Predicting Dengue Incidence Leveraging Internet-Based Data Sources. A Case Study in 20 cities in Brazil

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 it 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, most work has focused on prediction systems at the national level, rather than at finer spatial resolutions. 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 a random forest-based model effectively leverages these multiple data sources and provides robust predictions, while retaining interpretability. 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 very short delays (1-2 weeks), short-term and seasonal autocorrelation are dominant as predictors. Despite the difficulties inherent to city-level prediction, our framework achieves meaningful and actionable estimates across cities with different characteristics.

Dengue no Brasil: Uma análise epidemiológica do período compreendido entre 2014 e 2021

“…Possui 4 sorotipos conhecidos: DENV 1, DENV 2, DENV 3 e DENV 4, apresentando como transmissor o mosquito Aedes aegypti, que tem preferência por áreas urbanas e de elevada densidade populacional para sua proliferação (CAVALLI et al, 2019). Uma variedade de condições externas demonstrou afetar a transmissão da dengue como a precipitação, temperatura e padrões climáticos sazonais que convergem para a propagação da doença, desenvolvimento e vida útil dos mosquitos transmissores (KOPLEWITZ et al, 2022).…”