PLOS Global Public Health (Jan 2023)

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

  • Julie D Pourtois,
  • Krti Tallam,
  • Isabel Jones,
  • Elizabeth Hyde,
  • Andrew J Chamberlin,
  • Michelle V Evans,
  • Felana A Ihantamalala,
  • Laura F Cordier,
  • Bénédicte R Razafinjato,
  • Rado J L Rakotonanahary,
  • Andritiana Tsirinomen'ny Aina,
  • Patrick Soloniaina,
  • Sahondraritera H Raholiarimanana,
  • Celestin Razafinjato,
  • Matthew H Bonds,
  • Giulio A De Leo,
  • Susanne H Sokolow,
  • Andres Garchitorena

DOI
https://doi.org/10.1371/journal.pgph.0001607
Journal volume & issue
Vol. 3, no. 2
p. e0001607

Abstract

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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.