 Research
 Open Access
 Published:
Realtime quantification of the transmission advantage associated with a single mutation in pathogen genomes: a case study on the D614G substitution of SARSCoV2
BMC Infectious Diseases volume 21, Article number: 1039 (2021)
Abstract
Background
The COVID19 pandemic poses serious threats to global health, and the emerging mutation in SARSCoV2 genomes, e.g., the D614G substitution, is one of the major challenges of disease control. Characterizing the role of the mutation activities is of importance to understand how the evolution of pathogen shapes the epidemiological outcomes at population scale.
Methods
We developed a statistical framework to reconstruct variantspecific reproduction numbers and estimate transmission advantage associated with the mutation activities marked by single substitution empirically. Using likelihoodbased approach, the model is exemplified with the COVID19 surveillance data from January 1 to June 30, 2020 in California, USA. We explore the potential of this framework to generate early warning signals for detecting transmission advantage on a realtime basis.
Results
The modelling framework in this study links together the mutation activity at molecular scale and COVID19 transmissibility at population scale. We find a significant transmission advantage of COVID19 associated with the D614G substitution, which increases the infectivity by 54% (95%CI: 36, 72). For the early alarming potentials, the analytical framework is demonstrated to detect this transmission advantage, before the mutation reaches dominance, on a realtime basis.
Conclusions
We reported an evidence of transmission advantage associated with D614G substitution, and highlighted the realtime estimating potentials of modelling framework.
Introduction
The dynamics of the transmission of an infectious disease is largely determined by the pathogen’s infectiousness and the course of the transmission [1, 2]. The control of a contagious disease with high infectiousness requires the knowledge of the driven factors that may affect the transmission process [3, 4]. Virus mutation is one of the major challenges for controlling epidemics [5, 6]. The profile of pathogen in terms of viral fitness and functionality may be altered by mutations [7, 8], and in consequence change its transmissibility. Referring to the previous literature on seasonal influenza [9], a few key amino acid (AA) substitutions may lead to remarkable changing dynamics of antigenic property and epidemiological outcomes at population scale [10, 11]. Similar findings were also reported for other viral pathogens [12, 13].
The coronavirus disease 2019 (COVID19), whose etiological agent is the severe acute respiratory syndrome coronavirus 2 (SARSCoV2) [14], swept the world in a short period of time [15], and the ongoing COVID19 pandemic poses serious threat to public health [16]. As of December 31, 2020, over 81 million COVID19 cases are confirmed in the world with over 1.8 million associated deaths. In February 2020, genetic variants carrying the D614G substitution on the SARSCoV2 spike (S) protein began to spread first in Europe [17] and otherwhere globally, reaching fixation in many places rapidly. The D614G is potentially affecting viral transmission [5, 18]. Recent modelling analysis reported statistical evidence that SARSCoV2 strains with D614G substitution are likely to have an increased infectivity retrospectively [19]. In 2021, although 614D still can be detected in some places, e.g., Australia with around 25% frequency, the variants carrying 614G is predominant globally.
Some of these variant genomes upon the different selection pressure increase their frequency in the population. Recently, the SARSCoV2 Delta variants composed of several novel mutations on Spike protein increased their frequency [20]. This becomes one of the major challenges of COVID19 control because these variants have more competitive pathological features such higher transmission or resistance to vaccines [21, 22]. Exploring the relationship between the mutation activities and the disease transmissibility is of importance to understand how the evolutionary patterns at molecular scale may shape the epidemiological outcomes at population scale. Quantifying the advantage of mutations that affects the transmission may inform the disease control strategic decisionmaking process [23].
Given the intensity and the risk scale of the ongoing COVID19 pandemic, realtime surveillance and inference of the role of key mutations may be crucial for fighting against the pandemic. In this study, we adopted a statistical inference framework to estimate the transmission advantage associated with a single mutation in pathogen genomes empirically, and exemplify by using the COVID19 data in California, USA. We demonstrate the potentials of this analytical framework to produce an early warning signal for detecting transmission advantage on a realtime basis.
Methods
Reproduction number and transmission advantage: parameterization and likelihood framework
The timevarying reproduction number is commonly adopted to quantify the instantaneous transmissibility of infectious disease in an epidemic. Using the estimation framework in [24], the epidemic growth is modelled as a branching process, thus can be expressed as the ratio of C(t) over \({\int }_{0}^{\infty }w\left(k\right)C\left(tk\right)\mathrm{d}k\), which is commonly known as the renewable equation [25]. Here, the C(t) is the observed number of new COVID19 cases on the tth day. The function w(∙) is the distribution of the generation time (GT) of the disease. The GT is defined as the time interval between the time of exposure, i.e., being infected, of a primary case and that of his associated secondary case in the consecutive transmission generation [26]. The distribution w(∙) is predefined in our model, which is commonly estimated from contract tracing surveillance data [27,28,29,30].
The transmission advantage of the mutated variant against the original type is defined as the ratio, denoted by η, of the strainspecified reproduction numbers. We denote the reproduction number of cases infected by the original variant as R_{t}, and thus the reproduction number of cases infected by the mutated variant is η∙R_{t}. If η > 1, the mutated variant may be more infectious than the original genetic variant, and vice versa.
The observed proportion of original genetic variant is denoted by q_{t}, and the observed proportion of mutated variant is denoted by p_{t}. Since we consider the binary AA substituting process, we have p_{t} + q_{t} = 1 for all ts. By using the renewable equation backwardly, we model the expected number of cases on the tth day in Eq. (1).
Here, the E[∙] denotes the expectation function. Therefore, we construct the likelihood function \({L}_{t}^{(\mathrm{c})}\) of the daily number of cases using a Poissondistributed framework with observation at C_{t} and rate parameter at E[C_{t}] as in Eq. (2).
Here, the superscript ‘^{(c)}’ merely indicated the likelihood function is for the number of cases, which does not indicate the power. In addition, the overall reproduction number is (q_{t} + η∙p_{t})∙R_{t}.
For the observed sequencing data, we denote the numbers of original and mutated strains by m_{t} and n_{t}, respectively, for the tth day. The expected chance (or probability) that a randomly selected strain at the tth day carrying a specific mutation is given in Eq. (3).
Then, we have E[q_{t}] = 1 − E[p_{t}], which can be modelled with the same fashion. As such, by modelling the sampling of the genetic variants as a Bernoulli process, we construct the likelihood function (\({L}_{t}^{(\mathrm{s})}\)) of the observed genotype using a Bernoullidistributed framework with probability at E[p_{t}] as in Eq. (4).
Here, the superscript ‘^{(s)}’ merely indicated the likelihood function is for genetic variants, which does not indicate the power.
With Eqs. (2) and (4), we reconstruct the R_{t} time series, denoted by \(\left\{{R}_{t}\right\}\), and estimate η using the overall likelihood function defined in Eq. (5).
Similar formulations were adopted in previous studies [19, 31, 32].
COVID19 surveillance data and SARSCoV2 sequencing data
To demonstrate the application of the framework, we adopted the data of COVID19 in California, USA, and estimated the transmission advantage η of the D614G substitution. The surveillance data of daily number of COVID19 cases are collected from the R package “nCov2019” [33], which is extracted from the COVID19 surveillance platform launched by the New York Times. Figure 1A shows the daily number of COVID19 cases time series in California.
The SARSCoV2 strains are obtained via the Global initiative on sharing all influenza data (GISAID) with collection dates ranging from January 1 to June 30, 2020 in California [34]. A total of 4268 fulllength human SARSCoV2 strains are retrieved on December 31, 2020. All SARSCoV2 strains used for analysis are provided in the appendix (Additional file 1). We consider the study period from January 1 to June 30, 2020 when other mutated lineages, e.g., B.1.1.7, P.1, or B.1.617.2, were not yet detected. Multiple sequence alignment is performed using Clustal Omega [35], and the SARSCoV2 strain ‘China/WuhanHu1/2019EPI_ISL_402125’ is considered as the reference sequence.
Likelihoodbased inference and realtime estimation
To setup the model, we considered the w as a Gamma distribution having mean (± SD) values of 5.3 (± 2.1) days by averaging the GT estimates for COVID19 from the existing literatures [27,28,29, 36, 37]. Slight variation in the settings of the GT will not affect our main findings.
Using the likelihood framework defined in Eq. (5), we calculate the maximum likelihood estimation (MLE) of η to determine transmission advantage of D614G substitution. The 95% confidence intervals (95%CI) are calculated using the profile likelihood estimation framework with a Chisquare quantile as cutoff [38, 39], which is also adopted in [40,41,42,43].
For the realtime estimation, we repeat the statistical inference process of η using a part of dataset, instead of the full dataset, divided by the observing date. For example, the realtime estimate of η on the τth day is calculated by using the dataset with reporting date from the first day (i.e., January 1, 2020) to the τth day. We compare the consistency of the η estimates on a realtime basis in terms of their scales and 95%CIs. Moreover, we define the early warning signal as that a realtime estimate of η larger than 1 and of statistical significance can be obtained before the mutated strains (i.e., those SARSCoV2 strains with amino acid G) reach the dominance level in the population. For dominance level, it is considered as the proportion of the mutated strains (p_{t}) over 0.5, i.e., p_{t} > 0.5, which can be observed empirically. An early warning signal indicates the realtime estimating potentials of our analytical framework in detecting the transmission advantage due to mutation.
Results
In California, the epidemic curve grew since February, see Fig. 1A, peaked in July with daily number of COVID19 case over 10,000, declined in August, and has maintained at a steady level since September (data not shown). We reconstruct the daily instantaneous reproduction numbers of the cases infected by SARSCoV2 strains with D614 or G614 type in Fig. 1B. We observe that the overall trends of reproduction numbers are relatively high in the early March, but gradually decreasing thereafter since the local ‘stayathome’ order was issued on March 19, 2020 in California [44]. During the first half of March, which is regarded as the early phase of the outbreak, the average reproduction number is 2.4, which is largely consistent with most of previous estimates [15, 16, 45,46,47].
We report the estimated proportion of D614G substitution E[p_{t}] fits the observed sequencing data well, see Fig. 1C. We infer the transmission advantage η at 1.54 (95%CI: 1.36, 1.72), which means the D614G substitution increases 54% of the transmissibility. Hence, in Fig. 1B, the reproduction number of the SARSCoV2 variant with 614G appears higher than that of the original genotype. Although reproduction number R_{t} of the 614D are below 1 for most of the time after April 2020, the reproduction number η∙R_{t} of type G fluctuated around 1 during the same period, which led to a largescale epidemic wave in California during summer in 2020 (see Fig. 1A).
For the realtime estimating potentials, we find that the realtime estimates of η appear unstable in February and early March, when the D614G substitution emerge, and gradually converge and stabilize since March 12, see Fig. 1D. Specially, on March 12 (highlighted in Fig. 1C and Fig. 1D), when the proportion of D614G substitution (p_{t}) reaches 35% (< 0.5), the η estimate is 2.12 (95%CI: 1.24, 3.78), which is significantly larger than 1.
Discussion
Although the variants carrying D614G substitution might be introduced to California from aboard during the first few months of pandemic, the observed changes in SARSCoV2 mutations (p_{t}) were likely due to the spread of virus locally after the implementation of strict travelban measurements. The significant increase in transmissibility associated with the D614G substitution is biologically reasonable according to similar findings reported in previous studies. Consistent evidences of the transmission advantage of D614G substitution were also reported in previous literature both statistically [19, 48] and experimentally [49,50,51,52,53]. The D614G replacement leads to increased infectivity and stability of the virion and is shown to enhance viral replication in human lung epithelial cells [51, 52]. The interaction of the SARSCoV2 S protein with multiple epithelium components, e.g., glycocalyx, and proteases, govern the cellular entry [54]. Thus, the mutations on S protein with more effective interaction with these epithelium components enables SARSCoV2 variants to infect with relatively lower virus titer. Previous analysis implied that the D614G substitution may alter the conformation of spike protein trimer that shifted toward an ACE2 bindingcompetent state [50], and thus may functionally improve receptor binding capacity from a theoretical perspective [17, 18, 53]. The D614G substitution increases host cell entry via ACE2 and transmembrane protease serine 2 (TMPRSS2) [54]. Comparing to substitution, we learn from the influenza virus that major antigenic changes can be caused by a single AA substitution related to the receptor binding domain (RBD) [55].
Although a significant transmission advantage of D614G is found, we notice that the proportion of 614G variant generally increased, while the reproduction number series decreased in March and then remained constant. The reasons may include that the increase in transmissibility associated with D614G was counteracted by the effects of local nonpharmaceutical interventions that reduced the overall transmission of COVID19. For sensitivity checking, we repeat the estimating process of η with alternative mean GT using a shorter estimate of 4.0 days [30] and a longer estimate of 7.5 days [15]. We find that the η estimates are consistently and significantly larger than 1 in similar scales (data not shown), which validates our main results. The statistical inference framework is empirical, and thus can be extended to explore the transmission advantage attributed to single mutations for other infectious diseases.
Our analytical framework can yield an early warning signal in detecting the transmission advantage due to D614G substitution before the mutation reaching dominance on a realtime basis. Although some recent studies indicate that the D614G mutation is unlikely to undermine the neutralization from current SARSCoV2 vaccine candidates [53, 56], there are also other studies suggest the concerns should be raised oppositely [57, 58]. Similar concerns of the changes in the protective effect from vaccine or prior infection are frequently raised regarding other recent SARSCoV2 varaints [22, 59,60,61,62]. Under selection, viral quasispecies including closely related viral genomes might be generated by the accumulation of mutations [63]. As such, the early warning signal provides an opportunity for improving disease control strategies and healthcare planning against the mutated strains, which might have different diagnostic conditions or clinical outcomes [19, 50, 53]. Hence, we highlight the importance of our analytical framework, such that the public health risks related to viral mutations may be controllable with early preparedness.
For the limitations of this study, we have the following remarks. First, the reconstruction of R_{t} relies on the setting of the generation time (GT). We model the GT distribution, i.e., w(∙), of COVID19 as a fixed Gamma distribution, which follows previous studies [27,28,29,30, 36]. In the realworld situation, the time interval between transmission generations might be varying [45], which may affect the reconstruction of reproduction number. However, the overall trends of R_{t} estimates are unlikely changed due to slight variation in GT [45]. Thus, we consider the impact of this limitation on the inference of transmission advantage may be negligible, and our model can be extended to a more complex context with the timevarying GT data available. Second, theoretically, the GT distribution might be altered by the mutated strains. However, by screening the literature of COVID19, we find no evidence that GT is varied associated with the D614G substitution in SARSCoV2, and thus we presume w(∙) to be a fixed distribution. Third, for the R_{t} estimation parts, C(t) in the ‘methods’ section should be the numbers of COVID19 cases onset at time t. However, due to the surveillance data by onset date are unavailable, we adopted the current dataset by reporting data as a proxy for the COVID19 incidence time series. If one considers a constant reporting lag, the R_{t} estimates will have exactly same trends but shifted for the reporting lag. Considering the similar reporting delay also occurred for the SARSCoV2 sequencing data, the effects of the two reporting lags may be counteracted. We remark that this approximation in analysis is unlikely to affect the main conclusions in this study. Furthermore, with detailed reporting lag information of each individual case, adjustment for reporting delay can surely be carried out based on our current analytical framework. Fourth, this study focuses on exploring the effects on changing the disease transmissibility associated with a single mutation, e.g., D614G, but the realworld biological mechanisms, which are usually more complex, remain uncovered. As an example, on one hand, the R384G substitution in influenza A/H3N2 virus enhances ability of inhost immuneescape [64], which indicates an increase in infectivity [9], but this substitution appears detrimental. On the other hand, the comutations of R384G in nucleoprotein (NP) could improve and compensate the viral fitness or functionality of [7, 8], such that the mutated strains reached fixation rapidly in 1993–1994 flu season. Future studies are needed for exploring the mechanisms of how D614G in SARSCoV2 affects the transmissibility of COVID19. Fifth, the transmission advantage can be contributed by multiple factors such as increase in infectiousness or viral viability, change in the infection risk to different group of hosts [65], change in the escape from antibodies, shortening of generation interval, changes in clinical conditions, population size dynamics, and selective pressures [66]. Our analytical framework cannot disentangle the effects of each factor, which requires more complex methods, and detailed information [67]. Sixth, homogeneous mixing and equal contribution of all cases were assumed in our model. Thus, the reproduction numbers and transmission advantage estimates are interpreted as the average scales for the whole population in California. Seventh, there are multiple mutations in the SARSCoV2 variants carrying 614G, and we remark that the independent effects of each mutation cannot be disentangled in this study, where the interactions among these mutations are unassessed. Lastly, as a datadriven study, the estimated association should be interpreted with caution. With ecological setting, though our analysis provides statistical evidence about the likelihood of causality, the findings in this study cannot guarantee the causality, which needs further biomedical experiments in more sophisticated contexts.
Conclusions
The modelling framework in this study links together the mutation activity at molecular scale and COVID19 transmissibility at population scale. We report statistical evidence of the transmission advantage associated with the D614G substitution in SARSCoV2. We highlight that an early warning signal in detecting this transmission advantage can be generated on a realtime basis. Future studies on exploring the mechanism between SARSCoV2 mutation and COVID19 infectivity are needed.
Availability of data and materials
All data used in this work are publicly available. The processed data and codes are available via https://github.com/plxzpnxZBD/realtime_TransAdv.
Abbreviations
 AA:

Amino acid
 COVID19:

Coronavirus disease 2019
 D614G:

The amino acid substitution changing from Aspartic Acid (D) to Glycine (G) on the 614th codon (of the S protein of SARSCoV2)
 GT:

Generation time
 LR:

Likelihoodratio
 GISAID:

Global initiative on sharing all influenza data
 MLE:

Maximum likelihood estimation
 RBD:

Receptor binding domain
 SARSCoV2:

Severe acute respiratory syndrome coronavirus 2
 SD:

Standard deviation
 95%CI:

95% Confidence interval
References
Tuite AR, Fisman DN. Reporting, Epidemic Growth, and Reproduction Numbers for the 2019 Novel Coronavirus (2019nCoV) Epidemic. Ann Intern Med. 2020;172(8):567–8.
Riou J, Althaus CL: Pattern of early humantohuman transmission of Wuhan 2019 novel coronavirus (2019nCoV), December 2019 to January 2020. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 2020, 25(4):2000058.
Kutter JS, Spronken MI, Fraaij PL, Fouchier RA, Herfst S. Transmission routes of respiratory viruses among humans. Curr Opin Virol. 2018;28:142–51.
Fraser C, Riley S, Anderson RM, Ferguson NM. Factors that make an infectious disease outbreak controllable. Proc Natl Acad Sci U S A. 2004;101(16):6146–51.
Baum A, Fulton BO, Wloga E, Copin R, Pascal KE, Russo V, Giordano S, Lanza K, Negron N, Ni M, et al. Antibody cocktail to SARSCoV2 spike protein prevents rapid mutational escape seen with individual antibodies. Science. 2020;369(6506):1014–8.
Tsetsarkin KA, Vanlandingham DL, McGee CE, Higgs S: A single mutation in chikungunya virus affects vector specificity and epidemic potential. PLoS Pathog 2007, 3(12):e201.
Rimmelzwaan GF, Berkhoff EGM, Nieuwkoop NJ, Fouchier RAM, Osterhaus A. Functional compensation of a detrimental amino acid substitution in a cytotoxicTlymphocyte epitope of influenza a viruses by comutations. J Virol. 2004;78(16):8946–9.
Rimmelzwaan GF, Berkhoff EGM, Nieuwkoop NJ, Smith DJ, Fouchier RAM, Osterhaus A. Full restoration of viral fitness by multiple compensatory comutations in the nucleoprotein of influenza A virus cytotoxic Tlymphocyte escape mutants. J Gen Virol. 2005;86(6):1801–5.
Gog JR, Rimmelzwaan GF, Osterhaus ADME, Grenfell BT. Population dynamics of rapid fixation in cytotoxic T lymphocyte escape mutants of influenza A. Proc Natl Acad Sci. 2003;100(19):11143–7.
Smith DJ, Lapedes AS, de Jong JC, Bestebroer TM, Rimmelzwaan GF, Osterhaus AD, Fouchier RA. Mapping the antigenic and genetic evolution of influenza virus. Science. 2004;305(5682):371–6.
Zhao S, Lou J, Cao L, Chen Z, Chan RW, Chong MK, Zee BC, Chan PK, Wang MH. Quantifying the importance of the key sites on haemagglutinin in determining the selection advantage of influenza virus: Using A/H3N2 as an example. J Infect. 2020;81(3):452–82.
Botosso VF, Zanotto PM, Ueda M, Arruda E, Gilio AE, Vieira SE, Stewien KE, Peret TC, Jamal LF, Pardini MI et al: Positive selection results in frequent reversible amino acid replacements in the G protein gene of human respiratory syncytial virus. PLoS Pathog 2009, 5(1):e1000254.
Tolle MA. Mosquitoborne diseases. Curr Probl Pediatr Adolesc Health Care. 2009;39(4):97–140.
Hu B, Guo H, Zhou P, Shi ZL. Characteristics of SARSCoV2 and COVID19. Nat Rev Microbiol. 2021;19(3):141–54.
Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KSM, Lau EHY, Wong JY, et al. Early Transmission Dynamics in Wuhan, China, of Novel CoronavirusInfected Pneumonia. N Engl J Med. 2020;382(13):1199–207.
Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019nCoV outbreak originating in Wuhan, China: a modelling study. Lancet. 2020;395(10225):689–97.
Wan Y, Shang J, Graham R, Baric RS, Li F: Receptor Recognition by the Novel Coronavirus from Wuhan: an Analysis Based on DecadeLong Structural Studies of SARS Coronavirus. J Virol 2020, 94(7).
Benvenuto D, Demir AB, Giovanetti M, Bianchi M, Angeletti S, Pascarella S, Cauda R, Ciccozzi M, Cassone A. Evidence for mutations in SARSCoV2 Italian isolates potentially affecting virus transmission. J Med Virol. 2020;92(10):2232–7.
Volz E, Hill V, McCrone JT, Price A, Jorgensen D, O'Toole A, Southgate J, Johnson R, Jackson B, Nascimento FF et al: Evaluating the Effects of SARSCoV2 Spike Mutation D614G on Transmissibility and Pathogenicity. Cell 2021, 184(1):64–75 e11.
Ito K, Piantham C, Nishiura H. Predicted domination of variant Delta of SARSCoV2 before Tokyo Olympic games, Japan. Eurosurveillance. 2021;26(27):2100570.
Yadav PD, Sapkal GN, Abraham P, Ella R, Deshpande G, Patil DY, Nyayanit DA, Gupta N, Sahay RR, Shete AM et al: Neutralization of Variant Under Investigation B.1.617.1 With Sera of BBV152 Vaccinees. Clin Infect Dis 2021.
Planas D, Veyer D, Baidaliuk A, Staropoli I, GuivelBenhassine F, Rajah MM, Planchais C, Porrot F, Robillard N, Puech J et al: Reduced sensitivity of SARSCoV2 variant Delta to antibody neutralization. Nature 2021.
Ferguson NM, Cummings DA, Cauchemez S, Fraser C, Riley S, Meeyai A, Iamsirithaworn S, Burke DS. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature. 2005;437(7056):209–14.
Cori A, Ferguson NM, Fraser C, Cauchemez S. A new framework and software to estimate timevarying reproduction numbers during epidemics. Am J Epidemiol. 2013;178(9):1505–12.
Zhao S, Musa SS, Hebert JT, Cao P, Ran J, Meng J, He D, Qin J: Modelling the effective reproduction number of vectorborne diseases: the yellow fever outbreak in Luanda, Angola 2015–2016 as an example. PeerJ 2020, 8:e8601.
Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 2007;274(1609):599–604.
Ferretti L, Wymant C, Kendall M, Zhao L, Nurtay A, AbelerDorner L, Parker M, Bonsall D, Fraser C: Quantifying SARSCoV2 transmission suggests epidemic control with digital contact tracing. Science 2020, 368(6491):eabb6936.
Ganyani T, Kremer C, Chen D, Torneri A, Faes C, Wallinga J, Hens N: Estimating the generation interval for coronavirus disease (COVID19) based on symptom onset data, March 2020. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 2020, 25(17):2000257.
Tindale LC, Stockdale JE, Coombe M, Garlock ES, Lau WYV, Saraswat M, Zhang L, Chen D, Wallinga J, Colijn C: Evidence for transmission of COVID19 prior to symptom onset. Elife 2020, 9:e57149.
Zhao S. Estimating the time interval between transmission generations when negative values occur in the serial interval data: using COVID19 as an example. Math Biosci Eng. 2020;17(4):3512–9.
Leung K, Lipsitch M, Yuen KY, Wu JT. Monitoring the fitness of antiviralresistant influenza strains during an epidemic: a mathematical modelling study. Lancet Infect Dis. 2017;17(3):339–47.
Zhao S, Lou J, Cao L, Zheng H, Chong MKC, Chen Z, Chan RWY, Zee BCY, Chan PKS, Wang MH: Quantifying the transmission advantage associated with N501Y substitution of SARSCoV2 in the UK: an early datadriven analysis. J Travel Med 2021, 28(2):taab011.
Wu T, Hu E, Ge X, Yu G. nCov2019: an R package for studying the COVID19 coronavirus pandemic. PeerJ. 2021;9:e11421.
Shu Y, McCauley J. GISAID: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance. 2017;22(13):30494.
Sievers F, Higgins DG: Clustal Omega, accurate alignment of very large numbers of sequences. In: Multiple sequence alignment methods. edn.: Springer; 2014: 105–116.
He X, Lau EHY, Wu P, Deng X, Wang J, Hao X, Lau YC, Wong JY, Guan Y, Tan X: Temporal dynamics in viral shedding and transmissibility of COVID19. Nat Med 2020:1–4.
Zhao S, Tang B, Musa SS, Ma S, Zhang J, Zeng M, Yun Q, Guo W, Zheng Y, Yang Z et al: Estimating the generation interval and inferring the latent period of COVID19 from the contact tracing data. Epidemics 2021, 36:100482.
Fan JQ, Huang T. Profile likelihood inferences on semiparametric varyingcoefficient partially linear models. Bernoulli. 2005;11(6):1031–57.
Bolker BM: Ecological models and data in R: Princeton University Press; 2008.
Breto C, He DH, Ionides EL, King AA. Time Series Analysis Via Mechanistic Models. Annals of Applied Statistics. 2009;3(1):319–48.
He D, Ionides EL, King AA. Plugandplay inference for disease dynamics: measles in large and small populations as a case study. J R Soc Interface. 2010;7(43):271–83.
Lin Q, Chiu AP, Zhao S, He D. Modeling the spread of Middle East respiratory syndrome coronavirus in Saudi Arabia. Stat Methods Med Res. 2018;27(7):1968–78.
Zhao S, Lou J, Chong MKC, Cao L, Zheng H, Chen Z, Chan RWY, Zee BCY, Chan PKS, Wang MH: Inferring the Association between the Risk of COVID19 Case Fatality and N501Y Substitution in SARSCoV2. Viruses 2021, 13(4).
California Health Officials Announce a Regional Stay at Home Order [https://www.gov.ca.gov/wpcontent/uploads/2020/03/3.19.20attestedEON3320COVID19HEALTHORDER.pdf]
Ali ST, Wang L, Lau EHY, Xu XK, Du Z, Wu Y, Leung GM, Cowling BJ. Serial interval of SARSCoV2 was shortened over time by nonpharmaceutical interventions. Science. 2020;369(6507):1106–9.
Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, Pastore YPA, Mu K, Rossi L, Sun K, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID19) outbreak. Science. 2020;368(6489):395–400.
Gatto M, Bertuzzo E, Mari L, Miccoli S, Carraro L, Casagrandi R, Rinaldo A. Spread and dynamics of the COVID19 epidemic in Italy: Effects of emergency containment measures. Proc Natl Acad Sci U S A. 2020;117(19):10484–91.
Leung K, Pei Y, Leung GM, Lam TTY, Wu JT: Empirical transmission advantage of the D614G mutant strain of SARSCoV2. medRxiv 2020. https://doi.org/10.1101/2020.09.22.20199810.
Weissman D, Alameh MG, de Silva T, Collini P, Hornsby H, Brown R, LaBranche CC, Edwards RJ, Sutherland L, Santra S et al: D614G Spike Mutation Increases SARS CoV2 Susceptibility to Neutralization. Cell Host Microbe 2021, 29(1):23–31 e24.
Yurkovetskiy L, Wang X, Pascal KE, TomkinsTinch C, Nyalile TP, Wang Y, Baum A, Diehl WE, Dauphin A, Carbone C et al: Structural and Functional Analysis of the D614G SARSCoV2 Spike Protein Variant. Cell 2020, 183(3):739–751 e738.
Plante JA, Liu Y, Liu J, Xia H, Johnson BA, Lokugamage KG, Zhang X, Muruato AE, Zou J, FontesGarfias CR et al: Spike mutation D614G alters SARSCoV2 fitness. Nature 2020.
Hou YJ, Chiba S, Halfmann P, Ehre C, Kuroda M, Dinnon KH 3rd, Leist SR, Schafer A, Nakajima N, Takahashi K, et al. SARSCoV2 D614G variant exhibits efficient replication ex vivo and transmission in vivo. Science. 2020;370(6523):1464–8.
Korber B, Fischer WM, Gnanakaran S, Yoon H, Theiler J, Abfalterer W, Hengartner N, Giorgi EE, Bhattacharya T, Foley B et al: Tracking Changes in SARSCoV2 Spike: Evidence that D614G Increases Infectivity of the COVID19 Virus. Cell 2020, 182(4):812–827 e819.
Seyran M, Takayama K, Uversky VN, Lundstrom K, Palù G, Sherchan SP, Attrish D, Rezaei N, Aljabali AAA, Ghosh S et al: The structural basis of accelerated host cell entry by SARSCoV2. The FEBS Journal 2020, n/a(n/a).
Koel BF, Burke DF, Bestebroer TM, van der Vliet S, Zondag GC, Vervaet G, Skepner E, Lewis NS, Spronken MI, Russell CA, et al. Substitutions near the receptor binding site determine major antigenic change during influenza virus evolution. Science. 2013;342(6161):976–9.
Dearlove B, Lewitus E, Bai H, Li Y, Reeves DB, Joyce MG, Scott PT, Amare MF, Vasan S, Michael NL, et al. A SARSCoV2 vaccine candidate would likely match all currently circulating variants. Proc Natl Acad Sci U S A. 2020;117(38):23652–62.
van Dorp L, Acman M, Richard D, Shaw LP, Ford CE, Ormond L, Owen CJ, Pang J, Tan CCS, Boshier FAT et al: Emergence of genomic diversity and recurrent mutations in SARSCoV2. Infect Genet Evol 2020, 83:104351.
Weisblum Y, Schmidt F, Zhang F, DaSilva J, Poston D, Lorenzi JC, Muecksch F, Rutkowska M, Hoffmann HH, Michailidis E et al: Escape from neutralizing antibodies by SARSCoV2 spike protein variants. Elife 2020, 9:e61312.
Xie XP, Liu Y, Liu JY, Zhang XW, Zou J, FontesGarfias CR, Xia HJ, Swanson KA, Cutler M, Cooper D et al: Neutralization of SARSCoV2 spike 69/70 deletion, E484K and N501Y variants by BNT162b2 vaccineelicited sera. Nat Med 2021:1–2.
Moore JP, Offit PA. SARSCoV2 Vaccines and the Growing Threat of Viral Variants. JAMA. 2021;325(9):821–2.
Muik A, Wallisch AK, Sänger B, Swanson KA, Mühl J, Chen W, Cai H, Maurus D, Sarkar R, Türeci Ö: Neutralization of SARSCoV2 lineage B. 1.1. 7 pseudovirus by BNT162b2 vaccine–elicited human sera. Science 2021.
Supasa P, Zhou D, Dejnirattisai W, Liu C, Mentzer AJ, Ginn HM, Zhao Y, Duyvesteyn HME, Nutalai R, Tuekprakhon A: Reduced neutralization of SARSCoV2 B. 1.1. 7 variant by convalescent and vaccine sera. Cell 2021.
Andino R, Domingo E. Viral quasispecies. Virology. 2015;479–480:46–51.
Berkhoff EGM, Boon ACM, Nieuwkoop NJ, Fouchier RAM, Sintnicolaas K, Osterhaus A, Rimmelzwaan GF. A mutation in the HLAB* 2705restricted NP383391 epitope affects the human influenza A virusspecific cytotoxic Tlymphocyte response in vitro. J Virol. 2004;78(10):5216–22.
Faria NR, Mellan TA, Whittaker C, Claro IM, Candido DdS, Mishra S, Crispim MAE, Sales FCS, Hawryluk I, McCrone JT: Genomics and epidemiology of the P. 1 SARSCoV2 lineage in Manaus, Brazil. Science 2021.
SaadRoy CM, Morris SE, Metcalf CJE, Mina MJ, Baker RE, Farrar J, Holmes EC, Pybus OG, Graham AL, Levin SA. Epidemiological and evolutionary considerations of SARSCoV2 vaccine dosing regimes. Science. 2021;372(6540):363–70.
Ong SWX, Young BE, Lye DC: Lack of detail in populationlevel data impedes analysis of SARSCoV2 variants of concern and clinical outcomes. The Lancet Infectious Diseases.
Acknowledgements
This study is conducted using the resources of Alibaba Cloud Intelligence High Performance Cluster computing facilities, which is made free for COVID19 research.
Disclaimer
The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
Funding
This work is supported by CUHK grant [PIEF/Ph2/COVID/06, 4054456], the Health and Medical Research Fund (HMRF) Commissioned Research on COVID19 [INFCUHK1] of Hong Kong, China, and partially supported by the National Natural Science Foundation of China (NSFC) [31871340, 71974165].
Author information
Authors and Affiliations
Contributions
Conceptualization: SZ. Methodology: SZ, and MKCC. Software: SZ. Validation: SZ. Formal analysis: SZ. Investigation: SZ. Resources: SZ, and JZ. Data Curation: SZ, and JZ. Writing—Original Draft: SZ. Writing—Review and Editing: JZ, LC, HZ, MKCC, ZC, RWYC, BCYZ, PKSC, and MHW. Visualization: SZ. Supervision: MHW. Project Administration: JZ. Funding acquisition: MHW. All authors critically read the manuscript, and gave final approval for publication.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
The COVID19 number of cases and sequencing data are collected via public domains, and thus neither ethical approval nor individual consent is applicable.
Consent for publication
Not applicable.
Competing interests
MHW is a shareholder of Beth Bioinformatics Co., Ltd. BCYZ is a shareholder of Beth Bioinformatics Co., Ltd and Health View Bioanalytics Ltd. Other authors declared no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Additional file 1.
The acknowledgement table of SARSCoV2 strain sequences used in this study.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Zhao, S., Lou, J., Cao, L. et al. Realtime quantification of the transmission advantage associated with a single mutation in pathogen genomes: a case study on the D614G substitution of SARSCoV2. BMC Infect Dis 21, 1039 (2021). https://doi.org/10.1186/s1287902106729w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s1287902106729w
Keywords
 COVID19
 Mutation
 Transmission advantage
 Realtime estimation
 Statistical modelling