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.