A reproducible ensemble machine learning approach to forecast dengue outbreaks

Alessandro Sebastianelli, Dario Spiller, Raquel Carmo, James Wheeler, Artur Nowakowski, Ludmilla Viana Jacobson, Dohyung Kim, Hanoch Barlevi et al.

Scientific Reports 2024 article cited by 66

Source Open-access PDF More in Medicine & Health

importance 7/10 ai assisted Mosquito-borne diseases and control infectious disease forecasting stage: research

Problem

Dengue outbreaks are hard to predict countrywide because incidence varies across regions and time.

Why it matters

Dengue burdens public health and economies in tropical regions; early warnings enable response.

What the AI did

Built an ensemble machine learning model integrating spatial and temporal data to forecast one-month-ahead dengue incidence at Brazilian state level, transferable to Peru.

Results

Outperformed dummy model across 27 Brazilian Federal Units; transferability to Peru shown; no numeric metrics stated.

Impact

Could help local governments implement timely dengue control measures.

Record

Venue	Scientific Reports
Published	2024
Work type	article
Authors	Alessandro Sebastianelli, Dario Spiller, Raquel Carmo, James Wheeler, Artur Nowakowski, Ludmilla Viana Jacobson, Dohyung Kim, Hanoch Barlevi et al.
Citations	66 (OpenAlex, at import time)
References	48
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1038/s41598-024-52796-9
Open access	gold
Keywords	dengue forecasting, machine learning, ensemble model, epidemiology, Brazil

Importance rating

7 / 10

significant — thousands to millions affected

Abstract states dengue poses substantial public health and economic challenges in tropical/sub-tropical regions worldwide; countrywide early-warning forecasts could enable timely control measures.

Basis: stated. The source itself states the scale. Stated scale: 27 Brazilian Federal Units; transferable to Peru. Method and limits.

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