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Related Experiment Video

Updated: Jan 20, 2026

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A Machine Learning-Based Dynamic SST Index for Long-Lead Malaria Prediction in the Peruvian Amazon.

Mengxin Pan1,2, Shineng Hu1, Mark M Janko3

  • 1Nicholas School of the Environment Duke University Durham NC USA.

Geohealth
|January 19, 2026
PubMed
Summary
This summary is machine-generated.

Tropical sea surface temperature (SST) variability can predict malaria in the Peruvian Amazon. A new machine learning model using a dynamic SST index offers improved long-lead malaria forecasting over traditional methods.

Keywords:
Peruvian amazonmalaria early warningsea surface temperatureself‐organizing map

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Area of Science:

  • Environmental science
  • Epidemiology
  • Machine learning

Background:

  • Malaria poses a significant health challenge in the Peruvian Amazon.
  • Early warning systems are crucial for effective malaria prevention and control.

Purpose of the Study:

  • To develop a machine learning methodology for predicting malaria in the Peruvian Amazon using tropical sea surface temperature (SST) variability.
  • To identify a dynamic SST index for improved malaria prediction with long lead times.

Main Methods:

  • Correlating tropical SST anomalies with Peruvian malaria occurrence across seasons and time lags.
  • Utilizing self-organizing maps to synthesize SST-malaria relationships and derive a dynamic SST index.
  • Comparing the performance of the dynamic SST index against the traditional El Niño-Southern Oscillation (ENSO) index in a generalized linear model.

Main Results:

  • Significant correlations were found between tropical SST anomalies and Peruvian malaria.
  • The dynamic SST index demonstrated superior performance (higher correlation, lower RMSE) compared to the ENSO index for malaria prediction.
  • The dynamic SST index, linked to the Pacific Meridional Mode, influences local temperature and humidity, providing a plausible mechanism for prediction.

Conclusions:

  • Tropical SST variability offers potential for long-lead malaria prediction in the Peruvian Amazon.
  • The developed machine learning approach and dynamic SST index provide a more effective tool for malaria forecasting.
  • Open-source code is provided for broader applications in climate-sensitive disease transmission research.