Spatiotemporal trends in P. falciparum malaria and identification of high-risk villages in Eastern Myanmar: an 8-year observational study
Issued Date
2026-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-105027896792
Pubmed ID
41381817
Journal Title
Scientific Reports
Volume
16
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.16 No.1 (2026)
Suggested Citation
Rae J.D., Parker D.M., Maude R.J., Thu A.M., Devine A., Nosten F.H., Simpson J.A. Spatiotemporal trends in P. falciparum malaria and identification of high-risk villages in Eastern Myanmar: an 8-year observational study. Scientific Reports Vol.16 No.1 (2026). doi:10.1038/s41598-025-32065-z Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/114367
Title
Spatiotemporal trends in P. falciparum malaria and identification of high-risk villages in Eastern Myanmar: an 8-year observational study
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Corresponding Author(s)
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Abstract
One barrier to achieving Plasmodium falciparum elimination is the persistence of villages where transmission remains high. While targeted interventions can effectively reduce transmission in these areas, identifying priority target villages is often resource-intensive. This study investigates the use of a geostatistical model to analyse routinely collected surveillance data and identify high-risk villages in Hpapun Township, Myanmar. A geostatistical model was fitted using routine surveillance data (2014–2021) collected from 507 village-based malaria posts to assess temporal changes in P. falciparum incidence and make incidence predictions while accounting for elevation, prior interventions and spatial correlation between villages. Between 2014 and 2019, P. falciparum incidence decreased by 93.9%. Villages that received targeted interventions were characterised by higher pre-intervention incidence (incidence rate ratio (IRR) = 4.72, 95% confidence interval (CI) 4.56–4.90) relative to non-intervention villages and were associated with lower incidence post-intervention (IRR = 0.26, 95% CI 0.24–0.27). In 2021, 12 high-risk villages were identified, with a reported incidence exceeding the predicted incidence for at least three months, and eight villages were identified as persistently high-risk (≥ 90th percentile difference in at least six months). Our findings suggest that geostatistical modelling can be utilised to identify persistent high-risk villages, thereby efficiently supporting malaria elimination efforts.