Pourtois J.D. auteur aut IRD Tallam K. auteur aut IRD Jones I. auteur aut IRD Hyde E. auteur aut IRD Chamberlin A.J. auteur aut IRD Evans M.V. auteur aut IRD Ihantamalala F.A. auteur aut IRD Cordier L.F. auteur aut IRD Razafinjato B.R. auteur aut IRD Rakotonanahary R. J. L. auteur aut IRD Tsirinomen'ny Aina A. auteur aut IRD Soloniaina P. auteur aut IRD Raholiarimanana S.H. auteur aut IRD Razafinjato C. auteur aut IRD Bonds M.H. auteur aut IRD De Leo G.A. auteur aut IRD Sokolow S.H. auteur aut IRD Garchitorena Andres auteur aut IRD text journalArticle eng text/pdf reformatted digital access 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. specialized MADAGASCAR 052ANOPAL04 021CLIMAT 3 2 e0001607 [20 ] 2023 2767-3375 https://www.documentation.ird.fr/hor/fdi:010094510 10.1371/journal.pgph.0001607 2767-3375 [F B010094510] https://www.documentation.ird.fr/hor/fdi:010094510 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2025-08/010094510.pdf IRD - Base Horizon / Pleins textes 2025-07-16 2025-08-06 fdi:010094510 fre