Publication: Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data
Issued Date
2021-12-01
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ISSN
14712288
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2-s2.0-85121456479
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Mahidol University
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SCOPUS
Bibliographic Citation
BMC Medical Research Methodology. Vol.21, No.1 (2021)
Suggested Citation
Chawarat Rotejanaprasert, Nattwut Ekapirat, Prayuth Sudathip, Richard J. Maude Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data. BMC Medical Research Methodology. Vol.21, No.1 (2021). doi:10.1186/s12874-021-01480-x Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/77457
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Title
Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data
Abstract
Background: In 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. Methods: In 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. Results: From 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. Conclusions: A 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.