Spatiotemporal mapping of malaria prevalence in Madagascar using routine surveillance and health survey data

TL;DR

This study developed a Bayesian model combining survey and routine health data to produce detailed monthly malaria prevalence maps in Madagascar, revealing spatial, seasonal, and temporal trends from 2013 to 2016.

Contribution

It introduces a novel multi-metric approach that integrates survey and routine data for dynamic malaria mapping in Madagascar.

Findings

High prevalence in coastal regions

Prevalence peaked in 2015 and was lowest in 2014

Seasonality significantly affects malaria transmission patterns

Abstract

Malaria transmission in Madagascar is highly heterogeneous, exhibiting spatial, seasonal and long-term trends. Previous efforts to map malaria risk in Madagascar used prevalence data from Malaria Indicator Surveys. These cross-sectional surveys, conducted during the high transmission season most recently in 2013 and 2016, provide nationally representative prevalence data but cover relatively short time frames. Conversely, monthly case data are collected at health facilities but suffer from biases, including incomplete reporting. We combined survey and case data to make monthly maps of prevalence between 2013 and 2016. Health facility catchments were estimated and incidence surfaces, environmental and socioeconomic covariates, and survey data informed a Bayesian prevalence model. Prevalence estimates were consistently high in the coastal regions and low in the highlands. Prevalence was lowest in 2014 and peaked in 2015, highlighting the importance of estimates between survey years. Seasonality was widely observed. Similar multi-metric approaches may be applicable across sub-Saharan Africa.