Epidemiological Characteristics and Regional Risk Prediction of Hemorrhagic Fever with Renal Syndrome in Shandong Province, China

Kaili She, Chunyu Li, Chang Qi, Tingxuan Liu, Yan Jia, Yuchen Zhu, Lili Liu, Zhiqiang Wang, Ying Zhang and Xiujun Li
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Kaili She: Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
Chunyu Li: Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
Chang Qi: Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
Tingxuan Liu: Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
Yan Jia: Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
Yuchen Zhu: Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
Lili Liu: Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
Zhiqiang Wang: Institute of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan 250014, China
Ying Zhang: Faculty of Medicine and Health, School of Public Health, University of Sydney, Camperdown, NSW 2006, Australia
Xiujun Li: Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China

IJERPH, 2021, vol. 18, issue 16, 1-12

Abstract: Background: Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne disease caused by different species of hantaviruses, is widely endemic in China. Shandong Province is one of the most affected areas. This study aims to analyze the epidemiological characteristics of HFRS, and to predict the regional risk in Shandong Province. Methods: Descriptive statistics were used to elucidate the epidemiological characteristics of HFRS cases in Shandong Province from 2010 to 2018. Based on environmental and socioeconomic data, the boosted regression tree (BRT) model was applied to identify important influencing factors, as well as predict the infection risk zones of HFRS. Results: A total of 11,432 HFRS cases were reported from 2010 to 2018 in Shandong, with groups aged 31–70 years (81.04%), and farmers (84.44%) being the majority. Most cases were from central and southeast Shandong. There were two incidence peak periods in April to June and October to December, respectively. According to the BRT model, we found that population density (a relative contribution of 15.90%), elevation (12.02%), grassland (11.06%), cultivated land (9.98%), rural settlement (9.25%), woodland (8.71%), and water body (8.63%) were relatively important influencing factors for HFRS epidemics, and the predicted high infection risk areas were concentrated in central and eastern areas of Shandong Province. The BRT model provided an overall prediction accuracy, with an area under the receiver operating characteristic curve of 0.91 (range: 0.83–0.95). Conclusions: HFRS in Shandong Province has shown seasonal and spatial clustering characteristics. Middle-aged and elderly farmers are a high-risk population. The BRT model has satisfactory predictive capability in stratifying the regional risk of HFRS at a county level in Shandong Province, which could serve as an important tool for risk assessment of HFRS to deploy prevention and control measures.

Keywords: hemorrhagic fever with renal syndrome; epidemiological characteristics; regional risk prediction; boosted regression tree model; influencing factors (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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