METHODS: Blood samples from 1874 patients were tested for Plasmodium species by microscopy and nested polymerase chain reaction. P. knowlesi was characterized by sequencing the merozoite surface protein 1 gene (msp-1).
RESULTS: Of all Plasmodium species identified, P. falciparum, P. vivax, P. malariae, P. ovale, and P. knowlesi contributed 43.52%, 68.08%, 1.37%, 1.03%, and 0.57%, respectively. Mixed-species infections were more common in northwestern and southwestern regions bordering Myanmar (23%-24%) than in eastern and southern areas (3%-5%). In northwestern and southwestern regions, mixed-species infections had a significantly higher prevalence in dry than in rainy seasons (P < .001). P. knowlesi was found in 10 patients, mostly from southern and southwestern areas-9 were coinfected with either P. falciparum or P. vivax. Most of the P. knowlesi Thai isolates were more closely related to isolates from macaques than to isolates from Sarawak patients. The msp-1 sequences of isolates from the same area of endemicity differed and possessed novel sequences, indicating genetic polymorphism in P. knowlesi infecting humans.
CONCLUSIONS: This survey highlights the widespread distribution of P. knowlesi in Thailand, albeit at low prevalence and mostly occurring as cryptic infections.
METHODOLOGY/PRINCIPAL FINDINGS: A total of 439 records of P. knowlesi infections in humans, macaque reservoir and vector species were collated. To predict spatial variation in disease risk, a model was fitted using records from countries where the infection data coverage is high. Predictions were then made throughout Southeast Asia, including regions where infection data are sparse. The resulting map predicts areas of high risk for P. knowlesi infection in a number of countries that are forecast to be malaria-free by 2025 (Malaysia, Cambodia, Thailand and Vietnam) as well as countries projected to be eliminating malaria (Myanmar, Laos, Indonesia and the Philippines).
CONCLUSIONS/SIGNIFICANCE: We have produced the first map of P. knowlesi malaria risk, at a fine-scale resolution, to identify priority areas for surveillance based on regions with sparse data and high estimated risk. Our map provides an initial evidence base to better understand the spatial distribution of this disease and its potential wider contribution to malaria incidence. Considering malaria elimination goals, areas for prioritised surveillance are identified.