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Exploring Environmental and Geographical Factors Influencing the Spread of Infectious Diseases with Interactive Maps

Author

Listed:
  • Saturnino Luz

    (Usher Institute, Edinburgh Medical School, The University of Edinburgh, Edinburgh EH16 4UX, UK)

  • Masood Masoodian

    (School of Arts, Design and Architecture, Aalto University, 02150 Espoo, Finland)

Abstract

Environmental problems due to human activities such as deforestation, urbanisation, and large scale intensive farming are some of the major factors behind the rapid spread of many infectious diseases. This in turn poses significant challenges not only in as regards providing adequate healthcare, but also in supporting healthcare workers, medical researchers, policy makers, and others involved in managing infectious diseases. These challenges include surveillance, tracking of infections, communication of public health knowledge and promotion of behavioural change. Behind these challenges lies a complex set of factors which include not only biomedical and population health determinants but also environmental, climatic, geographic, and socioeconomic variables. While there is broad agreement that these factors are best understood when considered in conjunction, aggregating and presenting diverse information sources requires effective information systems, software tools, and data visualisation. In this article, we argue that interactive maps, which couple geographical information systems and advanced information visualisation techniques, provide a suitable unifying framework for coordinating these tasks. Therefore, we examine how interactive maps can support spatial epidemiological visualisation and modelling involving distributed and dynamic data sources and incorporating temporal aspects of disease spread. Combining spatial and temporal aspects can be crucial in such applications. We discuss these issues in the context of support for disease surveillance in remote regions, utilising tools that facilitate distributed data collection and enable multidisciplinary collaboration, while also providing support for simulation and data analysis. We show that interactive maps deployed on a combination of mobile devices and large screens can provide effective means for collection, sharing, and analysis of health data.

Suggested Citation

  • Saturnino Luz & Masood Masoodian, 2022. "Exploring Environmental and Geographical Factors Influencing the Spread of Infectious Diseases with Interactive Maps," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:9990-:d:886639
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    References listed on IDEAS

    as
    1. Geoffrey M. Jacquez, 2000. "Spatial analysis in epidemiology: Nascent science or a failure of GIS?," Journal of Geographical Systems, Springer, vol. 2(1), pages 91-97, March.
    2. Swen P. M. Bos & Tina Cornioley & Anne Dray & Patrick O. Waeber & Claude A. Garcia, 2020. "Exploring Livelihood Strategies of Shifting Cultivation Farmers in Assam through Games," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    3. Itai Kloog & Lara Ifat Kaufman & Kees De Hoogh, 2018. "Using Open Street Map Data in Environmental Exposure Assessment Studies: Eastern Massachusetts, Bern Region, and South Israel as a Case Study," IJERPH, MDPI, vol. 15(11), pages 1-21, November.
    4. Stephen Eubank & Hasan Guclu & V. S. Anil Kumar & Madhav V. Marathe & Aravind Srinivasan & Zoltán Toroczkai & Nan Wang, 2004. "Modelling disease outbreaks in realistic urban social networks," Nature, Nature, vol. 429(6988), pages 180-184, May.
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