Optimizing Public Health Interventions through Geospatial Data and Machine Learning

  • Authors

    • Dr. Arvind Kumar Singh Professor, Jawaharlal Nehru University, India. Author
    • Dr. Lakshmi Narayanan Associate Professor, Bharathidasan University, India. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2021PI4I6E

    Published 03-04-2021

  • Geospatial Data, Machine Learning, Public Health Interventions, Geoai, Healthcare Planning, Vaccine Allocation, Disease Surveillance, Data Science, Predictive Analysis, Resource Optimization

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    [1]
    A. K. Singh and L. Narayanan, “Optimizing Public Health Interventions through Geospatial Data and Machine Learning”, IJETMR, vol. 4, no. 1, pp. 01–08, Mar. 2021, doi: 10.67228/30715636/IJETMR-2021PI4I6E.

  • Abstract

    The integration of geospatial data and machine learning has the potential to revolutionize public health interventions by enabling precise targeting and efficient resource allocation. This paper explores the synergistic application of these technologies to enhance public health outcomes. We examine case studies such as the utilization of geospatial artificial intelligence (GeoAI) in planning healthcare services in Varanasi's peri-urban areas, where AI-driven models have identified gaps in healthcare provision and optimized resource distribution. Similarly, the development of a decision support framework combining geospatial analysis and AI has demonstrated effective prioritization of COVID-19 vaccine allocation in resource-poor settings, ensuring that socially vulnerable populations receive timely vaccinations. Furthermore, the application of data science in Mumbai's public health campaigns showcases how predictive modeling and machine learning can enhance disease surveillance, predict outbreaks, and optimize healthcare resource allocation. The paper also discusses the broader implications of AI in India's public health sector, highlighting its role in predictive healthcare analysis, disease screening, and personalized treatment planning. By synthesizing these diverse applications, the paper underscores the transformative potential of combining geospatial data with machine learning to optimize public health interventions, particularly in densely populated and resource-constrained settings.​

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