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

Abstract

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.

Increasing the resolution of malaria early warning systems for use by local health actors

“…The sensitivity of malaria to climate and environmental variables makes it an ideal candidate for disease forecasting efforts (Wimberly et al 2021). Indeed, we found that ecological variables related to temperature and vegetation dynamics were strongly associated with malaria incidence, in agreement with past work (Arisco et al 2020, Rakotoarison et al 2020, Pourtois et al 2023, Epstein et al 2023). Interestingly, after accounting for seasonal dynamics via a cyclical temporal structure, MNDWI was more strongly associated with malaria incidence than precipitation.…”

“…The dominant land covers are savanna and agricultural land for rice production. This diversity of ecoregions and climates translates into spatio-temporal heterogeneities in malaria burden in the district (Hyde et al 2021, Pourtois et al 2023).…”