Spatiotemporal epidemiology, environmental correlates, and demography of malaria in Tak Province, Thailand (2012-2015)

BACKGROUND:Tak Province, at the Thai-Myanmar border, is one of three high malaria incidence areas in Thailand. This study aimed to describe and identify possible factors driving the spatiotemporal trends of disease incidence from 2012 to 2015. METHODS:Climate variables and forest cover were correlated with malaria incidence using Pearson's r. Statistically significant clusters of high (hot spots) and low (cold spots) annual parasite incidence per 1000 population (API) were identified using Getis-Ord Gi* statistic. RESULTS:The total number of confirmed cases declined by 76% from 2012 to 2015 (Plasmodium falciparum by 81%, Plasmodium vivax by 73%). Incidence was highly seasonal with two main annual peaks. Most cases were male (62.75%), ≥ 15 years (56.07%), and of Myanmar (56.64%) or Thai (39.25%) nationality. Median temperature (1- and 2-month lags), average temperature (1- and 2-month lags) and average relative humidity (2- and 3-month lags) correlated positively with monthly total, P. falciparum and P. vivax API. Total rainfall in the same month correlated with API for total cases and P. vivax but not P. falciparum. At sub-district level, percentage forest cover had a low positive correlation with P. falciparum, P. vivax, and total API in most years. There was a decrease in API in most sub-districts for both P. falciparum and P. vivax. Sub-districts with the highest API were in the Tha Song Yang and Umphang Districts along the Thai-Myanmar border. Annual hot spots were mostly in the extreme north and south of the province. CONCLUSIONS:There has been a large decline in reported clinical malaria from 2012 to 2015 in Tak Province. API was correlated with monthly climate and annual forest cover but these did not account for the trends over time. Ongoing elimination interventions on one or both sides of the border are more likely to have been the cause but it was not possible to assess this due to a lack of suitable data. Two main hot spot areas were identified that could be targeted for intensified elimination activities