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In this paper, a simple susceptible-infected (SI) model is build for simulating the early phase of COVID-19 transmission process. By using the data collected from the newest epidemiological investigation, the parameters of SI model is estimated and compared with those from some other studies. The population migration data during Spring festival in China are collected from Baidu.com and also extracted from different news sources, the migration characteristic of Wuhan city in the early phase of the epidemic situation is captured, and substituted into a simple difference equation model which is modified from the SI model for supporting migrations. Then several simulations are performed for the spatiotemporal transmission process of COVID-19 in China. Some conclusions are drawn from simulations and experiments below. 1) With 95% confidence, the infection rate of COVID-19 is estimated to be in a range of 0.2068–0.2073 in general situation, and the corresponding basic reproduction number R 0is estimated to be in a range of 2.5510–2.6555. A case study shows that under an extreme condition, the infection rate and R 0are estimated to be 0.2862 and 3.1465, respectively. 2) The Pearson correlation coefficient between Baidu migration index and the number of travelers sent by railway is 0.9108, which indicates a strong linear correlation between them, thus it can be deduced that Baidu migration index is an efficient tool for estimating the migration situation. 3) The epidemic arrival times for different provinces in China are estimated via simulations, specifically, no more than 1 day within an estimation error of 41.38%; no more than 3 days within an error of 79.31%, and no more than 5 days with an error of 95.55%. An average estimation error is 2.14 days. -
Keywords:
- COVID-19/
- epidemic model/
- traffic flow/
- migration
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日期 人数 日期 人数 日期 人数 日期 人数 日期 人数 12/09 1 12/17 12 12/21 29 12/25 47 12/29 78 12/11 2 12/18 14 12/22 37 12/26 49 12/30 90 12/12 5 12/19 16 12/23 40 12/27 59 12/31 102 12/15 8 12/20 25 12/24 45 12/28 68 
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截至时点 估计发病人数 实际发病人数 上报CCDC人数 软件抓取法 蒙特卡罗法 2019/12/31 102 49 102 0 2020/01/10 738 459 738—781 41 2020/01/20 6162 5800 6143—6187 291 2020/01/31 32661 29654 32633—32677 11821 2020/02/11 44692 69163 44672 44730 DownLoad:
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截止日期 $\beta $ 置信区间 R2 截止日期 $\beta $ 置信区间 R2 2019/12/31 0.2213 [0.2152, 0.2274] 0.868 2020/01/11 0.2066 [0.2056, 0.2274] 0.990 2020/01/01 0.2171 [0.2116, 0.2225] 0.878 2020/01/12 0.2063 [0.2056, 0.2071] 0.993 2020/01/02 0.2168 [0.2127, 0.2209] 0.923 20200/1/13 0.2060 [0.2054, 0.2067] 0.995 2020/01/03 0.2159 [0.2127, 0.2191] 0.949 2020/01/14 0.2059 [0.2054, 0.2064] 0.997 2020/01/04 0.2155 [0.2130, 0.2179] 0.967 2020/01/15 0.2056 [0.2052, 0.2060] 0.998 2020/01/05 0.2138 [0.2118, 0.2159] 0.973 2020/10/16 0.2058 [0.2054, 0.2061] 0.998 2020/01/06 0.2127 [0.2109, 0.2144] 0.980 2020/01/17 0.2060 [0.2057, 0.2063] 0.999 2020/01/07 0.2109 [0.2093, 0.2126] 0.980 2020/01/18 0.2064 [0.2061, 0.2066] 0.999 2020/01/08 0.2091 [0.2075, 0.2107] 0.979 2020/01/19 0.2065 [0.2063, 0.2067] 0.999 2020/01/09 0.2080 [0.2067, 0.2094] 0.984 2020/01/20 0.2066 [0.2064, 0.2068] 0.999 2020/01/10 0.2067 [0.2054, 0.2080] 0.985 2020/01/21 0.2070 [0.2068, 0.2073] 0.999 DownLoad:
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截至时点 $\beta $ 0.2213 0.2159 0.2080 0.2066 2019/12/31 130 116 97 95 2020/01/10 1190 1001 777 753 2020/01/20 10870 8666 6220 5964 DownLoad:
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日期 迁徙指数 人数/万 日期 迁徙指数 人数/万 日期 迁徙指数 人数/万 2020/01/10 6.6232 27 2020/01/22 11.8403 29.96 2019/01/29 7.0282 27.2 2020/01/11 7.5612 29.8 2019/01/21 4.5718 21.6 2019/01/30 7.1072 27.7 2020/01/12 6.2165 27 2019/01/22 4.6892 21.4 2019/01/31 7.4800 28.1 2020/01/13 5.7620 24.8 2019/01/23 4.8062 23 2019/02/01 8.7140 29.8 2020/01/15 5.9087 26.5 2019/01/24 4.8605 21.7 2019/02/02 9.6043 31.5 2020/01/16 6.0028 27.7 2019/01/26 7.0436 27 2019/02/03 9.2247 29.1 2020/01/19 7.4060 30 2019/01/28 6.7706 26.8 DownLoad:
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省份 $\beta $ 实际日期 省份 $\beta $ 实际日期 0.2213 0.2159 0.2070 0.2213 0.2159 0.2070 安徽 01/06 01/06 01/07 01/07 辽宁 01/11 01/12 01/13 01/09 北京 01/07 01/07 01/08 01/08* 内蒙古 01/13 01/14 01/16 01/16 福建 01/09 01/09 01/10 01/06 宁夏 01/17 01/18 01/20 01/17 甘肃 01/10 01/11 01/12 01/04 青海 01/19 01/21 01/23 01/21 广东 01/05 01/05 01/06 01/04 山东 01/08 01/08 01/09 01/08 广西 01/08 01/09 01/09 01/13 山西 01/09 01/10 01/10 01/14 贵州 01/08 01/08 01/09 01/06 陕西 01/09 01/09 01/10 01/12 海南 01/10 01/11 01/12 01/13 上海 01/08 01/08 01/09 01/10 河北 01/08 01/09 01/09 01/13 四川 01/08 01/08 01/09 01/07 河南 01/04 01/04 01/05 01/03 天津 01/14 01/15 01/16 01/11 黑龙江 01/12 01/13 01/14 01/12 西藏 > 01/23 > 01/23 > 01/23 01/30 湖南 01/05 01/05 01/06 01/05 新疆 01/12 01/12 01/14 01/17 吉林 01/15 01/15 01/17 01/14 云南 01/09 01/11 01/10 01/07 江苏 01/06 01/07 01/07 01/10 浙江 01/07 01/07 01/08 01/04 江西 01/06 01/07 01/07 01/07 重庆 01/08 01/08 01/09 01/06 DownLoad:
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