An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China
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
Abstract
Responding to an outbreak of a novel coronavirus [agent of coronavirus disease 2019 (COVID-19)] in December 2019, China banned travel to and from Wuhan city on 23 January 2020 and implemented a national emergency response. We investigated the spread and control of COVID-19 using a data set that included case reports, human movement, and public health interventions. The Wuhan shutdown was associated with the delayed arrival of COVID-19 in other cities by 2.91 days. Cities that implemented control measures preemptively reported fewer cases on average (13.0) in the first week of their outbreaks compared with cities that started control later (20.6). Suspending intracity public transport, closing entertainment venues, and banning public gatherings were associated with reductions in case incidence. The national emergency response appears to have delayed the growth and limited the size of the COVID-19 epidemic in China, averting hundreds of thousands of cases by 19 February (day 50).
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 (1, 2) was identified as the etiological agent (3, 4), and human-to-human transmission of the virus [severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2)] has been since confirmed (5, 6). 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 (7, 8).
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” (9). 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 (10–15), including explicit studies of the role of human movement (16, 17), 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 (6, 7). 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.
Fig. 1 Dates of discovery of the novel coronavirus causing COVID-19 and of the implementation of control measures in China, from 31 December 2019.
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) (7). 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 (7, 8).
Fig. 2 The dispersal of COVID-19 in China 15 days before and 25 days after the Spring Festival (Chinese Lunar New Year).
(A) Movement outflows from Wuhan city during Spring Festival travel in 2017, 2018, and 2020. The vertical dotted line is the date of the Spring Festival (Chinese Lunar New Year). (B) The number of recorded movements from Wuhan city to other provinces during the 15 days before the Spring Festival in 2020. The shading from light to dark represents the number of human movements from Wuhan to each province. The areas of circles represent the cumulative number of cases reported by 30 January 2020, 1 week after the Wuhan travel ban on 23 January. (C) Association between the cumulative number of confirmed cases reported before 30 January and the number of movements from Wuhan to other provinces.
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.
Fig. 3 Spatial dispersal of COVID-19 in China.
(A) Cumulative number of cities reporting cases by 19 February 2020. Arrival days are defined as the time interval (days) from the date of the first case in the first infected city (Wuhan) to the date of the first case in each newly infected city (a total of 324 cities), to characterize the intercity transmission rate of COVID-19. The dashed line indicates the date of the Wuhan travel ban (shutdown). (B) Before (blue) and after (red) the intervention by 30 January 2020, 1 week after the Wuhan travel ban (shutdown). The blue line and points show the fitted regression of arrival times up to the shutdown on day 23 (23 January, vertical dashed line). Gray points show the expected arrival times after day 23, without the shutdown. The red line and points show the fitted regression of delayed arrival times after the shutdown on day 23. Each observation (point) represents one city. Error bars give ±2 standard deviations.
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).
| Covariates | Coefficient | 95% CI | P |
|---|---|---|---|
| Intercept | 25.95 | (23.43, 28.48) | <0.01 |
| Longitude (degrees) | –0.03 | (–0.05, –0.01) | <0.01 |
| Latitude (degrees) | 0.03 | (0.01, 0.06) | <0.05 |
| log10 (population) | –0.70 | (–1.12, –0.28) | <0.01 |
| log10 (total movements) | –0.12 | (–0.22, –0.02) | <0.05 |
| Travel ban (days) | 2.91 | (2.54, 3.29) | <0.01 |
Table 1 Association between the Wuhan travel ban and COVID-19 dispersal to other cities in China.
The dependent variable Y is the arrival time (days) of the outbreak in each city.
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).
| Covariates | Coefficient | 95% CI | P |
|---|---|---|---|
| (Intercept) | –9.10 | (–9.56, –8.64) | <0.01 |
| Arrival time | 0.44 | (0.43, 0.46) | <0.01 |
| Distance from Wuhan City (log10) | 0.61 | (0.49, 0.73) | <0.01 |
| Suspension of intra-city public transport | |||
| Implementation | –3.50 | (–4.28, –2.73) | <0.01 |
| Timing | 0.11 | (0.08, 0.14) | <0.01 |
| Closure of entertainment venues | |||
| Implementation | –2.28 | (–2.98, –1.57) | <0.01 |
| Timing | 0.09 | (0.06, 0.11) | <0.01 |
Table 2 Associations between the type and timing of transmission control measures and the number of COVID-19 cases reported in city outbreaks the first week.
Data were evaluated by means of a generalized linear regression model.
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).
| Parameter | Definition | Mean | 95% BCI |
|---|---|---|---|
| ρ | Reporting proportion | 0.002 | 0.001 to 0.003 |
| R0 | Basic reproduction number | 3.15 | 3.04 to 3.26 |
| 1/δ | Mean latent period (days) | 4.90 | 4.32 to 5.47 |
| C1_high | Lower effect of control at the first stage | 0.97 | 0.94 to 0.99 |
| C1_medium | Medium effect of control at the first stage | 0.65 | 0.58 to 0.72 |
| C1_low | Higher effect of control at the first stage | 0.31 | 0.24 to 0.38 |
| C2 | Effect of control at the second stage | 0.01 | 0.001 to 0.03 |
| 1/γ | Infectious period before isolation (days) | 5.19 | 4.51 to 5.86 |
| Iw0 | Minimum number of cases when none detected | 1.12 | 0.91 to 1.32 |
Table 3 Parameter estimates of the SEIR (susceptible-exposed-infectious-recovered) epidemic model.
BCI, Bayesian confidence interval; C1_high, Heilongjiang, Shanghai, Tianjin, Zhejiang, and Hubei (excluding Wuhan); C1_medium, Anhui, Beijing, Fujian, Guangdong, Guangxi, Guizhou, Hunan, Jilin, Jiangsu, Jiangxi, Inner Mongolia, Shandong, and Tibet; C1_low, Gansu, Hainan, Hebei, Henan, Liaoning, Ningxia, Qinghai, Shanxi, Shaanxi, Sichuan, Xinjiang, Yunnan, and Chongqing.
Fig. 4 The role of interventions in controlling the COVID-19 outbreak across China.
(A) Epidemic model (line) fitted to daily reports of confirmed cases (points) summed across 31 provinces. Hubei excludes Wuhan city. (B) Expected epidemic trajectories without the Wuhan travel ban (shutdown), and with (blue) or without (red) interventions carried out as part of the Level 1 national emergency response, with the Wuhan travel ban and with (black) or without (orange) the intervention. Vertical dashed lines in (A) and (B) mark the date of the Wuhan travel ban and the start of the emergency response on 23 January. Shaded regions in (A) and (B) mark the 95% prediction envelopes.
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.
Acknowledgments
We thank the thousands of Centers for Disease Control (CDC) staff and local health workers in China who collected data and continue to work to contain COVID-19 in China and elsewhere. Funding: This study was provided by the National Natural Science Foundation of China (81673234), Beijing Natural Science Foundation (JQ18025), Beijing Advanced Innovation Program for Land Surface Science, and Young Elite Scientist Sponsorship Program by CAST (YESS) (2018QNRC001). H.T., M.U.G.K., O.G.P., and C.D. acknowledge support from the Oxford Martin School; H.T. acknowledges support from the Military Logistics Research Program. The funders had no role in study design, data collection and analysis, the decision to publish, or in preparation of the manuscript. Author contributions: H.T., P.Z., R.Y., O.G.P., B.T.G., and C.D. designed the study. B.C. and Y.S. collected and processed the Tencent’s LBS data. Y.Liu, B.L., B.X., Q.Y., B.W., P.Y., Y.C., and Q.W. collected the statistical data. H.T., Y.Li, C.-H.W., and J.C. conducted the analyses. M.U.G.K., O.N.B., R.Y., O.G.P., B.T.G., and C.D. edited the manuscript. H.T. and C.D. wrote the manuscript. All authors read and approved the manuscript. Competing interests: All other authors declare no competing interests. Data and materials availability: Code and data are available on the following GitHub repository: https://github.com/huaiyutian/COVID-19_TCM-50d_China (18). This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/. This license does not apply to figures/photos/artwork or other content included in the article that is credited to a third party; obtain authorization from the rights holder before using such material.
Supplementary Material
Summary
Materials and Methods
Figs. S1 to S7
Tables S1 to S4
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Volume 368 | Issue 6491
8 May 2020
8 May 2020
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Received: 6 March 2020
Accepted: 27 March 2020
Published in print: 8 May 2020
Acknowledgments
We thank the thousands of Centers for Disease Control (CDC) staff and local health workers in China who collected data and continue to work to contain COVID-19 in China and elsewhere. Funding: This study was provided by the National Natural Science Foundation of China (81673234), Beijing Natural Science Foundation (JQ18025), Beijing Advanced Innovation Program for Land Surface Science, and Young Elite Scientist Sponsorship Program by CAST (YESS) (2018QNRC001). H.T., M.U.G.K., O.G.P., and C.D. acknowledge support from the Oxford Martin School; H.T. acknowledges support from the Military Logistics Research Program. The funders had no role in study design, data collection and analysis, the decision to publish, or in preparation of the manuscript. Author contributions: H.T., P.Z., R.Y., O.G.P., B.T.G., and C.D. designed the study. B.C. and Y.S. collected and processed the Tencent’s LBS data. Y.Liu, B.L., B.X., Q.Y., B.W., P.Y., Y.C., and Q.W. collected the statistical data. H.T., Y.Li, C.-H.W., and J.C. conducted the analyses. M.U.G.K., O.N.B., R.Y., O.G.P., B.T.G., and C.D. edited the manuscript. H.T. and C.D. wrote the manuscript. All authors read and approved the manuscript. Competing interests: All other authors declare no competing interests. Data and materials availability: Code and data are available on the following GitHub repository: https://github.com/huaiyutian/COVID-19_TCM-50d_China (18). This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/. This license does not apply to figures/photos/artwork or other content included in the article that is credited to a third party; obtain authorization from the rights holder before using such material.
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Funding Information
Young Elite Scientist Sponsorship Program by CAST (YESS): 2018QNRC001
Beijing Natural Science Foundation: JQ18025
Beijing Advanced Innovation Program for Land Surface Science: None
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Reply to Drs Nax, Casalone, Di Guardo and Yezhao Wang
Dr Nax usefully raises the age-old question of how to determine causality in epidemiological studies. Not neglecting Popper, we have usually sought guidance from Bradford Hill (see e.g. Broutet, N. et al., New England Journal of Medicine, 374(16), 1506-1509. doi:10.1056/NEJMp1602708). Mindful of the pitfalls for COVID-19, we carried out the most exhaustive possible analysis of the data available from China, combining three different kinds of analysis in our paper. Since we published this paper, a consensus (if not formal proof) has emerged that COVID-19 control has been highly effective in China, as we concluded ourselves.
We agree with Drs Casalone and Di Guardo that it is important to remember the lessons of previous health emergencies. Bovine spongiform encephalopathy (BSE) is a good example, and the One Health concept is a helpful framework for thinking about pathogen transmission at the animal-human interface. Much of their exposition on BSE is beyond the scope of our paper, but we are less enthusiastic than they are about the "principle of precaution" (POP): we do not necessarily agree that "all necessary measures and actions aimed at minimizing human exposure should be implemented." The snag with POP is that all actions have a cost as well as a benefit and taking precautions could be unjustifiably costly under some circumstances. Both the costs and benefits of any action must be weighed when making decisions with practical consequences.
In response to Dr Yezhao Wang, we refute the suggestion that there was any plagiarism in our study ─ from concept to analysis to publication. Plagiarism (claiming someone else's work to be one's own) should not be confused with reaching similar conclusions through independent investigations of a question of common interest. For the record, we began to make public the results of our study on 2 February 2020 in www.medrxiv.org/content/10.1101/2020.01.30.20019844v4.article-info, well before the March publications Dr Wang cites, and nearly two months before our Science paper appeared online 31 March.
We are authors of the paper under discussionThe effectiveness of transmission control measures on the COVID-19 epidemic cannot be evaluated scientifically like this
A new type of research is emerging that evaluates the effectiveness of various COVID-19 response policies as have been adopted across the globe. A prominent example is this one (see also Science 368(6490), 493-497). What is common to these studies is a sequential timelining of control measure introductions, changes in mobility data as compared with data from pre COVID-19, and changes in case numbers and other epidemiological data. While these studies admit that with this kind of methodology it is impossible to determine the effectiveness of each individual intervention, the studies nevertheless claim to show that the chosen control measures were a "success" in substantially mitigating the spread of COVID-19. Here, we argue that the author's methodology is unsuited to determine in any scientific sense the actual impact of any intervention. The reason is Popper 101: COVID-19 constitutes a unicum, that is, a historically singular outbreak of an infectious respiratory disease amongst humans who are intelligent, aware, decision-making and influenced by information about the disease, news about its spread and –amongst other things—mobility control and restriction policies. Without appropriate controls for other relevant factors that influence human decisions, there is no way to estimate cleanly the aggregate of all or ceteris paribus effect of any measure. Moreover, there is no way that an analysis focusing on a single sequence of introductions of the same policy types in the same country can inform us about what kinds of policies are truly successful, not in that same country and definitely not elsewhere. There is an urgent need for integration of methods to evaluate COVID-19 responses with those that are standardly used to evaluate policy interventions in the social sciences.
***Disclaimer
We are in favor of good policymaking to fight the pandemic. But we argue against the rather crass introductions of anti-liberal policies that directly limited personal liberties in the forms of bans on movements and contacts. We are not in general against policies aimed at promoting social distancing at all such as through education, information, school closures, event cancellations, infrastructural adjustments, etc. We actually believe that not curtailing personal liberties in ways as was done not only is more in line with democratic ideals but might actually produce more sustainable behavioral change.
CoViD-19 and mad cow disease: So different yet so similar
In the current situation in which the entire world is fighting SARS-CoV-2, the virus causing one of the largest global health emergencies to date, it is important to remember the lessons learned from previous health emergencies. A very impressive example in this direction is that provided by bovine spongiform encephalopathy (BSE), popularly known as mad cow disease. CoViD-19 and BSE differ enormously: CoViD-19, in fact, is caused by a respiratory virus (1), whereas BSE by a prion agent transmitted through the food chain (2). Notwithstanding the above, the two diseases share several common features.
The first is the "principle of precaution" (POP), the minimum common denominator applied in the management of emergencies, including health emergencies, in order to mitigate the potential risks arising from human and/or animal exposure to agents for which the available scientific evidence is either insufficient, inconclusive or uncertain. When an infectious agent is transmissible to humans like the one responsible for BSE (3) or is highly contagious and no drugs and/or vaccines to deal with it are available, as in the case of SARS-CoV-2, POP should be applied in tight association with the "worst case scenario" (WCS) concept. More in detail, within the dual POP-WCS framework, all necessary measures and actions aimed at minimizing human exposure should be implemented. The BSE crisis management-related actions which were adopted, aimed at minimizing the BSE human exposure risk, required the removal from the human food chain of all products of animal origin harboring BSE infectivity (4). Likewise, during the present CoViD-19 epidemic and pandemic, draconian measures have been implemented, starting from the city of Wuhan and the China province of Hubei (the epicenter of the SARS-CoV-2 pandemic), thus being subsequently put in force by many other Countries like Italy, where they were adopted before other European and extra-European Nations (5).
The most significant hurdle in POP application to SARS-CoV-2 management is the lack of adequate knowledge about the "enemy", a tiny pathogen perceived as a life-threatening agent. Forced to give a "name and surname" to the "enemy", the scientific community must identify the tissues and cells where the virus can reside and replicate, alongside the immune mechanisms through which infected hosts limit the spread of infection inside their organism. Such information can be acquired from post-mortem analyses, as demonstrated by numerous studies on animal species naturally (cattle, cats, humans, etc.) and experimentally infected with the BSE agent. There is no question that our understanding of the pathogenesis of SARS-CoV-2 infection, currently very limited, could greatly benefit from the study of deceased patients.
In this respect, while we have noticed that a wide time window has been granted in interviews to authoritative virologists, infectious disease specialists, epidemiologists, public health experts, et cetera - in order to explain SARS-CoV-2/CoViD-19 nosology and nosography -, we have not seen yet a single pathologist
being interviewed in the media. This obviously results in a large "asymmetry" between the "intra vitam" and the "post mortem dimension" of SARS-CoV-2 infection/CoVid-19, the latter being investigated and told only by pathologists. Thanks to the work carried out by pathologists, a detailed picture of the pathogenetic evolution of SARS-CoV-2 infection will be made available, along with the substantial contribution made (also) by pathologists in providing "ad hoc" answers to the many crucial and still open issues regarding SARS-CoV-2 infection's pathogenesis in relation to the virus- and the host-related drivers involved in it.
As previously demonstrated for the "strains" responsible for "atypical" prion diseases (6), which are distinct from the "original" strain, both in humans and in animals, different SARS-CoV-2 strains with different levels of pathogenicity for humans could also exist. Within such context, post-mortem investigations may provide crucial informations about the biology of the virus and its interactions with the human host.
During the BSE epidemic, rapid diagnostic tests were used to check asymptomatic adult cattle and remove them from the food chain. By doing so, the exposure to the BSE agent was greatly reduced. The implementation of an "active surveillance"-based approach - "active" meaning that the disease was actively sought - required a huge technical and organizational effort. To this aim, new laboratories were set up in which 1,500 to 2,500 tissue samples were analyzed every day (7). In this respect, a recent study on experimentally infected macaques has shown that SARS-CoV-2 can be early detected in asymptomatic animals (8). Accordingly, the use of biomolecular tests on swabs taken from the general population could help in considerably reducing the number of new cases and to self-quarantining those patients testing positive for the virus.
Never before in the history of mankind has the One Health concept – human, animal and environmental health are mutually interwoven – emerged so clearly. Equally clear is the link between human and veterinary medicine, coupled with environmental protection. Multidisciplinary and intersectorial collaboration are of paramount relevance within such context. Modeling the course of an epidemic and the impact of climate change on the environmental and epidemiological characteristics of causative agents is gaining increasing importance. In a recent statement the World Health Organization (WHO) reported that 75% of emerging infectious diseases are caused by agents of proven or suspected zoonotic potential, i.e., transmissible from animals to humans (9).
Today, 35 years after the discovery of the first case of BSE in England (10), we can state that the fight against the disease has been won. The victory was achieved through the implementation of restrictive measures that partially limited individual liberty and led to huge economic losses in the sectors more directly involved in the epidemic in the United Kingdom.
"Mutatis mutandis", despite the huge differences existing between BSE and CoViD-19 as well as between their causative pathogens, along with those concerning the management of the related epidemics, "the light at the end of the tunnel" will appear the sooner whenever the actions and measures aimed at reducing SARS-CoV-2 spread will be strictly enforced. This has been successfully achieved in China, thus far, the same being also true for Italy as well as for other European and non-European Countries.
How and when the SARS-CoV-2 pandemic will end depends on our behaviours as well as on our perseverance in following the strict rules and measures imposed by the health authorities, which are undoubtedly producing strong limitations to our individual freedom. It worked for BSE, it will also work for CoVid-19, despite their big differences!
And, when coming out of the long and frightening "CoViD-19 tunnel", we will find a very different world from the one we knew before we entered it.
References
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2) Wilesmith JW Wells GAH Cranwell MP Ryan JBM. Bovine spongiform encephalopathy: epidemiological studies. Vet Rec. 1988; 123: 638-644.
3) Collinge J Variant Creutzfeldt-Jakob disease. Lancet 1999 jul 24; 354(9175):317-23. doi:10.1016/S0140-6736(99)05128-4. PMID: 10440324.
4) M B Bowling 1, K E Belk, K K Nightingale, L D Goodridge, J A Scanga, J N Sofos, J D Tatum, G C Smith Central Nervous System Tissue in Meat Products: An Evaluation of Risk, Prevention Strategies, and Testing Producers. Rewiew Adv Food Nutr Res.53,39-64 2007. PMID: 17900496 DOI:10.1016/S1043-4526(07)53002-0.
5) Remuzzi A, Remuzzi G. COVID-19 and Italy: what next? Lancet. 2020 Mar 13. pii: S0140-6736(20)30627-9. doi: 10.1016/S0140-6736(20)30627-9. [Epub ahead of print] Review. PubMed PMID: 32178769; PubMed Central PMCID: PMC7102589.
6) Casalone C, Zanusso G, Acutis P, Ferrari S, Capucci L, Tagliavini F, Mnaco S, Caramelli M. Identification of a second bovine amyloidotic spongiform encephalopathy: molecular similarities with sporadic Creutzfeldt-Jakob disease. Proc Natl Acad Sci USA. 2004 Mar 2; 101(9):3065-70. doi:10.1073/pnas.0305777101. Epub 2004 Feb 17. PMID:14970340.
7) Maurella C, Casalone C, Iulini B, Caramelli M. Bovine spongiform encephalopathy: history and diagnosis of a decreasing epidemic. Food safety assurance and veterinary public health. Volume 6. Foodborne viruses and prions and their significance for public health.
8) Barry Rockx, Thijs Kuiken, Sander Herfst, Theo Bestebroer, Mart M. Lamers, Dennis de Meulder, Geert van Amerongen, Judith van den Brand, Nisreen M.A. Okba, Debby Schipper, Peter van Run, Lonneke Leijten, Ernst Verschoor, Babs Verstrepen, Jan Langermans, Christian Drosten, Martje Fentener van Vlissingen, Ron Fouchier, Rik de Swart, Marion Koopmans, Bart L. Haagmans. Comparative pathogenesis of COVID-19, MERS and SARS in a non-human primate model. BioRxiv preprint doi: https://doi.org/10.1101/2020.03.17.995639.
9) One Health Initiative. About the one health initiative. Available at: <http://www.onehealthinitiative.com/>; [accessed 09.01. 15].
10) Wells GA, Scott AC, Johnson CT, Gunning RT, H ancock RD, Jeffrey M, Dawson M, Bradley R. A. Novel Progressive Spongiform Encephalopathy in Cattle. Vet Rec, 121 (18), 419-20 1987 Oct31.
The effectiveness of travel restrictions and social distancing measures in preventing the spread of SARS-CoV2 is certain.
Dear editor:
The recent coronavirus pneumonia that emerged in Wuhan (SARS-CoV2) at the end of 2019 quickly spread to all Chinese provinces, as of April 4, 2020 , confirmed cases was reported has reached 1100000 worldwide according to statistical data, which attracted global attention (https://www.who.int/). Although the Chinese government provided a quick response and took drastic measures, including quarantining Wuhan City on January 23, the spread of SARS-CoV2 has become a major public health threat and economic burden globally.
Therefore, I read the recent article by Tian et al with great interest (1). Nonetheless, there are obvious problems of plagiarism in this article.
Firstly, there are issues of plagiarism in Figure 1 of the article (1, 2), In their manuscript, the authors stated that "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)" (1). However, some researchers have also investigate the role of travel restrictions and transmission control measures of the COVID-19 epidemic in China and published it on N Engl J Med. (2). In addition, Fig. 1 Dates of discovery of the novel coronavirus causing COVID-19, and of the implementation of control measures in China, from 31 December 2019 of this article (1) is accused of plagiarism, because a part of Fig. 1 has been published before on N Engl J Med. (2).
Secondly, the authors stated that "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, coronavirus transmission patterns and the impact of interventions are still poorly understood. " (1). However, I have published two e-letters before them entitled "The effectiveness of efforts to control SARS-CoV2 is known" and "Travel restrictions are effective to control SARS-CoV2. " on March 17, 2020 and March 27, 2020, respectively (3, 4). In addition, some similar manuscripts have been published by other researchers (2, 5-7), and several articles published on N Engl J Med. have made clear explanation of travel restrictions and social distancing measures in preventing the spread of infection, coronavirus transmission patterns and the impact of interventions (2, 8). Besides, China has completely contained the dissemination of novel coronavirus (SARS-CoV2) through drastic measures such as controlling the source of infection, cutting off the route of transmission, and protecting vulnerable groups including travel restrictions and quarantining Wuhan City on January 23 as of March 31, 2020 (3, 7, 8). Thus, what's value of these old findings in controlling the outbreak of SARS-CoV2 for China and the world? It was also reported that "initial testing was focused mainly on travelers from Wuhan, potentially biasing estimates of travel related infections upwards " (7).
Thirdly, as the authors have made a strong comment as " We first investigated the role of the Wuhan city travel ban", Indeed, other authors have published a few similar study before them (2, 5-7).
Also the conclusion in their manuscript is stated "In summary, this analysis shows that transmission control (non-pharmaceutical) measures initiated during Chinese Spring Festival holiday, including the unprecedented Wuhan city travel ban and the Level 1 national emergency response, were strongly associated with, though 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." This conclusion has been made in other manuscripts before (2, 8).
This work was supported by grants from the National Undergraduate Training Program for Innovation and Entrepreneurship (no. 201811646025 to YW), the Student Research and Innovation Program of Ningbo University (no. 2017SRIP1918, no. 2018SRIP2507 and no. 2019SRIP1902 to YW). The author has nothing to disclose.
References:
1. H. Tian, Y. Liu, Y. Li, et al. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science. 2020;eabb6105. doi:10.1126/science.abb6105
2. Q. Li, X. Guan, P. Wu, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020;382(13):1199–1207. doi:10.1056/NEJMoa2001316
3. Yezhao Wang. The effectiveness of efforts to control SARS-CoV2 is known.https://science.sciencemag.org/content/early/2020/03/24/science.abb3221/....
4. Yezhao Wang. Travel restrictions are effective to control SARS-CoV2. https://science.sciencemag.org/content/early/2020/03/24/science.abb3221/....
5. M. Chinazzi, J. T. Davis, M. Ajelli, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 2020;eaba9757. doi:10.1126/science.aba9757
6. R. Li, S. Pei, B. Chen, et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science. 2020;eabb3221. doi:10.1126/science.abb3221
7. M.U.J. Kraemer, C.H. Yang, B. Gutierrez, C.H. Wu, B. Klein, D. M. Pigott, Open COVID-19 Data Working Group, et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science. 2020; doi:10.1126/science.abb4218.
8. W.J. Guan, Z.Y. Ni, Y. Hu, W.H. Liang, C.Q. Ou, J.X. He, L. Liu, H. Shan, C.L. Lei, D.S.C. Hui, B. Du, L.J. Li, G. Zeng, K.Y. Yuen, R.C. Chen, C.L. Tang, T. Wang, P.Y. Chen, J. Xiang, S.Y.Li, J.L. Wang, Z.J. Liang, Y.X. Peng, L. Wei, Y. Liu, Y.H. Hu, P. Peng, J.M. Wang, J.Y. Liu, Z. Chen, G. Li, Z.J. Zheng, S.Q. Qiu, J. Luo, C.J. Ye, S.Y. Zhu, N.S. Zhong; China Medical Treatment Expert Group for Covid-19. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020 Feb 28. doi: 10.1056/NEJMoa2002032.
9. Yezhao Wang. The most important thing is to save life. https://science.sciencemag.org/content/367/6485/1436.2/tab-e-letters.
Yezhao Wang 1,2*
1Zhejiang Province Key Laboratory of Pathophysiology, Ningbo University School of Medicine, Ningbo, 315211, China.
2Department of Biochemistry and Molecular Biology, Ningbo University School of Medicine, Ningbo, 315211, China.
*Correspondence to Yezhao Wang
Department of Biochemistry and Molecular Biology, Ningbo University School of Medicine, Ningbo, 315211, China.
E-mail: [email protected].
The effectiveness of travel restrictions and social distancing measures in preventing the spread of infection is certain.
Dear editor:
The recent coronavirus pneumonia that emerged in Wuhan (SARS-CoV2) at the end of 2019 quickly spread to all Chinese provinces, as of April 3, 2020 , confirmed cases was reported has reached 1000000 worldwide according to statistical data, which attracted global attention (https://www.who.int/). Although the Chinese government provided a quick response and took drastic measures, including quarantining Wuhan City on January 23, the spread of SARS-CoV2 has become a major public health threat and economic burden globally.
Therefore, I read the recent article by Tian et al with great interest (1). In their manuscript, the authors stated that "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, coronavirus transmission patterns and the impact of interventions are still poorly understood. " (1). However, I have published an e-letter before them entitled "The effectiveness of efforts to control SARS-CoV2 is known" on March 17, 2020 (2). In addition, some similar manuscripts have been published by other researchers (3-6), and several article published on N Engl J Med. have made clear explanation of travel restrictions and social distancing measures in preventing the spread of infection, coronavirus transmission patterns and the impact of interventions (3, 7). Besides, China has completely contained the dissemination of novel coronavirus (SARS-CoV2) through drastic measures such as controlling the source of infection, cutting off the route of transmission, and protecting vulnerable groups including travel restrictions and quarantining Wuhan City on January 23 as of March 31, 2020 (2, 7). Thus, what's value of these old findings in controlling the outbreak of SARS-CoV2 for China and the world? It was also reported that "initial testing was focused mainly on travelers from Wuhan, potentially biasing estimates of travel related infections upwards " (6).
As the authors have made a strong comment as " We first investigated the role of the Wuhan city travel ban", Indeed, other authors have published a few similar study before them (3-6).
Also the conclusion in their manuscript is stated "In summary, this analysis shows that transmission control (non-pharmaceutical) measures initiated during Chinese Spring Festival holiday, including the unprecedented Wuhan city travel ban and the Level 1 national emergency response, were strongly associated with, though 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." This conclusion has been made in other manuscripts before (4, 6, 7).
This work was supported by grants from the National Undergraduate Training Program for Innovation and Entrepreneurship (no. 201811646025 to YW), the Student Research and Innovation Program of Ningbo University (no. 2017SRIP1918, no. 2018SRIP2507 and no. 2019SRIP1902 to YW). The author has nothing to disclose.
References:
1. H. Tian, Y. Liu, Y. Li, et al. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science. 2020;eabb6105. doi:10.1126/science.abb6105
2. Yezhao Wang. The effectiveness of efforts to control SARS-CoV2 is known.https://science.sciencemag.org/content/early/2020/03/24/science.abb3221/....
3. Li Q, Guan X, Wu P, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020;382(13):1199–1207. doi:10.1056/NEJMoa2001316
4. Chinazzi M, Davis JT, Ajelli M, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 2020;eaba9757. doi:10.1126/science.aba9757
5. Li R, Pei S, Chen B, et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science. 2020;eabb3221. doi:10.1126/science.abb3221
6. M.U.J. Kraemer, C.H. Yang, B. Gutierrez, C.H. Wu, B. Klein, D. M. Pigott, Open COVID-19 Data Working Group, et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science. 2020; doi:10.1126/science.abb4218.
7. W.J. Guan, Z.Y. Ni, Y. Hu, W.H. Liang, C.Q. Ou, J.X. He, L. Liu, H. Shan, C.L. Lei, D.S.C. Hui, B. Du, L.J. Li, G. Zeng, K.Y. Yuen, R.C. Chen, C.L. Tang, T. Wang, P.Y. Chen, J. Xiang, S.Y.Li, J.L. Wang, Z.J. Liang, Y.X. Peng, L. Wei, Y. Liu, Y.H. Hu, P. Peng, J.M. Wang, J.Y. Liu, Z. Chen, G. Li, Z.J. Zheng, S.Q. Qiu, J. Luo, C.J. Ye, S.Y. Zhu, N.S. Zhong; China Medical Treatment Expert Group for Covid-19. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020 Feb 28. doi: 10.1056/NEJMoa2002032.
Yezhao Wang 1,2*
1Zhejiang Province Key Laboratory of Pathophysiology, Ningbo University School of Medicine, Ningbo, 315211, China.
2Department of Biochemistry and Molecular Biology, Ningbo University School of Medicine, Ningbo, 315211, China.
*Correspondence to Yezhao Wang
Department of Biochemistry and Molecular Biology, Ningbo University School of Medicine, Ningbo, 315211, China.
E-mail: [email protected].
Yours sincerely
Yezhao Wang
Ningbo University
School of Medicine
CHINA