Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations.

Influenza virus continuously evolves to escape human adaptive immunity and generates seasonal epidemics. Therefore, influenza vaccine strains need to be updated annually for the upcoming flu season to ensure vaccine effectiveness. We develop a computational approach, beth-1, to forecast virus evolution and select representative virus for influenza vaccine. The method involves modelling site-wise mutation fitness. Informed by virus genome and population sero-positivity, we calibrate transition time of mutations and project the fitness landscape to future time, based on which beth-1 selects the optimal vaccine strain. In season-to-season prediction in historical data for the influenza A pH1N1 and H3N2 viruses, beth-1 demonstrates superior genetic matching compared to existing approaches. In prospective validations, the model shows superior or non-inferior genetic matching and neutralization against circulating virus in mice immunization experiments compared to the current vaccine. The method offers a promising and ready-to-use tool to facilitate vaccine strain selection for the influenza virus through capturing heterogeneous evolutionary dynamics over genome space-time and linking molecular variants to population immune response.

The mechanism shaping the logistic growth of mutation proportion in epidemics at population scale.

Virus evolution is a common process of pathogen adaption to host population and environment. Frequently, a small but important fraction of virus mutations are reported to contribute to higher risks of host infection, which is one of the major determinants of infectious diseases outbreaks at population scale. The key mutations contributing to transmission advantage of a genetic variant often grow and reach fixation rapidly. Based on classic epidemiology theories of disease transmission, we proposed a mechanistic explanation of the process that between-host transmission advantage may shape the observed logistic curve of the mutation proportion in population. The logistic growth of mutation is further generalized by incorporating time-varying selective pressure to account for impacts of external factors on pathogen adaptiveness. The proposed model is implemented in real-world data of COVID-19 to capture the emerging trends and changing dynamics of the B.1.1.7 strains of SARS-CoV-2 in England. The model characterizes and establishes the underlying theoretical mechanism that shapes the logistic growth of mutation in population.

Rapid evaluation of COVID-19 vaccine effectiveness against symptomatic infection with SARS-CoV-2 variants by analysis of genetic distance.

Timely evaluation of the protective effects of Coronavirus Disease 2019 (COVID-19) vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is urgently needed to inform pandemic control planning. Based on 78 vaccine efficacy or effectiveness (VE) data from 49 studies and 1,984,241 SARS-CoV-2 sequences collected from 31 regions, we analyzed the relationship between genetic distance (GD) of circulating viruses against the vaccine strain and VE against symptomatic infection. We found that the GD of the receptor-binding domain of the SARS-CoV-2 spike protein is highly predictive of vaccine protection and accounted for 86.3% (P = 0.038) of the VE change in a vaccine platform-based mixed-effects model and 87.9% (P = 0.006) in a manufacturer-based model. We applied the VE-GD model to predict protection mediated by existing vaccines against new genetic variants and validated the results by published real-world and clinical trial data, finding high concordance of predicted VE with observed VE. We estimated the VE against the Delta variant to be 82.8% (95% prediction interval: 68.7-96.0) using the mRNA vaccine platform, closely matching the reported VE of 83.0% from an observational study. Among the four sublineages of Omicron, the predicted VE varied between 11.9% and 33.3%, with the highest VE predicted against BA.1 and the lowest against BA.2, using the mRNA vaccine platform. The VE-GD framework enables predictions of vaccine protection in real time and offers a rapid evaluation method against novel variants that may inform vaccine deployment and public health responses.

Quantifying the effect of government interventions and virus mutations on transmission advantage during COVID-19 pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has become a major public health threat. This study aims to evaluate the effect of virus mutation activities and policy interventions on COVID-19 transmissibility in Hong Kong. METHODS: In this study, we integrated the genetic activities of multiple proteins, and quantified the effect of government interventions and mutation activities against the time-varying effective reproduction number Rt. FINDINGS: We found a significantly positive relationship between Rt and mutation activities and a significantly negative relationship between Rt and government interventions. The results showed that the mutations that contributed most to the increase of Rt were from the spike, nucleocapsid and ORF1b genes. Policy of prohibition on group gathering was estimated to have the largest impact on mitigating virus transmissibility. The model explained 63.2% of the Rt variability with the R2. CONCLUSION: Our study provided a convenient framework to estimate the effect of genetic contribution and government interventions on pathogen transmissibility. We showed that the S, N and ORF1b protein had significant contribution to the increase of transmissibility of SARS-CoV-2 in Hong Kong, while restrictions of public gathering and suspension of face-to-face class are the most effective government interventions strategies.

Temporal Patterns in the Evolutionary Genetic Distance of SARS-CoV-2 during the COVID-19 Pandemic.

During coronavirus disease 2019 (COVID-19) pandemic, the genetic mutations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurred frequently. Some mutations in the spike protein are considered to promote transmissibility of the virus, while the mutation patterns in other proteins are less studied and may also be important in understanding the characteristics of SARS-CoV-2. We used the sequencing data of SARS-CoV-2 strains in California to investigate the time-varying patterns of the evolutionary genetic distance. The accumulative genetic distances were quantified across different time periods and in different viral proteins. The increasing trends of genetic distance were observed in spike protein (S protein), the RNA-dependent RNA polymerase (RdRp) region and nonstructural protein 3 (nsp3) of open reading frame 1 (ORF1), and nucleocapsid protein (N protein). The genetic distances in ORF3a, ORF8, and nsp2 of ORF1 started to diverge from their original variants after September 2020. By contrast, mutations in other proteins appeared transiently, and no evident increasing trend was observed in the genetic distance to the original variants. This study presents distinct patterns of the SARS-CoV-2 mutations across multiple proteins from the aspect of genetic distance. Future investigation shall be conducted to study the effects of accumulative mutations on epidemics characteristics.

Differences in the case fatality risks associated with SARS-CoV-2 Delta and non-Delta variants in relation to vaccine coverage: An early ecological study in the United Kingdom.

The circulation of SARS-CoV-2 Delta (i.e., B.1.617.2) variants challenges the pandemic control. Our analysis showed that in the United Kingdom (UK), the reported case fatality ratio (CFR) decreased from May to July 2021 for non-Delta variant, whereas the decreasing trends of the CFR of Delta variant appeared weak and insignificant. The association between vaccine coverage and CFR might be stratified by different circulating variants. Due to the limitation of ecological study design, the interpretation of our results should be treated with caution.

Real-time quantification of the transmission advantage associated with a single mutation in pathogen genomes: a case study on the D614G substitution of SARS-CoV-2.

BACKGROUND: The COVID-19 pandemic poses serious threats to global health, and the emerging mutation in SARS-CoV-2 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 variant-specific reproduction numbers and estimate transmission advantage associated with the mutation activities marked by single substitution empirically. Using likelihood-based approach, the model is exemplified with the COVID-19 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 real-time basis. RESULTS: The modelling framework in this study links together the mutation activity at molecular scale and COVID-19 transmissibility at population scale. We find a significant transmission advantage of COVID-19 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 real-time basis. CONCLUSIONS: We reported an evidence of transmission advantage associated with D614G substitution, and highlighted the real-time estimating potentials of modelling framework.

Characterization of key amino acid substitutions and dynamics of the influenza virus H3N2 hemagglutinin.

The annual epidemics of seasonal influenza is partly attributed to the continued virus evolution. It is challenging to evaluate the effect of influenza virus mutations on evading population immunity. In this study, we introduce a novel statistical and computational approach to measure the dynamic molecular determinants underlying epidemics using effective mutations (EMs), and account for the time of waning mutation advantage against herd immunity by measuring the effective mutation periods (EMPs). Extensive analysis is performed on the sequencing and epidemiology data of H3N2 epidemics in ten regions from season to season. We systematically identified 46 EMs in the hemagglutinin (HA) gene, in which the majority were antigenic sites. Eight EMs were located in immunosubdominant stalk domain, an important target for developing broadly reactive antibodies. The EMs might provide timely information on key substitutions for influenza vaccines antigen design. The EMP suggested that major genetic variants of H3N2 circulated in Southeast Asia for an average duration of 4.5 years (SD 2.4) compared to a significantly shorter 2.0 years (SD 1.0) in temperate regions. The proposed method bridges population epidemics and molecular characteristics of infectious diseases, and would find broad applications in various pathogens mutation estimations.

A Bayesian method for synthesizing multiple diagnostic outcomes of COVID-19 tests.

The novel coronavirus disease 2019 (COVID-19) has spread worldwide and threatened human life. Diagnosis is crucial to contain the spread of SARS-CoV-2 infections and save lives. Diagnostic tests for COVID-19 have varying sensitivity and specificity, and the false-negative results would have substantial consequences to patient treatment and pandemic control. To detect all suspected infections, multiple testing is widely used. However, it may be challenging to build an assertion when the testing results are inconsistent. Considering the situation where there is more than one diagnostic outcome for each subject, we proposed a Bayesian probabilistic framework based on the sensitivity and specificity of each diagnostic method to synthesize a posterior probability of being infected by SARS-CoV-2. We demonstrated that the synthesized posterior outcome outperformed each individual testing outcome. A user-friendly web application was developed to implement our analytic framework with free access via http://www2.ccrb.cuhk.edu.hk/statgene/COVID_19/. The web application enables the real-time display of the integrated outcome incorporating two or more tests and calculated based on Bayesian posterior probability. A simulation-based assessment demonstrated higher accuracy and precision of the Bayesian probabilistic model compared with a single-test outcome. The online tool developed in this study can assist physicians in making clinical evaluations by effectively integrating multiple COVID-19 tests.

Inferring the Association between the Risk of COVID-19 Case Fatality and N501Y Substitution in SARS-CoV-2.

As COVID-19 is posing a serious threat to global health, the emerging mutation in SARS-CoV-2 genomes, for example, N501Y substitution, is one of the major challenges against control of the pandemic. Characterizing the relationship between mutation activities and the risk of severe clinical outcomes is of public health importance for informing the healthcare decision-making process. Using a likelihood-based approach, we developed a statistical framework to reconstruct a time-varying and variant-specific case fatality ratio (CFR), and to estimate changes in CFR associated with a single mutation empirically. For illustration, the statistical framework is implemented to the COVID-19 surveillance data in the United Kingdom (UK). The reconstructed instantaneous CFR gradually increased from 1.0% in September to 2.2% in November 2020 and stabilized at this level thereafter, which monitors the mortality risk of COVID-19 on a real-time basis. We identified a link between the SARS-CoV-2 mutation activity at molecular scale and COVID-19 mortality risk at population scale, and found that the 501Y variants may slightly but not significantly increase 18% of fatality risk than the preceding 501N variants. We found no statistically significant evidence of change in COVID-19 mortality risk associated with 501Y variants, and highlighted the real-time estimating potentials of the modelling framework.

Differential Influence of Age on the Relationship between Genetic Mismatch and A(H1N1)pdm09 Vaccine Effectiveness.

Assessment of influenza vaccine effectiveness (VE) and identification of relevant influencing factors are the current priorities for optimizing vaccines to reduce the impacts of influenza. To date, how the difference between epidemic strains and vaccine strains at genetic scale affects age-specific vaccine performance remains ambiguous. This study investigated the association between genetic mismatch on hemagglutinin and neuraminidase genes and A(H1N1)pdm09 VE in different age groups with a novel computational approach. We found significant linear relationships between VE and genetic mismatch in children, young adults, and middle-aged adults. In the children's group, each 3-key amino acid mutation was associated with an average of 10% decrease in vaccine effectiveness in a given epidemic season, and genetic mismatch exerted no influence on VE for the elderly group. We demonstrated that present vaccines were most effective for children, while protection for the elderly was reduced and indifferent to vaccine component updates. Modeling such relationships is practical to inform timely evaluation of VE in different groups of populations during mass vaccination and may inform age-specific vaccination regimens.

Modelling the association between COVID-19 transmissibility and D614G substitution in SARS-CoV-2 spike protein: using the surveillance data in California as an example.

BACKGROUND: The COVID-19 pandemic poses a serious threat to global health, and pathogenic mutations are a major challenge to disease control. We developed a statistical framework to explore the association between molecular-level mutation activity of SARS-CoV-2 and population-level disease transmissibility of COVID-19. METHODS: We estimated the instantaneous transmissibility of COVID-19 by using the time-varying reproduction number (Rt). The mutation activity in SARS-CoV-2 is quantified empirically depending on (i) the prevalence of emerged amino acid substitutions and (ii) the frequency of these substitutions in the whole sequence. Using the likelihood-based approach, a statistical framework is developed to examine the association between mutation activity and Rt. We adopted the COVID-19 surveillance data in California as an example for demonstration. RESULTS: We found a significant positive association between population-level COVID-19 transmissibility and the D614G substitution on the SARS-CoV-2 spike protein. We estimate that a per 0.01 increase in the prevalence of glycine (G) on codon 614 is positively associated with a 0.49% (95% CI: 0.39 to 0.59) increase in Rt, which explains 61% of the Rt variation after accounting for the control measures. We remark that the modeling framework can be extended to study other infectious pathogens. CONCLUSIONS: Our findings show a link between the molecular-level mutation activity of SARS-CoV-2 and population-level transmission of COVID-19 to provide further evidence for a positive association between the D614G substitution and Rt. Future studies exploring the mechanism between SARS-CoV-2 mutations and COVID-19 infectivity are warranted.

In silico prediction of influenza vaccine effectiveness by sequence analysis.

The effectiveness of seasonal influenza vaccines varies with the matching of vaccine strains to circulating strains. Based on the genetic distance of hemagglutinin and neuraminidase gene of the influenza viruses to vaccine strains, we statistically quantified the relationship between the genetic mismatch and vaccine effectiveness (VE) for influenza A/H1N1pdm09, A/H3N2 and B. We also proposed a systematic approach to integrate multiple genes and influenza types for overall VE estimation. Evident linear relationships were identified and validated in independent data. The modelling framework may enable in silico prediction for VE on a real-time basis and inform the influenza vaccine selection strategy.

Predicting the dominant influenza A serotype by quantifying mutation activities.

OBJECTIVES: Characterizing and predicting the evolutionary process of influenza, which remains challenging, are of importance in capturing the patterns of influenza activities and the development of prevention and control strategies. In this study, we quantified genetic mutation activity and developed a statistical model to predict dominant influenza A serotype with limited sequencing data. DATA AND METHODS: A total number of 8097 and 7090 HA sequences for A/H1N1 and A/H3N2 were collected from 2008/09 to 2018/19 flu season in seven countries or regions. And g-measure, which reflected the overall level of genetic activity through time, was considered to predict dominant flu serotype in population. RESULTS: The model discriminated the influenza serotypes well with the sensitivity = 0.84, precision = 0.79 and AUC = 0.78 (95% CI: 0.54 - 0.97), and explained 42% of the serotypes variability with the R2. CONCLUSIONS: Our study suggests that the dominance of flu serotype in population can be well discriminated by genetic mutation activities from sample strains. By the data-driven computational framework, the genetic mutation can be quantified to trace the genetic activities on a real-time basis, and provide early warning for the coming flu season.

[Investigation of the status of perioperative blood transfusion in grade III-A general hospitals in Zhejiang province].

OBJECTIVE: To investigate the situation of perioperative blood transfusion in Grade III-A hospitals in Zhejiang province, in order to provide statistics for improving appropriateness of blood transfusion. METHOD: The questionnaire was conducted in 9 Grade III-A hospitals in Zhejiang province according to "The Technical Criterion of Clinical Blood Transfusion". The data including total quantity, whole blood and component blood transfusion in 2007 were analyzed. RESULTS: Among 19 102 cases, the percentage of component blood transfusion was 99.3%, but 44.1% transfusion is conducted just according to doctors' experience without any medical indication, including 603 patients not re-examining the level of Hct or Hb in 72 h after operation. For the patients with complete transfusion record, the irrational rate of whole blood, RBC and platelet transfusion were 39.2%, 39.2%, 43.7%, the mainly reason was the relax demand on the transfusion indication. CONCLUSIONS: Although Grade III-A hospitals in Zhejiang did fairly well in perioperative blood component transfusion, there are still some seriously unreasonable phenomenons. Every medical organization should pay more attention to improve the quality of clinical blood transfusion.