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Emerging human infectious diseases of aquatic origin: a comparative biogeographic approach using Bayesian spatial modelling

Abstract : Background With the increase in unprecedented and unpredictable disease outbreaks due to human-driven environmental changes in recent years, we need new analytical tools to map and predict the spatial distribution of emerging infectious diseases and identify the biogeographic drivers underpinning their emergence. The aim of the study was to identify and compare the local and global biogeographic predictors such as landscape and climate that determine the spatial structure of leptospirosis and Buruli Ulcer (BU). Methods We obtained 232 hospital-confirmed leptospirosis (2007–2017) cases and 236 BU cases (1969–2017) in French Guiana. We performed non-spatial and spatial Bayesian regression modeling with landscape and climate predictor variables to characterize the spatial structure and the environmental drivers influencing the distribution of the two diseases. Results Our results show that the distribution of both diseases is spatially dependent on environmental predictors such as elevation, topological wetness index, proximity to cropland and increasing minimum temperature at the month of potential infection. However, the spatial structure of the two diseases caused by bacterial pathogens occupying similar aquatic niche was different. Leptospirosis was widely distributed across the territory while BU was restricted to the coastal riverbeds. Conclusions Our study shows that a biogeographic approach is an effective tool to identify, compare and predict the geographic distribution of emerging diseases at an ecological scale which are spatially dependent to environmental factors such as topography, land cover and climate.
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Submitted on : Thursday, June 3, 2021 - 3:34:48 PM
Last modification on : Wednesday, September 28, 2022 - 4:20:10 PM
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Soushieta Jagadesh, Marine Combe, Pierre Couppié, Paul Le Turnier, Loïc Epelboin, et al.. Emerging human infectious diseases of aquatic origin: a comparative biogeographic approach using Bayesian spatial modelling. International Journal of Health Geographics, BioMed Central, 2019, 18 (1), pp.23. ⟨10.1186/s12942-019-0188-6⟩. ⟨hal-02862872⟩



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