Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data

Chawarat Rotejanaprasert,Richard J Maude,Prayuth Sudathip,Nattwut Ekapirat

doi:10.1186/s12874-021-01480-x

Abstract

BackgroundIn many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission. With a regional goal of elimination by 2030, it is important to use appropriate methods to analyze and predict trends in incidence in these remaining transmission foci to inform planning efforts. Climatic variables have been associated with malaria incidence to varying degrees across the globe but the relationship is less clear in the GMS and standard methodologies may not be appropriate to account for the lag between climate and incidence and for locations with low numbers of cases.MethodsIn this study, a methodology was developed to estimate the spatio-temporal lag effect of climatic factors on malaria incidence in Thailand within a Bayesian framework. A simulation was conducted based on ground truth of lagged effect curves representing the delayed relation with sparse malaria cases as seen in our study population. A case study to estimate the delayed effect of environmental variables was used with malaria incidence at a fine geographic scale of sub-districts in a western province of Thailand.ResultsFrom the simulation study, the model assumptions which accommodated both delayed effects and excessive zeros appeared to have the best overall performance across evaluation metrics and scenarios. The case study demonstrated lagged climatic effect estimation of the proposed modeling with real data. The models appeared to be useful to estimate the shape of association with malaria incidence.ConclusionsA new method to estimate the spatiotemporal effect of climate on malaria trends in low transmission settings is presented. The developed methodology has potential to improve understanding and estimation of past and future trends in malaria incidence. With further development, this could assist policy makers with decisions on how to more effectively distribute resources and plan strategies for malaria elimination.

Highlights

In many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission
We described research motivation in the context of malaria elimination and provided the basic model used in spatiotemporal analysis
Spatio‐temporal distributed lag modeling for sparse areal malaria incidence data To investigate the association with climatic factors, we propose a spatio-temporal distributed lag space-time model for excessive zeros in our study population

Summary

Introduction

In many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission. Among the parasitic diseases worldwide, malaria is one of the most prevalent [1]. Rotejanaprasert et al BMC Medical Research Methodology (2021) 21:287 malaria by 2030, the number of P. falciparum incidence has markedly decreased in recent years across the GMS [3]. This includes on the Thai side [4] and in Kayin State in Myanmar [5]. Suitable climatic conditions can create circumstances appropriate for malaria transmission in endemic areas. The relationship between climate and malaria incidence is complex with inconsistent findings perhaps due to regional variations and limited availability of suitable methods [16]

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