Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals

TL;DR

This study uses machine learning and climate data analysis to identify key weather patterns that predict dengue outbreaks in Brazilian cities, highlighting city-specific climate signatures that influence epidemic timing.

Contribution

It introduces a novel approach combining dimensionality reduction and machine learning to identify city-specific climate predictors for dengue outbreaks in Brazil.

Findings

Precipitation and temperature during winter are highly predictive of dengue outbreaks.

Each city exhibits unique climate signatures associated with outbreaks.

Climate variables influence mosquito populations months before outbreaks occur.

Abstract

Local climate conditions play a major role in the development of the mosquito population responsible for transmitting Dengue Fever. Since the {\em Aedes Aegypti} mosquito is also a primary vector for the recent Zika and Chikungunya epidemics across the Americas, a detailed monitoring of periods with favorable climate conditions for mosquito profusion may improve the timing of vector-control efforts and other urgent public health strategies. We apply dimensionality reduction techniques and machine-learning algorithms to climate time series data and analyze their connection to the occurrence of Dengue outbreaks for seven major cities in Brazil. Specifically, we have identified two key variables and a period during the annual cycle that are highly predictive of epidemic outbreaks. The key variables are the frequency of precipitation and temperature during an approximately two month window of the winter season preceding the outbreak. Thus simple climate signatures may be influencing Dengue outbreaks even months before their occurrence. Some of the more challenging datasets required usage of compressive-sensing procedures to estimate missing entries for temperature and precipitation records. Our results indicate that each Brazilian capital considered has a unique frequency of precipitation and temperature signature in the winter preceding a Dengue outbreak. Such climate contributions on vector populations are key factors in dengue dynamics which could lead to more accurate prediction models and early warning systems. Finally, we show that critical temperature and precipitation signatures may vary significantly from city to city, suggesting that the interplay between climate variables and dengue outbreaks is more complex than generally appreciated.