Construction of a forecast system for prediction of schistosomiasis risk in China based on the flood information
10.16250/j.32.1374.2020253
- VernacularTitle:一种基于洪水信息的血吸虫病风险预警系统的构建
- Author:
Jin-Xin ZHENG
1
;
Shang XIA
1
;
Shan LÜ
1
;
Yi ZHANG
1
;
Xiao-Nong ZHOU
1
Author Information
1. National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai 200025, China
- Publication Type:Journal Article
- Keywords:
Schistosomiasis;
Oncomelania snail;
Flood;
Forecast system;
Transmission index;
Species distribution model
- From:
Chinese Journal of Schistosomiasis Control
2021;33(2):133-137
- CountryChina
- Language:Chinese
-
Abstract:
Objective To create a model based on meteorological data to predict the regions at risk of schistosomiasis during the flood season, so as to provide insights into the surveillance and forecast of schistosomiasis. Methods An interactive schistosomiasis forecast system was created using the open-access R software. The schistosomiasis risk index was used as a basic parameter, and the species distribution model of Oncomelania hupensis snails was generated according to the cumulative rainfall and temperature to predict the probability of O. hupensis snail distribution, so as to identify the regions at risk of schistosomiasis transmission during the flood season. Results The framework of the web page was built using the Shiny package in the R program, and an interactive and visualization system was successfully created to predict the distribution of O. hupensis snails, containing O. hupensis snail surveillance site database, meteorological and environmental data. In this system, the snail distribution area may be displayed and the regions at risk of schistosomiasis transmission may be predicted using the species distribution model. This predictive system may rapidly generate the schistosomiasis transmission risk map, which is simple and easy to perform. In addition, the regions at risk of schistosomiasis transmission were predicted to be concentrated in the middle and lower reaches of the Yangtze River during the flood period. Conclusions A schistosomiasis forecast system is successfully created, which is accurate and rapid to utilize meteorological data to predict the regions at risk of schistosomiasis transmission during the flood period.
