Predictive analysis across spatial scales links zoonotic malaria to deforestation

Patrick M. Brock, Kimberly M. Fornace, Matthew J. Grigg, Nicholas M. Anstey, Timothy William, Jon Cox, Chris J. Drakeley, Heather M. Ferguson, Rowland R. Kao

    Research output: Contribution to journalArticleResearchpeer-review

    Abstract

    The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria (Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case - control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. By contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.

    Original languageEnglish
    Article number20182351
    Pages (from-to)1-9
    Number of pages9
    JournalProceedings of the Royal Society B: Biological Sciences
    Volume286
    Issue number1894
    DOIs
    Publication statusPublished - 16 Jan 2019

    Fingerprint

    Plasmodium knowlesi
    Deforestation
    Spatial Analysis
    malaria
    Conservation of Natural Resources
    Zoonoses
    deforestation
    spatial analysis
    Malaria
    disease occurrence
    Satellite imagery
    Ecology
    regrowth
    households
    Land use
    Satellite Imagery
    satellite imagery
    land use change
    Learning systems
    environmental change

    Cite this

    Brock, Patrick M. ; Fornace, Kimberly M. ; Grigg, Matthew J. ; Anstey, Nicholas M. ; William, Timothy ; Cox, Jon ; Drakeley, Chris J. ; Ferguson, Heather M. ; Kao, Rowland R. / Predictive analysis across spatial scales links zoonotic malaria to deforestation. In: Proceedings of the Royal Society B: Biological Sciences. 2019 ; Vol. 286, No. 1894. pp. 1-9.
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    abstract = "The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria (Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case - control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. By contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.",
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    Brock, PM, Fornace, KM, Grigg, MJ, Anstey, NM, William, T, Cox, J, Drakeley, CJ, Ferguson, HM & Kao, RR 2019, 'Predictive analysis across spatial scales links zoonotic malaria to deforestation', Proceedings of the Royal Society B: Biological Sciences, vol. 286, no. 1894, 20182351, pp. 1-9. https://doi.org/10.1098/rspb.2018.2351

    Predictive analysis across spatial scales links zoonotic malaria to deforestation. / Brock, Patrick M.; Fornace, Kimberly M.; Grigg, Matthew J.; Anstey, Nicholas M.; William, Timothy; Cox, Jon; Drakeley, Chris J.; Ferguson, Heather M.; Kao, Rowland R.

    In: Proceedings of the Royal Society B: Biological Sciences, Vol. 286, No. 1894, 20182351, 16.01.2019, p. 1-9.

    Research output: Contribution to journalArticleResearchpeer-review

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    AU - Fornace, Kimberly M.

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    AU - William, Timothy

    AU - Cox, Jon

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