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

• AcceptedFeb 26, 2020
• PublishedMar 3, 2020
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### 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.

### Supplement

Appendix

2020年1月30日至2020年2月22日预测结果

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

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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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