Interactive and lag effects of environmental factors on the density of schistosome-transmitting Oncomelania hupensis: A twelve-year monthly repeated survey

• Published in: Parasitology research (1987) Vol. 123; no. 8; p. 301
• Main Authors: Wang, Jiamin, Huang, Junhui, Gong, Yanfeng, Xu, Ning, Zhou, Yu, Zhu, Liyun, Shi, Liang, Chen, Yue, Jiang, Qingwu, Zhou, Yibiao
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024; Springer; Springer Nature B.V
• Subjects: Animals; Bayes Theorem; Bayesian analysis; Bayesian theory; Biomedical and Life Sciences; Biomedicine; China; China - epidemiology; Environment; Environmental aspects; Environmental factors; Environmental monitoring; Host-parasite relationships; Immunology; intermediate hosts; lakes; Lakes - parasitology; Medical Microbiology; Microbiology; normalized difference vegetation index; Oncomelania; Oncomelania hupensis; Parasitological research; Population Density; Precipitation; Public health; regression analysis; Risk factors; Schistosoma japonicum; Schistosoma japonicum - physiology; Schistosomiasis; Schistosomiasis - epidemiology; Schistosomiasis - parasitology; Schistosomiasis - transmission; Schistosomiasis japonica - epidemiology; Schistosomiasis japonica - parasitology; Schistosomiasis japonica - transmission; Seasons; snails; Snails - parasitology; Statistics; Surveys; Temperature; Trends; Water levels; China; Snail density; Interactive effect; Lag effect; Oncomelania hupensis; Bayesian
• DOI: 10.1007/s00436-024-08323-w
• ISSN: 0932-0113; 1432-1955

Schistosomiasis is a significant public health threat, and Oncomelania hupensis is the only intermediate host for schistosoma japonicum . We conducted 12-year monthly repeated surveys to explore the interactive and lag effects of environmental factors on snail density and to monitor their long-term and seasonal trends in a bottomland around the Dongting Lake region in China. Relevant environmental data were obtained from multiple sources. A Bayesian kernel machine regression model and a Bayesian temporal model combined with a distributed lag model were constructed to analyze interactive and lag effects of environmental factors on snail density. The results indicated the average annual snail density in the study site exhibited an increasing and then decreasing trend, peaking in 2013. Snail densities were the highest in October and the lowest in January in a year. Normalized Difference Vegetation Index (NDVI) and water level were the most effective predictors of snail density, with potential interactions among temperature, precipitation, and NDVI. The mean minimum temperature in January, water level, precipitation and NDVI were positively correlated with snail density at lags ranging from 1 to 4 months. These findings could serve as references for relevant authorities to monitor the changing trend of snail density and implement control measures, thereby reducing the occurrence of schistosomiasis.