Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors: Evidence from rural Madagascar.

• Main Authors: Julie D Pourtois (Author), Krti Tallam (Author), Isabel Jones (Author), Elizabeth Hyde (Author), Andrew J Chamberlin (Author), Michelle V Evans (Author), Felana A Ihantamalala (Author), Laura F Cordier (Author), Bénédicte R Razafinjato (Author), Rado J L Rakotonanahary (Author), Andritiana Tsirinomen'ny Aina (Author), Patrick Soloniaina (Author), Sahondraritera H Raholiarimanana (Author), Celestin Razafinjato (Author), Matthew H Bonds (Author), Giulio A De Leo (Author), Susanne H Sokolow (Author), Andres Garchitorena (Author)
• Format: Book
• Published: Public Library of Science (PLoS), 2023-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2767-3375; 10.1371/journal.pgph.0001607
• Keywords: Public aspects of medicine; RA1-1270
• Source: PLOS Global Public Health, Vol 3, Iss 2, p e0001607 (2023)

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.