Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
Yi-Jing Feng
Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
Pham Ngoc Doanh
Department of Parasitology, Institute of Ecology and Biological Resources, Graduate University of Science and Technology, Vietnam Academy of Sciences and Technology, Cau Giay, Hanoi, Viet Nam
Somphou Sayasone
Lao Tropical and Public Health Institute, Ministry of Health, Vientiane, Lao People's Democratic Republic
Virak Khieu
National Center for Parasitology, Entomology and Malaria Control, Ministry of Health, Phnom Penh, Cambodia
Choosak Nithikathkul
Tropical and Parasitic Diseases Research Unit, Faculty of Medicine, Mahasarakham University, Mahasarakham, Thailand
Men-Bao Qian
National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China; WHO Collaborating Centre for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, China
Yuan-Tao Hao
Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
Opisthorchiasis is an overlooked danger to Southeast Asia. High-resolution disease risk maps are critical but have not been available for Southeast Asia. Georeferenced disease data and potential influencing factor data were collected through a systematic review of literatures and open-access databases, respectively. Bayesian spatial-temporal joint models were developed to analyze both point- and area-level disease data, within a logit regression in combination of potential influencing factors and spatial-temporal random effects. The model-based risk mapping identified areas of low, moderate, and high prevalence across the study region. Even though the overall population-adjusted estimated prevalence presented a trend down, a total of 12.39 million (95% Bayesian credible intervals [BCI]: 10.10–15.06) people were estimated to be infected with O. viverrini in 2018 in four major endemic countries (i.e., Thailand, Laos, Cambodia, and Vietnam), highlighting the public health importance of the disease in the study region. The high-resolution risk maps provide valuable information for spatial targeting of opisthorchiasis control interventions.
