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

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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)

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.

Suggested Citation

  • Kaili She & Chunyu Li & Chang Qi & Tingxuan Liu & Yan Jia & Yuchen Zhu & Lili Liu & Zhiqiang Wang & Ying Zhang & Xiujun Li, 2021. "Epidemiological Characteristics and Regional Risk Prediction of Hemorrhagic Fever with Renal Syndrome in Shandong Province, China," IJERPH, MDPI, vol. 18(16), pages 1-12, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8495-:d:612590
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    References listed on IDEAS

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    1. Shujuan Li & Hongyan Ren & Wensheng Hu & Liang Lu & Xinliang Xu & Dafang Zhuang & Qiyong Liu, 2014. "Spatiotemporal Heterogeneity Analysis of Hemorrhagic Fever with Renal Syndrome in China Using Geographically Weighted Regression Models," IJERPH, MDPI, vol. 11(12), pages 1-19, November.
    2. Junyu He & George Christakos & Jiaping Wu & Bernard Cazelles & Quan Qian & Di Mu & Yong Wang & Wenwu Yin & Wenyi Zhang, 2018. "Spatiotemporal variation of the association between climate dynamics and HFRS outbreaks in Eastern China during 2005-2016 and its geographic determinants," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 12(6), pages 1-22, June.
    3. Changjun Bao & Wanwan Liu & Yefei Zhu & Wendong Liu & Jianli Hu & Qi Liang & Yuejia Cheng & Ying Wu & Rongbin Yu & Minghao Zhou & Hongbing Shen & Feng Chen & Fenyang Tang & Zhihang Peng, 2014. "The Spatial Analysis on Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China Based on Geographic Information System," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-8, September.
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    Cited by:

    1. Shujuan Li & Lingli Zhu & Lidan Zhang & Guoyan Zhang & Hongyan Ren & Liang Lu, 2023. "Urbanization-Related Environmental Factors and Hemorrhagic Fever with Renal Syndrome: A Review Based on Studies Taken in China," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    2. Qimeng Ren & Ming Sun, 2023. "Exploring the Quantitative Assessment of Spatial Risk in Response to Major Epidemic Disasters in Megacities: A Case Study of Qingdao," IJERPH, MDPI, vol. 20(4), pages 1-24, February.
    3. Ming Sun & Xueyu Jiao, 2023. "Quantitative Identification Study of Epidemic Risk in the Spatial Environment of Harbin City," Sustainability, MDPI, vol. 15(9), pages 1-22, May.

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