Los Alamos Lab Coronavirus Report Proves the CDC and WHO Are Massively Lying to an Unsuspecting Public

epidemic

Both the CDC qnd the W.H.O. are lying about the spread and severity of the virus. This excerpted report speaks for itself and is an official US government document. One does not need to go beyond the abstract to discover the truth. 

The Novel Coronavirus, 2019‐nCoV, is Highly Contagious and More Infectious  Than Initially Estimated

Authors: Steven Sanche 1,2,† , Yen Ting Lin 3,† , Chonggang Xu 4 , Ethan Romero‐Severson 1 , Nicolas W.
Hengartner 1 , Ruian Ke 1,*
Affiliations:
1 T‐6 Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory,
NM87544, USA.
2 T‐CNLS Center for Nonlinear Studies, Los Alamos National Laboratory, NM87544, USA.
3 CCS‐3 Information Sciences Group, Computer, Computational and Statistical Sciences Division,
Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
4 EES‐14 Earth Systems Observations Group, Earth and Environmental Sciences Division, Los
Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

† S.S. and Y.T.L. contributed equally to the work.

* Correspondences should be addressed to:
Ruian Ke
Email: rke@lanl.gov
Phone: 1‐505‐667‐7135
Mail: Mail Stop K710,
T‐6 Theoretical Biology and Biophysics,
Los Alamos National Laboratory,
NM87544, USA.

Short title: The 2019 novel coronavirus is highly infectious

Word counts:
Abstract: 124
Main text including references and figure captions: 3,945

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Abstract
The novel coronavirus (2019‐nCoV) is a recently emerged human pathogen that has spread
widely since January 2020. Initially, the basic reproductive number, R 0 , was estimated to be 2.2
to 2.7. Here we provide a new estimate of this quantity. We collected extensive individual case
reports and estimated key epidemiology parameters, including the incubation period.
Integrating these estimates and high‐resolution real‐time human travel and infection data with
mathematical models, we estimated that the number of infected individuals during early
epidemic double every 2.4 days, and the R 0 value is likely to be between 4.7 and 6.6. We further
show that quarantine and contact tracing of symptomatic individuals alone may not be
effective and early, strong control measures are needed to stop transmission of the virus.

One‐sentence summary
By collecting and analyzing spatiotemporal data, we estimated the transmission potential for
2019‐nCoV.
Main Text
2019‐nCoV is the etiological agent of the current rapidly growing outbreak originating from
Wuhan, Hubei province, China (1). At the end of December 2019, 41 cases of ‘pneumonia of
unknown etiology’ were reported by the Wuhan Municipal Health Committee (2). On January 1,
2020, the Huanan Seafood Wholesale Market in Wuhan, which was determined to be the
epicenter of the outbreak, was closed. Seven days later, the causative agent of new disease was
formally announced by China CDC as 2019‐nCoV. Human‐to‐human transmission was later
reported, i.e. infection of medical workers reported by the news and infection of individuals
with no recent history of Wuhan visit (3). In response, China CDC upgraded the emergency
response to Level 1 (the highest level) on January 15. By January 21, 2019‐nCoV infection had
spread to most of the other provinces. On January 23, the city of Wuhan was locked
down/quarantined, all transportations into and out of the city and all mass gatherings was
canceled. However, the number of confirmed cases has continued to increase exponentially
since January 16 (Fig. 1A and B). On January 30, the World Health Organization (WHO) declared
the outbreak a public health emergency of international concern (4) . As of February 5, 2020,
the virus outbreak lead to more than 24,000 total confirmed cases and 494 deaths, and the
virus has spread to 25 countries. Initial estimates of the growth rate of the outbreak based on
early case count data in Wuhan and international flight data up to mid‐January were 0.1 per
day (a doubling time between 6‐7 days) and a basic reproductive number, R 0 (defined as the
average number of secondary cases an index case infects when it is introduced in a susceptible
population), of 2.2 and 2.7 (1, 5); however, the rates of growth in the number of confirmed
cases during late January (Fig. 1A and B) suggest a doubling time much shorter than 6‐7 days.
Here, with more up‐to‐date and high‐resolution datasets across China until the end of January,
we estimated that the exponential growth rate and R 0 are much higher than these previous
estimates. We improved on previous estimates in three distinct ways. First, we used an

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expanded dataset of individual case reports based on our collection and direct translations of
documents published daily from official health commissions across provinces and special
municipalities in China (see Data Collection in Supplementary Materials). Second, we integrated
high‐resolution real‐time domestic travel data in China. Third, to address the issue of potential
data collection and methodological bias or incomplete control of confounding variables, we
implemented two distinct modeling approaches using different sets of data. These analyses
produced estimates of the exponential growth rates that are consistent with one another and
higher than previous estimates.
A unique feature of our case report dataset (Table S1) is that it includes case reports of many of
the first or the first few individuals who were confirmed with the virus infection in each
province, where dates of departure from Wuhan were reported. All together, we collected 140
individual case reports (Table S1). These reports include demographic information including
age, sex and location of hospitalization, as well as epidemiological information including
potential time periods of infection, dates of symptom onset, hospitalization and case
confirmation.
Using this dataset, we estimated the basic parameter distributions of durations from initial
exposure to symptom onset to hospitalization to discharge or death. Our estimate of the time
from initial exposure to symptom onset is 4.2 days with a 95% confidence interval (CI for short
below) between 3.5 and 5.1 days (Fig. 1C). This estimated period is about 1 day shorter and has
lower variance than a previous estimate (1). The shorter time is likely caused by the expanded
temporal range of our data that includes cases occurring after broad public awareness of the
disease. Patients reported in the Li et al. study (1) are all from Wuhan and most had symptom
onset before mid‐January; in our dataset, many patients had symptom onset during or after
midJanuary and were reported in provinces other than Hubei province (where Wuhan is the
capital). The time from symptom onset to hospitalization showed evidence of time dependence
(Fig. 1D and S1). Before January 18, the time from symptom onset to hospitalization was 5.5
days (CI: 4.6 to 6.6 days); whereas after January 18, the duration shortened significantly to 1.5
days only (CI: 1.2 to 1.9 days) (p‐value <0.001 by Mann‐Whitney U test). The change in the
distribution coincides with the period when infected cases were first confirmed in Thailand,
news reports of potential human‐to‐human transmission and upgrading of emergency response
level to Level 1 by China CDC. The emerging consensus about the risk of 2019‐nCoV likely led to
significant behavior change in symptomatic people seeking more timely medical care over this
period. We also found that the time from initial hospital admittance to discharge is 11.5 days
(Fig. 1E; CI: 8.0 to 17.3 days) and the time from initial hospital admittance to death is 11.2 days
(Fig. 1F; CI: 8.7 to 14.9 days).
Moving from empirical estimates of basic epidemiological parameters to an understanding of
the actual epidemiology of 2019‐nCoV requires model‐based inference. We thus used
mathematical models to integrate the empirical estimates with spatiotemporal domestic travel

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and infection data outside of Hubei province to infer the outbreak dynamics in Wuhan.
Inference based on data outside of Hubei is more reliable because, as a result of the awareness
of the risk of virus transmission, other provinces implemented intensive surveillance system to
detect individuals with high temperatures and closely track travelers out of Wuhan using digital
data to identify infected individuals (6) as the outbreak in Wuhan unfolded.
We collected real‐time travel data during the epidemic using the Baidu® Migration server (Fig.
2A and Table S2). The server an online platform summarizing mobile phone travel data through
Baidu® Huiyan [https://huiyan.baidu.com/]. Baidu® Huiyan is a widely used positioning system
in China. It processes >120 billion positioning requests daily through GPS, WIFI and other means
[https://huiyan.baidu.com/]. Therefore, the data represents a reliable, real‐time and
highresolution source of travel patterns in China. We extracted daily travel data from Wuhan to
each of the provinces. We found that in general, between 40,000 to 140,000 people in Wuhan
traveled to destinations outside of Hubei province daily before the lock‐down of the city on
January 23, with travel peaks on January 9, 21 and 22 (Fig. 2B). Thus, it is likely that this massive
flow of people from Hubei province during January facilitated the rapid dissemination of virus.
We integrated the travel data into our inferential models using two approaches. The rationale
of the first model, the ‘first‐arrival’ approach, is that an increasing fraction of people infected in
Wuhan increases the likelihood that one such case is exported to the other provinces. Hence,
how soon new cases are observed in other provinces can inform disease progression in Wuhan
(Fig. 2C). This has similarities with earlier analyses to estimate the size of the 2019‐nCoV
outbreak in Wuhan based on international travel data (5, 7, 8), though inference based infected
cases outside of China may suffer large uncertainty due to the low volume of international
travel. In our model, we assumed exponential growth for the infected population I* in Wuhan,
�� ∗ �� , where �� is the exponential growth rate and �� is the time of the exponential growth
initiation, i.e. �� ∗ ��. Note that �� is likely to be later than the date of the first infection event,
because multiple infections may be needed before the onset of exponential growth (9). We
used travel data to each of the provinces (Table S3) and the earliest times that an infected
individual arrived at a province across a total of 26 provinces (Fig. 2D) to infer �� and �� (see
Supplementary Materials for details). Model predictions of arrival times in the 26 provinces
fitted the actual data well (Fig. S2). We estimated that the date of the beginning of an
exponential growth is December 20, 2019 (CI: December 11 to 26). This suggests that human
infections in early December may be due to spillovers from the animal reservoir or limited
chains of transmission (10, 11). The growth rate of the outbreak is estimated to be 0.29 per day
(CI: 0.21 to 0.37 per day), a much higher rate than two recent estimates (1, 5). This growth rate
corresponds to a doubling time of 2.4 days. We further estimated that the total infected
population size in Wuhan was approximately 4,100 (CI: 2,423 to 6,178) on January 18, which is
remarkably consistent with a recently posted estimate (7). The estimated number of infected
individuals is 18,700 (CI: 7,147, 38,663) on January 23, i.e. the date when Wuhan started lock
down. We projected that without any control measure, the infected population would be
approximately 233,400 (CI: 38,757 to 778,278) by the end of January (Fig. S3).

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An alternative model, the ‘case count’ approach, used daily case count data between January
19 and 26 from provinces outside of Hubei to infer the initiation and the growth rate of the
outbreak. We restricted the data to this period because during this time infected persons
found outside of Hubei province generally reported visiting Wuhan within 14 days of becoming
symptomatic, i.e. cases during that time period were indicative of the dynamics in Wuhan. We
developed a metapopulation model based on the classical SEIR model (12). We assumed a
deterministic exponential growth for the infected populations in Wuhan, whereas in other
provinces, we represented the trajectory of infected individuals who travelled from Wuhan
using a stochastic agent‐based model. The transitions of the infected individuals from symptom
onset to hospitalization and then to case confirmation were assumed to follow the distributions
inferred from the case report data (see Supplementary Materials for detail). Simulation of the
model using best fit parameters showed that the model described the observed case counts
over time well (Fig. 2E). The estimated date of exponential growth initiation is December 16,
2019 (CI: December 12 to Dec 21) and the exponential growth rate is 0.30 per day (CI: 0.26 to
0.34 per day). These estimates are consistent with estimates in the ‘first arrival’ approach (Fig.
2F and G, and Fig. S4).
We note that in both approaches, we assumed perfect detection of infected cases outside of
Hubei province, i.e. the dates of first arrival and the number of case counts are accurate. This
could be a reasonable assumption to make for symptomatic individuals because of the intensive
surveillance implemented in China, for example, tracking individuals’ movement from digital
transportation data (6). However, it is possible that a fraction of infected individuals, for
example, individuals with mild or no symptoms (13), were not hospitalized, in which case we
will underestimate the true size of the infected population in Wuhan. We undertook sensitivity
analyses to investigate how our current estimates are affected by this issue using both
approaches (see Supplementary Materials for detail). We found that if a proportion of cases
remained undetected, the time of exponential initiation would be earlier than December 20,
translating into a larger population of infected individuals in January, but the estimation of the
growth rate remained the same. Overall, the convergence of the estimates of the exponential
growth rate from the two approaches emphasizes the robustness of our estimates to
modeldependent assumptions.
Our estimated outbreak growth rate is significantly higher than two recent reports where the
growth rate was estimated to be 0.1 per day (1, 5). This estimate were based on early case
counts from Wuhan (1) or international air travel data (5). However, these data suffer from
important limitations. The reported case counts in Wuhan during early outbreak are likely to be
underreported because of many factors, and because of the low numbers of individuals
traveling abroad compared to the total population size in Wuhan, inference of the infected
population size and outbreak growth rate from infected cases outside of China suffers from

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large uncertainty (7, 8). Our estimated exponential growth rate, 0.29/day (a doubling time of
2.4 days) is consistent the rapidly growing outbreak during late January (Fig. 1A).
Using the exponential growth rate, we next estimated the range of the basic reproductive
number, R 0 . It has been shown that this estimation depends on the distributions of the latent
period (defined as the period between the times when an individual infected and become
infectious) and the infectious period (14). For both periods, we assumed a gamma distribution
and varied the mean and the shape parameter of the gamma distributions in a large range to
reflect the uncertainties in these distributions (see Supplementary Materials). It is not clear
when an individual becomes infectious; thus, we considered two scenarios: 1) the latent period
is the same as the incubation period, and 2) the latent period is 2 days shorter than the
incubation period, i.e. individuals start to transmit 2 days before symptom onset. Integrating
uncertainties in the exponential growth rate estimated from the ‘first arrival’ approach and the
uncertainties in the duration of latent and infectious periods, we estimated the values of R 0 to
be 6.3 (CI: 3.3 to 11.3) and 4.7 (CI: 2.8 to 7.6), for the first and second scenarios, respectively
(Fig. 3A). When using the estimates from the ‘case count’ approach, we estimated slightly
higher R 0 values of 6.6
(CI: 4.0 to 10.5) and 4.9 (CI: 3.3 to 7.2), for the first and second scenarios, respectively (Fig. S5).
Overall, we report R 0 values are likely be between 4.7 and 6.6 with a CI between 2.8 to 11.3. We
argue that the high R 0 and a relatively short incubation period lead to the extremely rapid
growth of the of 2019‐nCoV outbreak as compared to the 2003 SARS epidemic where R 0 was
estimated to be between 2.2 to 3.6 (15, 16).
The high R 0 values we estimated have important implications for disease control. For example,
basic theory predicts that the force of infection has to be reduced by 1 to guarantee
extinction of the disease. At � 2.2 this fraction is only 55%, but at � 6.7 this fraction rises to
85%. To translate this into meaningful predictions, we use the framework proposed by Lipsitch
et al (16) with the parameters we estimated for 2019‐nCoV. Importantly, given the recent
report of transmission of the virus from asymptomatic individuals (13), we considered the
existence of a fraction of infected individuals who is asymptomatic and can transmit the virus
(see Supplementary Materials). Results show that if as low as 20% of infected persons are
asymptomatic and can transmit the virus, then even 95% quarantine efficacy will not be able to
contain the virus (Fig. 3B). Given the rapid rate of spread, the sensitivity of control effort
effectiveness to asymptomatic infections and the potential of transmission before symptom
onset, we need to be aware of the difficulty of controlling 2019‐nCoV once it establishes in a
new population (17). Future field, laboratory and modeling studies aimed to address the
unknowns, such as the fraction of asymptomatic individuals, the time when individuals become
infectious and the existence of superspreaders are needed to accurately predict the impact of
various control strategies (9, 17).

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Fortunately, we see evidence that control efforts have a measurable effect on the rate of
spread. Since January 23, Wuhan and other cities in Hubei province implemented vigorous
control measures, such as closing down transportation and mass gatherings in the city;
whereas, other provinces also escalated the public health alert level and implemented strong
control measures. We noted that the growth rate of the daily number of new cases in provinces
outside of Hubei slowed down gradually since late January (Fig. 3B). Due to the closure of
Wuhan (and other cities in Hubei), the number of cases reported in other provinces during this
period shall start to track local infection dynamics rather than imports from Wuhan. We
estimated that the exponential growth rate is decreased to 0.14 per day (CI: 0.12 to 0.15 per
day) since January 30. Based on this growth rate and an R 0 between 4.7 to 6.6 before the
control measures, a calculation following the formula in Ref. (14) suggested that a growth rate
decreasing from 0.29 per day to 0.14 per day translates to a 50%‐59% decrease in R 0 to
between 2.3 to 3.0. This is in agreement with previous estimates of the impact of effective
social distancing during 1918 influenza pandemic (18). Thus, the reduction in growth rate may
reflect the impact of vigorous control measures implemented and individual behavior changes
in China during the course of the outbreak

.
The 2019‐nCoV epidemic is still rapidly growing and spread to more than 20 countries as of
February 5, 2020. Here, we estimated the growth rate of the early outbreak in Wuhan to be
0.29 per day (a doubling time of 2.4 days), and the reproductive number, R 0 , to be between 4.7
to 6.6 (CI: 2.8 to 11.3). Among many factors, the Lunar New Year Travel rush in early and
mid‐January 2020 may or may not play a role in the high outbreak growth rate, although SARS
epidemic also overlapped with the Lunar New Year Travel rush. How contiguous the 2019‐nCoV
is in other countries remains to be seen. If the value of R 0 is as high in other countries, our
results suggest that active and strong population‐wide social distancing efforts, such as closing
down transportation system, schools, discouraging travel, etc., might be needed to reduce the
overall contacts to contain the spread of the virus.
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