LitCovid-sentences  

PMC:7210464 / 63880-75445 JSONTXT 8 Projects

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Id Subject Object Predicate Lexical cue
T577 0-61 Sentence denotes Assessment of the effects of non-pharmaceutical interventions
T578 62-128 Sentence denotes Several factors may contribute to the containment of the epidemic.
T579 129-324 Sentence denotes 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.
T580 325-466 Sentence denotes Therefore, we have split the sample into two sub-samples, and the estimated coefficients can be different across the sub-samples (Section 4).
T581 467-653 Sentence denotes 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.
T582 654-987 Sentence denotes 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.
T583 988-1200 Sentence denotes 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.
T584 1201-1460 Sentence denotes 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.
T585 1461-1592 Sentence denotes These modifications coincide with the adoption of local NPIs and can significantly affect the observed dynamics of confirmed cases.
T586 1593-1732 Sentence denotes The adoption of closed management or stay at home is likely affected by the severity of the epidemic and correlated with the unobservables.
T587 1733-1898 Sentence denotes 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).
T588 1899-1935 Sentence denotes Details are displayed in Appendix A.
T589 1936-2009 Sentence denotes The estimation results of OLS and IV regressions are reported in Table 8.
T590 2010-2067 Sentence denotes Table 8 Effects of local non-pharmaceutical interventions
T591 2068-2091 Sentence denotes (1) (2) (3) (4) (5) (6)
T592 2092-2112 Sentence denotes OLS IV OLS IV OLS IV
T593 2113-2147 Sentence denotes Average # of new cases, 1-week lag
T594 2148-2210 Sentence denotes Own city 0.642*** 0.780*** 0.684*** 0.805*** 0.654*** 0.805***
T595 2211-2264 Sentence denotes (0.0644) (0.0432) (0.0496) (0.0324) (0.0566) (0.0439)
T596 2265-2326 Sentence denotes × closed management − 0.593*** − 0.244*** − 0.547*** − 0.193*
T597 2327-2359 Sentence denotes (0.162) (0.0619) (0.135) (0.111)
T598 2360-2413 Sentence denotes × stay at home − 0.597*** − 0.278*** − 0.0688 − 0.110
T599 2414-2446 Sentence denotes (0.186) (0.0800) (0.121) (0.143)
T600 2447-2513 Sentence denotes Other cities 0.00121 − 0.00159 0.00167 − 0.00108 0.00129 − 0.00142
T601 2514-2592 Sentence denotes wt. = inv. dist. (0.000852) (0.00167) (0.00114) (0.00160) (0.000946) (0.00183)
T602 2593-2647 Sentence denotes Wuhan 0.00184 0.00382 0.00325* 0.00443 0.00211 0.00418
T603 2648-2724 Sentence denotes wt. = inv. dist. (0.00178) (0.00302) (0.00179) (0.00314) (0.00170) (0.00305)
T604 2725-2786 Sentence denotes Wuhan 0.00298 0.00110 − 0.00187 − 0.000887 0.00224 − 3.26e-07
T605 2787-2862 Sentence denotes wt. = pop. flow (0.00264) (0.00252) (0.00304) (0.00239) (0.00254) (0.00260)
T606 2863-2897 Sentence denotes Average # of new cases, 2-week lag
T607 2898-2956 Sentence denotes Own city 0.0345 − 0.0701 − 0.0103 − 0.0818 0.0396 − 0.0533
T608 2957-3010 Sentence denotes (0.0841) (0.0550) (0.0921) (0.0523) (0.0804) (0.0678)
T609 3011-3066 Sentence denotes × closed management − 0.367*** − 0.103 − 0.259** 0.0344
T610 3067-3099 Sentence denotes (0.0941) (0.136) (0.111) (0.222)
T611 3100-3150 Sentence denotes × stay at home − 0.294*** − 0.102 − 0.124* − 0.162
T612 3151-3184 Sentence denotes (0.0839) (0.136) (0.0720) (0.212)
T613 3185-3263 Sentence denotes Other cities − 0.00224 − 0.00412** − 0.00190 − 0.00381** − 0.00218 − 0.00397**
T614 3264-3340 Sentence denotes wt. = inv. dist. (0.00135) (0.00195) (0.00118) (0.00177) (0.00129) (0.00192)
T615 3341-3400 Sentence denotes Wuhan − 0.00512 0.00197 − 0.00445 0.00231 − 0.00483 0.00227
T616 3401-3477 Sentence denotes wt. = inv. dist. (0.00353) (0.00367) (0.00328) (0.00348) (0.00340) (0.00376)
T617 3478-3549 Sentence denotes Wuhan 0.00585*** 0.00554*** 0.00534*** 0.00523*** 0.00564*** 0.00516***
T618 3550-3626 Sentence denotes wt. = pop. flow (0.00110) (0.000929) (0.00112) (0.00104) (0.00109) (0.00116)
T619 3627-3669 Sentence denotes Observations 8064 8064 8064 8064 8064 8064
T620 3670-3710 Sentence denotes Number of cities 288 288 288 288 288 288
T621 3711-3751 Sentence denotes Weather controls Yes Yes Yes Yes Yes Yes
T622 3752-3783 Sentence denotes City FE Yes Yes Yes Yes Yes Yes
T623 3784-3815 Sentence denotes Date FE Yes Yes Yes Yes Yes Yes
T624 3816-3897 Sentence denotes The sample is from February 2 to February 29, excluding cities in Hubei province.
T625 3898-3964 Sentence denotes The dependent variable is the number of daily new confirmed cases.
T626 3965-4254 Sentence denotes 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.
T627 4255-4466 Sentence denotes 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).
T628 4467-4560 Sentence denotes The weather controls include weather characteristics in the preceding first and second weeks.
T629 4561-4656 Sentence denotes Standard errors in parentheses are clustered by provinces. *** p < 0.01, ** p < 0.05, * p < 0.1
T630 4657-4751 Sentence denotes We find that closed management and stay at home significantly decrease the transmission rates.
T631 4752-4898 Sentence denotes 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.
T632 4899-4983 Sentence denotes The effect in the second week is also negative though not statistically significant.
T633 4984-5188 Sentence denotes 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.
T634 5189-5252 Sentence denotes The effect in the second week is not statistically significant.
T635 5253-5418 Sentence denotes 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.
T636 5419-5425 Sentence denotes 2020).
T637 5426-5462 Sentence denotes Many cities implement both policies.
T638 5463-5626 Sentence denotes However, it is not conclusive to ascertain the effect of further imposing family outdoor restrictions in cities that have adopted closed management of communities.
T639 5627-6002 Sentence denotes 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).
T640 6003-6235 Sentence denotes 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.
T641 6236-6453 Sentence denotes 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.
T642 6454-6543 Sentence denotes We further assess the effects of NPIs by conducting a series of counterfactual exercises.
T643 6544-6598 Sentence denotes After estimating (3) by 2SLS, we obtain the residuals.
T644 6599-6810 Sentence denotes 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).
T645 6811-6887 Sentence denotes In scenario A, no cities adopted family outdoor restrictions (stay at home).
T646 6888-6969 Sentence denotes Similarly, in scenario B, no cities implemented closed management of communities.
T647 6970-7100 Sentence denotes We use the estimates in columns (2) and (4) of Table 8 to conduct the counterfactual analyses for scenarios A and B, respectively.
T648 7101-7361 Sentence denotes 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.
T649 7362-7501 Sentence denotes 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.
T650 7502-7545 Sentence denotes 2020), and the effect would then be larger.
T651 7546-7969 Sentence denotes 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.
T652 7970-8050 Sentence denotes Appendix C contains the technical details on the computation of counterfactuals.
T653 8051-8223 Sentence denotes 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.
T654 8224-8339 Sentence denotes We also report the predicted cumulative effect in each scenario at the bottom of the corresponding panel in Fig. 7.
T655 8340-8542 Sentence denotes 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).
T656 8543-8609 Sentence denotes Assuming a fatality rate of 4%, there would be 56,339 more deaths.
T657 8610-8697 Sentence denotes The magnitude of the effect from Wuhan lockdown and local NPIs is considerably smaller.
T658 8698-8841 Sentence denotes 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).
T659 8842-9080 Sentence denotes 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.
T660 9081-9271 Sentence denotes 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.
T661 9272-9313 Sentence denotes Fig. 7 Counterfactual policy simulations.
T662 9314-9540 Sentence denotes 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.
T663 9541-9726 Sentence denotes 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)
T664 9727-9977 Sentence denotes 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.
T665 9978-10087 Sentence denotes Our retrospective analysis of the data from China complements the simulation study of Ferguson et al. (2020).
T666 10088-10226 Sentence denotes Our estimates indicate that suppressing local transmission rates at low levels might have avoided one million or more infections in China.
T667 10227-10350 Sentence denotes Chinazzi et al. (2020) also find that reducing local transmission rates is necessary for effective containment of COVID-19.
T668 10351-10509 Sentence denotes 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.
T669 10510-10622 Sentence denotes 2020) in keeping local transmission rates in cities outside Hubei at low levels throughout January and February.
T670 10623-10906 Sentence denotes 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.
T671 10907-11114 Sentence denotes 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.
T672 11115-11183 Sentence denotes It should be noted that these factors may overlap in the real world.
T673 11184-11354 Sentence denotes 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.
T674 11355-11452 Sentence denotes In China, the arrival of the COVID-19 epidemic coincided with the Lunar New Year for many cities.
T675 11453-11565 Sentence denotes Had the outbreak started at a different time, the effects and costs of these policies would likely be different.