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Correction: Javaid et al. WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases. <i>Int. J. Environ. Res. Public Health</i> 2023, <i>20</i>, 3740.

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WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases.

Momna Javaid1, Muhammad Shahzad Sarfraz1, Muhammad Umar Aftab1

  • 1Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan.

International Journal of Environmental Research and Public Health
|February 25, 2023
PubMed
Summary

Identifying vector-borne disease (VBD) breeding sites using climate data and GIS is crucial for control. Machine learning models, particularly Random Forest, accurately predict VBD spread, aiding public health strategies.

Keywords:
Geographical Information SystemWebGISclimatemachine learningvector-borne disease

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Area of Science:

  • Environmental Science
  • Epidemiology
  • Machine Learning

Background:

  • Vector-borne diseases (VBDs) like malaria, dengue, and leishmaniasis pose significant public health challenges.
  • Effective control of VBDs relies on identifying and managing vector breeding sites.
  • Geographical Information System (GIS) offers a powerful tool for spatial analysis and vector control.

Purpose of the Study:

  • To investigate the relationship between climatic factors (temperature, humidity, precipitation) and vector breeding sites for VBDs.
  • To develop and evaluate machine learning models for predicting VBD occurrence based on environmental data.
  • To identify the most effective machine learning model for VBD prediction in Punjab, Pakistan.

Main Methods:

  • Data oversampling techniques were employed to address class imbalance in the dataset.
  • Multiple machine learning algorithms, including Light Gradient Boosting Machine, Random Forest, Decision Tree, Support Vector Machine, and Multi-Layer Perceptron, were trained and compared.
  • Model performance was assessed using metrics such as F score, precision, and recall.

Main Results:

  • The Random Forest model demonstrated superior performance, achieving 93.97% accuracy in predicting VBDs.
  • Climatic factors, including temperature, precipitation, and specific humidity, were identified as significant drivers influencing the spread of dengue, malaria, and leishmaniasis.
  • A user-friendly, web-based GIS platform was developed for public and policymaker access.

Conclusions:

  • Machine learning, particularly Random Forest, combined with GIS and climate data, provides an accurate and effective approach for predicting and controlling VBDs.
  • Understanding the impact of climatic factors is essential for targeted vector control interventions.
  • The developed GIS platform can support evidence-based decision-making for VBD management.