Mapping malaria seasonality: a case study from Madagascar

TL;DR

This paper introduces a statistical framework for mapping malaria seasonality directly from case data, providing detailed insights and uncertainty estimates to aid decision-making in endemic regions.

Contribution

It develops a novel spatiotemporal regression model and algorithm to characterize malaria seasonality from case data, surpassing threshold-based methods.

Findings

The model accurately captures seasonal transmission patterns in Madagascar.

The seasonality index quantifies how seasonal each location is relative to its surroundings.

Uncertainty estimates improve the reliability of seasonality maps for policymakers.

Abstract

Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we develop a statistical modelling framework for characterising the seasonal patterns derived directly from case data. The procedure involves a spatiotemporal regression model for estimating the monthly proportions of total annual cases and an algorithm to identify operationally relevant characteristics such as the transmission start and peak months. A seasonality index combines the monthly proportion estimates and existing estimates of annual case incidence to provide a summary of "how seasonal" locations are relative to their surroundings. An advancement upon past seasonality mapping endeavours is the presentation of the uncertainty associated with each map, which will enable policymakers to make more statistically sound decisions. The methodology is illustrated using health facility data from Madagascar.