Lauren A. Castro 1 , 2 , * , Nicholas Generous 3 , Wei Luo 4 , 5 , 6 , Ana Pastore y Piontti 7 , Kaitlyn Martinez 1 , 8 , Marcelo F. C. Gomes 9 , Dave Osthus 10 , Geoffrey Fairchild 1 , Amanda Ziemann 11 , Alessandro Vespignani 7 , Mauricio Santillana 4 , 5 , 12 , Carrie A. Manore 1 , Sara Y. Del Valle 1
21 May 2021
Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.
Dengue virus spreads through mosquitoes in many tropical and subtropical parts of the world, including Brazil. Each year, dengue virus causes seasonal outbreaks that vary in magnitude and timing across the country. This variation makes tailoring preparation efforts for fine spatio-temporal scales challenging. In this study, we described four properties of historical dengue time series at the mesoregion level, the Brazilian subdivision below state, and examined how they varied across the country. We found that the duration and timing of seasonal outbreaks are largely driven by climate factors, while relational properties, i.e., the similarity in outbreak timing and magnitude between two mesoregions, are explained by a mix of mobility patterns and climate similarities. Surprisingly, we found that remote sensing derived products and movement inferred through Twitter were adequate proxies for climate and mobility patterns respectively. Knowledge of how dengue outbreaks differ across the country and the factors that may influence specific outbreak properties may be important for improving efforts to build forecasting and prediction models.