Abstract
Background“Schistosomiasis” is a highly recurrent parasitic disease that affects a wide range of areas and a large number of people worldwide. In China, schistosomiasis has seriously affected the life and safety of the people and restricted the economic development. Schistosomiasis is mainly distributed along the Yangtze River and in southern China. Anhui Province is located in the Yangtze River Basin of China, with dense water system, frequent floods and widespread distribution of Oncomelania hupensis that is the only intermediate host of schistosomiasis, a large number of cattle, sheep and other livestock, which makes it difficult to control schistosomiasis. It is of great significance to monitor and analyze spatiotemporal risk of schistosomiasis in Anhui Province, China. We compared and analyzed the optimal spatiotemporal interpolation model based on the data of schistosomiasis in Anhui Province, China and the spatiotemporal pattern of schistosomiasis risk was analyzed.MethodsIn this study, the root-mean-square-error (RMSE) and absolute residual (AR) indicators were used to compare the accuracy of Bayesian maximum entropy (BME), spatiotemporal Kriging (STKriging) and geographical and temporal weighted regression (GTWR) models for predicting the spatiotemporal risk of schistosomiasis in Anhui Province, China.ResultsThe results showed that (1) daytime land surface temperature, mean minimum temperature, normalized difference vegetation index, soil moisture, soil bulk density and urbanization were significant factors affecting the risk of schistosomiasis; (2) the spatiotemporal distribution trends of schistosomiasis predicted by the three methods were basically consistent with the actual trends, but the prediction accuracy of BME was higher than that of STKriging and GTWR, indicating that BME predicted the prevalence of schistosomiasis more accurately; and (3) schistosomiasis in Anhui Province had a spatial autocorrelation within 20 km and a temporal correlation within 10 years when applying the optimal model BME.ConclusionsThis study suggests that BME exhibited the highest interpolation accuracy among the three spatiotemporal interpolation methods, which could enhance the risk prediction model of infectious diseases thereby providing scientific support for government decision making.
Highlights
Schistosomiasis, an important zoonotic parasitic disease caused by three main species of the trematode worm Schistosoma, is reported from 78 countries on the tropical and subtropical parts of the world where it affects more than 200 million people [1]
The Daytime land surface temperature (LSTd), Mean minimum temperature (MTmin), normalized difference vegetation index (NDVI), soil moisture, night-time light, and soil bulk density were included in the model; their variance inflation factor (VIF) values were all less than 5, and their P-values less than 0.05
This indicates that the collinearity between these influencing factors is small and that there is a significant relationship between these factors and the prevalence of schistosomiasis in the province
Summary
Schistosomiasis, an important zoonotic parasitic disease caused by three main (and three less common) species of the trematode worm Schistosoma, is reported from 78 countries on the tropical and subtropical parts of the world where it affects more than 200 million people [1]. Schistosomiasis japonicum is endemic in China [2], where its endemic areas are classified into three types based on geographical topography and the ecological characteristics of breeding areas of the only intermediate snail host Oncomelania: lakes and swamp areas, plain areas of waterway networks, and hilly and mountainous areas [3, 4]. The large number of livestock, such as cattle and sheep that play the role of reservoir hosts in endemic areas, exacerbate the difficulty of controlling transmission of the disease facilitating the persistence of schistosomiasis in the country. This situation contributes to the great significance of the disease and the need to study its risk potential in Anhui Province [2]
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