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SCIENTIA SINICA Mathematica, Volume 50 , Issue 6 : 885(2020) https://doi.org/10.1360/SSM-2020-0043

Studies of the strategies for controlling the COVID-19 epidemic in China: Estimation of control efficacy and suggestions for policy makers

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  • ReceivedFeb 19, 2020
  • AcceptedFeb 26, 2020
  • PublishedMar 3, 2020

Abstract

Based on the theory of the transmission dynamics and the general SEIR (susceptible-exposed-infectious-recovered/removed) model, using the software EpiSIX (involved with only 10 parameters), we study the COVID-19 epidemic courses in China.By fitting the real-time data of diagnosed cases from December 12, 2019 to our model,we estimate the most important epidemiological parameters of COVID-19 such as the basic reproductive number, the mean latency/infectious period, the proportion of asymptomatic infectives, as well as the ending times, peaks and end sizes of the epidemic courses. From the very early stages of the epidemic courses, we estimate the time-dependent control efficacy and make suggestions for the policy makers. We have established a webpage for updating our predictions.


Funded by

南开大学冠状病毒应急专项(63201104)

国家自然科学基金(61873154,81673275)

国家科技重大专项(2017ZX10201101,2018ZX10715002)


Acknowledgment

感谢物美集团、张文中博士以及南开大学 的鼎力支持. 感谢曹雪涛教授和王兆军教授(南开大学)、 魏凤英教授(福州大学, 数据整理) 和徐铣明博士(南开大学, 可视化)对我们的帮助. 特别要感谢陈兰荪教授(中国科学院)、庾建设教授(广州大学)、 侯自新教授、 龚克教授和孔德领教授(南开大学)以及教育部和国家卫健委 等对我们工作的支持. 第一作者还要感谢南开大学团队里的高建召教授、王金杰博士和王博灵及马驰宇同学的支持. 最后, 我们要特别感谢两位匿名审稿人的非常有建设性的意见 (例如,建立网页,将我们的预测公布以使我们的工作有更大的公众影响).


Supplement

Appendix

2020年1月30日至2020年2月22日预测结果

自2020年1月30日开始, 我们将预测公布在南开大学统计与数据科学学院的微信群“南开统计” 上. 自2020年2月14日起, 我们将预测范围扩展到全国重要直辖市和省份. 预测每3天更新一次, 可访问网页 https://www.nkdacs.com/2019-nCoV/item/charts/predict.htmlhttps://www.nkdacs.com/2019-nCoV/item/index.html

AA1 给出2020年1月30日至2020年2月22日共24天 武汉、 湖北、 全国 的预测值和国家卫健委公布的截至当日的确诊总数(括号内)的比较, 注remarkA.1remarkA.2 给出对预测结果的解读.

remark 自2月5日起是爬坡和下坡期, 波动较大, 故我们变更为区间预测. 自2月10日起, 实际数据大都靠近我们预测区间的下界, 这是实际数据下坡的特征.

remark 2月12日, 武汉的临床病例约1.3万被计入统计数. 受此影响, 2月12日和2月13日 武汉市、湖北省以及全国的实际数据严重偏离我们的预测区间. 但是, 若将这两天的实际数据减去新增的临床病例数约1.3万, 则所得的值依然在我们的预测区间的偏下界些许. 为应对这种数据统计方法的突然改变, 我们自2020年2月14日以后采用的策略是, 模型不变, 但重新解读数据, 即所谓的“先割后补” 法.


References

[1] 第一财经. 假如武汉的警铃有机会被拉响, 可以是哪天? Https://mp.weixin.qq.com/s/_TQj7IIUZkwIf0M3I8PquA 2020. Google Scholar

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  • Figure 1

  • Figure 3

  • Table 1   3天预测的部分结果
    时间 全国确诊总数 湖北省确诊总数 武汉市确诊总数
    2020.1.30 9,118 (8,149) 6,026 (4,903) 2,950 (2,639)
    2020.1.31 10,602 (11,791) 6,998 (7,153) 3,720 (3,215)
    2020.2.1 12,136 (13,831) 8,033 (9,074) 4,427 (4,109)
    2020.2.2 15,363 (17,205) 9,790 (11,177) 4,596 (5,142)
    2020.2.3 17,074 (20,471) 10,987 (13,522) 5,120 (6,384)
    2020.2.4 18,627 (23,718) 12,091 (16,678) 5,568 (8,351)
    2020.2.5 25,245–28,531 (28,018) 12,091–13,328 (16,678) 9,739–11,406 (10,117)
    2020.2.6 27,742–30,929 (31,211) 18,211–21,139 (19,665) 10,839–12,529 (11,618)
    2020.2.7 30,200–33,255 (34,546) 20,213–23,159 (24,953) 11,985–13,681 (13,603)
    2020.2.8 36,168–40,444 (37,198) 26,704–30,168 (27,100) 14,520–16,023 (14,982)
    2020.2.9 39,069–43,233 (40,171) 28,942–32,341 (29,631) 15,588–17,021 (16,902)
    2020.2.10 41,914–45,937 (42,638) 31,159–34,467 (31,728) 16,612–17,966 (18,454)
    2020.2.11 44,665–48,081 (44,653) 33,977–37,261 (33,366) 19,013–20,466 (19,558)
    2020.2.12 46,937–50,139 (59,804) 36,142–39,292 (48,206) 20,075–21,433 (32,994)
    2020.2.13 49,044–52,030 (63,922) 38,220–41,223 (51,986) 21,064–22,323 (35,991)
    2020.2.14 63,983–66,757 (66,492) 52,495–55,412 (54,406) 35,975–37,052 (37,914)
    2020.2.15 65,758–68,326 (68,500) 54,341–57,097 (56,249) 36,739–37,723 (39,462)
    2020.2.16 67,375–69,744 (70,548) 56,074–58,668 (58,182) 37,428–38,324 (41,152)
    2020.2.17 71,992–74,817 (72,436) 59,061–61,650 (59,989) 42,236–43,657 (42,752)
    2020.2.18 73,638–76,266 (74,185) 60,668–63,090 (61,682) 42,897–44,185 (44,412)
    2020.2.19 75,147–77,587 (74,646) 62,156–64,415 (62,031) 43,497–44,660 (45,027)
    2020.2.20 76,516–78,778 (75,891) 63,813–65,924 (63,088) 45,541–47,186 (45,346)
    2020.2.21 77,774–79,868 (76,288) 65,083–67,042 (63,454) 46,584–48,123 (45,660)
    2020.2.22 78,921–80,857 (77,035) 66,249–68,064 (64,084) 47,553–48,989 (46,201)
    2020.2.23 77,889–78,513 (77,150) 65,423–65,988 (64,287) 46,399–46,656 (46,660)
    2020.2.24 78,661–79,232 (77,658) 66,152–66,669 (64,786) 46,848–47,078 (47,071)
  • Table 1   COVID-19、SARS和MERS的比较
    COVID-19 $\tau$ $~(95%,\,~99%)$ $\sg$ $~(95%,~99%)$ $\beta$ $\theta$ (%) $R_0$ mbox致死率
    mbox武汉市 4.45 (11.5, 15.2) 4.86 (18.0, 25.8) 0.6258 23.6 3.04 4.16$%$–4.53$%$
    mbox湖北省 4.77 (12.1, 21.0) 6.74 (19.1, 35.5) 0.5876 21.1 3.96 2.69$%$–4.53$%$
    mbox湖北省以外 6.18 (16.2, 19.3) 6.24 (25.4, 32.5) 0.6225 17.3 3.88 0.08$%$–3.0$%$
    mbox全国 4.98 (12.7, 22.2) 7.04 (19.9, 37.2) 0.6175 21.8 4.35
    文献[10,11] 5.2 (12.5, 24.0) 5.3 (19.0, -) 1.4–3.9 0.08$%$–4.53$%$
    SARS [8] 5–7 (20, 30) 9–12 (29, 60) 0.20–0.30 11.0 2.0–3.7 7.2$%$
    MERS [12,13] 6–7 (-, -) 3–6 (-, -) 0.3–0.8 30.4$%$–58.0$%$

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