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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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Leptospirosis modelling using hydrometeorological indices and random forest machine learning.

Veianthan Jayaramu1, Zed Zulkafli2, Simon De Stercke3

  • 1Department of Civil Engineering, Universiti Putra Malaysia, Serdang, Malaysia.

International Journal of Biometeorology
|January 31, 2023
PubMed
Summary

Hydrometeorological data, including averages and extremes, can predict leptospirosis outbreaks. Mixed models using these indices showed the highest accuracy, with extreme rainfall being the most significant predictor.

Keywords:
Cross-correlation analysisFeature selectionHydrometeorological indicesLeptospirosisRandom forestVariable importance

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Area of Science:

  • Environmental Health
  • Infectious Disease Epidemiology
  • Climate Science

Background:

  • Leptospirosis is a zoonotic disease influenced by climate variability.
  • The predictive power of hydrometeorological data for leptospirosis remains underexplored.
  • Understanding these links is crucial for disease prevention in vulnerable regions.

Purpose of the Study:

  • To assess the importance of hydrometeorological indices in predicting leptospirosis.
  • To evaluate the performance of predictive models based on different index types (average, extreme, mixed).
  • To identify key hydrometeorological drivers of leptospirosis in Kelantan, Malaysia.

Main Methods:

  • Transformed 164 weekly hydrometeorological indices (average and extreme) from rainfall, streamflow, water level, humidity, and temperature data.
  • Utilized a random forest classifier to build seventeen predictive models for leptospirosis.
  • Optimized feature selection using Mean Decrease Gini (MDG) scores and assessed variable importance via cross-correlation.

Main Results:

  • Mixed hydrometeorological models achieved the highest prediction accuracy (71.7-82.6%).
  • Extreme models showed higher sensitivity, while average models demonstrated greater specificity.
  • Extreme rainfall was identified as the most critical predictor, despite streamflow showing higher correlations.

Conclusions:

  • Hydrometeorological indices, particularly extreme rainfall, are valuable for leptospirosis prediction.
  • Mixed index models offer improved accuracy for forecasting disease outbreaks.
  • Findings support integrating climate and environmental data into public health surveillance for leptospirosis.