Brazil accounts for ~80% of dengue (DENV) cases in the Americas. DENV outbreaks vary spatially within a season and temporally across seasons due to cocirculating serotypes, coexistence of competent vectors, and five distinct climatic zones, making prediction efforts challenging. Identifying specific environmental and sociodemographic factors that explain this variation is key to improving such prediction efforts. Ecological proxy data (i.e., indirect data sources) can be used to capture the complex interactions between the host, pathogen, and environment and provide information for local conditions in the absence of granular statistics collected on the ground. Here, we used a combination of 36 satellite imagery, weather, clinical, and census data streams to characterize DENV outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset at the Brazilian state (N=27) and meso region (N=137) scales. Using cross-validated regularized regression models to analyze weekly DENV case data from 2010-2016, we found a parsimonious set of ecological data that explains each outbreak property and measured how well each model captures the property’s variation through several performance metrics. Finally, we compared results from the state and the meso region scales to identify local versus global effects of environmental conditions on DENV transmission.
Results/Conclusions
From 2010-2016, the average difference in seasonal DENV onset spanned 17 weeks and followed a counterclockwise pattern, starting in the Amazon region and ending in the northeastern coastal states. The seasonal onset in Para (northern region) was on average 6.74 weeks ahead of other states, positioning it as a potential sentinel state. At the state level, our models explained 62.8% of the variation in outbreak shape, 51.1% of pairwise correlation in outbreak timing, 48.5% of seasonal onset, and 13.0% of pairwise correlation in outbreak magnitude. Outbreak properties were generally better explained at the state scale rather than the meso region scale. The normalized burn ratio (NBR) had the strongest effect on outbreak shape, while the mean daily temperature range most impacted the state seasonal onset. The pairwise correlation in outbreak timing between states was best predicted by distance, while the pairwise correlation in outbreak magnitude was best predicted by the similarity in population density. Overall, our results highlight the utility of diverse and disparate ecological data streams for understanding the complex mechanisms that drive DENV transmission dynamics across geographic regions, and how this knowledge can be used to improve the formulation of spatially linked forecasting models of DENV transmission.