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An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China

Huaiyu Tian, Yonghong Liu, Yidan Li and 18 more

Science · 2020 · CC BY

doi:10.1126/science.abb6105

Abstract

The most effective interventions By 23 January 2020, China had imposed a national emergency response to restrict travel and impose social distancing measures on its populace in an attempt to inhibit the transmission of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2). However, which measures were most effective is uncertain. Tian et al. performed a quantitative analysis of the impact of control measures between 31 December 2019 and 19 February 2020, which encompasses the Lunar New Year period when millions of people traveled across China for family visits. Travel restrictions in and out of Wuhan were too late to prevent the spread of the virus to 262 cities within 28 days. However, the epidemic peaked in Hubei province on 4 February 2020, indicating that measures such as closing citywide public transport and entertainment venues and banning public gatherings combined to avert hundreds of thousands of cases of infection. It is unlikely that this decline happened because the supply of susceptible people was exhausted, so relaxing control measures could lead to a resurgence. Science , this issue p. 638

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 15, 2026. Paragraph numbers are ours, so an answer can point at one.

On 31 December 2019—less than a month before the 2020 Spring Festival holiday, including the Chinese Lunar New Year—a cluster of pneumonia cases caused by an unknown pathogen was reported in Wuhan, a city of 11 million inhabitants and the largest transport hub in Central China. A novel coronavirus was identified as the etiological agent, and human-to-human transmission of the virus [severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2)] has been since confirmed. Further spatial spread of this disease was of great concern in view of the upcoming Spring Festival (chunyun), during which there are typically 3 billion travel movements over the 40-day holiday period, which runs from 15 days before the Spring Festival (Chinese Lunar New Year) to 25 days afterward.

Because there is currently neither a vaccine nor a specific drug treatment for coronavirus disease 2019 (COVID-19), a range of public health (nonpharmaceutical) interventions has been used to control the epidemic. In an attempt to prevent further dispersal of COVID-19 from its source, all transport was prohibited in and out of Wuhan city from 10:00 a.m. on 23 January 2020, followed by the whole of Hubei Province a day later. In terms of the population covered, this appears to be the largest attempted cordon sanitaire in human history.

On 23 January, China also raised its national public health response to the highest state of emergency: Level 1 of 4 levels of severity in the Chinese Emergency System, defined as an “extremely serious incident”. As part of the national emergency response, and in addition to the Wuhan city travel ban, suspected and confirmed cases have been isolated, public transport by bus and subway rail suspended, schools and entertainment venues have been closed, public gatherings banned, health checks carried out on migrants (“floating population”), travel prohibited in and out of cities, and information widely disseminated. Despite all of these measures, COVID-19 remains a danger in China. Control measures taken in China potentially hold lessons for other countries around the world.

Although the spatial spread of infectious diseases has been intensively studied, including explicit studies of the role of human movement, the effectiveness of travel restrictions and social distancing measures in preventing the spread of infection is uncertain. For COVID-19, SARS-CoV-2 transmission patterns and the impact of interventions are still poorly understood. We therefore carried out a quantitative analysis to investigate the role of travel restrictions and transmission control measures during the first 50 days of the COVID-19 epidemic in China, from 31 December 2019 to 19 February 2020 (Fig. 1). This period encompassed the 40 days of the Spring Festival holiday, 15 days before the Chinese Lunar New Year on 25 January and 25 days afterward. The analysis is based on a geocoded repository of data on COVID-19 epidemiology, human movement, and public health (nonpharmaceutical) interventions. These data include the numbers of COVID-19 cases reported each day in each city of China, information on 4.3 million human movements from Wuhan city, and data on the timing and type of transmission control measures implemented across cities of China.

We first investigated the role of the Wuhan city travel ban, comparing travel in 2020 with that in previous years and exploring how holiday travel links to the dispersal of infection across China. During Spring Festival travel in 2017 and 2018, there was an average outflow of 5.2 million people from Wuhan city during the 15 days before the Chinese Lunar New Year. In 2020, this travel was interrupted by the Wuhan city shutdown, but 4.3 million people traveled out of the city between 11 January and the implementation of the ban on 23 January (Fig. 2A). In 2017 and 2018, travel out of the city during the 25 days after the Chinese Lunar New Year averaged 6.7 million people each year. In 2020, the travel ban prevented almost all of that movement and markedly reduced the number of exportations of COVID-19 from Wuhan.

The dispersal of COVID-19 from Wuhan was rapid (Fig. 3A). A total of 262 cities reported cases within 28 days. For comparison, the 2009 influenza H1N1 pandemic took 132 days to reach the same number of cities in China (Supplementary materials, materials and methods). The number of cities providing first reports of COVID-19 peaked at 59 per day on 23 January, the date of the Wuhan travel ban.

The total number of cases reported from each province by 30 January, 1 week after the Wuhan shutdown, was strongly associated with the total number of travellers from Wuhan [correlation coefficient (r) = 0.98, P < 0.01; excluding Hubei, r = 0.69, P < 0.01] (Fig. 2, B and C). COVID-19 arrived sooner in those cities that had larger populations and had more travellers from Wuhan (Table 1 and table S1). However, the Wuhan travel ban was associated with a delayed arrival time of COVID-19 in other cities by an estimated 2.91 days [95% confidence interval (CI), 2.54 to 3.29 days] on average (Fig. 3B and Table 1).

This delay provided extra time to prepare for the arrival of COVID-19 in more than 130 cities across China but would not have curbed transmission after infection had been exported to new locations from Wuhan. The timing and implementation of emergency control measures in 342 cities across China are shown in Fig. 1 (figs. S2 and S4). School closure, the isolation of suspected and confirmed patients, plus the disclosure of information were implemented in all cities. Public gatherings were banned and entertainment venues closed in 220 cities (64.3%). Intracity public transport was suspended in 136 cities (39.7%), and intercity travel was prohibited by 219 cities (64.0%). All three measures were applied in 136 cities (table S2).

Cities that implemented a Level 1 response (any combination of control measures) (figs. S2 and S4) preemptively, before discovering any COVID-19 cases, reported 33.3% (95% CI, 11.1 to 44.4%) fewer laboratory-confirmed cases during the first week of their outbreaks (13.0 cases; 95% CI, 7.1 to 18.8, n = 125 cities) compared with cities that started control later (20.6 cases, 95% CI, 14.5 to 26.8, n = 171 cities), with a statistically significant difference between the two groups (Mann-Whitney U = 8197, z = –3.4, P < 0.01). A separate analysis using regression models shows that among specific control measures, cities that suspended intracity public transport and/or closed entertainment venues and banned public gatherings, and did so sooner, had fewer cases during the first week of their outbreaks (Table 2 and table S3). This analysis provided no evidence that the prohibition of travel between cities, which was implemented after and in addition to the Wuhan shutdown on 23 January, reduced the number of cases in other cities across China. These results are robust to the choice of statistical regression model (table S3).

The reported daily incidence of confirmed cases peaked in Hubei province (including Wuhan) on 4 February (3156 laboratory-confirmed cases, 5.33 per 100,000 population in Hubei) and in all other provinces on 31 January (875 cases, 0.07 per 100,000 population) (fig. S1). The low level of peak incidence per capita, the early timing of the peak, and the subsequent decline in daily case reports suggest that transmission control measures were associated not only with a delay in the growth of the epidemic but also with a marked reduction in the number of cases. By fitting an epidemic model to the time series of cases reported in each province (fig. S3), we estimate that the (basic) case reproduction number (R0) was 3.15 before the implementation of the emergency response on 23 January (Table 3). As control was scaled-up from 23 January onward (stage 1), the case reproduction number declined to 0.97, 2.01, and 3.05 (estimated as C1R0) in three groups of provinces, depending on the rate of implementation in each group (Table 3 and table S4). Once the implementation of interventions was 95% complete everywhere (stage 2), the case reproduction number had fallen to 0.04 on average (C2R0), far below the replacement rate (≪1) and consistent with the rapid decline in incidence (Fig. 4A, Table 3, fig. S5, and table S4).

On the basis of the fit of the model to daily case reports from each province, and on the preceding statistical analyses, we investigated the possible effects of control measures on the trajectory of the epidemic outside Wuhan city (Fig. 4B). Our model suggests that without the Wuhan travel ban or the national emergency response, there would have been 744,000 (±156,000) confirmed COVID-19 cases outside Wuhan by 19 February, day 50 of the epidemic. With the Wuhan travel ban alone, this number would have decreased to 202,000 (±10,000) cases. With the national emergency response alone (without the Wuhan travel ban), the number of cases would have decreased to 199,000 (±8500). Thus, neither of these interventions would, on their own, have reversed the rise in incidence by 19 February (Fig. 4B). But together and interactively, these control measures offer an explanation of why the rise in incidence was halted and reversed, limiting the number of confirmed cases reported to 29,839 (fitted model estimate 28,000 ± 1400 cases), 96% fewer than expected in the absence of interventions.

This analysis shows that transmission control (nonpharmaceutical) measures initiated during the Chinese Spring Festival holiday, including the unprecedented Wuhan city travel ban and the Level 1 national emergency response, were strongly associated with, although not necessarily the cause of, a delay in epidemic growth and a reduction in case numbers during the first 50 days of the COVID-19 epidemic in China.

The number of people who have developed COVID-19 during this epidemic, and therefore the number of people who were protected by control measures, is not known precisely, given that cases were almost certainly underreported. However, in view of the small fraction of people known to have been infected by 19 February (75,532 cases, 5.41 per 100,000 population), it is unlikely that the spread of infection was halted and epidemic growth reversed because the supply of susceptible people had been exhausted. This implies that a large fraction of the Chinese population remains at risk of COVID-19; control measures may need to be reinstated, in some form, if there is a resurgence of transmission. Further investigations are needed to verify that proposition, and population surveys of infection are needed to reveal the true number of people who have been exposed to this novel coronavirus.

We could not investigate the impact of all elements of the national emergency response because many were introduced simultaneously across China. However, this analysis shows that suspending intracity public transport, closing entertainment venues, and banning public gatherings, which were introduced at different times in different places, were associated with the overall containment of the epidemic. However, other factors are likely to have contributed to control, especially the isolation of suspected and confirmed patients and their contact, and it is not yet clear which parts of the national emergency response were most effective. We did not find evidence that prohibiting travel between cities or provinces reduced the numbers of COVID-19 cases outside Wuhan and Hubei, perhaps because such travel bans were implemented as a response to, rather than in anticipation of, the arrival of COVID-19.

This study has drawn inferences not from controlled experiments but from statistical and mathematical analyses of the temporal and spatial variation in case reports, human mobility, and transmission control measures. With that caveat, control measures were strongly associated with the containment of COVID-19, potentially averting hundreds of thousands of cases by 19 February, day 50 of the epidemic. Whether the means and the outcomes of control can be replicated outside China and which of the interventions are most effective are now under intense investigation as the virus continues to spread worldwide.

References 28

  1. 1.

    Zhu N., Zhang D., Wang W., Li X., Yang B., Song J., Zhao X., Huang B., Shi W., Lu R., Niu P., Zhan F., Ma X., Wang D., Xu W., Wu G., Gao G. F., Tan W., China Novel Coronavirus Investigating and Research Team, A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 382, 727–733 (2020 ).

    Not in our library10.1056/nejmoa2001017

  2. 2.

    Lu R., Zhao X., Li J., Niu P., Yang B., Wu H., Wang W., Song H., Huang B., Zhu N., Bi Y., Ma X., Zhan F., Wang L., Hu T., Zhou H., Hu Z., Zhou W., Zhao L., Chen J., Meng Y., Wang J., Lin Y., Yuan J., Xie Z., Ma J., Liu W. J., Wang D., Xu W., Holmes E. C., Gao G. F., Wu G., Chen W., Shi W., Tan W., Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. Lancet 395, 565–574 (2020 ).

    Not in our library10.1016/s0140-6736(20)30251-8

  3. 3.
  4. 4.
  5. 5.

    Cai J., Xu B., Chan K. K. Y., Zhang X., Zhang B., Chen Z., Xu B., Roles of different transport modes in the spatial spread of the 2009 influenza A(H1N1) pandemic in mainland China. Int. J. Environ. Res. Public Health 16, 222 (2019 ).

    Not in our library10.3390/ijerph16020222

  6. 6.

    Wang C., Horby P. W., Hayden F. G., Gao G. F., A novel coronavirus outbreak of global health concern. Lancet 395, 470–473 (2020 ).

    Not in our library10.1016/s0140-6736(20)30185-9

  7. 7.

    Chen S., Yang J., Yang W., Wang C., Bärnighausen T., COVID-19 control in China during mass population movements at New Year. Lancet 395, 764–766 (2020 ).

    Not in our library10.1016/s0140-6736(20)30421-9

  8. 8.
  9. 9.

    Chinadaily, “Tibet activates highest-level public health alert” (29 January 2020); www.chinadaily.com.cn/a/202001/29/WS5e318a36a3101282172739c1.html.

    Not in our library

  10. 10.

    Grenfell B. T., Bjørnstad O. N., Kappey J., Travelling waves and spatial hierarchies in measles epidemics. Nature 414, 716–723 (2001 ).

    Not in our library10.1038/414716a

  11. 11.

    Brockmann D., Helbing D., The hidden geometry of complex, network-driven contagion phenomena. Science 342, 1337–1342 (2013 ).

    Not in our library10.1126/science.1245200

  12. 12.

    Wesolowski A., Eagle N., Tatem A. J., Smith D. L., Noor A. M., Snow R. W., Buckee C. O., Quantifying the impact of human mobility on malaria. Science 338, 267–270 (2012 ).

    Not in our library10.1126/science.1223467

  13. 13.

    Ferguson N. M., Cummings D. A., Cauchemez S., Fraser C., Riley S., Meeyai A., Iamsirithaworn S., Burke D. S., Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437, 209–214 (2005 ).

    Not in our library10.1038/nature04017

  14. 14.

    Jones K. E., Patel N. G., Levy M. A., Storeygard A., Balk D., Gittleman J. L., Daszak P., Global trends in emerging infectious diseases. Nature 451, 990–993 (2008 ).

    Not in our library10.1038/nature06536

  15. 15.

    Morens D. M., Folkers G. K., Fauci A. S., The challenge of emerging and re-emerging infectious diseases. Nature 430, 242–249 (2004 ).

    Not in our library10.1038/nature02759

  16. 16.

    Viboud C., Bjørnstad O. N., Smith D. L., Simonsen L., Miller M. A., Grenfell B. T., Synchrony, waves, and spatial hierarchies in the spread of influenza. Science 312, 447–451 (2006 ).

    Not in our library10.1126/science.1125237

  17. 17.

    Wesolowski A., Qureshi T., Boni M. F., Sundsøy P. R., Johansson M. A., Rasheed S. B., Engø-Monsen K., Buckee C. O., Impact of human mobility on the emergence of dengue epidemics in Pakistan. Proc. Natl. Acad. Sci. U.S.A. 112, 11887–11892 (2015 ).

    Not in our library10.1073/pnas.1504964112

  18. 18.
  19. 19.

    Wu J. T., Leung K., Leung G. M., Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: A modelling study. Lancet 395, 689–697 (2020 ).

    Not in our library10.1016/s0140-6736(20)30260-9

  20. 20.

    B. Ripley, B. Venables, D. M. Bates, K. Hornik, A. Gebhardt, D. Firth, M. B. Ripley, Package ‘MASS’. CRAN Repository, See http://cran.r-projectorg/web/packages/MASS/MASS.pdf (2013).

    Not in our library

  21. 21.

    B. R. Kirkwood, J. A. Sterne, Essential Medical Statistics (John Wiley & Sons, 2010).

    Not in our library

  22. 22.

    D. E. Bailey, Probability and Statistics (John Wiley & Sons, 1971).

    Not in our library

  23. 23.

    A. Canty, B. Ripley, boot: Bootstrap R (S-Plus) functions. R package version 1.3-24. (2019).

    Not in our library

  24. 24.

    A. C. Davison, D. V. Hinkley, Bootstrap Methods and Their Application (Cambridge Univ. Press, 1997).

    Not in our library

  25. 25.

    R. M. Anderson, R. M. May, Infectious Diseases of Humans: Dynamics and Control (Oxford Univ. Press, 1992).

    Not in our library

  26. 26.

    Morton A., Finkenstädt B. F., Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods. J. R. Stat. Soc. 54, 575–594 (2005 ).

    Not in our library10.1111/j.1467-9876.2005.05366.x

  27. 27.

    Li Q., Guan X., Wu P., Wang X., Zhou L., Tong Y., Ren R., Leung K. S. M., Lau E. H. Y., Wong J. Y., Xing X., Xiang N., Wu Y., Li C., Chen Q., Li D., Liu T., Zhao J., Liu M., Tu W., Chen C., Jin L., Yang R., Wang Q., Zhou S., Wang R., Liu H., Luo Y., Liu Y., Shao G., Li H., Tao Z., Yang Y., Deng Z., Liu B., Ma Z., Zhang Y., Shi G., Lam T. T. Y., Wu J. T., Gao G. F., Cowling B. J., Yang B., Leung G. M., Feng Z., Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N. Engl. J. Med. 382, 1199–1207 (2020 ).

    Not in our library10.1056/nejmoa2001316

  28. 28.

    Bjørnstad O. N., Ims R. A., Lambin X., Spatial population dynamics: Analyzing patterns and processes of population synchrony. Trends Ecol. Evol. 14, 427–432 (1999 ).

    Not in our library10.1016/s0169-5347(99)01677-8

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