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Contemporary neighborhoods nested within modern cities integrate innovative public health strategies for tackling infectious disease outbreaks. The dynamic environment of smart cities and built-environment landscapes have highlighted the need for a transition from conventional epidemiological methods to spatial-epidemiological approaches, highlighting their role in enabling swift and precise outbreak detection, prediction, investigation, monitoring, and evaluation. The extension of these novel tools delves further into the integration of Geographic Information Systems (GIS) for outbreak investigations, concurrently addressing population and environmental challenges through cartographic geovisualizations of potential risks for outbreaks to occur. Coupled with these is the innovative use of artificial intelligence (AI) solutions for contact tracing and spatial-epidemiological analysis in critical areas for urban architecture and their risks for outbreaks. This chapter discusses contemporary modeling approaches like SEIR modelling linked to machine learning algorithms, presenting AI-driven predictions for outbreaks such as COVID-19 and dengue fever via real-time data case studies. The concepts, strengths, and limitations of AI in managing outbreaks within smart cities, emphasizing its pivotal role in modern geographical epidemiology, are discussed, concurrently reflecting the importance of AI integration for enhancing disease control and prevention efforts in smart urban environments.

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Revolutionizing Infectious Disease Epidemiology in Smart Cities: Geo-AI Modeling for Outbreak Investigation, Prediction, and Control

  • 章节

  • 发表日期：

    2026年

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  • Kurubaran Ganasegeran,
  • Mohd Kamarulariffin Kamarudin,
  • Jeff Blossom,
  • Nur’ain Mohd Ghazali,
  • Mohd Rizal Abdul Manaf

摘要

Contemporary neighborhoods nested within modern cities integrate innovative public health strategies for tackling infectious disease outbreaks. The dynamic environment of smart cities and built-environment landscapes have highlighted the need for a transition from conventional epidemiological methods to spatial-epidemiological approaches, highlighting their role in enabling swift and precise outbreak detection, prediction, investigation, monitoring, and evaluation. The extension of these novel tools delves further into the integration of Geographic Information Systems (GIS) for outbreak investigations, concurrently addressing population and environmental challenges through cartographic geovisualizations of potential risks for outbreaks to occur. Coupled with these is the innovative use of artificial intelligence (AI) solutions for contact tracing and spatial-epidemiological analysis in critical areas for urban architecture and their risks for outbreaks. This chapter discusses contemporary modeling approaches like SEIR modelling linked to machine learning algorithms, presenting AI-driven predictions for outbreaks such as COVID-19 and dengue fever via real-time data case studies. The concepts, strengths, and limitations of AI in managing outbreaks within smart cities, emphasizing its pivotal role in modern geographical epidemiology, are discussed, concurrently reflecting the importance of AI integration for enhancing disease control and prevention efforts in smart urban environments.