%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Pourtois, J.D.
%A Tallam, K.
%A Jones, I.
%A Hyde, E.
%A Chamberlin, A.J.
%A Evans, M.V.
%A Ihantamalala, F.A.
%A Cordier, L.F.
%A Razafinjato, B.R.
%A Rakotonanahary, R. J. L.
%A Tsirinomen'ny Aina, A.
%A Soloniaina, P.
%A Raholiarimanana, S.H.
%A Razafinjato, C.
%A Bonds, M.H.
%A De Leo, G.A.
%A Sokolow, S.H.
%A Garchitorena, Andres
%T Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors : evidence from rural Madagascar
%D 2023
%L fdi:010094510
%G ENG
%J PLOS Global Public Health
%@ 2767-3375
%K MADAGASCAR
%K IFANADIANA
%M ISI:001421100400001
%N 2
%P e0001607 [20 ]
%R 10.1371/journal.pgph.0001607
%U https://www.documentation.ird.fr/hor/fdi:010094510
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2025-08/010094510.pdf
%V 3
%W Horizon (IRD)
%X While much progress has been achieved over the last decades, malaria surveillance and control remain a challenge in countries with limited health care access and resources. High-resolution predictions of malaria incidence using routine surveillance data could represent a powerful tool to health practitioners by targeting malaria control activities where and when they are most needed. Here, we investigate the predictors of spatio-temporal malaria dynamics in rural Madagascar, estimated from facility-based passive surveillance data. Specifically, this study integrates climate, land-use, and representative household survey data to explain and predict malaria dynamics at a high spatial resolution (i.e., by Fokontany, a cluster of villages) relevant to health care practitioners. Combining generalized linear mixed models (GLMM) and path analyses, we found that socio-economic, land use and climatic variables are all important predictors of monthly malaria incidence at fine spatial scales, via both direct and indirect effects. In addition, out-of-sample predictions from our model were able to identify 58% of the Fokontany in the top quintile for malaria incidence and account for 77% of the variation in the Fokontany incidence rank. These results suggest that it is possible to build a predictive framework using environmental and social predictors that can be complementary to standard surveillance systems and help inform control strategies by field actors at local scales.
%$ 052ANOPAL04 ; 021CLIMAT