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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Updated: Nov 25, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables.

Amy Marie Campbell1, Marie-Fanny Racault2,3, Stephen Goult2,3

  • 1European Space Agency, Climate Office, ECSAT, Harwell OX11 0FD, UK.

International Journal of Environmental Research and Public Health
|December 18, 2020
PubMed
Summary

Machine learning accurately forecasts cholera outbreaks in coastal India using satellite data. Key predictors include chlorophyll-a, sea surface salinity, and land surface temperature, enabling better environmental risk assessment.

Keywords:
AIcholeraclimatecoastal environmentessential climate variablesmachine learningrandom forestremote sensing

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Area of Science:

  • Environmental science
  • Epidemiology
  • Machine learning applications

Background:

  • Climate change impacts oceanic and coastal ecosystems, influencing water-borne disease dynamics.
  • Essential climate variables are linked to the distribution and seasonality of Vibrio cholerae, the cause of cholera.
  • Coastal India faces significant public health challenges due to water-borne diseases.

Purpose of the Study:

  • To explore the potential of machine learning for forecasting environmental cholera risk in coastal India.
  • To identify key climate variables influencing cholera outbreaks using satellite-derived data.
  • To develop and validate a predictive model for cholera risk assessment.

Main Methods:

  • Development and testing of a Random Forest classifier model using a cholera outbreak dataset (2010-2018).
  • Utilisation of atmospheric, terrestrial, and oceanic satellite-derived essential climate variables as input features.
  • Analysis of model performance metrics including accuracy, F1 score, and sensitivity.

Main Results:

  • The Random Forest model achieved high performance: 0.99 accuracy, 0.942 F1 score, and 0.895 sensitivity.
  • Chlorophyll-a concentration, sea surface salinity, and land surface temperature were identified as the strongest predictors.
  • Spatio-temporal patterns in model performance were observed across seasons and coastal locations.

Conclusions:

  • Machine learning, particularly Random Forest classifiers, shows significant potential for environmental cholera risk forecasting.
  • Remotely-sensed essential climate variables are valuable for developing predictive cholera risk applications.
  • Further research is needed to assess model transferability to other coastal regions and enhance cholera surveillance systems.