Bayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in Anhui Province, China

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 main...

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Autores principales:	Fuju Wang, Xin Liu, Robert Bergquist, Xiao Lv, Yang Liu, Fenghua Gao, Chengming Li, Zhijie Zhang
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Bayesian maximum entropy Geographical and temporal weighted regression Schistosomiasis Spatiotemporal kriging Spatiotemporal interpolation Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/67fccb1dd083469c8dcefe403922caa9
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spelling	oai:doaj.org-article:67fccb1dd083469c8dcefe403922caa92021-11-28T12:41:46ZBayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in Anhui Province, China10.1186/s12879-021-06854-61471-2334https://doaj.org/article/67fccb1dd083469c8dcefe403922caa92021-11-01T00:00:00Zhttps://doi.org/10.1186/s12879-021-06854-6https://doaj.org/toc/1471-2334Abstract 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. Methods In 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. Results The 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. Conclusions This 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.Fuju WangXin LiuRobert BergquistXiao LvYang LiuFenghua GaoChengming LiZhijie ZhangBMCarticleBayesian maximum entropyGeographical and temporal weighted regressionSchistosomiasisSpatiotemporal krigingSpatiotemporal interpolationInfectious and parasitic diseasesRC109-216ENBMC Infectious Diseases, Vol 21, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bayesian maximum entropy Geographical and temporal weighted regression Schistosomiasis Spatiotemporal kriging Spatiotemporal interpolation Infectious and parasitic diseases RC109-216
spellingShingle	Bayesian maximum entropy Geographical and temporal weighted regression Schistosomiasis Spatiotemporal kriging Spatiotemporal interpolation Infectious and parasitic diseases RC109-216 Fuju Wang Xin Liu Robert Bergquist Xiao Lv Yang Liu Fenghua Gao Chengming Li Zhijie Zhang Bayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in Anhui Province, China
description	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. Methods In 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. Results The 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. Conclusions This 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.
format	article
author	Fuju Wang Xin Liu Robert Bergquist Xiao Lv Yang Liu Fenghua Gao Chengming Li Zhijie Zhang
author_facet	Fuju Wang Xin Liu Robert Bergquist Xiao Lv Yang Liu Fenghua Gao Chengming Li Zhijie Zhang
author_sort	Fuju Wang
title	Bayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in Anhui Province, China
title_short	Bayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in Anhui Province, China
title_full	Bayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in Anhui Province, China
title_fullStr	Bayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in Anhui Province, China
title_full_unstemmed	Bayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in Anhui Province, China
title_sort	bayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in anhui province, china
publisher	BMC
publishDate	2021
url	https://doaj.org/article/67fccb1dd083469c8dcefe403922caa9
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Bayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in Anhui Province, China

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