a framework for addressing other mosquito-borne diseases, including Zika and chikungunya.