Spatio-temporal trends of malaria incidence from 2011 to 2017 and environmental predictors of malaria transmission in Myanmar

Abstract

Abstract Background Myanmar bears the heaviest malaria burden in the Greater Mekong Subregion (GMS). This study assessed the spatio-temporal dynamics and environmental predictors of Plasmodium falciparum and Plasmodium vivax malaria in Myanmar. Methods Monthly reports of malaria cases at primary health centers during 2011–2017 were analyzed to describe malaria distribution across Myanmar at the township and state/region levels by spatial autocorrelation (Moran index) and spatio-temporal clustering. Negative binomial generalized additive models identified environmental predictors for falciparum and vivax malaria, respectively. Results From 2011 to 2017, there was an apparent reduction in malaria incidence in Myanmar. Malaria incidence peaked in June each year. There were significant spatial autocorrelation and clustering with extreme spatial heterogeneity in malaria cases and test positivity across the nation (P < 0.05). Areas with higher malaria incidence were concentrated along international borders. Primary clusters of P. falciparum persisted in western townships, while clusters of P. vivax shifted geographically over the study period. The primary cluster was detected from January 2011 to December 2013 and covered two states (Sagaing and Kachin). Annual malaria incidence was highest in townships with a mean elevation of 500‒600 m and a high variance in elevation (states with both high and low elevation). There was an apparent linear relationship between the mean normalized difference vegetative index and annual P. falciparum incidence (P < 0.05). Conclusion The decreasing trends reflect the significant achievement of malaria control efforts in Myanmar. Prioritizing the allocation of resources to high-risk areas identified in this study can achieve effective disease control. Graphical Abstract

Keywords

• Spatial distribution
• Temporal clustering
• Spatiotemporal clustering
• Environmental predictor
• Plasmodium falciparum
• Plasmodium vivax