%0 Journal Article
%A GUAN Peng
%A HAN Wen-ju
%A HUANG De-sheng
%A LI De-qiang
%A LI Hao-chen
%A QI Ying-ying
%A SHEN Tie-feng
%A ZHANG Chong
%T A Bayesian network-based prediction model for the incidence of hemorrhagic fever with renal syndrome
%D 2021
%R 10.11853/j.issn.1003.8280.2021.04.019
%J Chinese Journal of Vector Biology and Control
%P 475-480
%V 32
%N 4
%X **Objective** To study the influencing factors for hemorrhagic fever with renal syndrome (HFRS) in Huludao, Liaoning province, China and to construct an incidence prediction model using a Bayesian network. **Methods** Data about incidence, host, and meteorological conditions of HFRS were collected at surveillance sites in Huludao from January 2008 to October 2018. The tabu search algorithm was used to learn the structure of the Bayesian network, and maximum likelihood estimation was applied to estimate Bayesian network parameters. **Results** At the 0.01 level, factors associated with the incidence of HFRS in Huludao included: rodent density; the average wind speed and sunshine hours in the contemporaneous month; the average maximum temperature, relative humidity, and normalized difference vegetation index with a one-month lag; and the average temperature, average minimum temperature, average atmospheric pressure, and precipitation with a two-months lag. The correlation coefficients were 0.691, 0.689, 0.345, -0.635, -0.631, -0.674, -0.714, -0.746, 0.650, and -0.643, respectively. When using meteorological and host data to predict the incidence of HFRS, the accuracy of the Bayesian network model was 85.00% (17/20), the precision was 83.33% (10/12), and the area under the receiver operating characteristic curve was 0.919. **Conclusion** The Bayesian network-based incidence prediction model shows a relatively high prediction accuracy for HFRS in Huludao, providing a certain reference for HFRS prevention and control.
%U http://www.bmsw.net.cn/EN/10.11853/j.issn.1003.8280.2021.04.019