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

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

2

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

3

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

4

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).

5

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

6

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).

7

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.

References

1. Q. Li et al., Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus‐Infected

Pneumonia. N Engl J Med, (2020).

2. WHO, Pneumonia of unknown cause – China

(https://www.who.int/csr/don/05‐january2020‐pneumonia‐of‐unkown‐cause‐china/en/;

accessed January 30, 2020), (2020).

3. J. F. Chan et al., A familial cluster of pneumonia associated with the 2019 novel

coronavirus indicating person‐to‐person transmission: a study of a family cluster. Lancet,

(2020).

4. WHO, Statement on the second meeting of the International Health Regulations (2005)

Emergency Committee regarding the outbreak of novel coronavirus (2019‐nCoV).

(https://www.who.int/news‐room/detail/30‐01‐2020‐statement‐on‐the‐secondmeeting‐

of‐the‐international‐health‐regulations‐(2005)‐emergency‐committee‐

8

regarding‐the‐outbreak‐of‐novel‐coronavirus‐(2019‐ncov); accessed January 30, 2020),

(2020).

5. J. T. Wu, K. Leung, G. M. Leung, Nowcasting and forecasting the potential domestic and

international spread of the 2019‐nCoV outbreak originating in Wuhan, China: a

modelling study. The Lancet, (2020).

6. ChinaDailyNews, Railway corporation using big data to trace potential virus carrier.

(https://www.chinadaily.com.cn/a/202001/30/WS5e329ca2a310128217273b89.ht…;

accessed Feburary 1, 2020), (2020).

7. N. Imai et al., Report 2: Estimating the potential total number of novel Coronavirus

cases

in Wuhan City, China

(https://www.imperial.ac.uk/media/imperialcollege/medicine/sph/ide/gida‐fellowships/

2019‐nCoV‐outbreak‐report‐22‐01‐2020.pdf; accessed Feburary 2, 2020), (2020).

8. N. Imai, I. Dorigatti, A. Cori, S. Riley, N. M. Ferguson, Estimating the potential total

number of novel Coronavirus cases in Wuhan City, China.

(https://www.imperial.ac.uk/media/imperial‐college/medicine/sph/ide/gida‐

fellowships/2019‐nCoV‐outbreak‐report‐17‐01‐2020.pdf; accessed Feburary 2, 2020),

(2020).

9. J. O. Lloyd‐Smith, S. J. Schreiber, P. E. Kopp, W. M. Getz, Superspreading and the effect

of individual variation on disease emergence. Nature 438, 355‐359 (2005).

10. J. O. Lloyd‐Smith et al., Epidemic dynamics at the human‐animal interface. Science 326,

1362‐1367 (2009).

11. R. K. Plowright et al., Pathways to zoonotic spillover. Nat Rev Microbiol 15, 502‐510

(2017).

12. R. M. Anderson, R. M. May, Infectious Diseases of Humans: Dynamics and Control.

Oxford science publications (Oxford University Press, 1991), pp. 768.

13. C. Rothe et al., Transmission of 2019‐nCoV Infection from an Asymptomatic Contact in

Germany. N Engl J Med, (2020).

14. H. J. Wearing, P. Rohani, M. J. Keeling, Appropriate models for the management of

infectious diseases. PLoS Med 2, e174 (2005).

15. C. A. Donnelly et al., Epidemiological determinants of spread of causal agent of severe

acute respiratory syndrome in Hong Kong. Lancet 361, 1761‐1766 (2003).

16. M. Lipsitch et al., Transmission dynamics and control of severe acute respiratory

syndrome. Science 300, 1966‐1970 (2003).

17. C. Fraser, S. Riley, R. M. Anderson, N. M. Ferguson, Factors that make an infectious

disease outbreak controllable. Proc Natl Acad Sci U S A 101, 6146‐6151 (2004).

18. M. C. Bootsma, N. M. Ferguson, The effect of public health measures on the 1918

influenza pandemic in U.S. cities. Proc Natl Acad Sci U S A 104, 7588‐7593 (2007).