Spatio-temporal modelling of weekly malaria incidence in children under 5 for early epidemic detection in Mozambique

Kathryn L Colborn,Eduardo Gudo,James M Colborn,Tatiana J Marrufo,Emanuele Giorgi,Baltazar Candrinho,Andrew J Monaghan

doi:10.1038/s41598-018-27537-4

Abstract

Malaria is a major cause of morbidity and mortality in Mozambique. We present a malaria early warning system (MEWS) for Mozambique informed by seven years of weekly case reports of malaria in children under 5 years of age from 142 districts. A spatio-temporal model was developed based on explanatory climatic variables to map exceedance probabilities, defined as the predictive probability that the relative risk of malaria incidence in a given district for a particular week will exceed a predefined threshold. Unlike most spatially discrete models, our approach accounts for the geographical extent of each district in the derivation of the spatial covariance structure to allow for changes in administrative boundaries over time. The MEWS can thus be used to predict areas that may experience increases in malaria transmission beyond expected levels, early enough so that prevention and response measures can be implemented prior to the onset of outbreaks. The framework we present is also applicable to other climate-sensitive diseases.

Highlights

Malaria is a major cause of morbidity and mortality in Mozambique, with an estimated 8.3 million cases and 15,000 deaths in 20151
Over the past two decades, researchers have developed malaria early warning systems (MEWS) to predict and detect malaria outbreaks across different parts of the world with mixed success[3]. This effort was in part inspired by a World Health Organization (WHO) Roll Back Malaria report published in 2001 detailing the need for developing epidemic warning systems for malaria in Africa[4]
In order to address unpredictable patterns in transmission, we developed a MEWS for early epidemic detection in children

Summary

Introduction

Malaria is a major cause of morbidity and mortality in Mozambique, with an estimated 8.3 million cases and 15,000 deaths in 20151. In low-resource settings, where MEWS are most needed, cost-effective malaria surveillance systems are based on passive detection of cases reported as an aggregated count over large administrative units (e.g., county or district level) on a weekly or monthly basis. These data are only available at coarse spatial resolution, the association of climatic factors with malaria risk can still be established if enough geographic variation occurs at the administrative unit level, enabling prediction of future levels of transmission[7,10,14]. The implied spatial correlation structure by such models between neighbouring regions is often not realistic[19]

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