Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

Initial public health response and interim clinical guidance for the 2019 novel coronavirus outbreak - United States, December 31, 2019-February 4, 2020.

Extracorporeal membrane oxygenation in patients with SARS-CoV-2

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract We collated contact tracing data from COVID-19 clusters in Singapore and Tianjin, China and estimated the extent of pre-symptomatic transmission by estimating incubation periods and serial intervals. The mean incubation periods accounting for intermediate cases were 4.91 days (95%CI 4.35, 5.69) and 7.54 (95%CI 6.76, 8.56) days for Singapore and Tianjin, respectively. The mean serial interval was 4.17 (95%CI 2.44, 5.89) and 4.31 (95%CI 2.91, 5.72) days (Singapore, Tianjin). The serial intervals are shorter than incubation periods, suggesting that pre-symptomatic transmission may occur in a large proportion of transmission events (0.4–0.5 in Singapore and 0.6–0.8 in Tianjin, in our analysis with intermediate cases, and more without intermediates). Given the evidence for pre-symptomatic transmission, it is vital that even individuals who appear healthy abide by public health measures to control COVID-19. eLife digest The first cases of COVID-19 were identified in Wuhan, a city in Central China, in December 2019. The virus quickly spread within the country and then across the globe. By the third week in January, the first cases were confirmed in Tianjin, a city in Northern China, and in Singapore, a city country in Southeast Asia. By late February, Tianjin had 135 cases and Singapore had 93 cases. In both cities, public health officials immediately began identifying and quarantining the contacts of infected people. The information collected in Tianjin and Singapore about COVID-19 is very useful for scientists. It makes it possible to determine the disease's incubation period, which is how long it takes to develop symptoms after virus exposure. It can also show how many days pass between an infected person developing symptoms and a person they infect developing symptoms. This period is called the serial interval. Scientists use this information to determine whether individuals infect others before showing symptoms themselves and how often this occurs. Using data from Tianjin and Singapore, Tindale, Stockdale et al. now estimate the incubation period for COVID-19 is between five and eight days and the serial interval is about four days. About 40% to 80% of the novel coronavirus transmission occurs two to four days before an infected person has symptoms. This transmission from apparently healthy individuals means that staying home when symptomatic is not enough to control the spread of COVID-19. Instead, broad-scale social distancing measures are necessary. Understanding how COVID-19 spreads can help public health officials determine how to best contain the virus and stop the outbreak. The new data suggest that public health measures aimed at preventing asymptomatic transmission are essential. This means that even people who appear healthy need to comply with preventive measures like mask use and social distancing. Introduction The novel coronavirus disease, COVID-19, was first identified in Wuhan, Hubei Province, China in December 2019 (Li et al., 2020b; Huang et al., 2020). The virus causing the disease was soon named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Hui et al., 2020) and quickly spread to other regions of China and then across the globe, causing a pandemic with over 5 million cases and 300,000 deaths at the time of writing (Johns Hopkins University, 2020). In Tianjin, a metropolis located at the north of China, the first case was confirmed on January 21, 2020 (Tianjin Health Commission, 2020). Two days later, the first case was confirmed in Singapore (Ministry of Health Singapore, 2020), a city country in Southeast Asia. As of February 28, 2020, 93 and 135 cases had been confirmed in Singapore and Tianjin (Ministry of Health Singapore, 2020; Tianjin Health Commission, 2020). The first Singapore COVID-19 case was confirmed as an individual who had travelled to Singapore from Wuhan. Many of the initial cases were imported from Wuhan, with later cases being caused by local transmission. Singaporean officials worked to identify potential contacts of confirmed cases; close contacts were monitored and quarantined for 14 days from their last exposure to the patient, and other low-risk contacts were put under active surveillance and contacted daily to monitor their health status. These early outbreaks continue to provide the opportunity to estimate key parameters to understand COVID-19 transmission dynamics. We screened publicly available data to identify datasets for two COVID-19 clusters that could be used to estimate transmission dynamics. In both Singapore and Tianjin, the COVID-19 outbreak occurred within a relatively closed system where immediate public health responses were implemented, contacts were identified and quarantined, and key infection dates were tracked and updated daily. With its experiences in control of the SARS outbreak, the Singaporean government had been adopting a case-by-case control policy from January 2, 2020. Only close contacts of a confirmed case were monitored and quarantined for 14 days. In Tianjin, a number of COVID-19 cases were traced to a department store, where numerous customers and sales associates were likely infected. Additional customers who had potential contact were asked to come forward through state news and social media, as well as asked if they had visited the department store at various checkpoints in the city. All individuals identified as having visited the store in late January were quarantined and sections of the Baodi District where the store is located were sealed and put under security patrol. We estimate the serial interval and incubation period of COVID-19 from clusters of cases in Singapore and Tianjin. The serial interval is defined as the length of time between symptom onset in a primary case (infector) and symptom onset in a secondary case (infectee), whereas the incubation period is defined as the length of time between an infectee's exposure to a virus and their symptom onset. Both are important parameters that are widely used in modeling in infectious disease, as they impact model dynamics and hence fits of models to data. While the pandemic has progressed far beyond these early outbreaks, it remains the case that mathematical modelling, using parameters derived from estimates like these, is widely used in forecasting and policy. The serial interval and incubation period distributions, in particular, can be used to identify the extent of pre-symptomatic transmission (i.e. viral transmission from an individual that occurs prior to symptom onset). There is evidence that pre-symptomatic transmission accounts for a considerable portion of COVID-19 spread (Arons et al., 2020; Baggett et al., 2020; Li et al., 2020a) and it is important to determine the degree to which this is occurring (Peak et al., 2020). Early COVID-19 estimates borrowed parameters from SARS (Wu et al., 2020; Jiang et al., 2020; Abbott et al., 2020), but more recent estimates have been made using information from early clusters of COVID-19 cases, primarily in Wuhan. Depending on the population used, estimates for incubation periods have ranged from 3.6 to 6.4 days and serial intervals have ranged from 4.0 to 7.5 days (Li et al., 2020b; Ki and Task Force for 2019-nCoV, 2020; Backer et al., 2019; Linton et al., 2020; Nishiura et al., 2020); however, it is crucial that the estimates of incubation period and serial interval are based on the same outbreak, and are compared to those obtained from outbreaks in other populations. Distinct outbreak clusters are ideal for understanding how COVID-19 can spread through a population with no prior exposure to the virus. Here, we estimate the portion of transmission that is pre-symptomatic based on estimates of the incubation period and serial interval. We estimate both quantities under two frameworks: first, we use samples as directly as is feasible from the data, for example assuming that the health authorities' epidemiological inferences regarding who exposed whom and who was exposed at which times are correct. Second, we use estimation methods that allow for unknown intermediate cases, such that the presumed exposure and infection events may not be complete. We also separate the analysis of incubation period according to earlier and later phases of the outbreaks, since measures were introduced during the time frame of the data. Results Descriptive analyses Figures 1 and 2 show the daily counts, putative origin of the exposure and individual time courses for the Singapore and Tianjin data. In the Singapore dataset, new hospitalization and discharge cases were documented daily from January 23 to February 26, 2020. 66.7% (62/93) of the confirmed cases recovered and were discharged from the hospital by the end of the study period (Figure 1(a)). The disease progression timeline of the 93 documented cases in Figure 1(c) indicates that symptom onset occurred 1.71 ± 3.01 (mean ± SD) days after the end of possible viral exposure window and cases were confirmed 7.43 ± 5.28 days after symptom onset. The mean length of hospital stay was 13.3 ± 6.01 days before individuals recovered and were discharged. Figure 1 Download asset Open asset Singapore COVID-19 cases. (a) Daily hospitalized cases and cumulative hospitalized and discharged cases. (b) Daily incidence with probable source of infection. (C) Disease timeline, including dates at which each case is unexposed, exposed, symptomatic, hospitalized, and discharged. Not all cases go through each status as a result of missing dates for some cases. Figure 2 Download asset Open asset Tianjin COVID-19 cases. (a) Daily and cumulative confirmed cases, cumulative discharges and daily death cases. (b) Daily incidence with probable source of infection. (c) Disease progression timeline; not all cases go through each status as a result of missing dates for some cases. In the Tianjin dataset, new confirmed cases were documented daily from January 21 to February 22, 2020. 48.1% (65/135) recovered and 2.2% (3/135) had died by the end of the study period (Figure 2(a)). The timeline of the 135 cases is shown in Figure 2(c). Symptom onset occurred 4.98 ± 4.83 (mean ± SD) days after the end of the possible viral exposure window. Cases were confirmed 5.23 ± 4.15 days after symptom onset. The duration of hospital stay of the Tianjin cases is unknown as the discharge date of each case was not available. In both datasets, daily counts decline over time, which is likely a combination of delays to symptom onset and between symptom onset and reporting, combined with the effects of strong social distancing and contact tracing. Incubation period In the Singapore dataset, we find that the median incubation period in our direct analysis (without accounting for intermediate cases) is 5.32 days with the gamma distribution; shape 3.05 (95%CI 2.0, 3.84); and scale 1.95 (1.23, 2.34). The mean incubation period is 5.99 (95%CI 4.97, 7.14) days. In Tianjin, we find a median 8.06 days; shape 4.74 (3.35, 5.72); scale 1.83 (1.29, 2.04). The mean is 8.68 (7.72, 9.7) days. These results are summarised in Table 1, and we also fitted Weibull and log normal distributions; see Appendix 1—table 1. These are consistent with, or slightly longer than, previous estimates, see Appendix 1—table 5 for comparison. Table 1 Mean incubation period, serial interval and pre-symptomatic transmission. Incubation periods are based on the gamma estimates because these are the most convenient for taking the covariation of serial intervals and incubation periods into account (done throughout the table). 95% CIs are provided in brackets. Without intermediatesIncubation (days)Serial interval (days)Mean difference (days)Portion pre-symptomatic(-)Singapore (all)5.99 (4.97, 7.14)4.0 (2.73, 5.57)1.990.74Singapore (early)5.91 (4.50,7.64)1.910.742Singapore (late)6.06 (4.70, 7.67 )2.060.744Tianjin (all)8.68 (7.72, 9.7)5.0 (3.82, 6.12)3.680.81Tianjin (early)6.88 (5.97,7.87)1.880.72Tianjin (late)12.4 (11.1,13.7)7.40.96Account for intermediatesSingapore r=0.054.914.17 (2.44, 5.89)0.770.53Singapore r=0.14.430.260.46Singapore r=0.154.12−0.050.41Singapore r=0.23.89−0.280.38Tianjin r=0.057.544.31 (2.91, 5.72)3.230.79Tianjin r=0.16.892.580.74Tianjin r=0.156.301.990.67Tianjin r=0.25.911.60.64 In Singapore, these estimates are based on a combination of cases for whom last possible exposure is given by travel, and later cases (for whom the presumed infector was used). In Tianjin, social distancing measures were implemented during the outbreak. We find that the estimated incubation period is different, particularly in Tianjin, for cases with symptom onset on or prior to January 31st: see Figure 3 and Figure 4. The estimated median incubation period for pre-Feb one cases in Tianjin is 6.48 days; the q=(0.025,0.975) quantiles are (2.5, 13.3) days. In contrast, post-Jan 31 the median is 12.13 days with q=(0.025,0.975) quantiles (7.3, 18.7) days. The means are 6.88 (5.97, 7.87) days for early cases and 12.4 (11.1, 13.7) days for later cases. Social distancing seems unlikely to change the natural course of infection, but these results might be explained if exposure occurred during group quarantine or otherwise later than the last time individuals thought they could have been exposed. Pre-symptomatic transmission would enable this, if an individual was thought to have been exposed before group quarantine, but in actuality was exposed during quarantine by a pre-symptomatic individual. The time interval in the data would then not be a sample of the incubation period, instead it would be a sample of one or more generation times plus an incubation period. Figure 3 Download asset Open asset Fitted gamma COVID-19 incubation period distributions (without intermediates). Cases are defined as 'early' if they have symptom onset on or prior to January 31, and are classified 'late' otherwise. Figure 4 Download asset Open asset COVID-19 incubation period Kaplan-Meier curves for (a) Singapore and (b) Tianjin. Top panels show unstratified data (all cases with symptom onset given). Bottom panels show 'early' and 'late' cases, where early cases are defined as those with symptom onset on or prior to January 31, and late otherwise. In Singapore, we find the same effect, although much less pronounced. The estimated median incubation time is 5.26, with (0.025, 0.975) quantiles of (1.30, 13.8) days for early cases (also defined as cases with symptom onset on or prior to January 31st) and 5.35 (quantiles (1.22, 14.6)) days for late-arising cases. The means are 5.91 (4.50, 7.64) days for early cases and 6.06 (4.70, 7.67) days for later cases. Fits of gamma and log-normal distributions are similar; see Appendix 1—table 2. Changes in perception of exposure times after control measures were introduced (i.e. people may assume that they must have been exposed prior to control measures), together with pre-symptomatic transmission, could result in missing intermediate transmission events and hence lengthened incubation period estimates. This in part motivates our analysis with intermediate cases. Our estimates of the incubation period with intermediates are similar, under the assumption that intermediates are relatively rare. Results are shown in Figure 5 and Table 1. We find that the median of the bootstrapped mean incubation periods for Singapore with a low (0.05 per day) rate of unknown intermediates is 4.91 days (4.35, 5.69 95% bootstrap CI), compared to a generation time of 3.71 (2.36, 4.91) days. The Tianjin bootstrapped mean incubation period is 7.54 (6.76, 8.56 95% CI) days and the generation time is only 2.82 (1.83, 3.52) days. The estimates are lower when the assumed probability of unknown intermediates is higher. Indeed, if intermediates were present between assumed exposure and onset, naturally the generation time would be shorter than if they were not. The mean generation times are consistently shorter than the mean incubation periods, indicating that infection can occur prior to symptom onset. The difference is particularly pronounced in Tianjin, where long intervals were observed. Figure 5 Download asset Open asset Mean incubation period and generation time estimates from the incubation period intermediates analysis, under the assumption that the scale parameter for both distributions is equal, shown with dependence on the mean number of unknown intermediate cases per day of the empirical time elapsed between exposure and symptom onset. The incubation period is longer than the generation time, so this analysis suggests that symptom onset occurs after infectiousness begins. Top: Singapore. Bottom: Tianjin. The means are the scale times the shape, which is fixed at 2.1 in Singapore and 2.2 in Tianjin. Varying this fixed value for the shape parameter was not found to significantly impact the results. However, this approach makes a number of assumptions and is limited by the fact that if we do not know the true infectors then we are also unlikely to know the true exposure. The data we have is well suited to this method in the sense that there were particular events where exposure is thought to have occurred, and so we can account for intermediates in the manner we have done, but we do not have information for the alternative scenario in which the true exposures were prior to those given in the data. This could happen if, for example, individuals were exposed before attending an event or before known contact, and developed symptoms well after it. Exposure would thus be wrongly attributed to the event or contact. We have accommodated this with uncertainty in the exposure intervals, in particular not insisting that individuals who are likely to be the index case for a cluster (e.g. who developed symptoms on the same day as an event) must have been exposed then, but instead allowing the possibility that they were exposed earlier. Serial intervals Figure 6 represents the empirical serial intervals between all potential transmission case-pairs as noted in the data and represented in Figure 7, split into groups based on date of first symptom onset for each case-pair. The empirical mean serial intervals shorten in the 'late' group in both Singapore and Tianjin; however, the empirically derived 95% confidence intervals overlap (Singapore early 4.44 (-2.81, 11.7) vs. late 3.18 (-1.52, 7.88); Tianjin early 5.48 (-0.968, 11.9) vs. late 4.18 (-2.33, 10.7)). Negative lower bounds are due to the high standard deviation. Figure 6 Download asset Open asset Serial intervals of possible case pairs in (a) Singapore and (b) Tianjin. Pairs represent a presumed infector and their presumed infectee plotted by date of symptom onset. Cases are defined as 'early' if they have symptom onset on or prior to January 31st. Figure 7 Download asset Open asset Network diagram for (a) Singapore (b) Tianjin. Shortening serial intervals are expected as increased quarantine measures are enacted during the course of an outbreak and can be an indication of improved control through successful contact tracing, as seen in SARS (Lipsitch et al., 2003). Our results suggest that serial intervals shortened as the outbreak progressed in both clusters, but they could also be due to right truncation. Accounting for this, we found that the mean serial intervals were 4 and 5 days (Singapore, Tianjin); a Cox regression found no significant difference between the early and late groups' serial intervals. This estimate is made directly from case pairs in the data without accounting for intermediate infectors and co-primary infection, as in the ICC analysis. Table 1 shows our ICC estimates of the mean and standard deviation for the serial intervals, with comparison to other analyses and assumptions in Appendix 1—table 5. The ICC method finds the mean serial interval to be 4.17 (2.44, 5.89 95% bootstrap CI) days (0.882 bootstrap standard deviation) for Singapore and 4.31 (2.91, 5.72) days (0.716 bootstrap sd) for Tianjin, using the first four cases in each cluster. This is consistent with the results with right truncation. Pre-symptomatic transmission We estimated incubation periods and serial intervals with and without accounting for intermediate unknown cases. To estimate the portion of transmission that occurs before symptom onset, we compare the 'direct' (no intermediate) estimates of each, and the 'indirect' (accounting for intermediates) estimates of each. We estimate consistently shorter serial intervals than incubation period, suggesting that there is pre-symptomatic transmission. We took the covariation of incubation periods and serial intervals (and of generation times and incubation periods) into account by sampling the intervals jointly before estimating the fraction of the relevant differences that are negative. Even accounting for correlation, the estimated fraction of pre-symptomatic transmission for Singapore is 0.74 (regardless of early/late split) and for Tianjin is 0.72, 0.96, 0.81 (early, late, based on the direct estimates of the incubation periods and serial intervals also Figure we use the incubation period estimates that account for the pre-symptomatic transmission are in Singapore and in Tianjin, when the assumed of of intermediates is (i.e. when we assume a relatively low rate of unknown intermediates). this rate is the portion of pre-symptomatic transmission but even for we estimate the pre-symptomatic portion to be in Singapore and in Tianjin. Figure Download asset Open asset Pre-symptomatic infection as estimated by samples of interval incubation accounting for Top: Singapore. Bottom: Tianjin. without accounting for pre-symptomatic transmission. In all cases there is pre-symptomatic transmission. These results were obtained under an estimated between the incubation period and serial interval of in Tianjin. instead the were the portion of pre-symptomatic transmission in Tianjin under and is estimated as and With the are and We find that the degree of not impact our estimates of pre-symptomatic transmission. We high estimates of the fraction pre-symptomatic in Tianjin, due to the long incubation It seems likely that these are an of pre-symptomatic transmission during or of other assumptions made about exposures in the of the We that for this data and under we see evidence of at of transmission occurring before symptom onset. In our direct analysis, we estimate that infection occurred on and days before symptom onset of the infector (Singapore, Tianjin). the incubation period is for and cases in our data, on transmission for cases is and days before symptom onset (Singapore, and days before (Singapore, for cases. a low rate of potential unknown intermediate cases into the mean difference to and days (Singapore, we estimate a significant portion of pre-symptomatic transmission as serial intervals are shorter than incubation periods in our analyses These estimates are by the fact that we have estimated both incubation period and serial interval in the same and by the fact that we the same result in two In both of estimates, samples of the incubation period serial interval are with probability or and or and these lower bounds a high rate of unknown intermediates early in the outbreak. This indicates that a portion of transmission may occur before symptom onset Appendix 1 and Figure consistent with the by et al., 2020 and et al., 2020). serial intervals lower number estimates. example, if the at a rate of time of days et al., scenario an estimated number using the mean of the bootstrapped estimates is with a serial interval of 4.17 days and with a serial interval of days In contrast, if a longer serial interval days et al., 2020; Li et al., is used, the estimate is This is based on the between serial and and is a estimate that not into account a and natural of infection and It primarily to how our estimated serial intervals impact in models for COVID-19 dynamics. Discussion Here, we use transmission clusters in two where cases have exposure and symptom onset times to estimate both the incubation period and serial interval of COVID-19. We these datasets available in a convenient they were available publicly but the Singapore was in and the Tianjin cluster was on and in in We that the datasets themselves useful for understanding early spread in these The incubation period and serial interval are key parameters for transmission modeling and for public health modeling remains one of the primary policy in use in local and COVID-19 Serial intervals, together with control the shape and of the et al., the disease's incidence and how quickly an and how quickly methods need to be implemented by public health officials to control the disease et al., et al., In particular, the portion of transmission events that occur before symptom onset is a for infection control et al., and impact the of contact tracing and case (Peak et al., 2020). Singapore and Tianjin officials both quickly when COVID-19 cases and contact tracing and however, there was a difference in the of the measures The first case was identified in Singapore on 2020 and in Tianjin on By Singapore had identified close contacts and implemented a to all to Hubei and all to China, asked to monitor their health for 2 to Singapore, and asked the public to including close contact with people who are and and a mask if they had respiratory symptoms (Ministry of Health Singapore, 2020). by February in Tianjin, contacts were under and the Baodi of 1 million people was under with one person per could 2 days to public were no one could their between and without an to Tianjin were put under and all the and were While Singapore the virus spread relatively well they confirmed cases on cases on 1, cases on 22, and cases on (Ministry of Health Singapore, 2020); Tianjin began to their by and had at confirmed cases as of In Singapore and Tianjin we estimated relatively serial intervals. particular early estimates of for COVID-19 used the SARS serial interval of days et al., 2020; and 2020; et al., 2020). Our serial interval from two those of et al., 2020 and et al., 2020), who estimated a serial interval of and 4.0 days. et al., 2020 a estimate for the serial interval days with 95% but with standard deviation based on cases in we estimate the serial interval to be shorter than the incubation period in both clusters, which suggests pre-symptomatic transmission. This indicates that spread of is likely to be to stop by of cases However, shorter serial intervals also to lower estimates of and our serial intervals if this means that of the need to be to contain We the incubation per

AGA Rapid Recommendations for Gastrointestinal Procedures During the COVID-19 Pandemic

Since the outbreak of novel coronavirus infection pneumonia in Wuhan City, China, in late 2019, such cases have been gradually reported in other parts of China and abroad. Children have become susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) because of their immature immune function. As the outbreak has progressed, more cases of novel coronavirus infection/pneumonia in children have been reported. Compared with adults, the impact of SARS-CoV-2 infection in children is less severe, with a lower incidence and susceptibility in children, which results in fewer children being tested, thereby underestimating the actual number of infections. Therefore, strengthening the diagnosis of the disease is particularly important for children, and early and clear diagnosis can determine treatment strategies and reduce the harm caused by the disease to children. According to the Novel Coronavirus Infection Pneumonia Diagnosis and Treatment Standards (trial version 7) issued by National Health Committee and the latest diagnosis and treatment strategies for novel coronavirus infection pneumonia in children, this review summarizes current strategies on diagnosis and treatment of SARS-CoV-2 infection in children.

The role of close contacts tracking management in COVID-19 prevention: A cluster investigation in Jiaxing, China

Challenges with the management of older patients with cancer during the COVID-19 pandemic

Effects of Chinese Strategies for Controlling the Growth Speed of the Cumulative Confirmed Cases of Novel Coronavirus–Infected Pneumonia in China

Characteristics of COVID-19 infection in Beijing

Trends in Transmissibility of 2019 Novel Coronavirus-Infected Pneumonia in Wuhan and 29 Provinces in China

This project does not involve primary data collection. All data used are from: Li, Qun, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England Journal of Medicine (2020). Nishiura, Hiroshi, Natalie M. Linton, and Andrei R. Akhmetzhanov. Serial interval of novel coronavirus (COVID-19) infections. International Journal of Infectious Diseases (2020). Bi, Qifang, et al. Epidemiology and Transmission of COVID-19 in Shenzhen China: Analysis of 391 cases and 1,286 of their close contacts. MedRXiv (2020).

Early in the epidemic: impact of preprints on global discourse about COVID-19 transmissibility

In December 2019, a new type viral pneumonia cases occurred in Wuhan, Hubei Province; and then named “2019 novel coronavirus (2019-nCoV)” by the World Health Organization (WHO) on 12 January 2020. For it is a never been experienced respiratory disease before and with infection ability widely and quickly, it attracted the world’s attention but without treatment and control manual. For the request from frontline clinicians and public health professionals of 2019-nCoV infected pneumonia management, an evidence-based guideline urgently needs to be developed. Therefore, we drafted this guideline according to the rapid advice guidelines methodology and general rules of WHO guideline development; we also added the first-hand management data of Zhongnan Hospital of Wuhan University. This guideline includes the guideline methodology, epidemiological characteristics, disease screening and population prevention, diagnosis, treatment and control (including traditional Chinese Medicine), nosocomial infection prevention and control, and disease nursing of the 2019-nCoV. Moreover, we also provide a whole process of a successful treatment case of the severe 2019-nCoV infected pneumonia and experience and lessons of hospital rescue for 2019-nCoV infections. This rapid advice guideline is suitable for the first frontline doctors and nurses, managers of hospitals and healthcare sections, community residents, public health persons, relevant researchers, and all person who are interested in the 2019-nCoV.

The basic reproduction number of novel coronavirus (2019-nCoV) estimation based on exponential growth in the early outbreak in China from 2019 to 2020: A reply to Dhungana

<h3>Abstract</h3> <h3>Background</h3> The 2019 novel coronavirus infected pneumonia (COVID-19) represents a significant public health threat. The COVID-19 emerged in December 2009 in Wuhan, China and rapidly spread to other regions and countries. The variation in transmission patterns and disease spread in regard to time or among different locations, partially reflecting the public health intervention effects, remains to be quantified. As most transmissibility-related epidemic parameters are unknown, we sought, with minimal assumptions, to estimate real-time transmissibility and forecast new cases using dynamic modelling. <h3>Methods</h3> Using the cases reported from the National Health Commission of China and transportation data, including the total number of travelling hours through railway, airplane, and car outbound from Wuhan, we have built a time-series model to estimate real-time underlying transmission rates of newly generated cases sequentially from January 20, 2020 to Feb 13, 2020 in Wuhan, Hubei province and other 28 provinces in China. We quantified the instantaneous transmission rate and relative reproduction number (<i>R</i>_<i>t</i>) of COVID-19, and evaluated whether public health intervention affected the case transmissibility in each province. Based on the current estimates, we have predicted the trend of disease spread with a high level of certainty. <h3>Findings</h3> We estimated that <i>R</i>_<i>t</i> declined from the range of 4 to 5 towards 1 and remained below unity, while there was an initial growth followed by a decline in a shorter period in Hubei and other provinces. The ratio of transmission rates decreased dramatically from January 23 to 27 likely due to the rigorous public health intervention implemented by the government beginning on January 23, 2020. The mean duration of the infectious period was 6 to 9 days. We have predicted the trend of infection sizes which became stable in provinces around February 19 to 24, 2020, and the date of containment would be one-week later in Wuhan. <h3>Interpretation</h3> Public health interventions implemented at both the social and personal levels are effective in preventing outbreaks of COVID-19 in Wuhan and other provinces. Model prediction results suggested that COVID-19 will be contained around the end of February 2020 in China.