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山东大学学报 (医学版) ›› 2023, Vol. 61 ›› Issue (4): 103-110.doi: 10.6040/j.issn.1671-7554.0.2022.1106

• 公共卫生与管理学 • 上一篇    下一篇

基于MaxEnt的山东省SFTS影响因素评估及风险区域预测

韩璐怿1*,田雪莹2*,高琦1,佘凯丽1,曹云贤1,魏淑淑1,丁淑军2,李秀君1   

  1. 1. 山东大学齐鲁医学院公共卫生学院生物统计学系, 山东 济南 250012;2. 山东省疾病预防控制中心传染病防制所, 山东 济南 250014
  • 发布日期:2023-04-11
  • 通讯作者: 李秀君. E-mail:xjli@sdu.edu.cn丁淑军. E-mail:dsj_jn@126.com*共同第一作者
  • 基金资助:
    国家重点研发计划(2019YFC1200500,2019YFC1200502);山东省医药卫生科技发展计划项目(202012051299)

MaxEnt modeling to analyze influencing factors of severe fever with thrombocytopenia syndrome and predict the potential distribution in Shandong Province

HAN Luyi1*, TIAN Xueying2*, GAO Qi1, SHE Kaili1, CAO Yunxian1, WEI Shushu1, Ding Shujun2, LI Xiujun1   

  1. 1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Department of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan 250014, Shandong, China
  • Published:2023-04-11

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摘要： 目的 探讨山东省发热伴血小板减少综合征(SFTS)发病的适宜环境因素,预测其高风险区域,为山东省SFTS防控提供科学依据。 方法 基于山东省2016—2019年SFTS报告病例和环境因子训练最大熵生态位模型(MaxEnt),分析SFTS发病的影响因素,并预测山东省2017—2020年SFTS发病高风险区域。 结果 对2016—2020年山东省报告的2 548例SFTS病例及环境因子的建模分析显示,14个环境因子不同程度影响山东省SFTS的发生,其中坡度贡献最大;日照时数、年累计降水量、年相对湿度、年平均风速为SFTS发生主要影响因子。ROC曲线显示,MaxEnt模型预测效果较优,预测模型的AUC分别为0.862、0.842、0.830、0.832、0.814。模型预测的高风险区范围相对集中,围绕胶东半岛及鲁中地区集群。 结论 山东省SFTS高风险区域较为稳定,易发生在缓坡周围的平坡所在区域,且受到多种气象因素的影响。应针对高发地区采取综合措施防控SFTS。

关键词: 发热伴血小板减少综合征, 生态位模型, 影响因素, 风险区域

Abstract: Objective To explore the appropriate environmental factors for the incidence of severe fever with thrombocytopenia syndrome(SFTS)in Shandong Province and to predict the potential high-risk areas, so as to provide scientific basis for the prevention and control of SFTS. Methods Based on the data of confirmed SFTS cases and environmental factors in Shandong Province from 2016 to 2019, MaxEnt model was trained to analyze the influencing factors of SFTS, and to predict the high-risk areas of SFTS from 2017 to 2020. Results From 2016 to 2020, 2,548 cases of SFTS were reported in Shandong Province, and 14 environmental factors were found to affect the distribution of SFTS to varying degrees, including slope, which contributed the most, and meteorological factors such as average sunshine duration, annual accumulated precipitation, annual relative humidity and annual average wind speed. The receiver operating characteristic(ROC)curve showed that the prediction effects of MaxEnt model was good, with the area under the curve(AUC)being 0.862, 0.842, 0.830, 0.832, and 0.814, respectively. The potential high-risk areas predicted by the model was around the Jiaodong Peninsula and central region of Shandong Province. Conclusion The high-risk areas of SFTS in Shandong Province are relatively stable. SFTS tends to occur in flat slope areas around gentle slopes, and is affected by meteorological factors. Comprehensive measures should be taken to prevent and control SFTS.

Key words: Severe fever with thrombocytopenia syndrome, Maximum entropy model, Influencing factors, Risk areas

中图分类号: 

  • R183.5
[1] Yu XJ, Liang MF, Zhang SY, et al. Fever with thrombocytopenia associated with a novel bunyavirus in China[J]. N Engl J Med, 2011, 364(16): 1523-1532.
[2] Zhang YZ, Zhou DJ, Qin XC, et al. The ecology, genetic diversity, and phylogeny of Huaiyangshan virus in China[J]. J Virol, 2012, 86(5): 2864-2868.
[3] Huang X, Li J, Li A, et al. Epidemiological characteristics of severe fever with thrombocytopenia syndrome from 2010 to 2019 in mainland China[J]. Int J Environ Res Public Health, 2021, 18(6): 3092.
[4] 陈秋兰, 朱曼桐, 陈宁, 等. 2011—2021年全国发热伴血小板减少综合征流行特征分析[J]. 中华流行病学杂志, 2022, 43(6): 852-859. CHEN Qiulan, ZHU Mantong, CHEN Ning, et al. Epidemiological characteristics of severe fever with thtrombocytopenia syndrome in China, 2011-2021[J]. Chinese Journal of Epidemiology, 2022, 43(6): 852-859.
[5] Sun J, Lu L, Wu H, et al. The changing epidemiological characteristics of severe fever with thrombocytopenia syndrome in China, 2011-2016[J]. Sci Rep, 2017, 7(1): 9236.
[6] Peterson AT, Vieglais DA. Predicting species invasions using ecological niche modeling: new approaches from bioinformatics attack a pressing problem[J]. Bioscience, 2001, 51(5): 363-371.
[7] 林晓玲, 肖洪, 田怀玉. 生态位模型在传染病风险预测中的应用[J]. 中华预防医学杂志, 2013, 47(4): 294-296.
[8] Peterson AT, Lash RR, Carroll DS, et al. Geographic potential for outbreaks of Marburg hemorrhagic fever[J]. Am J Trop Med Hyg, 2006, 75(1): 9-15.
[9] Hay SI, Guerra CA, Gething PW, et al. A world malaria map: Plasmodium falciparum endemicity in 2007[J]. PLoS Med, 2009, 6(3): e1000048.
[10] Stein ML. Interpolation of spatial data: some theory for kriging[M]. New York: Springer, 1999: 1-14.
[11] Sun JM, Wu HX, Lu L, et al. Factors associated with spatial distribution of severe fever with thrombocytopenia syndrome[J]. Sci Total Environ, 2021, 750: 141522. doi:10.1016/j.scitotenv.2020.141522.
[12] Yang XQ, Kushwaha S, Saran S, et al. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills[J]. Ecol Eng, 2013, 51: 83-87. doi: 10.1016/j.ecoleng.2012.12.004.
[13] Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions[J]. Ecol Model, 2006, 190(3): 231-259.
[14] Moreno R, Zamora R, Molina JR, et al. Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using maximum entropy(Maxent)[J]. Ecol Inform, 2011, 6(6): 364-370.
[15] Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic(ROC)curve[J]. Radiology, 1982, 143(1): 29-36.
[16] Du Z, Wang Z, Liu Y, et al. Ecological niche modeling for predicting the potential risk areas of severe fever with thrombocytopenia syndrome[J]. Int J Infect Dis, 2014, 26(9): 1-8.
[17] 雷军成,徐海根.基于MaxEnt的加拿大一枝黄花在中国的潜在分布区预测[J].生态与农村环境学报, 2010, 26(2): 137-141. LEI Junchen, XU Haigen. MaxEnt-based prediction of potential distribution of solidago canadensis in China[J]. Journal of Ecology and Rural Environment, 2010, 26(2): 137-141.
[18] Miao D, Liu MJ, Wang YX, et al. Epidemiology and ecology of severe fever with thrombocytopenia syndrome in China, 2010-2018[J]. Clin Infect Dis, 2021, 73(11): e3851-e3858.
[19] 段青, 逄博, 张晓梅, 等. 2011—2020年山东省发热伴血小板减少综合征流行特征及空间聚集性[J]. 中华疾病控制杂志, 2022, 26(7): 790-797. DUAN Qing, PANG Bo, ZHANG Xiaomei, et al. Epidemiological characteristics and spatial aggregation of severe fever with thrombocytopenia syndrome in Shandong Province from 2011 to 2020[J]. Chinese Journal of Disease Control & Prevention, 2022, 26(7): 790-797.
[20] 姜晓林, 张晓梅, 逄博, 等. 山东省发热伴血小板减少综合征时空流行特征分析[J]. 中国人兽共患病学报, 2020, 36(9): 740-745. JIANG Xiaolin, ZHANG Xiaomei, PANG Bo, et al. Temporal-spatial analysis of severe fever with thrombocytopenia syndrome(SFTS)in Shandong Province, China[J]. Chinese Journal of Zoonoses, 2020, 36(9): 740-745.
[21] 罗成旺, 刘起勇. 自然疫源性疾病流行因素分析及对策[J]. 中国媒介生物学及控制杂志, 2007, 18(4): 293-297. LUO Chengwang, LIU Qiyong. The epidemic factors analysis of natural focus diseases and their countermeasures[J]. Chinese Journal of Vector Biology and Control, 2007, 18(4): 293-297.
[22] Zhang D, Sun C, Yu H, et al. Environmental risk factors and geographic distribution of severe fever with thrombocytopenia syndrome in Jiangsu Province, China[J]. Vector Borne Zoonotic Dis, 2019, 19(10): 758-766.
[23] Liu Q, He B, Huang SY, et al. Severe fever with thrombocytopenia syndrome, an emerging tick-borne zoonosis[J]. Lancet Infect Dis, 2014, 14(8): 763-772.
[24] 李佳宸, 王玉娜, 赵静, 等. 发热伴血小板减少综合征流行病学研究进展[J]. 中华流行病学杂志, 2021, 42(12): 2226-2233. LI Jiachen, WANG Yuna, ZHAO Jing, et al. A review on the epidemiology of severe fever with thrombocytopenia syndrome[J]. Chinese Journal of Epidemiology, 2021, 42(12): 2226-2233.
[25] Wang T, Li XL, Liu M, et al. Epidemiological characteristics and environmental risk factors of severe fever with thrombocytopenia syndrome in Hubei Province, China, from 2011 to 2016[J]. Front Microbiol, 2017, 8: 387. doi:10.3389/fmicb.2017.00387.
[26] Sun JM, Lu L, Liu KK, et al. Forecast of severe fever with thrombocytopenia syndrome incidence with meteorological factors[J]. Sci Total Environ, 2018, 626: 1188-1192. doi:10.1016/j.scitotenv.2018.01.196.
[27] Wang X, Qi C, Zhang DD, et al. Epidemic character and environmental factors in epidemic areas of severe fever with thrombocytopenia syndrome in Shandong Province[J]. Ticks Tick Borne Dis, 2021, 12(1): 101593.
[28] Wu L, Deng F, Xie Z, et al. Spatial analysis of severe fever with thrombocytopenia syndrome virus in China using a geographically weighted logistic regression model[J]. Int J Environ Res Public Health, 2016, 13(11): 1125.
[29] Booth M. Climate change and the neglected tropical diseases[J]. Adv Parasitol, 2018, 100: 39-126. doi:10.1016/bs.apar.2018.02.001.
[30] 徐泽华, 韩美, 张广彩, 等. 山东降水时空分布变化及其影响因素[J]. 水土保持研究, 2018, 25(3): 272-278. XU Zehua, HAN Mei, ZHANG Guangcai, et al. Spatiotemporal distribution of the precipitation and its impact factors in Shandong Province[J]. Research of Soil and Water Conservation, 2018, 25(3): 272-278.
[31] 刘洋, 黄学勇, 杜燕华, 等. 河南发热伴血小板减少综合征流行区蜱类分布及媒介携带新布尼亚病毒状况调查[J]. 中华预防医学杂志, 2012, 46(6): 500-504. LIU Yang, HUANG Xueyong, DU Yanhua, et al. Survey on ticks and detection of new bunyavirus in some vect in the endemic areas of fever, thrombocytopenia and leukopenia syndrome(FTLS)in Henan Province[J]. Chinese Journal of Preventive Medicine, 2012, 46(6): 500-504.
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