1 Introduction
The novel coronavirus disease (COVID19) as an acute infection has rapidly spread over 200 countries in the world since December 2019 (World Health Organization, 2020). There were 2,804,799 reported cumulative confirmed cases and 193,711 cumulative deaths around the world and 84,341 cumulative confirmed cases and 4,643 cumulative deaths from China until April 26 , 2020 (National Health Commission of the People’s Republic of China, 2020b). These numbers are rapidly arising daily. This coronavirus family includes the virus that caused to Severe Acute Respiratory Syndrome (SARS) in 2003 and another that caused to Middle East Respiratory Syndrome (MERS) in 2012 (Zhu et al., 2020). Early cases of COVID19 suggested that it might not be as severe as SARSCoV and MERSCoV, but the rapid increase in confirmed cases and the evidence of humantohuman transmission indicated that this coronavirus was more contagious than both SARSCoV and MERSCoV (Wang et al., 2020; Guan et al., 2020; Chan et al., 2020).
Compared with the outbreak of SARS, the Chinese government mounted responses rapidly to contain COVID19 by reducing the risk of personal exposure and transmission (Tu et al., 2020). As the city with the largest number of confirmed cases in Zhejiang province (National Health Commission of the People’s Republic of China, 2020a), where it activated the firstlevel public health emergency response on January 23, 2020, Wenzhou had suspended all interprovincial and intercity shuttle buses, and chartered buses, and carried out temperature testing for passengers of buses arriving in Wenzhou as its own policies to deal with the epidemic situation since January 27, 2020 (Wenzhou Municipal People’s Government, 2020a). These strict control measures were implemented across Wenzhou, where one family member was assigned to go out for necessity purchase of each household every two days since February 1, 2020 (Wenzhou Municipal People’s Government, 2020b). Thus, Wenzhou had adopted the stringent intervention policies, including both traffic blockade inside and outside the city, and the strictly closed management of the community, to intensively prevent and control of the epidemic situation of COVID19. In addition, an industrial city of Guangdong province, which activated the firstlevel public health emergency response on January 23, 2020 (Health Commission of Guangdong Province, 2020), Shenzhen adopted closely following the close contact of confirmed cases and isolating them on the same time. Comparatively, Shenzhen implemented relatively mild but early interventions.
This paper concentrated on the strict intervention policies of COVID19 that started on February 1, 2020 in Wenzhou and evaluated the treatment effects of those strict interventions, providing insights into the effects of the strict interventions. We also assessed what could have happened in Wenzhou if the stringent interventions were delayed by 0 to 5 days or if mild interventions were implemented instead. We accomplished this through simulation by using the timevarying SusceptibleInfected and infectious without isolationHospitalized in isolationRemoved (SIHR) model (Tan et al., 2020). Finally, we simulated the likely outcomes if relatively mild interventions as used in Shenzhen, China, were implemented in lieu of the stringent interventions.
In the next section, we describe the classical causal effects methods and dynamic transmission models of COVID19 in detail. The corresponding analysis of epidemic data in Wenzhou are illustrated in Section 3. Section 4 discusses the effects of stringent interventions of COVID19.
2 Methodologies
2.1 Data collection
We compiled a total of 504 confirmed cases (Health Commission of Wenzhou, 2000) from January 21, 2020 when the confirmed cases were first reported, to February 25, 2020 when no newly reported confirmed cases had been reported for a week, from the official website of the health commission of Wenzhou, and obtained the residential population of Wenzhou from the 2019 statistical yearbook of the Zhejiang Provincial Bureau of Statistics (Statistics Bureau of Zhejiang Province, 2019). The total confirmed cases of Shenzhen during the same period were collected. The cases per 100,000 individuals were calculated by the prevalence of COVID19 multiplying 100,000 individuals in Wenzhou. We searched the daily number of cumulative confirmed cases in each city of China using the R package ”nCov2019” (Wu et al., 2020), and collected the number of cumulative confirmed cases from the top 100 cities for the epidemic of COVID19 (except Hubei province) on February 25, 2020. The corresponding population density and GDP per capita statistics of these 100 cities were obtained from their latest statistical yearbooks.
2.2 Synthetic Control Method
To identify the treatment effects of the highly stringent interventions implemented on February 1, 2020, we compared Wenzhou with cities without those interventions. The synthetic control method was used to construct a datadriven controlgroup in the absence of those interventions in Wenzhou (Abadie and Gardeazabal, 2003; Abadie et al., 2010). The observed cases per 100,000 for Wenzhou (active intervention city) at the time with interventions were denoted by , and cases per 100,000 for cities at the time without interventions by . The difference of was the treatment effect of the interventions on the cases per 100,000 for the active intervention city in the postinterventions period. Note that
is counterfactual for the active intervention city in the postinterventions period. Reliable estimates of cases per 100,000 for a synthetic intervention city were used to derive
, where a synthetic intervention city resembled the active intervention city by possible preinterventions characteristics. We selected the GDP percapita (Zhang et al., 2020), population density (OtoPeralías, 2020), and threeday cases per 100,000 before the implementation of interventions to construct a resembling city of Wenzhou. The effects of unobserved factors varied over time and were controlled by the linear combination of cases per 100,000 of preinterventions (Abadie et al., 2010). Thus, the treatment effect of interventions in Wenzhou was formulated as (Abadie et al., 2011):(2.1) 
To determine
, the number of cities demographically similar to Wenzhou, we selected the top 100 cities of cumulative confirmed cases on February 25, 2020 in China (except Hubei province) and used hierarchical clustering
(Ward Jr, 1963) to group those cities based on population density and GDP per capita. A homogenous group including Wenzhou and other cities were grouped together. Thus, those cities could be considered as highly resembling to Wenzhou. The sum of weights for each of cities was 1 and the values of weights in Equation (2.1) were determined by preinterventions characteristics. The placebo tests were used to identify the magnitude of the treatment effect (i.e. the causal effect of interventions) (Abadie et al., 2010).2.3 Regression Discontinuity
For the strict intervention policies of Wenzhou, we vertically compared the difference in the epidemic situation of COVID19 before and after the implementation of such intervention policies. Regression Discontinuity (RD) (Thistlethwaite and Campbell, 1960; Hahn et al., 2001; Imbens and Lemieux, 2008) was used to evaluate whether the implementation of strict intervention policies of Wenzhou on February 1, 2020 had a significant effect on its own COVID19 epidemic. The outcome of these stringent interventions was formulated as:
(2.2) 
where the logarithmic transformation of the daily number of cumulative confirmed cases as a response variable. The values of
include 1 to 36, representing the number of days starting from January 21, 2020 as a rate variable in Equation (2.2) of RD, for example, the date of February 1 was denoted as 12 of . is an indicator variable for the implementation of the strict intervention policies in Wenzhou, given a value of 1 as the implementation of the policies but a value of 0 as no implementation of the policies. This indicates that the values are 0 and 1 before and after February 1, 2020 (including February 1 itself), respectively and the subscript of indicates the number of days as defined before. is the number of days corresponding to the cutoffpoint whose value is 12, representing the policy implemented on February 1, 2020. The values of could be denoted as the order of interaction effect between rate variable and policy. We compared the order of interaction effect item in the Equation (2.2) by AIC.In RD, to determine whether is an obvious jump point, the plot of both the rate variable and response variable was used to identify their relationship (Calonico et al., 2015a). Different numbers of bins could be used to divide both the lefthand side and righthand side of the cutoffpoint to small intervals. Thus, the scatter plot of both median values of the rate variable and mean values of the response variable could be drawn (Calonico et al., 2015b). The actual rate variables are weighted uniformly in each bin, and the fitted curves of both the lefthand side and righthand side are used to examine whether the cutoffpoint can be considered as a threshold.
2.4 SusceptibleInfected and infectious without isolationHospitalized in isolationRemoved (SIHR) model
The dynamic system of SIHR with four classes: Susceptible (), infected and infectious without isolation (), hospitalized in isolation (), removed () (Li, 2018; Hsieh et al., 2004; Tan et al., 2020) was defined as:
(2.3)  
where the transmission rate is the average rate of being infected given contact over unit time, is the mean of the incubation period, is the mean of the hospitalization period. To consider the effects of interventions, we introduced the timevarying transmission rate, which was defined as (Tan et al., 2020):
(2.4) 
where denotes the maximum transmission rate of COVID19 during the early outbreak, represents the time when the interventions start to be effective and the transmission rate starts to decline, is the duration of a process where the epidemic is to nearly vanish, is selected as and is specified to be 0.01. The smaller the values of and , the earlier effectiveness and the stronger intensity of interventions were implemented, respectively. We simulated the likely outcomes of delaying stringent interventions from 0 to 5 days by changing the values of and of mild interventions on the same starting time of Wenzhou policies by changing the value of to Shenzhen policies.
3 Case Study
3.1 Synthetic Control Method Analysis
We selected the top 100 cities outside Hubei province according to the cumulative confirmed cases on February 25, 2020 (Table 1 in the Supplementary Materials). Based on the per capita GDP and population density of each city, these cities (including Wenzhou) were clustered into 4 groups using hierarchical clustering. Among them, a homogenous group consisted of Wenzhou and other 45 cities (Figure 1 in the Supplementary Materials), where Taizhou of Zhejiang province implemented similar policies to Wenzhou and was excluded from our analysis. The remaining 44 cities formed a counterfactual city resembling Wenzhou (Table 2 in the Supplementary Materials).
Before the strict interventions were implemented on February 1, 2020, the trends of actual cases per 100,000 in both Wenzhou and ”synthetic Wenzhou” were highly similar, suggesting that this synthetic city can be used to estimate the ”counterfactual” results of Wenzhou. After 2 days of the implementation of the policies, the growth rate of actual cases per 100,000 in Wenzhou remarkably slower than that of ”synthetic Wenzhou”. The number of cases per 100,000 in ”Synthetic Wenzhou” on February 25, 2020 was 10.32, which is 1.69 times the actual Wenzhou (6.08) (Figure 1). In other words, Wenzhou had not implemented the strict interventions on February 1, 2020, the number of cumulative confirmed cases would have reached 954 on February 25, 2020, i.e., the epidemic of COVID19 in Wenzhou would have expanded to approximately 1.7 times.
A placebo test was performed to determine the significance level of the difference in the trends of COVID19 cases per 100,000. To this end, we plotted the gap curves between Wenzhou and synthetic Wenzhou by in turn exchanging Wenzhou and one of each 44 cities in the homogenous group of Wenzhou. The gap of COVID19 cases per 100,000 between Wenzhou and our synthetic Wenzhou was the largest compared to the rest, i.e. the negative effect of the intervention policies on COVID19 per 100,000 in Wenzhou was the lowest of all. For those 44 cities, the probability of having a gap for Wenzhou under a random permutation of the control measures was
, which is conventionally regarded as statistically significant. This suggested that the effect of the implementation of the policies in Wenzhou was significantly different from the implementation of the policies in the remaining 44 cities, indicating that the strict interventions of Wenzhou might have significantly reduced the COVID19 cases per 100,000 (Figure 2).3.2 Regression Discontinuity Analysis
The values of AIC for each regression discontinuity model for different order of interaction effects between rate variable and policy were shown in Table 1.
Models  AIC  

No interaction  4  97.344 
First order  5  28.861 
without interaction  5  44.057 
Second order  6  40.846 
without interaction  6  34.639 
Third order  7  42.019 
Looking at Table 1, the model included item * () had the lowest value of AIC but it had more parameters (). Also, the slopes of the fitted curves between the lefthand side and the righthand side of the cutoffpoint were different (Figure 3). The points lied on the righthand side of quadratic curve. Thus, RD could be used to examine the treatment effect of interventions implemented in Wenzhou and the model included the second order of interaction effects between policy and rate variable was reasonable. The coefficient of interventions is 0.350 (pvalue: 0.003) and the interaction effect between interventions and time is 0.438 (pvalue: 0.001) indicating that there was a significant treatment effect of highly stringent interventions implemented in Wenzhou on February 1, 2020 (Table 2).
Variables  Coefficients  pvalues 

Intercept  5.958  0.001 
()  0.350  0.003 
(())  0.511  0.001 
* ()  0.438  0.001 
* ()  0.002  0.001 
Adjusted  0.994 
3.3 Simulation of stringent interventions delay or mild interventions instead
Our simulation projected that the expected cumulative confirmed cases would be 1.84 times of the actual cases for a 2day delay, 2.45 times for a 3day delay, 3.26 times for a 4day delay and 4.30 times for a 5day delay on February 25, 2020. The corresponding 95credible interval (CI) for these projected numbers of cases were presented in Table 3. The full simulation results from January 21 to February 25, 2020 are presented in Figure 2 of the Supplementary Materials. According to Table 3, the expected cumulative confirmed cases for 0day delay (i.e. the predicted cumulative confirmed cases) was very close to the actual cumulative confirmed cases of Wenzhou. The expected cumulative confirmed cases were 925 with corresponding 95 CI (571,1547) for the 2day delay, 1233 with corresponding 95 CI (702,2233) for the 3day delay, 1644 with corresponding 95 CI (851,3171) for the 4day delay, and 2167 with corresponding 95 CI (1034, 4578) for the 5day delay. The observed cumulative confirmed cases had been stable since February 17, 2020, however, the expected cumulative confirmed continued to increase if the stringent interventions were delayed. Based on Figure 1, if the stringent interventions were delayed by 2 days or more, the epidemic of COVID19 in Wenzhou could have been more severe than that in synthetic Wenzhou (954 cumulative confirmed cases on February 25, 2020). If the mild interventions as those implemented in Shenzhen were implemented in Wenzhou on February 1, 2020, the expected number of cumulative confirmed cases would have been 2319 with corresponding 95 CI (1145,5189) on February 25, 2020 (Figure 4).
Date  Actual  0 day  1 day  2 days  3 days  4 days  5 days 

02/16  503  514(361,749)  678(453,1059)  892(553,1490)  1178(674,2128)  1556 (810,2998)  2034 (978,4264) 
02/17  504  517 (363,754)  683 (456,1066)  899 (557,1502)  1190 (680,2151)  1576 (819,3036)  2064 (990,4330) 
02/18  504  519 (364,757)  686 (458,1072)  905 (560,1513)  1200 (685,2170)  1591 (826,3067)  2087 (1000,4387) 
02/19  504  521 (365,760)  689 (460,1077)  910 (563,1521)  1209 (690,2185)  1604 (832,3092)  2106 (1008,4435) 
02/20  504  522 (366,763)  692 (462,1081)  914 (565,1528)  1215 (693,2198)  1615 (837,3112)  2122 (1015,4472) 
02/21  504  524 (367,764)  694 (463,1084)  917 (567,1533)  1220 (696,2208)  1624 (841,3129)  2134 (1021,4502) 
02/22  504  525 (368,766)  695 (464,1087)  919 (568,1538)  1224 (698,2217)  1630 (845,3143)  2145 (1025,4527) 
02/23  504  525 (368,767)  697(465,1089)  922 (570,1542)  1228 (700,2224)  1636 (847,3154)  2153 (1029,4548) 
02/24  504  526 (369,769)  698 (465,1091)  923 (570,1545)  1230 (701,2229)  1640 (850,3164)  2161 (1032,4564) 
02/25  504  526 (369,769)  698 (466,1092)  925 (571,1547)  1233 (702,2233)  1644 (851,3171)  2167 (1034,4578) 
4 Discussion
We used daily reported cumulative confirmed case data from January 21 to February 25, 2020 in Wenzhou. By using the synthetic control method, the trend of COVID19 cases per 100,000 for the synthetic Wenzhou as a control group of no intervention were compared with those for Wenzhou as a treatment group with interventions. The COVID19 cases per 100,000 in Wenzhou were significantly lower than those in synthetic Wenzhou after February 3, 2020. This indicated that the implementation of the strict interventions on February 1, 2020 in Wenzhou had a significant effect on controlling the epidemic of COVID19.
By using regression discontinuity analysis, we also concluded that the implementation of strict intervention policies had a significant treatment effect on the epidemic in Wenzhou. Moreover, the statistical treatment effects were evaluated. That is, since the intervention policies were implemented in Wenzhou (corresponding coefficient of policy : 0.350), the growth rate of the reported cumulative confirmed cases of COVID19 were reduced, and this ”reduction” effect would increase over time (corresponding coefficient of interaction between time and policy: 0.438). Conversely, if the policies were not implemented, the reported cumulative confirmed cases of COVID19 would increase over time (corresponding coefficient of time: 0.511).
The horizontal and longitudinal comparisons were made to examine the treatment effects of the strict intervention policies implemented in Wenzhou, including the suspension of public transportation in the city, the closure of highway junctions, and strict community access control. Since January 21, 2020, compared with other cities outside Hubei province, the number of reported cumulative confirmed cases in Wenzhou had been consistently in the top two places, i.e., its COVID19 epidemic situation was relatively severe. If strict intervention policies were not implemented, the outbreak of COVID19 would be expected to expand to 1.7 times. Therefore, it can be concluded from the results of the two methods that the strict intervention policies in Wenzhou, where the epidemic was severe, had significantly suppressed its epidemic situation.
Based on our simulation, if the stringent interventions were delayed by a few days, such as 2 days, the epidemic situation would have been remarkably more severe, where the expected number of cumulative confirmed cases would have been nearly 2 times of the actual number of cases on February 25, 2020. If the mild interventions were implemented in lieu of the stringent interventions, the expected number of cumulative confirmed cases would have been 4.60 times of the actual number of cases on February 25, 2020. Overall, if the stringent interventions were delayed or the mild interventions were implemented instead, though on the same day, the expected cumulative confirmed cases would have continued to increase while the actual epidemic situation was under control in the Wenzhou.
Supplementary Materials
Province  City  Cumulative confirmed cases  Population density  GDP percapita (RMB) 

Chongqing  Chongqing  576  376.43083  65933 
Zhejiang  Wenzhou  504  763.832  65055 
Guangdong  Shenzhen  417  6484  189568 
Beijing  Beijing  400  7794.8  192957.1 
Guangdong  Guangzhou  346  2005  155491 
Shanghai  Shanghai  336  3814  126634 
Henan  Xinyang  274  342.0925  36951.12 
Shandong  Jining  258  745.7689  58972 
Hunan  Changsha  242  689.9653  136920 
Jiangxi  Nanchang  229  749.1894  95116.22 
Heilongjiang  Harbin  198  204.4821  48345.92 
Anhui  Hefei  174  706.5906  116352.2 
Zhejiang  Hangzhou  169  518.958  140180 
Anhui  Bengbu  160  640.6788  50662 
Henan  Zhengzhou  157  1361.268  101352.1 
Zhejiang  Ningbo  157  835.575  132603 
Hunan  Yueyang  156  385.9844  59165 
Anhui  Fuyang  155  1058.312  21700 
Henan  Nanyang  155  376.4511  35554.64 
Zhejiang  Taizhou  146  610.846  79541 
Sichuan  Chengdu  143  1121.066  86911 
Henan  Zhumadian  139  466.5252  33773.42 
Tianjin  Tianjin  135  2596.509  118944 
Jiangxi  Xinyu  130  373.411  86791 
Jiangxi  Shangrao  123  298.7281  36899 
Shaanxi  Xi’an  120  989.6814  86000 
Jiangxi  Jiujiang  118  260.1498  55141.93 
Anhui  Bozhou  108  625.3881  24388.01 
Jiangxi  Yichun  106  297.4825  39268.42 
Hunan  Shaoyang  102  353.9426  24178 
Guangdong  Zhuhai  98  1111.758  159428 
Guangdong  Dongguan  97  3404.544  98939 
Jiangsu  Nanjing  93  1280.735  152886 
Henan  Shangqiu  91  684.3516  32669.96 
Jiangsu  Suzhou  87  1238.501  173765 
Guangdong  Foshan  84  2043.873  127691 
Hong Kong  Hong Kong  84  6733.915  336147 
Anhui  Anqing  83  388.8447  37243.79 
Hunan  Changde  82  320.3518  58160 
Hunan  Zhuzhou  80  357.0236  65442 
Jiangsu  Xuzhou  79  748.1513  76915 
Jiangxi  Ganzhou  76  219.4027  40212 
Henan  Zhoukou  76  725.6292  30820.63 
Hunan  Loudi  76  484.3908  39249 
Jiangxi  Fuzhou  72  215.0821  34156.95 
Fujian  Fuzhou  71  645  102037 
Sichuan  Garz  69  7.772442  22097 
Anhui  Lu’an  69  338.3381  24638.49 
Guangdong  Zhongshan  66  1838.889  110585 
Jiangsu  Huai’an  66  491.0269  73204 
Guangdong  Huizhou  62  431.25  85418 
Shandong  Qingdao  60  674.295  128459 
Hunan  Yiyang  59  363.4552  39937 
Hebei  Tangshan  58  586.1787  82692 
Henan  Pingdingshan  58  797.921  42586 
Henan  Xinxiang  57  700.7014  43696.44 
Guangxi  Nanning  55  328.0617  59259 
Jiangsu  Wuxi  55  1420.899  174270 
Zhejiang  Jinhua  55  512.155  73428 
Fujian  Putian  55  685.5792  77325 
Hainan  Sanya  54  393.8903  63046.1 
Yunnan  Kunming  53  326.1905  76387 
Henan  Anyang  53  924.4508  46443.24 
Heilongjiang  Shuangyashan  52  62.64733  35527 
Jiangsu  Changzhou  51  978.6368  149275 
Shandong  Linyi  49  617.9618  44534 
Jiangsu  Lianyungang  48  593.5653  61332 
Hebei  Cangzhou  48  563.0002  48226 
Hunan  Hengyang  48  473.1156  42163 
Shandong  Jinan  47  912.3639  106302 
Shandong  Yantai  47  521.5907  110231 
Fujian  Quanzhou  47  790.1907  97614 
Heilongjiang  Suihua  47  149.0159  29625.32 
Heilongjiang  Jixi  46  74.9566  27639.35 
Jilin  Changchun  45  373.3042  86465 
Zhejiang  Jiaxing  45  1119.11  103858 
Shandong  Weifang  44  580.8031  65721 
Guangxi  Beihai  44  525.6818  73074 
Hunan  Yongzhou  43  242.9526  33035 
Heilongjiang  Qiqihar  43  119.6062  23676.17 
Sichuan  Dazhou  42  342.6378  28066 
Zhejiang  Shaoxing  42  608.1652  107853 
Anhui  Suzhou  41  580.5048  28693.98 
Jiangsu  Nantong  40  692.9567  115320 
Hunan  Huaihua  40  180.6035  30449 
Sichuan  Nanchong  39  512.8576  28516 
Henan  Xuchang  39  888.1906  63987.61 
Hainan  Haikou  39  973.3623  56055.67 
Hunan  Chenzhou  39  244.7258  50482 
Anhui  Ma’anshan  38  577.1796  82075 
Shandong  Weihai  38  488.1835  128774 
Shandong  Liaocheng  38  696.5371  51935 
Shandong  Dezhou  37  560.7567  58252 
Shanxi  Jinzhong  37  206.2  42916 
Jiangsu  Taizhou  37  801.0181  110180 
Hunan  Xiangtan  36  572.2733  75609 
Gansu  Lanzhou  36  284.3636  73042 
Guizhou  Guiyang  36  606.9471  78449 
Shandong  Tai’an  35  726.7105  64714 
Henan  Luohe  35  1020.252  46318 
Province  City  Population density  GDP percapita (RMB)  Weight 

Chongqing  Chongqing  376.43083  65933  0 
Shandong  Jining  745.7689  58972  0 
Jiangxi  Nanchang  749.1894  95116.22  0 
Anhui  Hefei  706.5906  116352.2  0 
Henan  Zhengzhou  1361.268  101352.1  0 
Hunan  Yueyang  385.9844  59165  0 
Sichuan  Chengdu  1121.066  86911  0 
Tianjin  Tianjin  2596.509  118944  0 
Jiangxi  Xinyu  373.411  86791  0.69 
Shaanxi  Xi’an  989.6814  86000  0 
Jiangxi  Jiujiang  260.1498  55141.93  0 
Guangdong  Dongguan  3404.544  98939  0 
Hunan  Changde  320.3518  58160  0 
Hunan  Zhuzhou  357.0236  65442  0 
Jiangsu  Xuzhou  748.1513  76915  0 
Fujian  Fuzhou  645  102037  0 
Guangdong  Zhongshan  1838.889  110585  0 
Jiangsu  Huai’an  491.0269  73204  0 
Guangdong  Huizhou  431.25  85418  0 
Hebei  Tangshan  586.1787  82692  0 
Guangxi  Nanning  328.0617  59259  0 
Zhejiang  Jinhua  512.155  73428  0 
Fujian  Putian  685.5792  77325  0 
Hainan  Sanya  393.8903  63046.1  0.31 
Yunnan  Kunming  326.1905  76387  0 
Jiangsu  Lianyungang  593.5653  61332  0 
Shandong  Jinan  912.3639  106302  0 
Shandong  Yantai  521.5907  110231  0 
Fujian  Quanzhou  790.1907  97614  0 
Jilin  Changchun  373.3042  86465  0 
Zhejiang  Jiaxing  1119.11  103858  0 
Shandong  Weifang  580.8031  65721  0 
Guangxi  Beihai  525.6818  73074  0 
Zhejiang  Shaoxing  608.1652  107853  0 
Jiangsu  Nantong  692.9567  115320  0 
Henan  Xuchang  888.1906  63987.61  0 
Hainan  Haikou  973.3623  56055.67  0 
Anhui  Ma’anshan  577.1796  82075  0 
Shandong  Dezhou  560.7567  58252  0 
Jiangsu  Taizhou  801.0181  110180  0 
Hunan  Xiangtan  572.2733  75609  0 
Gansu  Lanzhou  284.3636  73042  0 
Guizhou  Guiyang  606.9471  78449  0 
Shandong  Tai’an  726.7105  64714  0 
References
 (1)
 Abadie et al. (2010) Abadie, A., Diamond, A., and Hainmueller, J. (2010), “Synthetic control methods for comparative case studies: Estimating the effect of California s tobacco control program,” Journal of the American statistical Association, 105(490), 493–505.
 Abadie et al. (2011) Abadie, A., Diamond, A., and Hainmueller, J. (2011), “Synth: An r package for synthetic control methods in comparative case studies,” Journal of Statistical Software, 42(13).
 Abadie and Gardeazabal (2003) Abadie, A., and Gardeazabal, J. (2003), “The economic costs of conflict: A case study of the Basque Country,” American economic review, 93(1), 113–132.
 Calonico et al. (2015a) Calonico, S., Cattaneo, M. D., and Titiunik, R. (2015a), “Optimal datadriven regression discontinuity plots,” Journal of the American Statistical Association, 110(512), 1753–1769.
 Calonico et al. (2015b) Calonico, S., Cattaneo, M. D., and Titiunik, R. (2015b), “rdrobust: An r package for robust nonparametric inference in regressiondiscontinuity designs,” R Journal, 7(1), 38–51.
 Chan et al. (2020) Chan, J. F. W., Yuan, S., Kok, K. H., To, K. K. W., Chu, H., Yang, J., Xing, F., Liu, J., Yip, C. C.Y., Poon, R. W.S. et al. (2020), “A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating persontoperson transmission: a study of a family cluster,” The Lancet, 395(10223), 514–523.
 Guan et al. (2020) Guan, W., Ni, Z., Hu, Y., Liang, W., Ou, C., He, J., Liu, L., Shan, H., Lei, C., Hui, D. S. et al. (2020), “Clinical characteristics of coronavirus disease 2019 in China,” New England journal of medicine, 382(18), 1708–1720.
 Hahn et al. (2001) Hahn, J., Todd, P., and Van der Klaauw, W. (2001), “Identification and estimation of treatment effects with a regressiondiscontinuity design,” Econometrica, 69(1), 201–209.

Health Commission of Guangdong Province (2020)
Health Commission of Guangdong Province (2020), “Firstlevel public health emergency response .
[accessed 2020 March 31],”.
http://wsjkw.gd.gov.cn/zwyw_yqxx/content/post_2878895.html  Health Commission of Wenzhou (2000) Health Commission of Wenzhou (2000), “Outbreak of COVID19 in Wenzhou,”.
 Hsieh et al. (2004) Hsieh, Y.H., Lee, J.Y., and Chang, H.L. (2004), “SARS epidemiology modeling,” Emerging infectious diseases, 10(6), 1165.
 Imbens and Lemieux (2008) Imbens, G. W., and Lemieux, T. (2008), “Regression discontinuity designs: A guide to practice,” Journal of econometrics, 142(2), 615–635.
 Li (2018) Li, M. Y. (2018), An introduction to mathematical modeling of infectious diseases, Vol. 2 Springer.

National Health Commission of the People’s Republic of China (2020a)
National Health Commission of the People’s Republic of China
(2020a), “Distribution of COVID19
cases in China. [accessed 2020 April 26],”.
http://2019ncov.chinacdc.cn/2019nCoV/index.html 
National Health Commission of the People’s Republic of China (2020b)
National Health Commission of the People’s Republic of China
(2020b), “Distribution of COVID19
cases in the world. [accessed 2020 April 26],”.
http://2019ncov.chinacdc.cn/2019nCoV/global.html  OtoPeralías (2020) OtoPeralías, D. (2020), “Regional correlations of COVID19 in Spain,” , .
 Statistics Bureau of Zhejiang Province (2019) Statistics Bureau of Zhejiang Province (2019), Zhejiang Statistical Yearbook 2019.
 Tan et al. (2020) Tan, J., Jiang, Y., Tian, T., and Wang, X. (2020), “PSIHR Probabilistic Graphical Model: an Applicable Model of COVID19 in Estimating the Number of Infectious Individuals,” Acta Mathematicae Applicatae Sinica, 43(2).
 Thistlethwaite and Campbell (1960) Thistlethwaite, D. L., and Campbell, D. T. (1960), “Regressiondiscontinuity analysis: An alternative to the ex post facto experiment.,” Journal of Educational psychology, 51(6), 309.
 Tu et al. (2020) Tu, W., Tang, H., Chen, F. et al. (2020), “Epidemic Update and Risk Assessment of 2019 Novel Coronavirus? China, January 28, 2020,” China CDC Weekly, 2(6), 83–86.
 Wang et al. (2020) Wang, C., Horby, P. W., Hayden, F. G., and Gao, G. F. (2020), “A novel coronavirus outbreak of global health concern,” The Lancet, 395(10223), 470–473.
 Ward Jr (1963) Ward Jr, J. H. (1963), “Hierarchical grouping to optimize an objective function,” Journal of the American statistical association, 58(301), 236–244.

Wenzhou Municipal People’s Government (2020a)
Wenzhou Municipal People’s Government (2020a), “Notice of the Leading Group for the Prevention and
Control of COVID19 in Wenzhou (No. 3) . [accessed 2020 March 31],”.
http://www.wenzhou.gov.cn/art/2020/1/28/art_1219304_41858870.html 
Wenzhou Municipal People’s Government (2020b)
Wenzhou Municipal People’s Government (2020b), “Notice of the Leading Group for the Prevention and
Control of COVID19 in Wenzhou (No. 6) . [accessed 2020 March 31],”.
http://www.wenzhou.gov.cn/art/2020/2/2/art_1219304_41867466.html 
World Health Organization (2020)
World Health Organization (2020), “Rolling
updates on coronavirus disease (COVID19). [accessed 2020 May 14],”.
https://www. who. int/emergencies/diseases/novelcoronavirus2019/eventsastheyhappen 
Wu et al. (2020)
Wu, T., Ge, X., Yu, G., and Hu, E. (2020), “Opensource analytics tools for studying the COVID19 coronavirus outbreak,”
medRxiv, .  Zhang et al. (2020) Zhang, Y., Tian, H., Zhang, Y., and Chen, Y. (2020), “Is the epidemic spread related to GDP? Visualizing the distribution of COVID19 in Chinese Mainland,” arXiv preprint arXiv:2004.04387, .
 Zhu et al. (2020) Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R. et al. (2020), “A novel coronavirus from patients with pneumonia in China, 2019,” New England Journal of Medicine, .
Comments
There are no comments yet.