%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Jagadesh, S.
%A Combe, Marine
%A Gozlan, Rodolphe
%T Human-altered landscapes and climate to predict human infectious disease hotspots
%D 2022
%L fdi:010085910
%G ENG
%J Tropical Medicine and Infectious Disease
%K emerging infectious diseases ; human-altered landscapes ; biogeography ; topography ; climate ; land-use
%K MONDE
%M ISI:000831447700001
%N 7
%P 124 [9 ]
%R 10.3390/tropicalmed7070124
%U https://www.documentation.ird.fr/hor/fdi:010085910
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2022-09/010085910.pdf
%V 7
%W Horizon (IRD)
%X Background: Zoonotic diseases account for more than 70% of emerging infectious diseases (EIDs). Due to their increasing incidence and impact on global health and the economy, the emergence of zoonoses is a major public health challenge. Here, we use a biogeographic approach to predict future hotspots and determine the factors influencing disease emergence. We have focused on the following three viral disease groups of concern: Filoviridae, Coronaviridae, and Henipaviruses. Methods: We modelled presence-absence data in spatially explicit binomial and zero-inflation binomial logistic regressions with and without autoregression. Presence data were extracted from published studies for the three EID groups. Various environmental and demographical rasters were used to explain the distribution of the EIDs. True Skill Statistic and deviance parameters were used to compare the accuracy of the different models. Results: For each group of viruses, we were able to identify and map areas at high risk of disease emergence based on the spatial distribution of the disease reservoirs and hosts of the three viral groups. Common influencing factors of disease emergence were climatic covariates (minimum temperature and rainfall) and human-induced land modifications. Conclusions: Using topographical, climatic, and previous disease outbreak reports, we can identify and predict future high-risk areas for disease emergence and their specific underlying human and environmental drivers. We suggest that such a predictive approach to EIDs should be carefully considered in the development of active surveillance systems for pathogen emergence and epidemics at local and global scales.
%$ 021 ; 050 ; 052 ; 020