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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.

Our previous research demonstrated that genetic distance (GD) on effective mutation (EM) sites can be used to evaluate vaccine effectiveness (VE)in silicoin real time. This study further investigates the relationship between VE and GD on antigenic sites (AS) and identifies key amino acid sites related to vaccine protection against influenza A/H1N1pdm09 and A/H3N2 between 2009 and 2019 flu seasons. We found that not any AS on hemagglutinin (HA) and neuraminidase (NA) may cause a decrease in VE, rather, GD on the intersection set of EM and AS is highly predictive of influenza VE. The integrated GD of HA and NA can explain up to 87.8% of VE variations for H3N2. Significant improvement is also found for VE prediction for pH1N1. Accurate prediction of influenza VE before vaccine deployment may facilitate reverse vaccinology to optimize vaccine antigen design and facilitate government preparedness of epidemics.

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.

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 South-east 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.

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.

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.

BackgroundThe 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.MethodsWe 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.ResultsThe 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.ConclusionsWe reported an evidence of transmission advantage associated with D614G substitution, and highlighted the real-time estimating potentials of modelling framework.

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.

The circulation of P.1 SARS-CoV-2 lineage becomes a challenge of pandemic control. Among the COVID-19 cases reported in Brazil, P.1 is estimated 54% more fatal than non-P.1 significantly. Considering the transmission advantage of P.1, we raise concerns regarding the rapid growth in critical patients.

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.

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.

The emerging N501Y mutation in severe acute respiratory syndrome coronavirus 2, which becomes prevalent in the UK rapidly, is one of the major challenges of COVID-19 control. To explore the transmission advantage, we estimate that the N501Y substitution increases the infectivity by 52% (95% confidence interval: 46, 58) in terms of the reproduction number.

BackgroundDuring the pandemic of coronavirus disease 2019 (COVID-19), the genetic mutations occurred in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cumulatively or sporadically. In this study, we employed a computational approach to identify and trace the emerging patterns of the SARS-CoV-2 mutations, and quantify accumulative genetic distance across different periods and proteins.MethodsFull-length human SARS-CoV-2 strains in United Kingdom were collected. We investigated the temporal variation in the evolutionary genetic distance defined by the Hamming distance since the start of COVID-19 pandemic.FindingsOur results showed that the SARS-CoV-2 was in the process of continuous evolution, mainly involved in spike protein (S protein), the RNA-dependent RNA polymerase (RdRp) region of open reading frame 1 (ORF1) and nucleocapsid protein (N protein). By contrast, mutations in other proteins were sporadic and genetic distance to the initial sequenced strain did not show an increasing trend.

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.

Virus evolution drives the annual influenza epidemics in human population worldwide. However, it has been challenging to evaluate the mutation effect of the influenza virus on evading the population immunity. In this study, we introduce a novel statistical and computational approach to measure the dynamic molecular determinants underlying epidemics by the effective mutations (EMs), and account for the time of waning mutation advantage against herd immunity by the effective mutation periods (EMPs). Extensive analysis is performed on the genome and epidemiology data of 13-year worldwide H3N2 epidemics involving nine regions in four continents. We showed that the identified EM processed similar profile in geographically adjacent regions, while only 40% are common to Europe, North America, Asia and Oceania, indicating that the regional specific mutations also contributed significantly to the global H3N2 epidemics. The mutation dynamics calibrated that around 90% of the common EMs underlying global epidemics were originated from South East Asia, led by Thailand and India, and the rest were originated from North America. New Zealand was found to be the dominate sink region of H3N2 circulation, followed by UK. All regions might act as the intersection in the H3N2 transmission network. The proposed methodology provided a way to characterize key amino acids from the genetic epidemiology point of view. This approach is not restricted by the genomic region or type of the virus, and will find broad applications in identifying therapeutic targets for combating infectious diseases.

A new device for detecting the position of an endotracheal tube is presented. This device consists of a highly sensitive linear Hall-effect sensor and a newly designed endotracheal tube in which two small magnets are embedded. The Hall-effect sensor can be placed on the skin of neck over the vocal cord to detect the position of the endotracheal tube by measuring the strength of its magnetic field when the magnet on the tube passes through the glottis during intubation. The results of our clinical tests on 38 cases of endotracheal intubation and 15 controls of esophageal intubation show that the device is sensitive to verify the esophageal intubation, and that it provides a useful means for the clinician to control the inserted length easily. Due to its unique principle of operation, the detector can be applied to all kinds of patients, especially in pre-hospital sites.