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The serial interval of COVID-19 from publicly reported confirmed cases

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We estimate the distribution of serial intervals for 468 confirmed cases of COVID-19 reported in 93 Chinese cities by February 8, 2020. The mean and standard deviation are 3.96 (95% CI 3.53-4.39) and 4.75 (95% CI 4.46-5.07) days, respectively, with 12.6% of reports indicating pre-symptomatic transmission. One sentence summary We estimate the distribution of serial intervals for 468 confirmed cases of COVID-19 reported in 93 Chinese cities by February 8, 2020.
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Title: The serial interval of COVID-19 from publicly reported confirmed cases
Running Head: The serial interval of COVID-19
Authors: Zhanwei Du1,+, Lin Wang2,+, Xiaoke Xu3, Ye Wu4,5, Benjamin J. Cowling6, and
Lauren Ancel Meyers1,7*
Affiliations:
1. The University of Texas at Austin, Austin, Texas 78712, The United States of
America
2. Institut Pasteur, 28 rue du Dr Roux, Paris 75015, France
3. Dalian Minzu University, Dalian 116600, China
4. Computational Communication Research Center, Beijing Normal University, Zhuhai,
519087, China
5. School of Journalism and Communication, Beijing Normal University, Beijing,
100875, China
6. The University of Hong Kong, Hong Kong SAR, China
7. Santa Fe Institute, Santa Fe, New Mexico, The United States of America
Corresponding author: Lauren Ancel Meyers
Corresponding author email: laurenmeyers@austin.utexas.edu
+ These first authors contributed equally to this article
Abstract
As a novel coronavirus (COVID-19) continues to emerge throughout China and threaten the
globe, its transmission characteristics remain uncertain. Here, we analyze the serial
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intervals–the time period between the onset of symptoms in an index (infector) case and the
onset of symptoms in a secondary (infectee) case–of 468 infector-infectee pairs with
confirmed COVID-19 cases reported by health departments in 18 Chinese provinces between
January 21, 2020, and February 8, 2020. The reported serial intervals range from -11 days to
20 days, with a mean of 3.96 days (95% confidence interval: 3.53-4.39), a standard deviation
of 4.75 days (95% confidence interval: 4.46-5.07), and 12.1% of reports indicating
pre-symptomatic transmission.
Keywords: Wuhan, coronavirus, epidemiology, serial interval
A new coronavirus (COVID-19) emerged in Wuhan, China in late 2019 and was declared a
public health emergency of international concern by the World Health Organization (WHO)
on January 30, 2020 (1). As of February 19, 2020, the WHO has reported over 75,204
COVID-19 infections and over 2,009 COVID-19 deaths (2), while key aspects of the
transmission dynamics of COVID-19 remain unclear (3). The serial interval of COVID-19 is
defined as the time duration between a primary case (infector) developing symptoms and
secondary case (infectee) developing symptoms (4,5). Obtaining robust estimates for the
distribution of COVID-19 serial intervals is a critical input for determining the reproduction
number which can indicate the extent of interventions required to control an epidemic (6).
However, this quantity cannot be inferred from daily case count data alone (7).
To obtain reliable estimates of the serial interval, we obtained data on 468 COVID-19
transmission events reported in mainland China outside of Hubei Province between January
21, 2020, and February 8, 2020. Each report consists of a probable date of symptom onset for
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both the infector and infectee as well as the probable locations of infection for both cases.
The data include only confirmed cases that were compiled from online reports from 18
provincial centers for disease control and prevention.
Notably, 59 of the 468 reports indicate that the infectee developed symptoms earlier than the
infector. Thus, pre-symptomatic transmission may be occurring, i.e., infected persons may be
infectious before their symptoms appear. In light of these negative-valued serial intervals, we
assume that COVID-19 serial intervals follow a normal distribution rather than the more
commonly assumed gamma or Weibull distributions that are limited to strictly positive values
(8,9). We estimate a mean serial interval for COVID-19 of 3.96 [95% CI 3.53-4.39] with a
standard deviation of 4.75 [95% CI 4.46-5.07], which is considerably lower than reported
mean serial intervals of 8.4 days for SARS (9) and 12.6 days (10) - 14.6 days (11) for MERS.
The mean serial interval is slightly but not significantly longer when the index case is
imported (4.06 [95% CI 3.55-4.57]) versus locally infected (3.66 [95% CI 2.84-4.47]).
Combining these findings with published estimates for the early exponential growth rate
COVID-19 in Wuhan (12,13), we estimate a basic reproduction number (R
0) of 1.33 (6),
which is lower than published estimates that assume a mean serial interval exceeding seven
days (13–15).
These estimates reflect reported symptom onset dates for 752 cases from 93 Chinese cities,
who range in age from 1 to 90 years (mean 45.2 years and SD 17.21 years). We note three
key caveats of the analysis. First, the data are restricted to online reports of confirmed cases
and therefore may be biased towards more severe cases in areas with a high-functioning
healthcare and public health infrastructure. Second, the distribution of serial intervals varies
throughout an epidemic, with the time between successive cases contracting around the
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epidemic peak (16). To provide intuition, a susceptible person is likely to become infected
more quickly if they are surrounded by two infected people rather than just one. Since our
estimates are based primarily on transmission events reported during the early stages of
outbreaks, we do not explicitly account for such compression and interpret the estimates as
basic
serial intervals at the outset of an epidemic. If some of the reported infections occurred
amidst growing clusters of cases, our estimates may instead reflect effective serial intervals
that would be expected during a period of epidemic growth. Finally, rapid isolation of
symptomatic cases in some locations may have prevented longer serial intervals, potentially
biasing our estimate downwards compared to serial intervals that might be observed in an
uncontrolled epidemic.
Given the heterogeneity in type and reliability of these sources, we caution that our findings
should be interpreted as working hypotheses regarding the infectiousness of COVID-19
requiring further validation as more data become available. The potential implications for
COVID-19 control are mixed. While our lower estimates for R
0 suggest easier containment,
the large number of reported asymptomatic transmission events is concerning.
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Figure. Estimated serial interval distribution for COVID-19 based on 468 reported
transmission events in China between January 21, 2020 and February 8, 2020. Bars
indicate the number of infection events with specified serial interval and blue lines indicate
fitted normal distributions for (a) all infection events (N
= 468) reported across 93 cities of
mainland China by February 8, 2020, and (b) the subset infection events (N
= 122) in which
both the infector and infectee were infected in the reporting city (i.e., the index case was not
an importation from another city). Negative serial intervals (left of the vertical dotted lines)
suggest the possibility of COVID-2019 transmission from asymptomatic or mildly
symptomatic cases.
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Table S1. Estimated serial interval distributions based on location of index infection. We
assume that the serial intervals follow normal distributions and report the estimated means
and standard deviations for (a) all 468 infector-infectee pairs reported from 93 cities in
mainland China by February 8, 2020, (b) a subset of 122 infection events in which the index
case was infected locally, and (c) a subset of 346 infection events in which the index case was
an importation from another city. The rightmost column provides the proportion of infection
events in which the secondary case developed symptoms prior to the index case.
Group
Mean [95 CI%]
SD [95 CI%]
Proportion of
serial intervals < 0
All (N
=468)
3.96 [3.53, 4.39]
4.75 [4.46, 5.07]
12.61% (N
= 59)
Locally infected index case
(N
=122)
3.66 [2.84, 4.47]
4.54 [4.03, 5.20]
14.75% (N
= 18)
Imported index case
(N
=346)
4.06 [3.55, 4.57]
4.82 [4.48, 5.21]
11.85% (N
= 41)
Supplementary Appendix
Data
We collected publicly available online data on 6,903 confirmed cases from 271 cities of
mainland China, that were available as of February 8, 2020. The data were extracted in
Chinese from the websites of provincial public health departments and translated to English.
We then filtered the data for clearly indicated transmission events consisting of: (i) a known
infector
and infectee
, (ii) reported locations of infection for both cases, and (iii) reported dates
and locations of symptom onset for both cases. We thereby obtained 468 infector-infectee
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pairs from 93 Chinese cities between January 21, 2020 and February 8, 2020 (Figure S1).
The index cases (infectors) for each pair are reported as either importations from the city of
Wuhan (N
= 239), importations from cities other than Wuhan (N
= 106) or local infections (N
= 122). The cases included 752 unique individuals, with 98 index cases who infected multiple
people and 17 individuals that appear as both infector and infectee. They range in age from 1
to 90 years and include 386 females, 363 males and 3 cases of unreported sex.
Inference Methods
Estimating serial interval distribution
For each pair, we calculated the number of days between the reported symptom onset date for
the infector and the reported symptom onset date for the infectee. Negative values indicate
that the infectee developed symptoms before the infectee. We then used the fitdist function in
Matlab (17) to fit a normal distribution to all 468 observations. It finds unbiased estimates of
the mean and standard deviation, with 95% confidence intervals. We applied the same
procedure to estimate the means and standard deviations with the data stratified by whether
the index case was imported or infected locally.
Estimating the basic reproduction number (R
0)
Given a epidemic growth rate r
and a normally distributed serial interval with mean ( ) and
standard deviation ( ), the basic reproduction number is given by
(6).
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Assuming our point estimates for the mean and standard deviation of the serial interval
distribution (Table S1) and a recently published estimate for the exponential growth rate of
COVID-19 infections in Wuhan of 0.10 per day (13), we estimate an R
0 of 1.33.
Supplementary Analysis
To facilitate interpretation and future analyses, we summarize key characteristics of the
COVID-2019 infection report data set.
Age distribution
: Of the 737 unique cases in the data set, 1.7%, 3.5%, 54.1%, 26.1% and
14.5% were ages 0-4, 5-17, 18-49, 50-64, and over 65 years, respectively. Across all
transmission events, approximately one third occurred between adults ages 18 to 49, ~92%
had an adult infector (over 18), and over 99% had an adult infectee (over 18) (Table S2).
Secondary case distribution
: Across the 468 transmission events, there were 301 unique
infectors. The mean number of transmission events per infector is 1.55 (Figure S2) with a
maximum of 16 secondary infections reported from a 40 year old male in Liaocheng city of
Shandong Province.
Geographic distribution
: The 468 transmission events were reported from 93 Chinese cities
in 17 Chinese provinces and Tianjin (Figure S3). There are 22 cities with at least five
infection events and 71 cities with fewer than five infection events in the sample. The
maximum number of reports from a city is 72 for Shenzhen, which reported 339 cumulative
cases as of February 8, 2020.
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Table S2. Age distribution for the 457 of 468 infector-infectee pairs. Each value denotes
the number of infector-infectee pairs in the specified age combination. Age was not reported
for the remaining 11 pairs.
Infectee
0-4
5-17
18-49
50-46
65+
Total
Infector
0-4
0
0
0
0
0
0
5-17
0
0
1
0
1
2
18-49
12
18
154
60
44
288
50-46
1
5
47
49
13
115
65+
0
1
22
10
19
52
Total
13
24
224
119
77
457
Figure S1. Geographic composition of the infection report data set. The data consist of
468 infector-infectee pairs reported by February 8, 2020 across 93 cities in mainland China.
Colors represent the number of reported events per city, which range from 1 to 72, with an
average of 5.03 (SD 8.54) infection events. The 71 cities with fewer than five events are
colored in blues; the 22 cities with at least five events are colored in shades of orange.
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Figure S2. Number of infections per unique index case in the infection report data set.
There are 301 unique infectors across the 468 infector-infectee pairs. The number of
transmission events reported per infector ranges from 1 to 16, with ~55% having only one.
Acknowledgments
We acknowledge the financial support from NIH (U01 GM087719) and the National Natural
Science Foundation of China (61773091).
Author Bio
Dr. Du is a postdoctoral researcher in the Department of Integrative Biology at the University
of Texas at Austin. He develops mathematical models to elucidate the transmission dynamics,
surveillance, and control of infectious diseases.
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... El parámetro γ en la última expresión es de aproximadamente 4 días, el inverso del intervalo serial (Du et al. 2020) El valor k_ (t-1) es el número de nuevos contagiosos en el tiempo t-1. De acuerdo con esto, es posible reformular la probabilidad en términos de R_t. ...
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The World Health Organization (WHO) has declared the 2019 novel coronavirus outbreak (COVID-19) as a pandemic on March 11th. As of the end of April 2020, more than 3 million COVID-19 cases and 200 thousands death have been reported from more than 200 countries. It is therefore important to know what to expect in terms of the growth of the number of cases, and to understand what is needed to arrest the very worrying trends. In this disruptive period of the COVID-19 pandemic, scientists are investing an unprecedented effort to try to forecast and suggest measures to mitigate the ill-fated effects of the pandemic. Although recent literature indicates that travel control and restrictions of public activities are effective in delaying the spreading of the COVID-19 epidemic in China (Heory et al. 2020; Chinazzi et al. 2020), there is still an urgent need for greater understanding of the intrinsic dynamics and effective control methods which can offer in emergency and pandemic management. This Research Topic aims to extend the angles and collect articles which propose data driven mathematical or statistical models of the spread of the COVID-19, and/or of its foreseen consequences on public health, society, industry, economics and technology. It also focuses on collecting the real-time big data of COVID-19 spreading, and further helps the scientists to establish the efficient databases for the risk management. Furthermore, we also want to understand the impact of the pandemic on the economy and society of the whole world, and provide efficient suggestions for economic recovery and social order maintenance. The editors and reviewers of this special issue will guarantee a fast, but fair, peer-to-peer review procedure, in order to provide to society a reliable injection of scientific insights. The scopes and topics include but are not limited to: • nonlinear dynamics and non-equilibrium processes of COVID-19; • complex system and complex networks modeling of COVID-19; • computational epidemiology, biophysics, systems biology and computational biology aspects of COVID-19; • artificial intelligence, machine learning and big data analytics of COVID-19; • self-organization and emergent phenomena of social organization with COVID-19 pandemics; • applications to social science, Public health, economics, engineering and other aspects related to COVID-19 pandemics.
Chapter
Qualitative–quantitative reasoning is the way we think informally about formal or numerical phenomena. It is ubiquitous in scientific, professional and day-to-day life. Mathematicians have strong intuitions about whether a theorem is true well before a proof is found – intuition that also drives the direction of new proofs. Engineers use various approximations and can often tell where a structure will fail. In computation we deal with order of magnitude arguments in complexity theory and data science practitioners need to match problems to the appropriate neural architecture or statistical method. Even in the supermarket, we may have a pretty good idea of about how much things will cost before we get to the checkout. This paper will explore some of the different forms of QQ–reasoning through examples including the author’s own experience numerically modelling agricultural sprays and formally modelling human–computer interactions. We will see that it is often the way in which formal and mathematical results become useful and also the importance for public understanding of key issues including Covid and climate change. Despite its clear importance, it is a topic that is left to professional experience, or sheer luck. In early school years pupils may learn estimation, but in later years this form of reasoning falls into the gap between arithmetic and formal mathematics despite being more important in adult life than either. The paper is partly an introduction to some of the general features of QQ-reasoning, and partly a ‘call to arms’ for academics and educators.
Article
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Objective(s): To estimate the impact of universal community face mask use in Victoria, Australia along with other routine disease control measures in place. Methods: A mathematical modeling study using an age structured deterministic model for Victoria, was simulated for 123 days between 1 June 2020 and 1 October 2020, incorporating lockdown, contact tracing, and case findings with and without mask use in varied scenarios. The model tested the impact of differing scenarios of the universal use of face masks in Victoria, by timing, varying mask effectiveness, and uptake. Results: A six-week lockdown with standard control measures, but no masks, would have resulted in a large resurgence by September, following the lifting of restrictions. Mask use can substantially reduce the epidemic size, with a greater impact if at least 50% of people wear a mask which has an effectiveness of at least 40%. Early mask use averts more cases than mask usage that is only implemented closer to the peak. No mask use, with a 6-week lockdown, results in 67,636 cases and 120 deaths by 1 October 2020 if no further lockdowns are used. If mask use at 70% uptake commences on 23 July 2020, this is reduced to 7,961 cases and 42 deaths. We estimated community mask effectiveness to be 11%. Conclusion(s): Lockdown and standard control measures may not have controlled the epidemic in Victoria. Mask use can substantially improve epidemic control if its uptake is higher than 50% and if moderately effective masks are used. Early mask use should be considered in other states if community transmission is present, as this has a greater effect than later mask wearing mandates.
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Background The lab-confirmed interval is the date from lab confirmation in a core case (infector) to lab confirmation in a second case (infectee); however, its distribution and application are seldom reported. This study aimed to investigate the lab-confirmed interval and its application in the preliminary evaluation of the strength of disease prevention and control measures. Methods Taking European countries and Chinese provinces outside Hubei as examples, we identified 63 infector-infectee pairs from European countries from Wikipedia, and 103 infector-infectee pairs from official public sources in Chinese provinces outside Hubei. The lab-confirmed intervals were obtained through analysis of the collected data and adopting the bootstrap method. Results The mean lab-confirmed interval was 2.6 (95% CI: 2.1–3.1) days for Europe and 2.6 (95% CI: 1.9–3.3) days for China outside Hubei, which were shorter than the reported serial intervals. For index patients aged ≥60 years old, the lab-confirmed interval in Europe was slightly longer (mean: 2.9; 95% CI: 2.0–3.6) and obviously longer in China outside Hubei (mean: 3.8; 95% CI: 1.9–5.5) than that for patients aged < 60 years. Conclusion Investigation of the lab-confirmed interval can provide additional information on the characteristics of emergent outbreaks and can be a feasible indication to evaluate the strength of prevention and control measures. When the lab-confirmed interval was shorter than the serial interval, it could objectively reflect improvements in laboratory capacity and the surveillance of close contacts.
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The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number-the average number of secondary cases generated by a single primary case in a naïve population. We modeled epidemic growth either from a single index case with illness onset on 8 December, 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% confidence interval [CI]: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data.
Preprint
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The exported cases of 2019 novel coronavirus (2019-nCoV) infection who were confirmed in other countries provide a chance to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in China. Knowledge of the cCFR is critical to characterize the severity and understand pandemic potential of 2019-nCoV in the early stage of epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number, i.e., the average number of secondary cases generated by a single primary case in a naïve population. As of 24 January 2020, with 23 exported cases, and estimating the growth rate from 8 December 2019 (scenario 1) and using the data since growth of exported cases (scenario 2), the cumulative incidence in China was estimated at 5433 cases (95% confidence interval (CI): 3883, 7160) and 17780 cases (95% CI: 9646, 28724), respectively. The latest estimates of the cCFR were 4.6% (95% CI: 3.1-6.6) for scenario 1 and 7.7% (95% CI: 4.9-11.3%) for scenario 2, respectively. The basic reproduction number was estimated to be 2.2 (95% CI: 2.1, 2.3) and 3.7 (95% CI: 3.1, 4.3) for scenarios 1 and 2, respectively. Based on the results, we note that current 2019-nCoV epidemic has a substation potential to cause a pandemic. The proposed approach can provide insights into early risk assessment using only publicly available data.
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Background: The initial cases of novel coronavirus (2019-nCoV)-infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods: We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results: Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions: On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.).
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Background: A Middle East Respiratory Syndrome coronavirus (MERS-CoV) outbreak in South Korea in 2015 started by a single imported case and was amplified by intra- and inter-hospital transmission. We describe two hospital outbreaks of MERS-CoV infection in Daejeon caused by a single patient who was infected by the first Korean case of MERS. Materials and methods: Demographic and clinical information involving MERS cases in the Daejeon cluster were retrospectively collected and potential contacts and exposures were assessed. The incubation periods and serial intervals were estimated. Viral RNAs were extracted from respiratory tract samples obtained from the index case, four secondary cases and one tertiary case from each hospital. The partial S2 domain of the MERS-CoV spike was sequenced. Results: In Daejeon, a MERS patient (the index case) was hospitalized at Hospital A in the first week of illness and was transferred to Hospital B because of pneumonia progression in the second week of illness, where he received a bronchoscopic examination and nebulizer therapy. A total of 23 secondary cases (10 in Hospital A and 13 in Hospital B) were detected among patients and caregivers who stayed on the same ward with the index case. There were no secondary cases among healthcare workers. Among close hospital contacts, the secondary attack rate was 15.8% (12/76) in Hospital A and 14.3% (10/70) in Hospital B. However, considering the exposure duration, the incidence rate was higher in Hospital B (7.7/100 exposure-days) than Hospital A (3.4/100 exposure-days). In Hospital B, the median incubation period was shorter (4.6 days vs. 10.8 days), the median time to pneumonia development was faster (3 days vs. 6 days) and mortality was higher (70% vs. 30.8%) than in Hospital A. MERS-CoV isolates from 11 cases formed a single monophyletic clade, with the closest similarity to strains from Riyadh. Conclusion: Exposure to the MERS case in the late stage (2nd week) of diseases appeared to increase the risk of transmission and was associated with shorter incubation periods and rapid disease progression among those infected. Early detection and isolation of cases is critical in preventing the spread of MERS in the hospital and decreasing the disease severity among those infected.
Article
Background: Since Dec 31, 2019, the Chinese city of Wuhan has reported an outbreak of atypical pneumonia caused by the 2019 novel coronavirus (2019-nCoV). Cases have been exported to other Chinese cities, as well as internationally, threatening to trigger a global outbreak. Here, we provide an estimate of the size of the epidemic in Wuhan on the basis of the number of cases exported from Wuhan to cities outside mainland China and forecast the extent of the domestic and global public health risks of epidemics, accounting for social and non-pharmaceutical prevention interventions. Methods: We used data from Dec 31, 2019, to Jan 28, 2020, on the number of cases exported from Wuhan internationally (known days of symptom onset from Dec 25, 2019, to Jan 19, 2020) to infer the number of infections in Wuhan from Dec 1, 2019, to Jan 25, 2020. Cases exported domestically were then estimated. We forecasted the national and global spread of 2019-nCoV, accounting for the effect of the metropolitan-wide quarantine of Wuhan and surrounding cities, which began Jan 23-24, 2020. We used data on monthly flight bookings from the Official Aviation Guide and data on human mobility across more than 300 prefecture-level cities in mainland China from the Tencent database. Data on confirmed cases were obtained from the reports published by the Chinese Center for Disease Control and Prevention. Serial interval estimates were based on previous studies of severe acute respiratory syndrome coronavirus (SARS-CoV). A susceptible-exposed-infectious-recovered metapopulation model was used to simulate the epidemics across all major cities in China. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credibile interval (CrI). Findings: In our baseline scenario, we estimated that the basic reproductive number for 2019-nCoV was 2·68 (95% CrI 2·47-2·86) and that 75 815 individuals (95% CrI 37 304-130 330) have been infected in Wuhan as of Jan 25, 2020. The epidemic doubling time was 6·4 days (95% CrI 5·8-7·1). We estimated that in the baseline scenario, Chongqing, Beijing, Shanghai, Guangzhou, and Shenzhen had imported 461 (95% CrI 227-805), 113 (57-193), 98 (49-168), 111 (56-191), and 80 (40-139) infections from Wuhan, respectively. If the transmissibility of 2019-nCoV were similar everywhere domestically and over time, we inferred that epidemics are already growing exponentially in multiple major cities of China with a lag time behind the Wuhan outbreak of about 1-2 weeks. Interpretation: Given that 2019-nCoV is no longer contained within Wuhan, other major Chinese cities are probably sustaining localised outbreaks. Large cities overseas with close transport links to China could also become outbreak epicentres, unless substantial public health interventions at both the population and personal levels are implemented immediately. Independent self-sustaining outbreaks in major cities globally could become inevitable because of substantial exportation of presymptomatic cases and in the absence of large-scale public health interventions. Preparedness plans and mitigation interventions should be readied for quick deployment globally. Funding: Health and Medical Research Fund (Hong Kong, China).
Book
Highly practical yet authoritative, the new edition of Modern Infectious Disease Epidemiology has been thoroughly updated and revised in line with changing health concerns. This successful book continues to outline the tools available to the infectious disease student or clinician seeking a thorough background in the epidemiology of infectious and communicable diseases. Building on many case studies and practical scenarios included, the book then uses the tools learnt to illustrate the fundamental concepts of the study of infectious diseases, such as infection spread, surveillance and control, infectivity, incubation periods, seroepidemiology, and immunity in populations. New edition of this popular book, completely revised and updated Retains the clarity and down-to-earth approach praised in previous editions Successfully combines epidemiological theory with the principles of infectious disease treatment and control A highly experienced author brings a personal and unique approach to this important subject All students of epidemiology, infectious disease medicine and microbiology will find this text invaluable, ensuring its continued popularity.
Article
South Korea is experiencing the largest outbreak of Middle East respiratory syndrome coronavirus infections outside the Arabian Peninsula, with 166 laboratory- confirmed cases, including 24 deaths up to 19 June 2015. We estimated that the mean incubation period was 6.7 days and the mean serial interval 12.6 days. We found it unlikely that infectiousness precedes symptom onset. Based on currently available data, we predict an overall case fatality risk of 21% (95% credible interval: 14–31). © 2015 European Centre for Disease Prevention and Control (ECDC). All rights reserved.
Article
The serial interval of an infectious disease represents the duration between symptom onset of a primary case and symptom onset of its secondary cases. A good evidence base for such values is essential, because they allow investigators to identify epidemiologic links between cases and serve as an important parameter in epidemic transmission models used to design infection control strategies. We reviewed the literature for available data sets containing serial intervals and for reported values of serial intervals. We were able to collect data on outbreaks within households, which we reanalyzed to infer a mean serial interval using a common statistical method. We estimated the mean serial intervals for influenza A(H3N2) (2.2 days), pandemic influenza A(H1N1)pdm09 (2.8 days), respiratory syncytial virus (7.5 days), measles (11.7 days), varicella (14.0 days), smallpox (17.7 days), mumps (18.0 days), rubella (18.3 days), and pertussis (22.8 days). For varicella, we found an evidence-based value that deviates substantially from the 21 days commonly used in transmission models. This value of the serial interval for pertussis is, to the best of our knowledge, the first that is based on observations. Our review reveals that, for most infectious diseases, there is very limited evidence to support the serial intervals that are often cited.