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基于安徽省卫生健康委员会截至2020年2月19日公布的800余例新型冠状病毒肺炎病例信息, 根据病例中公布的接触史构建确诊患者间的有向传播关系, 发现源传染患者中男性居多, 被传染患者中女性居多. 从病例信息中可知, 安徽省新型冠状病毒肺炎疫情的发展从初期的具有武汉居住或接触史的输入病例转入后期本地传播为主的小范围社区传播, 且严格的防控隔离措施有效切断了社区内的进一步传播. 源传染患者与被传染患者的确诊时间间隔可用Γ分布拟合, 确诊时间间隔的中位数为2 d, 平均值为2.67 d. 基于有向传播关系的统计特点, 构建安徽省疫情发展后期的自回归传播模型, 模型仿真结果与疫情发展数据符合. 对除湖北省的全国确诊病例数据同样采取自回归建模与仿真, 结果仍与疫情发展数据符合. 这一发现为控制疫情在湖北省以外区域的防控提供了参考: 通过严格的防控措施和隔离措施, 疫情在湖北省之外的传播具有很大的黏滞性, 多为家庭程度的密切接触传播, 且能有效控制新型冠状病毒肺炎在当地的传播深度, 有效控制了疫情的蔓延.
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关键词:
- 新型冠状病毒肺炎疫情 /
- 缓慢增长期 /
- 有向传播关系 /
- 自回归
Based on the information about more than 800 cases reported by Anhui provincial health commission on February 19 2020, the directional transmission relationship between the confirmed patients is constructed according to the contact history published in the cases, and it is found that the majority of the patients who can infect others are male and most of the patients who are infected are female. According to the analysis of case information, the early confirmed coronavirus disease 2019 (COVID-19) cases in Anhui province had the history of residence or exposure in Wuhan. In the later stage, the cases spread in small communities mainly by local transmission, and the strict prevention and control measures can effectively cut off further transmission in the communities. The time interval between diagnosing the source-infected patients and infected patients is fitted by Γ distribution, with a median of 2 days and an average of 2.67 days. Based on the statistical characteristics of directional transmission relationship, an autoregressive transmission model is constructed in the late stage of epidemic development in Anhui province, and the simulation results are consistent with the epidemic development data. Autoregressive model and simulation are also used for predicting the data of confirmed cases in the whole country except for Hubei province. This discovery can be referenced by regional epidemic prevention and control except for where it originated. Through strict protection measures and isolation measures, the spread of the epidemic outside the original place is highly viscous. It usually spreads by close contact between family members, and the local spread of COVID-19 can be effectively controlled.-
Keywords:
- coronavirus disease 2019 epidemic /
- slow growth period /
- directed transmission /
- autoregression
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图 1 不同性别每日新增确诊人数
Fig. 1. Number of newly confirmed patients per day of different genders.
图 3 不同来源每日新增确诊人数
Fig. 3. Number of newly confirmed patients per day from different sources.
图 5 源传染患者与被传染患者间确诊时间差的分布直方图, 图中拟合密度参考线为Γ (10.84, 1.08)分布, 并向左平移9
Fig. 5. Distribution histogram of the time interval of diagnosis between source infected patients and infected patients. In the figure, the fitting density reference line is the distribution of Γ (10.84, 1.08) and shifts 9 to the left.
图 6 安徽省每日新增确诊人数及模型仿真
Fig. 6. Number of newly confirmed patients per day and model simulation in Anhui province.
图 7 安徽省累计确诊人数及模型仿真
Fig. 7. Cumulative number of confirmed cases and model simulation in Anhui province.
图 8 每日新增确诊人数(除湖北省)及模型仿真
Fig. 8. Number of newly confirmed patients per day (except in Hubei province) and model simulation.
图 9 累计确诊人数(除湖北省)及模型仿真
Fig. 9. Cumulative number of confirmed cases (excluding Hubei province) and model simulation.
表 1 传播过程中源传染患者与被传染患者性别统计(有效样本233例)
Table 1. Gender statistics of source infected patients and infected patients during virus transmission. The number of valid sample is 233.
源传染者-被传染者 人数 比例 男-女 83 0.3562 男-男 54 0.2318 女-男 56 0.2403 女-女 40 0.1717 
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表 2 病毒传播过程中源传染患者与被传染患者关系统计 (有效样本233, 仅截取统计样本量大于等于5的情况, 其中比例为每个类别内的人数除以有效样本数233得到)
Table 2. Relationship statistics between source infected patients and infected patients during virus transmission. The number of valid sample is 233. Use only statistical sample size greater than or equal to 5. The ratio is the number of people in each category divided by valid sample number 233.
源传染者-被传染者 人数 比例 丈夫-妻子 36 0.1538 妻子-丈夫 21 0.0897 儿子-母亲 11 0.0470 母亲-儿子 10 0.0427 儿子-父亲 10 0.0427 母亲-女儿 7 0.0299 父亲-女儿 5 0.0214 哥哥-弟弟 5 0.0214 父亲-儿子 5 0.0214
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[1] National Health Commission of the People’s Republic of China http://www.nhc.gov.cn/ [2020−03−29]
[2] Huaxia.com http://www.huaxia.com/xw/dlxw/2003/08/234782.html [2020−03−29]
[3] Sina.com https://news.sina.cn/zt_d/yiqing0121 [2020−03−29]
[4] Sina.com https://weibointl.api.weibo.cn/share/129902340.html?weibo_id=4475585516343394 [2020-03-29]
[5] 李春梅, 钟晓祝, 杨金城 2004 重庆医学 395 947
Google Scholar
Li C M, Zhong X Z, Yang J C 2004 Chongqing Med. J. 395 947
Google Scholar
[6] Wu J T, Leung K, Leung G M 2020 Lancet 365 689
Google Scholar
[7] 周涛, 刘权辉, 杨紫陌, 廖敬仪, 杨可心, 白薇, 吕欣, 张伟 2020 中国循证医学杂志 20 117
Zhou T, Liu Q H, Yang Z M, Liao J Y, Yang K X, Bai W, Liu X, ZhangW 2020 Chin. J. Evid.-Based Med. 20 117
[8] Jasper F W C, Yuan S F, Kinhang K, Kelvin K W T, Hin C, Yang J, Xing F F, Liu J L, Cyril C Y, Rosana W S P, Hoiwah T, Simon K L, Kwokhung C, Vincent K P, Wanmui C, Jonathan D I, Jianpiao C, Vincent C C C, Honglin C, Christopher H, Kwokyung Y 2020 Lancet 395 514
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Zhang L H, Li B H, Jia P, Pu j, Bai P, Zhu P J, Li L, Zeng G J, Zhao X, Dong S S, Liu M H, Zhang N 2020 J. Biomedical Eng. 37 1
Google Scholar
[12] Maria N, Niamh O’Neill, Catrin S, Mehdi K, Maliha A, Riaz Ag 2020 Int. J. Surg. 76 71
Google Scholar
[13] 喻孜, 张贵清, 刘庆珍, 吕忠全 2020 电子科技大学学报 DOI: 10.12178/1001-0548.2020027Google Scholar
Yu Z, Zhang G Q, Liu Q Z, Lv Z Q 2020 Journal of University of Electronic Science and Technology of China DOI: 10.12178/1001-0548.2020027Google Scholar
[14] Gerardo C, Cécile V, Simonsen L, Moghadas S M 2016 The Royal Society. Collection DOI: 10.6084/m9.figshare.c.3473634.v2Google Scholar
[15] 张琳 2020 电子科技大学学报 DOI: 10.12178/1001-0548.2020037Google Scholar
Zhang L 2020 Journal of University of Electronic Science and Technology of China DOI: 10.12178/1001-0548.2020037Google Scholar
[16] Benjamin F M, Dirk B 2020 arXiv: 2002.07572 [q-bio.PE]
[17] Liu Q, Jiang D Q, Tasawar H, Ahmed A, Bashir A 2020 Physica A DOI: 10.1016/j.physa.2020.124152Google Scholar
[18] 蔡洁, 贾浩源, 王珂 2020 山东医药 60 1
Google Scholar
Cai J, Jia H Y, Wang K 2020 Shandong Med. J. 60 1
Google Scholar
[19] Luo Y T, Tang S T, Teng Z D, Zhang L 2019 Nonlinear Anal. Real World Appl. 50 365
Google Scholar
[20] 张彦平 2020 中华流行病学杂志 41 145
Google Scholar
Zhang Y P 2020 China J. Epidevmiol. 41 145
Google Scholar
[21] Du Z W, Xu X K, Wu Y, Wang L, Cowling B J, Meyers A L 2020 Emerg. Infect. Dis. DOI: 10.3201/eid2606.200357Google Scholar
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