A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles

Sarah F. McGough, Leonardo Clemente, J. Nathan Kutz, Mauricio Santillana

Journal of The Royal Society Interface 2021 article cited by 46

Source Open-access PDF More in Medicine & Health

importance 8/10 ai assisted Mosquito-borne diseases and control epidemic forecasting stage: research

Problem

Predicting dengue fever epidemic years in Brazilian cities months in advance is hard due to complex weather, mosquito and human immunity dynamics.

Why it matters

Dengue outbreaks cause significant illness and death, and early warnings enable public health preparedness.

What the AI did

A dynamic, ensemble machine learning approach learns local weather and population susceptibility patterns to make city-level epidemic predictions months ahead.

Results

not stated in source

Impact

Abstract claims forecasts improve when population susceptibility is included, but gives no numeric performance metrics.

Record

Venue	Journal of The Royal Society Interface
Published	2021
Work type	article
Authors	Sarah F. McGough, Leonardo Clemente, J. Nathan Kutz, Mauricio Santillana
Citations	46 (OpenAlex, at import time)
References	45
Model or method	dynamic ensemble learning (ML)
Organization	not stated in source
Primary source	https://doi.org/10.1098/rsif.2020.1006
Open access	hybrid
Keywords	dengue, forecasting, machine learning, ensemble, Brazil, epidemiology

Importance rating

8 / 10

major — millions of lives, an industry or a field

Abstract frames seasonal dengue outbreaks affecting locations globally, with forecasts supporting public health decision-making to mitigate outbreaks.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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