MRCA time and epidemic dynamics of the 2019 novel coronavirus

Zhang, Chi; Wang, Mei

doi:10.1101/2020.01.25.919688

Cited by 31 publications

(34 citation statements)

References 15 publications

(19 reference statements)

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“…1a). The excess of low frequency mutations is consistent with the recent origin of SARS-CoV-2 [19]. Both population reduction and positive selection can increase high frequency mutations [20, 21].…”

Section: Resultssupporting

confidence: 69%

The origin and underlying driving forces of the SARS-CoV-2 outbreak

Chaw

Tai

Chen

et al. 2020

Preprint

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bioRxiv preprint 23 impacted many aspects of human society. Here, we analyzed genetic variation of SARS- 24 CoV-2 and its related coronavirus and found the evidence of intergenomic recombination. 25 After correction for mutational bias, analysis of 137 SARS-CoV-2 genomes as of 2/23/2020 26 revealed the excess of low frequency mutations on both synonymous and nonsynonymous 27 sites which is consistent with recent origin of the virus. In contrast to adaptive evolution 28 previously reported for SARS-CoV in its brief epidemic in 2003, our analysis of SARS-CoV-29 2 genomes shows signs of relaxation of selection. The sequence similarity of the spike 30 receptor binding domain between SARS-CoV-2 and a sequence from pangolin is probably 31 due to an ancient intergenomic introgression. Therefore, SARS-CoV-2 might have cryptically 32 circulated within humans for years before being recently noticed. Data from the early 33 outbreak and hospital archives are needed to trace its evolutionary path and reveal critical 34 steps required for effective spreading. Two mutations, 84S in orf8 protein and 251V in orf3 35 protein, occurred coincidentally with human intervention. The 84S first appeared on 1/5/2020 36 and reached a plateau around 1/23/2020, the lockdown of Wuhan. 251V emerged on 37 1/21/2020 and rapidly increased its frequency. Thus, the roles of these mutations on 38 infectivity need to be elucidated. Genetic diversity of SARS-CoV-2 collected from China was 39 two time higher than those derived from the rest of the world. In addition, in network analysis, 40 haplotypes collected from Wuhan city were at interior and have more mutational connections, 41 both of which are consistent with the observation that the outbreak of cov-19 was originated 42 from China. SUMMARY 44In contrast to adaptive evolution previously reported for SARS-CoV in its brief 45 epidemic, our analysis of SARS-CoV-2 genomes shows signs of relaxation of selection. The 46 sequence similarity of the spike receptor binding domain between SARS-CoV-2 and a 47 sequence from pangolin is probably due to an ancient intergenomic introgression. Therefore, 48 SARS-CoV-2 might have cryptically circulated within humans for years before being 49 recently noticed. Data from the early outbreak and hospital archives are needed to trace its 50 evolutionary path and reveal critical steps required for effective spreading. Two mutations, 51 84S in orf8 protein and 251V in orf3 protein, occurred coincidentally with human 52 intervention. The 84S first appeared on 1/5/2020 and reached a plateau around 1/23/2020, the 53 lockdown of Wuhan. 251V emerged on 1/21/2020 and rapidly increased its frequency. Thus, 54 the roles of these mutations on infectivity need to be elucidated. 55 56 57 A newly emerging coronavirus was detected in patients during an outbreak of 58 respiratory illnesses starting in mid-December of 2019 in Wuhan, the capital of Hubei 59 Province, China [1 , 2, 3]. Due to the similarity of its symptoms to those induced by the 60 severe acute respiratory syndro...

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Section: Resultssupporting

confidence: 69%

The origin and underlying driving forces of the SARS-CoV-2 outbreak

Chaw

Tai

Chen

et al. 2020

Preprint

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“…For all periods the effective reproduction numbers decrease in time. As new data were reported daily, we followed with subsequent analysis in near-real time with three periods as shown in Table 2 In contrast, a simple linearization of Re in time (Figure 3) back-estimated the original basic reproduction number R0 at about 2.4 to 2.5, agreeing well with other values recently reported Liu et al, 2020;Majumder & Mandl, 2020;Zhang & Wang, 2020a, 2020bZhao et al, 2020a). The discrepancy between these two estimates can be attributed a potential loss of virulence of the virus but most likely due to extreme measures to contain virus spread in China.…”

Section: Resultssupporting

confidence: 80%

Forecasting the Wuhan coronavirus (2019-nCoV) epidemics using a simple (simplistic) model

Hermanowicz

2020

Preprint

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and FindingsConfirmed infection cases in mainland China were analyzed using the data up to January 28, 2020 (first 13 days of reliable confirmed cases). For the first period the cumulative number of cases followed an exponential function. However, from January 28, we discerned a downward deviation from the exponential growth. This slower-than-exponential growth was also confirmed by a steady decline of the effective reproduction number. A backtrend analysis suggested the original basic reproduction number R0 to be about 2.4 to 2.5. As data become available, we subsequently analyzed them during three consecutive periods obtaining a sequence of model predictions. All available data up were processed the same way. We used a simple logistic growth model that fitted very well with all data. Using this model and the three sets of data, we estimated maximum cases as about 21,000, 28,000 and 35,000 cases refining these predictions in near-real time. With slightly different approach (linearization in time) the estimate of maximum cases was even higher (about 65,000). Although the estimates of maximum cases increase as more data were reported all models show reaching a peak in mid-February in contrast to the unconfined exponential growth. These predictions do not account for any possible other secondary sources of infection.

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“…Using statistical approaches, namely exponential growth and maximum likelihood techniques, Liu and colleagues (Liu et al, 2020) estimated the value of the reproduction number ranging from 2.90 (95% CI 2.32e3.63) to 2.92 (95% CI 2.28e3.67). Zhang and Wang (Zhang & Wang, 2020), employing a Bayesian framework to infer time-calibrated phylogeny from 33 available genomic sequences, found that the time of the most recent common ancestor (MRCA) was December 17th , 2019 (95% highest posterior density interval from December 7th , 2019 to December 23rd , 2019) and that the value of the reproduction number oscillated between 1.1 and 1.6.…”

Section: Discussionmentioning

confidence: 99%

An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov)

Tang

Bragazzi

et al. 2020

Infectious Disease Modelling

697

686

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a b s t r a c tThe basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period, in a naïve, uninfected population. It is well-known that the estimation of this number may vary due to several methodological issues, including different assumptions and choice of parameters, utilized models, used datasets and estimation period. With the spreading of the novel coronavirus (2019-nCoV) infection, the reproduction number has been found to vary, reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate. Due to significant variations in the control strategies, which have been changing over time, and thanks to the introduction of detection technologies that have been rapidly improved, enabling to shorten the time from infection/symptoms onset to diagnosis, leading to faster confirmation of the new coronavirus cases, our previous estimations on the transmission risk of the 2019-nCoV need to be revised. By using time-dependent contact and diagnose rates, we refit our previously proposed dynamics transmission model to the data available until January 29th , 2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions. We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak. Our updated findings suggest that the best measure is persistent and strict self-isolation. The epidemics will continue to grow, and can peak soon with the peak time depending highly on the public health interventions practically implemented.

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MRCA time and epidemic dynamics of the 2019 novel coronavirus

Cited by 31 publications

References 15 publications

The origin and underlying driving forces of the SARS-CoV-2 outbreak

The origin and underlying driving forces of the SARS-CoV-2 outbreak

Forecasting the Wuhan coronavirus (2019-nCoV) epidemics using a simple (simplistic) model

An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov)

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