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Epidemiological and clinical features of the 2019 novel coronavirus outbreak in
China
Yang Yang1*, Qing-Bin Lu2, Ming-Jin Liu1, Yi-Xing Wang3, An-Ran Zhang1,3,4, Neda
Jalali1, Natalie E. Dean1, Ira Longini1, M. Elizabeth Halloran5 Bo Xu6, Xiao-Ai
Zhang3, Li-Ping Wang7*, Wei Liu3*, Li-Qun Fang3*
1Department of Biostatistics, College of Public Health and Health Professions, and
Emerging Pathogens Institute, University of Florida, Gainesville, Florida, U. S. A;
2Department of Laboratorial Science and Technology, School of Public Health,
Peking University, Beijing, P. R. China; 3State Key Laboratory of Pathogen and
Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, P. R. China;
4Department of Epidemiology, School of Public Health, Shandong University, Jinan,
P. R. China; 5Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer
Research Center, Seattle, Washington, U. S. A.; 6Army Engineering University of
PLA, Nanjing, P.R. China; and 7Division of Infectious Disease, Key Laboratory of
Surveillance and Early-warning on Infectious Disease, Chinese Centre for Disease
Control and Prevention.
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*Correspondence:
L. Q. Fang or W. Liu, State Key Laboratory of Pathogen and Biosecurity, 20 Dong-Da
Street, Beijing Institute of Microbiology and Epidemiology, Fengtai District, Beijing,
China 100071 (fang_lq@163.com or lwbime@163.com); Y. Yang, Department of
Biostatistics, College of Public Health and Health Professions, and Emerging
Pathogens Institute, University of Florida, Gainesville, FL, USA 32611
(yangyang@ufl.edu); L. P. Wang, Division of Infectious Disease, Key Laboratory of
Surveillance and Early-warning on Infectious Disease, Chinese Centre for Disease
Control and Prevention, Beijing, China 102206 (wanglp@chinacdc.cn)
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Abstract
Background The ongoing outbreak of the 2019 novel coronavirus (2019-nCoV) in
China has led to the declaration of Public Health Emergency of International Concern
by the World Health Organization.
Methods All 2019-nCoV infected patients reported to Chinese Center for Disease
Control and Prevention up to 26 January 2020 were included for analysis. Disease and
death incidence were compared between demographic groups and baseline conditions.
Case fatality rates (CFRs) and the basic reproductive number R0 was estimated with a
transmission model.
Results: As of 26 January 2020, a total of 8866 patients including 4021 (45.35%)
laboratory confirmed patients were reported from 30 provinces. Nearly half of the
patients were aged 50 years or older (47.7%). There was a clear gender difference in
incidence with 0.31 (male) vs. 0.27 (female) per 100,000 people (P<0.001). The
median incubation period was 4.75 (interquartile range: 3.0-7.2) days. About 25.5%,
69.9% and 4.5% patients were diagnosed with severe pneumonia, mild pneumonia,
and non-pneumonia, respectively. The overall CFR was estimated be 3.06% (95% CI
2.02-4.59%), but male patients, 60 years old, baseline diagnosis of severe
pneumonia and delay in diagnosis were associated with substantially elevated CFR.
The R0 was estimated to be 3.77 (95% CI 3.51-4.05), ranging 2.23 4.82 in sensitivity
analyses varying the incubation and infectious periods.
Conclusions Compared with SARS-CoV, 2019-nCoV had comparable
transmissibility and lower CFR. Our findings based on individual-level surveillance
data emphasize the importance of early detection of elderly patients, particularly
males, before symptoms progress to severe pneumonia.
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Introduction
The fast-growing outbreak of the 2019 novel coronavirus (2019-nCoV), which
originated from Wuhan in central China at the beginning of December 2019, reached
multiple continents in merely a month. On 30 January 2020, the World Health
Organization (WHO) declared it to be a Public Health Emergency of International
Concern.1,2 As of 31 January 2020, the disease was confirmed in more than 9,000
patients in all 31 provinces of mainland China and 138 patients in 23 other countries
and regions (including Hong Kong, Macau and Taiwan, China), with a total of 213
deaths in mainland China and 1 in Hong Kong.3 Despite the lockdown of the source
city since 23 January 2020, the massive human movement during the Chinese
traditional new year period may have fueled the spread of the disease. To date, there is
no antiviral treatment or vaccine specifically designed for this virus with field-proven
effectiveness. Nonpharmaceutical interventions such as shutdown of public gathering
places, wearing of facial masks and social distancing could slow the spread of the
disease; however, when used alone, these measures may not succeed in fully
containing an outbreak of a novel and highly transmissible pathogen.4
Despite the difficulty in accurate real-time assessment of the epidemic because of
the insufficient supply of testing kits and delayed reporting by overwhelmed
healthcare facilities, a few studies have made progress to understand the molecular,
clinical and epidemiological features of the 2019-nCoV.2,5-11 For example, the
2019-nCoV more severely affect older patients with comorbidities.12 Analysis on
family clusters and an assessment of the basic reproductive number based on early
investigation data have shown efficient person-to-person transmissibility of the
virus.7,10 Nevertheless, there is an urgent need to verify and update these early
findings as the number of patients accumulates. Here we summarize our findings on
clinical and epidemiological characteristics of the 2019-nCoV based on the
surveillance data of confirmed and suspected 2019-nCoV patients in China up to 26
January 2020.
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Methods
Data sources
Soon after 2019-nCoV was identified as the etiological pathogen of the pneumonia
outbreak, the disease was classified as Class B infectious disease and managed as
Class A.13 Confirmed and suspected patients are required to be reported within 24
hours to the National Notifiable Infectious Disease Surveillance System, according to
the standard protocol issued by National Health Commission of the People’s Republic
of China (NHCC).14 Publicly accessible cumulative numbers of confirmed and
suspected cases (see Supplementary Methods for definitions), which are updated daily
by the NHCC, were collected for preliminary analyses.15 The data on confirmed and
suspected patients reported by 26 January 2020 were obtained from the Chinese
Public Health Science Data Center. Case data included basic demographic
information, case classification, date of symptom onset, date of diagnosis, date of
hospitalization, date of discharge or date of death, etc. Population data at the
prefecture (city) level in the year of 2017 were obtained from the National Bureau of
Statistics of the People’s Republic of China.
Statistical analysis
Incidence (per 100,000 people) is defined as the number of patients as of 26 January
2020 divided by the population size in a given prefecture. Observed case fatality rate
(CFR) is defined as the observed number of deaths divided by the number of patients.
The patient’s location was mapped at the prefecture level using a geographic
information system (ArcGIS, Environmental Systems Research Institute, Redlands,
California). Statistical analyses such as between-sample comparisons were performed
using R 3.6.1 (R core team, 2017) and Stata, version 14.0 (Stata Corp LP, College
Station, TX, USA).
To account for the fact that the final clinical outcome has not resolved for the
majority of patients, especially the newly identified cases, we estimate the CFR by
restricting the analysis to patients with symptom onset at least d days earlier than 26
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January 2020, d varying from 10 to 14 days. For each value of d, a CFR is calculated
as the proportion of fatal cases among all patients meeting this restriction. The
average of these estimates serves as the final estimate of CFR, which is referred to as
adjusted CFR. We estimated adjusted CFR by age group, gender, baseline severity
level and delay in diagnosis for confirmed patients only.
To estimate the basic reproductive number, R0, defined as the average number of
secondary infections a patient can generate in a fully susceptible population, we used
a chain binomial model.16,17 Another important epidemiological quantity is the
effective reproductive number, Rt, that measures the variation of a pathogen’s
transmissibility over time in response to, e.g., climate change or intervention
programs. The estimation of Rt is achieved with a sliding time window of 5 days to
estimate within-window transmissibility. Due to lack of individual level contact data,
we assume homogeneous mixing and restricted the analysis to the transmission
dynamics in Wuhan. Sensitivity analyses were performed by varying settings of the
natural history of disease, epidemic growth phase, and for confirmed only vs.
confirmed and suspected patients. For comparison, we also estimated Rt using an R
package EpiEstim.18
Data collection and analysis were considered as part of a continuing public health
outbreak investigation and exempt from institutional review board approval.
Results
Epidemiological description
The earliest symptom onset of confirmed patients can be traced back to 7 December
2019. As of 26 January 2020, a total of 8866 probable patients were reported and
4021 (45.35%) were laboratory-confirmed, in 30 provinces of China (Table S1). The
mean (±standard deviation) age among confirmed patients was 49±16 years,
comparable with suspected patients. However, the proportion of females differs
slightly, 45% among confirmed and 49% among suspected patients. Fourteen children
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<10 years old were confirmed to be infected with 2019-nCoV. The median time from
disease onset to diagnosis among confirmed patients was 5 (IQR: 2-9) days (Fig.
S1A). The median delay from symptom onset to diagnosis decreased dramatically
from before 14 January 2020 to after 22 January 2020 (Fig. S1B-S1D). About 41.38%
(1664/4176) of confirmed patients occurred in Wuhan city (Table S1).
The disease incidence initially remained low and sporadic until January 1, 2020,
when an abrupt jump was seen, followed by an exponential growth until 23 January
2020 (Fig. S2A). This increasing phase overlaps with the population movement
period before the spring festival that officially started on 10 January 2020. The
declining trend during the last three days of the study period was likely due to the
delay in diagnosis or reporting, as the spring festival occurred on January 25. January
17 seems to be a tipping point of the epidemic, when the daily number of confirmed
patients outside Wuhan begin to surpass that in Wuhan (Fig. S2B). Suspected patients
had the similar pattern as that of the confirmed patients, however with a 2 3 days lag
(Fig. S2C). The fast increase in the number of patients in Wuhan during the middle of
January was followed by the spread to other provinces during the second half of the
month, particularly to the neighboring provinces such as Henan, Sichuan and Hunan
(Fig. S2D E). Zhejiang, and Guangdong, the provinces popular for migrant workers,
also saw sharp increases. When the number of affected towns was analyzed, a similar
trend was observed, except that a remarkable increase in affected towns was observed
outside Hubei province (Fig. S3).
Geographic clustering of patients is clearly seen at the township level, mostly
in Hubei province (65.35% of all patients), with incidences ranging from 0.75 per
100,000 to 15.81 per 100,000 at the prefecture level. Other clusters are notable in
neighboring provinces as well as in Beijing, the Yantze River delta near Shanghai,
southeast of Zhejiang, and the Pearl River delta near Guangzhou and Hong Kong. The
earlier the reporting of confirmed patients, the higher the incidence at the prefecture
level (Fig. 1A). The reporting time of the first case was negatively correlated with the
size of outflow population from Wuhan to each affected prefecture during 1 26
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January 2020 (Spearman correlation, r=-0.568, P<0.001) (Fig. 1B).
Patients 30 65 years old dominated confirmed patients (2873, 71.45%), with the
highest case number of 139 at 56 years old. A similar age distribution was observed in
the suspected patients (Fig. 2A-C). Highest incidence was observed among adults
50 years, with the lowest incidence in the age group younger than 20 years (Fig.
2D-F and Table S2). Overall, males experienced a higher incidence than females
(0.31 per 100, 000 vs. 0.27 per 100,000, P<0.001). However, age-gender pattern
differs between Wuhan, the source of the outbreak, and other areas. The high
incidence subpopulation outside Wuhan tended to be younger than that in Wuhan.
Significant gender difference was only found outside Wuhan, 0.19 per 100,000 among
males vs. 0.15 per 100,000 among females (P<0.001).
Among the confirmed patients, 935 (25.5%) and 2563 (69.9%) were diagnosed
with severe and mild pneumonia, respectively, and 167 (4.5%) had no evidence of
pneumonia (Table S3). Patients diagnosed with severe pneumonia were significantly
older (Mean±SD, 55±15 years old) and had a higher proportion of males (61.5%), in
comparison to those with mild pneumonia (45±15 years old and 52.71% male) and
non-pneumonia (42±16 years old and 52.1% male). For 13 pediatric patients 10
years old with severity information, 10 had mild pneumonia and 3 had
non-pneumonia. The median (IQR) interval from disease onset to diagnosis was
longer for severe pneumonia, 8 (4-12) days, than for mild pneumonia, 4 (2-7) days,
and non-pneumonia, 3 (1-5) days. The proportion of non-pneumonia increased from
earlier disease onset to late disease onset. Higher frequency of mild pneumonia and
non-pneumonia was diagnosed from Wuhan than from the regions outside Wuhan.
Case fatality of 2019-nCoV infection
Fatal outcome developed in 58 confirmed patients and 18 suspected patients, the
former leading to an observed CFR of 1.44% (95% CI 1.10%-1.86%). Confirmed
patients with severe pneumonia experienced a much higher observed CFR, 5.88%,
than those with mild pneumonia (0.12%) and non-pneumonia (0%). Additional results
on observed CFRs can be found in Supplementary Appendix, Table S4 and Fig. S3.
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We estimated the overall adjusted CFRs among confirmed patients to be 3.06% (95%
CI 2.02-4.59%) (Table 1). The adjusted CFR in male patients more than tripled that in
female patients, 4.45% (95% CI 2.81-6.93%) vs. 1.25% (95% CI 0.43-3.29%).
Patients 60 years or older were also subject to a much more excessive adjusted CFR
of 5.30% (95% CI 3.25-8.46%), compared to the younger patients, 1.43% (95% CI
0.61-3.15%). Diagnosis of severe pneumonia at baseline is another leading risk factor
for death, associated with an adjusted CFR of 6.23% (95% CI 3.87-9.79%). The
adjusted CFR among patients with mild or no pneumonia at baseline was relatively
low, 0.68-1.16%, depending on whether patients with unknown baseline severity were
classified as mild or not (Supplementary Methods). A delay from onset to
diagnosis >5 days also doubled the adjusted CFR from 1.34% (95% CI 0.35-5.12%)
to 3.07% (95% CI 2.02-4.60%). Further stratified analyses by more than one baseline
variables found that patients meeting any two of the three characteristics, male patient,
60 years or older and severe pneumonia at diagnosis, had further increased CFRs. In
particular, the adjusted CFR reached as high as 9.47% (5.34-15.99%) among older
male patients diagnosed with severe pneumonia (Table S5).
Person to person transmissibility of 2019-nCoV
As a necessary input for estimating R0, the distribution of the incubation period
was estimated from 125 patients with clearly defined exposure periods
(Supplementary Methods). We estimated a median (IQR) duration of 4.8 (3.0, 7.2)
days for the incubation period (Fig. S5), based on which we constructed several
sensitivity analysis settings for the incubation and infectious periods to assess
person-to-person transmissibility of the 2019-nCoV (Table S6). Using a mean
incubation period of 5 days and a mean infectious period of 7 days, we estimated the
R0 to be 3.77 (95% CI 3.51-4.05), assuming 100% initial reporting rate before 1
January 2020 (Table 2). The various settings of the natural history of disease and
reporting rate yielded R0 estimates ranging from 2.23 to 4.82. Higher estimates are
associated with longer incubation and infectious periods and a higher initial reporting
rate. Using all confirmed and suspected patients for estimation gives slightly higher
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estimates, ranging from 2.31 to 5.18 (Table S7). Moving the estimation window from
the period of 25 December 2019 – 12 January 2020 to the period of 28 December
2019 – 15 January 2020 lowered the estimate to 3.00 (95% CI 2.81-3.20) based on
confirmed patients (Table S8) and 3.28 (95% CI 3.14-3.43) based on all patients
(Table S9), under the median incubation and infectious periods and 100% initial
reporting . The effective reproductive number, Rt, started to cross the critical
threshold of 1 near 25 December 2019, and peaked at 8-15 around 3 January 2020
depending on the setting of the natural history of disease, and quickly descended to
below the threshold near 16 January 2020 (Fig. 3). The declining trend was most
likely due to delayed reporting rather than decreasing transmissibility. The shape of
the Rt curve is moderately sensitive to the incubation period but not to the infectious
period. Similar to the results for R0, a lower initial reporting rate was associated with a
lower Rt curve (Fig. S6-S7). Using all patients led to high Rt during the early phase of
the epidemics but peak values are similar (Figs. S8-S10). The Rt curve obtained using
the EpiEstim package shows a bimodal shape, with the two modes near 22 December
2019 and the middle of January in 2020 (Fig. S11). Assuming a mean serial interval
of 9 days, the average Rt during 25 December 2019 – 12 January 2020 is close to 3,
comparable to our estimate for R0. A longer mean serial interval is associated with a
higher Rt curve, consistent with our method.
Discussion
Similar to the severe acute respiratory syndrome coronavirus (SARS-CoV) and
the Middle East Respiratory Syndrome coronavirus (MERS-CoV), the 2019-nCoV
adversely affected the elderly male subpopulation more than any other subpopulations,
in terms of both the highest incidence of confirmed patients and the highest CFR.10
The higher prevalence of ACE2 receptors, to which the virus binds, in the lungs of
Asian males could have contributed to this gender difference.19 The age effect is also
obvious in female patients, although to a less extent. The shift to a younger age profile
among patients who were identified outside Wuhan could be due to the fact that these
patients who had travelled to or from Wuhan tend to represent a younger population.
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While more data are needed to exclude the possibility of viral adaption to younger
hosts, the observed CFR is similar between Wuhan and non-Wuhan patients after
adjusting for age and gender.
We estimated the overall adjusted CFR to be 3.06% for the 2019-nCoV, which is
lower than those of SARS-CoV (9.2%) and MERS-CoV (34.4%).20 The limited
number of discharges so far has impeded the use of any advanced method such as the
competing risk model for estimating CFR.21 We restricted our analyses to patients
with symptom onset at least 10 days before 26 January 2020 to reduce bias from
unresolved final clinical outcomes. However, another serious source of bias is
under-detection and under-reporting of mild cases especially those without
pneumonia (4.5% in our data), which in turn could have substantially inflated the
overall CFR estimate. Therefore, our estimate should be treated as an upper bound.
The shortened delay from symptom onset to diagnosis over the epidemic course might
have helped reducing CFR. Early diagnosis of elderly patients, especially males, with
fever or respiratory symptoms before they progress to severe pneumonia is thus an
important target for preventing fatal outcomes.
In addition to atypical non-pneumonia patients, a couple of asymptomatic
infections have also been reported for both adults and children.7 An asymptomatic
young woman who came back from Wuhan was suspected to be the source infector of
three of her relatives in her hometown in Henan Province who had no travel history to
Wuhan.22 It remains necessary to evaluate the transmissibility of mild or
asymptomatic infections and their contribution to the overall epidemic, using both
field contact tracing data and modeling approaches.
Our estimate for R0, 3.77, is higher than recently published estimates. An estimate
of R0=2.0 based on 425 early reported patients is likely an underestimation given the
serious delay in case confirmation during the early phase.11 Another estimate of
R0=2.7 was also based on surveillance data, but the methodology was different.23 Our
method considers right censoring of infections who might not have developed
symptoms by the end of the study period, which is important given the ongoing
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increasing trend of the epidemic. In addition, our analysis distinguished patients living
and diagnosed within Wuhan from those Wuhan residents who were diagnosed
outside Wuhan, a major source of exporting the disease to other parts of China. The
transmissibility of 2019-nCoV is comparable to that of SARS-CoV in the range of
2.9-3.324 and much higher than that of MERS-CoV. Both 2019-nCoV and SARS-CoV
resulted from occasional spill-over from non-specific intermediate host mammals,
whereas MERS-CoV has a clear and constant animal reservoir.
Our findings are constrained to a limited time frame and might have missed
time-changing disease features, given the fast evolution of the epidemic. The most
recent curve of daily new numbers of confirmed patients shows that the exponential
growth extended beyond 25 January 2020 with a steeper slope,25 indicating the
possibility of underestimation in our analysis. In particular, the declining trend of Rt
after the beginning of January was most likely due to delayed reporting rather than
reduced transmissibility, as many cases with symptom onsets during the last week of
the study period had not been reported to China CDC when this analysis was
performed. However, further evaluation of transmissibility will need to address the
complexity raised by implementation of nonpharmaceutical interventions such as
travel ban.
It remains a challenging task to contain an outbreak of a novel pathogen capable
of efficient person to person transmission in this highly mobile world, in particular
when treatment and prevention options are limited. Existing antiviral treatments such
as lopinavir/ritonavir and remdesivir have been evaluated for treating SARS-CoV and
MERS-CoV infections,26-28 and some have been used or are being considered for
treating 2019-nCoV infections.29 Clinical trials need to be carefully designed and
implemented to assess their efficacies, which could be challenging given the
overwhelmed healthcare resources in China. Equally important is improvement in
case detection and management in the most vulnerable elderly population.
Acknowledgement
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This work was financially supported by grants from the China Mega-Project on
Infectious Disease Prevention (No. 2018ZX10713001, 2018ZX10713002,
2017ZX10103004 and 2018ZX10101003-002), the National Natural Science Funds
(No. 81825019), and the U.S. National Institute of Health (R01 AI139761 and R01
AI116770). We thank the staff members at the China CDC for their assistance in data
preparation.
References
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Figure Legends:
Fig. 1. The geographic location of 4021 patients with confirmed 2019-nCoV
infection (Panel A) and the spatial dispersion of confirmed patients from Wuhan
city to the other 253 cities in China (Panel B), as of 26 January 2020.
Panel A, the illness onset day of each patient (blue) and the incidence rate of each city
(red) are color-differentiated.
Panel B, the time interval (days) from the first case-reporting date of each affected
city to 7 December 2019 (the first patient reported in China) is color-differentiated in
blue. The location of each dot shows the township of the first patient in each affected
city.
The proportion of Wuhan-originated outflow population to each affected city during
1-26 January 2020 is color-differentiated in red as background. Transportation
networks are shown in green for railways and light brown for highways and freeways.
Fig. 2. Age and sex patterns of the patients infected with 2019-nCoV.
Age distribution stratified by sex is shown for all patients (Panel A), for confirmed
patients (Panel B) and for suspected patients (Panel C). Disease incidence stratified by
sex is shown for confirmed patients in mainland China (Panel D), for confirmed
patients reported in Wuhan city (Panel E) and for confirmed patients reported outside
Wuhan city (Panel F).
Fig. 3. Estimtes (solid red) and the 95% pointwise confidence band (dashed red)
for real time effective reproductive numbers from 24 December 2019 to 18
January 2020 based on confirmed cases who are residents of Wuhan. Results are
stratified by assumption settings about the incubation (inc.) and infectious (inf.)
periods. Reporting rate before 1 January 2020 is assumed to be 100%. Numbers of
confirmed cases are shown as the histogram (gray). Model-predicted case numbers
(solid green) and 95% confidence band (green dashed) are also shown.
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Table 1. Estimates for adjusted case fatality rate (CFR) among 2019-nCoV patients,
stratified by case type and baseline characteristics.
Case type
Missing
severity
classified as
mild
No. of cases No. of deaths
(Observed
CFR, %)
CFR, %
(95% CI)
Confirmed patients
Gender
Male 2213
44 (1.99
)
4.45 (2.81, 6.93)
Female 1808
14 (0.77
)
1.25 (0.43, 3.29)
Age Group
<60 y 2969
15 (0.51
)
1.43 (0.61, 3.15)
60 y 1052
43 (4.09
)
5.30 (3.25, 8.46)
Severity
Severe 927
47
(5.07)
6.23 (3.87, 9.79)
Mild Ye s 3094
11
(0.36)
1.16 (0.47, 2.69)
No 2727
3
(0.11)
0.68 (0.13, 3.07)
Time from onset to diagnosis
5 days$ 2054
18
(0.88)
1.34 (0.35, 5.12)
>5 days 1967
40
(2.03)
3.07 (2.02, 4.60)
Overall 4021
5
8 (1.44)
3.06 (2.02, 4.59)
$ These cases all had symptom onset dates < 14 days before 26 Jan. 2020. CFR is
calculated for this group by varying d from 5 to 8 days, rather than from 10 to14 days
(see methods).
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Table 2. Estimates of R0 for different settings of reporting rate before 1 January 2020,
incubation period and infectious period. Only confirmed patients are used for
estimation. The time window used for estimation is from 25 December 2010 to 12
January 2020
Reporting
rate Incubation
period Infectious
period R0 Standard
error 95% CI
100% Short Short 2.805 0.111 (2.595, 3.031)
Median 3.204 0.126 (2.966, 3.461)
Long 3.643 0.143 (3.373, 3.934)
Median Short 3.308 0.122 (3.077, 3.557)
Median 3.772 0.139 (3.510, 4.054)
Long 4.281 0.156 (3.985, 4.598)
Long Short 3.737 0.134 (3.483, 4.010)
Median 4.255 0.152 (3.967, 4.563)
Long 4.819 0.171 (4.495, 5.167)
50% Short Short 2.511 0.100 (2.324, 2.714)
Median 2.790 0.110 (2.582, 3.014)
Long 3.081 0.121 (2.852, 3.328)
Median Short 2.930 0.109 (2.725, 3.152)
Median 3.249 0.120 (3.022, 3.494)
Long 3.581 0.132 (3.331, 3.849)
Long Short 3.274 0.119 (3.049, 3.515)
Median 3.621 0.131 (3.373, 3.887)
Long 3.980 0.144 (3.708, 4.272)
30% Short Short 2.225 0.088 (2.059, 2.405)
Median 2.408 0.095 (2.228, 2.601)
Long 2.590 0.102 (2.397, 2.798)
Median Short 2.560 0.096 (2.378, 2.755)
Median 2.763 0.103 (2.568, 2.973)
Long 2.966 0.111 (2.757, 3.191)
Long Short 2.822 0.104 (2.626, 3.034)
Median 3.037 0.112 (2.826, 3.264)
Long 3.251 0.119 (3.026, 3.494)
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author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is the(which was not peer-reviewed) The copyright holder for this preprint .https://doi.org/10.1101/2020.02.10.20021675doi: medRxiv preprint
