Policy response to the COVID-19 outbreak in China
As the 2002–2004 SARS outbreak has shown, non-pharmaceutical interventions (NPIs) or public health measures may decrease or effectively stop the transmission of COVID-19 even without vaccines. Although the effectiveness of a single intervention strategy can be limited, multiple interventions together may generate substantial impacts on containing the spread of the virus. Figure 6 depicts the timeline for a series of policies enacted at the national, provincial, and city levels in China since January 19. After the official confirmation of human-to-human transmission by the Chinese authorities on January 20, China has adopted a variety of NPIs to contain the COVID-19 outbreak. At the national level, COVID-19 was classified as a statutory class B infectious disease on January 20, and prevention and control measures for class A infectious diseases have been taken. Government agencies across the country were mobilized. The Joint Prevention and Control Mechanism of the State Council was established on January 20, and the Central Leadership Group for Epidemic Response was established on January 25. On January 23, National Healthcare Security Administration announced that expenses related to COVID-19 treatments would be covered by the medical insurance and the government if necessary, in order that all COVID-19 cases could be hospitalized19. At the provincial level, 30 provinces declared level I responses to major public health emergencies from January 23 to 25, and all provinces had declared level I responses by January 2920. Level I responses in China are designed for the highest state of emergencies. Measures taken include enhanced isolation and contact tracing of cases, suspension of public transport, cancelling public events, closing schools and entertainment venues, and establishment of health checkpoints (Tian et al. 2020). These policies together represent population-wide social distancing and case isolation (Ferguson et al. 2020).
Fig. 6 Timeline of China’s public health policies in curtailing the spread of COVID-19

Policy response to COVID-19 in Hubei Province
Early detection of COVID-19 importation and prevention of onward transmission are crucial to all areas at risk of importation from areas with active transmissions (Gilbert et al. 2020). To contain the virus at the epicenter, Wuhan was placed under lockdown with traffic ban for all residents starting on January 23. The lockdown is not expected to be lifted until April 8. Local buses, subways, and ferries ceased operation. Ride-hailing services were prohibited, and only a limited number of taxis were allowed on road by January 24. Residents are not permitted to leave the city. Departure flights and trains were canceled at the city airport and train stations. Checkpoints were set up at highway entrances to prevent cars from leaving the city. Since January 22, it became mandatory to wear masks at work or in public places.
In addition, all cities in Hubei province implemented the lockdown policy, and most Hubei cities had also adopted measures commensurate with class A infectious diseases by January 2821. Residents in those areas were strongly encouraged to stay at home and not to attend any activity involving public gathering.
Health facilities in Wuhan had been extremely overstretched with shortage in medical supplies and high rates of nosocomial infections until February 2 when (1) two new hospitals, i.e., Huoshenshan and Leishenshan, were built to treat patients of COVID-19 with severe symptoms, and (2) 14 makeshift health facilities were converted to isolate patients with mild symptoms and to quarantine people suspected of contracting COVID-19, patients with fever symptoms, and close contacts of confirmed patients. This centralized treatment and isolation strategy since February 2 has substantially reduced transmission and incident cases.
However, stringent public health measures within Hubei province enforced after the massive lockdown may have little to do with virus transmissions out of Hubei province due to the complete travel ban since January 23.

Reducing inter-city population flows
Quarantine measures have been implemented in other provinces that aim at restricting population mobility across cities and reducing the risk of importing infections22. Seven cities in Zhejiang, Henan, Heilongjiang, and Fujian provinces had adopted the partial shutdown strategy by February 4 (Fang et al. 2020)23. In Wenzhou, most public transportation was shut down, and traffic leaving the city was banned temporarily. On January 21, the Ministry of Transport of China launched level 2 responses to emergencies in order to cooperate with the National Health Commission in preventing the virus spread. On January 23, the Ministry of Transport of China, Civil Aviation Administration of China, and China State Railway Group Company, Ltd. (CSRGC) declared to waive the change fees for flight, train, bus, and ferry tickets that were bought before January 24. Later, the CSRGC extended the fee waiver policy to train tickets that were bought before February 6. By February 2, all railway stations in China had started to monitor body temperature of travelers when they enter and exit the station. Across the whole country, Transportation Departments set up 14,000 health checkpoints at bus and ferry terminals, at service centers and toll gates on highways, monitoring the body temperature of passengers and controlling the inflow of population (World Health Organization 2020b). Recent visitors to high COVID-19 risk areas are required to self-quarantine for 14 days at home or in designated facilities. On February 2, China’s Exit and Entry Administration temporarily suspended the approval and issuance of the travel permits to Hong Kong and Macau.
On January 23, Wuhan Municipal Administration of Culture and Tourism ordered all tour groups to cancel travels to Wuhan. On January 27, the Ministry of Education of China postponed start of the spring semester in 2020, and on February 7, it further announced that students were not allowed to return to school campus without approvals from school.

Encouraging social distancing in local communities
Recent studies suggest that there is a large proportion of asymptomatic or mild-symptomatic cases, who can also spread the virus (Dong et al. 2020; Mizumoto et al. 2020; Nishiura et al. 2020; Wang et al. 2020a). Thus, maintaining social distance is of crucial importance in order to curtail the local transmission of the virus.
The period from January 24 to 31, 2020, is the traditional Chinese Spring Festival holiday, when families are supposed to get together so that inter-city travel is usually much less. People were frequently reminded by official media (via TV news and phone messages) and social media to stay at home and avoid gathering activities. On January 26, China State Council extended this holiday to February 2 to delay people’s return travel and curtail the virus spread. Nevertheless, economic activities are still supposed to resume after the spring festival, bringing people back to workplaces, which may increase the risk of virus spread.
To help local residents keep social distance and decrease the risk of virus transmissions, many cities started to implement the “closed management of communities” and “family outdoor restrictions” policies since late January (Table 7), encouraging residents to restrict nonessential travels. From January 28 to February 20, more than 250 prefecture-level cities in China implemented “closed management of communities,” which typically includes (1) keeping only one entrance for each community, (2) allowing only community residents to enter and exit the community, (3) checking body temperature for each entrant, (4) testing and quarantining cases that exhibit fever immediately, and (5) tracing and quarantining close contacts of suspicious cases. Meanwhile, residents who had symptoms of fever or dry cough were required to report to the community and were quarantined and treated in special medical facilities. Furthermore, local governments of 127 cities also imposed more stringent “family outdoor restrictions”—residents are confined or strongly encouraged to stay at home with limited exceptions, e.g., only one person in each family may go out for shopping for necessities once every 2 days24. Exit permits were usually distributed to each family in advance and recollected when residents reenter the community. Contacts of those patients were also traced and quarantined. Table 7 summarizes the number of cities that had imposed “closed management of communities” or “family outdoor restrictions” by different dates in February.
Table 7 Number of cities with local quarantine measures by different dates
Date Closed management of communities Family outdoor restrictions
2020-02-01 10 1
2020-02-02 20 6
2020-02-03 33 16
2020-02-04 63 38
2020-02-05 111 63
2020-02-06 155 88
2020-02-07 179 92
2020-02-08 187 98
2020-02-09 196 102
2020-02-10 215 104
2020-02-11 227 105
2020-02-12 234 108
2020-02-13 234 109
2020-02-14 235 111
2020-02-15 237 111
2020-02-16 237 122
2020-02-17 237 122
2020-02-18 238 122
2020-02-19 238 122
2020-02-20‡ 241 123
‡No new cities adopt these measures after February 20
In order to help inform evidence-based COVID-19 control measures, we examine the effect of these local quarantine measures in reducing the virus transmission rates. Dummy variables for the presence of closed management of communities or family outdoor restrictions are created, and they are interacted with the number of infections in the preceding 2 weeks.

Assessment of the effects of non-pharmaceutical interventions
Several factors may contribute to the containment of the epidemic. The transmission dynamics may change during the course of this epidemic because of improved medical treatments, more effective case isolation and contact tracing, increased public awareness, etc. Therefore, we have split the sample into two sub-samples, and the estimated coefficients can be different across the sub-samples (Section 4). NPIs such as closed management of communities, city lockdowns, and restrictions on population flow out of areas with high infection risks may also directly affect the transmission rates. While many public health measures are implemented nationwide, spatial variations exist in the adoption of two types of measures: closed management of communities (denoted by closed management) and family outdoor restrictions (denoted by stay at home), which allow us to quantify the effect of these NPIs on the transmission dynamics.
Because most of these local NPIs are adopted in February and our earlier results indicate that the transmission of COVID-19 declines during late January, we restrict the analysis sample to February 2–February 29. We also exclude cities in Hubei province, which modified the case definition related to clinically diagnosed cases on February 12 and changed the case definition related to reduced backlogs from increased capacity of molecular diagnostic tests on February 20. These modifications coincide with the adoption of local NPIs and can significantly affect the observed dynamics of confirmed cases. The adoption of closed management or stay at home is likely affected by the severity of the epidemic and correlated with the unobservables. Additional weather controls that have a good predictive power for these NPIs are selected as the instrumental variables based on the method of Belloni et al. (2016). Details are displayed in Appendix A. The estimation results of OLS and IV regressions are reported in Table 8.
Table 8 Effects of local non-pharmaceutical interventions
(1) (2) (3) (4) (5) (6)
OLS IV OLS IV OLS IV
Average # of new cases, 1-week lag
Own city 0.642*** 0.780*** 0.684*** 0.805*** 0.654*** 0.805***
(0.0644) (0.0432) (0.0496) (0.0324) (0.0566) (0.0439)
× closed management − 0.593*** − 0.244*** − 0.547*** − 0.193*
(0.162) (0.0619) (0.135) (0.111)
× stay at home − 0.597*** − 0.278*** − 0.0688 − 0.110
(0.186) (0.0800) (0.121) (0.143)
Other cities 0.00121 − 0.00159 0.00167 − 0.00108 0.00129 − 0.00142
wt. = inv. dist. (0.000852) (0.00167) (0.00114) (0.00160) (0.000946) (0.00183)
Wuhan 0.00184 0.00382 0.00325* 0.00443 0.00211 0.00418
wt. = inv. dist. (0.00178) (0.00302) (0.00179) (0.00314) (0.00170) (0.00305)
Wuhan 0.00298 0.00110 − 0.00187 − 0.000887 0.00224 − 3.26e-07
wt. = pop. flow (0.00264) (0.00252) (0.00304) (0.00239) (0.00254) (0.00260)
Average # of new cases, 2-week lag
Own city 0.0345 − 0.0701 − 0.0103 − 0.0818 0.0396 − 0.0533
(0.0841) (0.0550) (0.0921) (0.0523) (0.0804) (0.0678)
× closed management − 0.367*** − 0.103 − 0.259** 0.0344
(0.0941) (0.136) (0.111) (0.222)
× stay at home − 0.294*** − 0.102 − 0.124* − 0.162
(0.0839) (0.136) (0.0720) (0.212)
Other cities − 0.00224 − 0.00412** − 0.00190 − 0.00381** − 0.00218 − 0.00397**
wt. = inv. dist. (0.00135) (0.00195) (0.00118) (0.00177) (0.00129) (0.00192)
Wuhan − 0.00512 0.00197 − 0.00445 0.00231 − 0.00483 0.00227
wt. = inv. dist. (0.00353) (0.00367) (0.00328) (0.00348) (0.00340) (0.00376)
Wuhan 0.00585*** 0.00554*** 0.00534*** 0.00523*** 0.00564*** 0.00516***
wt. = pop. flow (0.00110) (0.000929) (0.00112) (0.00104) (0.00109) (0.00116)
Observations 8064 8064 8064 8064 8064 8064
Number of cities 288 288 288 288 288 288
Weather controls Yes Yes Yes Yes Yes Yes
City FE Yes Yes Yes Yes Yes Yes
Date FE Yes Yes Yes Yes Yes Yes
The sample is from February 2 to February 29, excluding cities in Hubei province. The dependent variable is the number of daily new confirmed cases. The instrumental variables include weekly averages of daily maximum temperature, wind speed, precipitation, and the interaction between wind speed and precipitation, in the preceding third and fourth weeks, and the inverse log distance weighted averages of these variables in other cities. Additional instrumental variables are constructed by interacting these excluded instruments with variables that predict the adoption of closed management of communities or family outdoor restrictions (Table 10). The weather controls include weather characteristics in the preceding first and second weeks. Standard errors in parentheses are clustered by provinces. *** p < 0.01, ** p < 0.05, * p < 0.1
We find that closed management and stay at home significantly decrease the transmission rates. As a result of closed management of communities, one infection will generate 0.244 (95% CI, −0.366∼−0.123) fewer new infections in the first week. The effect in the second week is also negative though not statistically significant. Family outdoor restrictions (stay at home) are more restrictive than closing communities to visitors and reduce additional infections from one infection by 0.278 (95% CI, −0.435∼−0.121) in the first week. The effect in the second week is not statistically significant. To interpret the magnitude of the effect, it is noted that the reproduction number of SARS-CoV-2 is estimated to be around 1.4∼6.5 as of January 28, 2020 (Liu et al. 2020).
Many cities implement both policies. However, it is not conclusive to ascertain the effect of further imposing family outdoor restrictions in cities that have adopted closed management of communities. When both policies are included in the model, the OLS coefficients (column (5)) indicate that closed management reduces the transmission rate by 0.547 (95% CI, −0.824∼−0.270) in the first week, and by 0.259 (95% CI, −0.485∼−0.032) in the second week, while the additional benefit from stay at home is marginally significant in the second week (− 0.124, 95% CI, −0.272∼0.023). The IV estimates indicate that closed management reduces the transmission rate in the first week by 0.193 (95% CI, −0.411∼0.025), while the effect in the second week and the effects of stay at home are not statistically significant. Additional research that examines the decision process of health authorities or documents the local differences in the actual implementation of the policies may offer insights into the relative merits of the policies.
We further assess the effects of NPIs by conducting a series of counterfactual exercises. After estimating (3) by 2SLS, we obtain the residuals. Then, the changes in yct are predicted for counterfactual changes in the transmission dynamics (i.e., coefficients αwithin,τk) and the impositions of NPIs (i.e., h¯ctkτ, and the lockdown of Wuhan m¯c,Wuhan,tkτ). In scenario A, no cities adopted family outdoor restrictions (stay at home). Similarly, in scenario B, no cities implemented closed management of communities. We use the estimates in columns (2) and (4) of Table 8 to conduct the counterfactual analyses for scenarios A and B, respectively. In scenario C, we assume that the index of population flows out of Wuhan after the Wuhan lockdown (January 23) took the value that was observed in 2019 for the same lunar calendar date (Fig. 3), which would be plausible had there been no lockdown around Wuhan. It is also likely that in the absence of lockdown but with the epidemic, more people would leave Wuhan compared with last year (Fang et al. 2020), and the effect would then be larger. In scenario D, we assume that the within-city transmission dynamics were the same as those observed between January 19 and February 1, i.e., the coefficient of 1-week lag own-city infections was 2.456 and the coefficient of 2-week lag own-city infections was − 1.633 (column (4) of Table 4), which may happen if the transmission rates in cities outside Hubei increased in the same way as those observed for cities in Hubei. Appendix C contains the technical details on the computation of counterfactuals.
In Fig. 7, we report the differences between the predicted number of daily new cases in the counterfactual scenarios and the actual data, for cities outside Hubei province. We also report the predicted cumulative effect in each scenario at the bottom of the corresponding panel in Fig. 7. Had the transmission rates in cities outside Hubei province increased to the level observed in late January, by February 29, there would be 1,408,479 (95% CI, 815,585∼2,001,373) more cases (scenario D). Assuming a fatality rate of 4%, there would be 56,339 more deaths. The magnitude of the effect from Wuhan lockdown and local NPIs is considerably smaller. As a result of Wuhan lockdown, 31,071 (95% CI, 8296∼53,845) fewer cases would be reported for cities outside Hubei by February 29 (scenario C). Closed management of communities and family outdoor restrictions would reduce the number of cases by 3803 (95% CI, 1142∼6465; or 15.78 per city with the policy) and 2703 (95% CI, 654∼4751; or 21.98 per city with the policy), respectively. These estimates, combined with additional assumptions on the value of statistical life, lost time from work, etc., may contribute to cost-benefit analyses of relevant public health measures.
Fig. 7 Counterfactual policy simulations. This figure displays the daily differences between the total predicted number and the actual number of daily new COVID-19 cases for each of the four counterfactual scenarios for cities outside Hubei province in mainland China. The spike on February 12 in scenario C is due to a sharp increase in daily case counts in Wuhan resulting from changes in case definitions in Hubei province (see Appendix B for details)
Our counterfactual simulations indicate that suppressing local virus transmissions so that transmission rates are kept well below those observed in Hubei in late January is crucial in forestalling large numbers of infections for cities outside Hubei. Our retrospective analysis of the data from China complements the simulation study of Ferguson et al. (2020). Our estimates indicate that suppressing local transmission rates at low levels might have avoided one million or more infections in China. Chinazzi et al. (2020) also find that reducing local transmission rates is necessary for effective containment of COVID-19. The public health policies announced by the national and provincial authorities in the last 2 weeks in January may have played a determinant role (Tian et al. 2020) in keeping local transmission rates in cities outside Hubei at low levels throughout January and February. Among the measures implemented following provincial level I responses, Shen et al. (2020) highlight the importance of contact tracing and isolation of close contacts before onset of symptoms in preventing a resurgence of infections once the COVID-19 suppression measures are relaxed. We also find that travel restrictions on high-risk areas (the lockdown in Wuhan), and to a lesser extent, closed management of communities and family outdoor restrictions, further reduce the number of cases. It should be noted that these factors may overlap in the real world. In the absence of the lockdown in Wuhan, the health care systems in cities outside Hubei could face much more pressure, and local transmissions may have been much higher. In China, the arrival of the COVID-19 epidemic coincided with the Lunar New Year for many cities. Had the outbreak started at a different time, the effects and costs of these policies would likely be different.