Abstract
The dengue virus affects millions of people every year worldwide, causing large epidemic outbreaks that disrupt people’s lives and severely strain healthcare systems. In the absence of a reliable vaccine against dengue or an effective treatment to manage the illness in humans, most efforts to combat dengue infections have focused on preventing its vectors, mainly the Aedes aegypti mosquito, from flourishing across the world. These mosquito-control strategies need reliable disease activity surveillance systems to be deployed. Despite significant efforts to estimate dengue incidence using a variety of data sources and methods, little work has been done to understand the relative contribution of the different data sources to improved prediction. Additionally, scholarship on the topic had initially focused on prediction systems at the national- and state-levels, and much remains to be done at the finer spatial resolutions at which health policy interventions often occur. We develop a methodological framework to assess and compare dengue incidence estimates at the city level, and evaluate the performance of a collection of models on 20 different cities in Brazil. The data sources we use towards this end are weekly incidence counts from prior years (seasonal autoregressive terms), weekly-aggregated weather variables, and real-time internet search data. We find that both random forest-based models and LASSO regression-based models effectively leverage these multiple data sources to produce accurate predictions, and that while the performance between them is comparable on average, the former method produces fewer extreme outliers, and can thus be considered more robust. For real-time predictions that assume long delays (6–8 weeks) in the availability of epidemiological data, we find that real-time internet search data are the strongest predictors of dengue incidence, whereas for predictions that assume short delays (1–3 weeks), in which the error rate is halved (as measured by relative RMSE), short-term and seasonal autocorrelation are the dominant predictors. Despite the difficulties inherent to city-level prediction, our framework achieves meaningful and actionable estimates across cities with different demographic, geographic and epidemic characteristics.
Highlights
The incidence of dengue has risen dramatically over the past few decades
We develop machine learning models based on both Least Absolute Shrinkage and Selection Operator (LASSO) regression and on random forest ensembles, and proceed to apply and compare them across 20 cities in Brazil
We find that our methodology produces meaningful and actionable disease estimates at the city level with both underlying model classes, and that the two perform comparably across most metrics, the ensemble method produces fewer outliers
Summary
The incidence of dengue has risen dramatically over the past few decades. With an estimated 100–400 million infections each year, dengue threatens roughly 3.9 billion people in 128 countries and poses a growing health and economic problem throughout the tropical and sub-tropical world [1]. Though the disease often manifests asymptomatically, severe cases can lead to hemorrhage, shock and death [3]. In Brazil, which we examine in this paper, dengue has been endemic since 1986, and is today considered to be experiencing a “hyperendemic scenario,” in which both fatalities and severe cases are rising [4,5]. In the decades since 1986 over 40% of all dengue deaths in the country have been taken place in the Southeast region, but mortality from the disease has been reported in all but two of Brazil’s states
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