Spatiotemporal trends in P falciparum malaria and identification of high-risk villages in Eastern Myanmar: an 8-year observational study #MMPMID41381817
Rae JD; Parker DM; Maude RJ; Thu AM; Devine A; Nosten FH; Simpson JA
Sci Rep 2025[Dec]; ? (?): ? PMID41381817show ga
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.
Sci+Rep 2025 ; ? (?): ?
