A mathematical model of protein subunits COVID-19 vaccines.

We consider a general mathematical model for protein subunit vaccine with a focus on the MF59-adjuvanted spike glycoprotein-clamp vaccine for SARS-CoV-2, and use the model to study immunological outcomes in the humoral and cell-mediated arms of the immune response from vaccination. The mathematical model is fit to vaccine clinical trial data. We elucidate the role of Interferon-γ and Interleukin-4 in stimulating the immune response of the host. Model results, and results from a sensitivity analysis, show that a balance between the TH1 and TH2 arms of the immune response is struck, with the TH1 response being dominant. The model predicts that two-doses of the vaccine at 28 days apart will result in approximately 85% humoral immunity loss relative to peak immunity approximately 6 months post dose 1.

The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions.

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Long-term durability of immune responses to the BNT162b2 and mRNA-1273 vaccines based on dosage, age and sex.

The lipid nanoparticle (LNP)-formulated mRNA vaccines BNT162b2 and mRNA-1273 are a widely adopted multi vaccination public health strategy to manage the COVID-19 pandemic. Clinical trial data has described the immunogenicity of the vaccine, albeit within a limited study time frame. Here, we use a within-host mathematical model for LNP-formulated mRNA vaccines, informed by available clinical trial data from 2020 to September 2021, to project a longer term understanding of immunity as a function of vaccine type, dosage amount, age, and sex. We estimate that two standard doses of either mRNA-1273 or BNT162b2, with dosage times separated by the company-mandated intervals, results in individuals losing more than 99% humoral immunity relative to peak immunity by 8 months following the second dose. We predict that within an 8 month period following dose two (corresponding to the original CDC time-frame for administration of a third dose), there exists a period of time longer than 1 month where an individual has lost more than 99% humoral immunity relative to peak immunity, regardless of which vaccine was administered. We further find that age has a strong influence in maintaining humoral immunity; by 8 months following dose two we predict that individuals aged 18-55 have a four-fold humoral advantage compared to aged 56-70 and 70+ individuals. We find that sex has little effect on the immune response and long-term IgG counts. Finally, we find that humoral immunity generated from two low doses of mRNA-1273 decays at a substantially slower rate relative to peak immunity gained compared to two standard doses of either mRNA-1273 or BNT162b2. Our predictions highlight the importance of the recommended third booster dose in order to maintain elevated levels of antibodies.

A machine learning approach to differentiate between COVID-19 and influenza infection using synthetic infection and immune response data.

Data analysis is widely used to generate new insights into human disease mechanisms and provide better treatment methods. In this work, we used the mechanistic models of viral infection to generate synthetic data of influenza and COVID-19 patients. We then developed and validated a supervised machine learning model that can distinguish between the two infections. Influenza and COVID-19 are contagious respiratory illnesses that are caused by different pathogenic viruses but appeared with similar initial presentations. While having the same primary signs COVID-19 can produce more severe symptoms, illnesses, and higher mortality. The predictive model performance was externally evaluated by the ROC AUC metric (area under the receiver operating characteristic curve) on 100 virtual patients from each cohort and was able to achieve at least AUC = 91% using our multiclass classifier. The current investigation highlighted the ability of machine learning models to accurately identify two different diseases based on major components of viral infection and immune response. The model predicted a dominant role for viral load and productively infected cells through the feature selection process.

Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages.

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to better understand the evolution of this virus in humans. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic. We analyzed 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets. This approach enables real-time lineage identification, a clear description of the relationship between variants of concern, and efficient detection of recurrent mutations. Furthermore, time series change of Tajima's D by haplotype provides a powerful metric of lineage expansion. Finally, principal component analysis (PCA) highlights key steps in variant emergence and facilitates the visualization of genomic variation in the context of SARS-CoV-2 diversity. The computational framework presented here is simple to implement and insightful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of populations of humans and other organisms.

Disruption of long-term psychological distress trajectories during the COVID-19 pandemic: evidence from three British birth cohorts (preprint)

Importance Mental health disorders were among the leading global contributors to years lived with disability prior to the COVID-19 pandemic onset, and growing evidence suggests that population mental health outcomes have worsened since the pandemic started. The extent that these changes have altered common age-related trends in psychological distress, where distress typically rises until mid-life and then falls in both sexes, is unknown. Objective To analyse whether long-term pre-pandemic psychological distress trajectories have altered during the pandemic, and whether these changes have been different across generations and by sex. Design Cross-cohort study with prospective data collection over a 40-year period (earliest time point: 1981; latest time point: February/March 2021). Setting Population-based (adult general population), Great Britain. Participants Members of three nationally representative birth cohorts which comprised all people born in Great Britain in a single week of 1946, 1958, or 1970, and who participated in at least one of the data collection waves conducted after the start of the pandemic (40.6%, 42.8%, 39.4%, respectively). Exposure(s) Time, COVID-19 pandemic. Main Outcome(s) and Measure(s) Psychological distress factor scores, as measured by validated self-reported questionnaires. Results 16,389 participants (2,175 from the 1946 birth cohort, 52.8% women; 7,446 from the 1958 birth cohort, 52.4% women; and 6,768 from the 1970 birth cohort, 56.2% women) participated in the study. By September/October 2020, psychological distress levels had reached or exceeded the levels of the peak in the pre-pandemic life-course trajectories, with larger increases in younger cohorts: Standardised Mean Differences (SMD) and 95% confidence intervals (CIs) of -0.02 [-0.07, 0.04], 0.05 [0.02, 0.07], and 0.09 [0.07, 0.12] for the 1946, 1958, and 1970 birth cohorts, respectively. Increases in distress were larger among women than men, widening the pre-existing inequalities observed in the pre-pandemic peak and in the most recent pre-pandemic assessment. Conclusions and Relevance Pre-existing long-term psychological distress trajectories of adults born between 1946 and 1970 were disrupted during the COVID-19 pandemic, particularly among women, who reached the highest levels ever recorded in up to 40 years of follow-up data. This may impact future trends of morbidity, disability, and mortality due to common mental health problems.

Multiscale Model of Antiviral Timing, Potency, and Heterogeneity Effects on an Epithelial Tissue Patch Infected by SARS-CoV-2.

We extend our established agent-based multiscale computational model of infection of lung tissue by SARS-CoV-2 to include pharmacokinetic and pharmacodynamic models of remdesivir. We model remdesivir treatment for COVID-19; however, our methods are general to other viral infections and antiviral therapies. We investigate the effects of drug potency, drug dosing frequency, treatment initiation delay, antiviral half-life, and variability in cellular uptake and metabolism of remdesivir and its active metabolite on treatment outcomes in a simulated patch of infected epithelial tissue. Non-spatial deterministic population models which treat all cells of a given class as identical can clarify how treatment dosage and timing influence treatment efficacy. However, they do not reveal how cell-to-cell variability affects treatment outcomes. Our simulations suggest that for a given treatment regime, including cell-to-cell variation in drug uptake, permeability and metabolism increase the likelihood of uncontrolled infection as the cells with the lowest internal levels of antiviral act as super-spreaders within the tissue. The model predicts substantial variability in infection outcomes between similar tissue patches for different treatment options. In models with cellular metabolic variability, antiviral doses have to be increased significantly (>50% depending on simulation parameters) to achieve the same treatment results as with the homogeneous cellular metabolism.

A randomized controlled trial of renin-angiotensin-aldosterone system inhibitor management in patients admitted in hospital with COVID-19.

BACKGROUND: Renin-angiotensin aldosterone system inhibitors (RAASi) are commonly used among patients hospitalized with a severe acute respiratory syndrome coronavirus 2 infection coronavirus disease 2019 (COVID-19). We evaluated whether continuation versus discontinuation of RAASi were associated with short term clinical or biochemical outcomes. METHODS: The RAAS-COVID-19 trial was a randomized, open label study in adult patients previously treated with RAASi who are hospitalized with COVID-19 (NCT04508985). Participants were randomized 1:1 to discontinue or continue RAASi. The primary outcome was a global rank score calculated from baseline to day 7 (or discharge) incorporating clinical events and biomarker changes. Global rank scores were compared between groups using the Wilcoxon test statistic and the negative binomial test (using incident rate ratio [IRR]) and the intention-to-treat principle. RESULTS: Overall, 46 participants were enrolled; 21 participants were randomized to discontinue RAASi and 25 to continue. Patients' mean age was 71.5 years and 43.5% were female. Discontinuation of RAASi, versus continuation, resulted in a non-statistically different mean global rank score (discontinuation 6 [standard deviation [SD] 6.3] vs continuation 3.8 (SD 2.5); P = .60). The negative binomial analysis identified that discontinuation increased the risk of adverse outcomes (IRR 1.67 [95% CI 1.06-2.62]; P = .027); RAASi discontinuation increased brain natriuretic peptide levels (% change from baseline: +16.7% vs -27.5%; P = .024) and the incidence of acute heart failure (33% vs 4.2%, P = .016). CONCLUSION: RAASi continuation in participants hospitalized with COVID-19 appears safe; discontinuation increased brain natriuretic peptide levels and may increase risk of acute heart failure; where possible, RAASi should be continued.

Inequalities in mental and social wellbeing during the COVID-19 pandemic: prospective longitudinal observational study of five UK cohorts (preprint)

Background. Research suggests that there have been inequalities in the impact of the COVID-19 pandemic and related non-pharmaceutical interventions on population mental health. We explored these inequalities during the first year of the pandemic using nationally representative cohorts from the UK. Methods. We analysed data from 26,772 participants from five longitudinal cohorts representing generations born between 1946 and 2000, collected in May 2020, September-October 2020, and February-March 2021 across all five cohorts. We used a multilevel growth curve modelling approach to explore sociodemographic and socioeconomic differences in levels of anxiety and depressive symptomatology, loneliness, and life satisfaction over time. Results. Younger generations had worse levels of mental and social wellbeing throughout the first year of the pandemic. Whereas these generational inequalities narrowed between the first and last observation periods for life satisfaction (-0.33 [95% CI: -0.51, -0.15]), they became larger for anxiety (0.22 [0.10, 0.33]). Pre-existing generational inequalities in depression and loneliness did not change, but initial depression levels of the youngest cohort were worse than expected if the generational inequalities had not accelerated. Women and those experiencing financial difficulties had worse initial mental and social wellbeing levels than men and those financially living comfortably, respectively, and these gaps did not substantially differ between the first and last observation periods. Inequalities by additional factors are reported. Conclusions. By March 2021, mental and social wellbeing inequalities persisted in the UK adult population. Pre-existing generational inequalities may have been exacerbated with the pandemic onset. Policies aimed at protecting vulnerable groups are needed.

Analysis of Host Immunological Response of Adenovirus-Based COVID-19 Vaccines.

During the SARS-CoV-2 global pandemic, several vaccines, including mRNA and adenovirus vector approaches, have received emergency or full approval. However, supply chain logistics have hampered global vaccine delivery, which is impacting mass vaccination strategies. Recent studies have identified different strategies for vaccine dose administration so that supply constraints issues are diminished. These include increasing the time between consecutive doses in a two-dose vaccine regimen and reducing the dosage of the second dose. We consider both of these strategies in a mathematical modeling study of a non-replicating viral vector adenovirus vaccine in this work. We investigate the impact of different prime-boost strategies by quantifying their effects on immunological outcomes based on simple system of ordinary differential equations. The boost dose is administered either at a standard dose (SD) of 1000 or at a low dose (LD) of 500 or 250 vaccine particles. Results show dose-dependent immune response activity. Our model predictions show that by stretching the prime-boost interval to 18 or 20, in an SD/SD or SD/LD regimen, the minimum promoted antibody (Nab) response will be comparable with the neutralizing antibody level measured in COVID-19 recovered patients. Results also show that the minimum stimulated antibody in SD/SD regimen is identical with the high level observed in clinical trial data. We conclude that an SD/LD regimen may provide protective capacity, which will allow for conservation of vaccine doses.

COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes.

To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results suggest that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings suggest biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.

Management of Renin-Angiotensin-Aldosterone System blockade in patients admitted to hospital with confirmed coronavirus disease (COVID-19) infection (The McGill RAAS-COVID- 19): A structured summary of a study protocol for a randomized controlled trial.

OBJECTIVES: The aim of the RAAS-COVID-19 randomized control trial is to evaluate whether an upfront strategy of temporary discontinuation of renin angiotensin aldosterone system (RAAS) inhibition versus continuation of RAAS inhibition among patients admitted with established COVID-19 infection has an impact on short term clinical and biomarker outcomes. We hypothesize that continuation of RAAS inhibition will be superior to temporary discontinuation with regards to the primary endpoint of a global rank sum score. The global rank sum score has been successfully used in previous cardiovascular clinical trials. TRIAL DESIGN: This is an open label parallel two arm (1,1 ratio) randomized control superiority trial of approximately 40 COVID-19 patients who are on chronic RAAS inhibitor therapy. PARTICIPANTS: Adults who are admitted to hospital within the McGill University Health Centre systems (MUHC) including Royal Victoria Hospital (RVH), Montreal General Hospital (MGH) and Jewish General Hospital (JGH) and who are within 96 hours of COVID-19 diagnosis (confirmed via PCR on any biological sample) will be considered for the trial. Of note, the initial protocol to screen and enrol within 48 hours of COVID-19 diagnosis was extended through an amendment, to 96 hours to increase feasibility. Participants have to be 18 years or older and would have to be on RAAS inhibitors for at least a month to be considered eligible for the study. Additionally, RAAS inhibitors should not have been held for more than 48 hours before randomization. A list of inclusion and exclusion criteria can be found in the full protocol document. In order to prevent heart failure exacerbation, patients with reduced ejection fraction were excluded from the trial. Once a patient is admitted on the ward with a diagnosis of COVID-19, we will confirm with the treating physician if the participant is suitable for the RAAS-COVID trial and meets all the inclusion and exclusion criteria. If the patient is eligible and informed consent has been obtained we will collect data on sex, age, ethnicity, past medical history and list of medications (e.g. other anti-hypertensives or anticoagulants), for further analysis. INTERVENTION AND COMPARATOR: All the study participants will be randomized to a strategy of temporarily holding the RAAS inhibitor [intervention] versus continuing the RAAS inhibitor [continued standard of care]. Among participants who are randomized to the intervention arm, alternative guide-line directed anti-hypertensive medication will be provided to the treating physician team (detail in study protocol). In the intervention arm RAAS inhibitor will be withheld for a total of 7 days with the possibility of the withdrawn medication being initiated at any point after day 7 or on the day of discharge. The recommendation for re-initiating the withdrawn medication will be made to the treating physician. The re-initiation of these therapies are according to standard convention and follow-up as per Canadian guidelines. Additionally, the date of restarting the withdrawn medication or whether the medication was re-prescribed on discharge or not, will be collected. This will be used to conduct a sensitivity analysis. Furthermore, biomarkers such as troponin, c-reactive protein (CRP) and lymphocyte count will be assessed during the same time period. Samples will be collected on randomization, day 4 and day 7. MAIN OUTCOMES: PRIMARY ENDPOINT: In this study the primary end point is a global rank score calculated for all participants, regardless of treatment assignment ( score from 0 to 7). Please refer to table 4 in the full protocol. In the context of the current trial, it is estimated that death is the most meaningful endpoint, and therefore has the highest score ( score of 7). This is followed by admission to ICU, the need for mechanical ventilation etc. The lowest scores ( score of 1) are assigned to biomarker changes (e.g. change in troponin, change in CRP). This strategy has been used successfully in cardiovascular disease trials and therefore is applicable to the current trial. The primary endpoint for the present trial is assessed from baseline to day 7 (or discharge). Participants are ranked across the clinical and biomarker domains. Lower values indicate better health (or stability). Participants who died during the 7th day of the study will be ranked based on all events occurring before their death and also including the fatal event in the score. Next, participants who did not die but were transferred to ICU for invasive ventilation will be ranked based on all the events occurring before the ICU entry and also including the ICU admission in the score. Those participants who did not die were not transferred to ICU for invasive ventilation, will be ranked based on the subsequent outcomes. The mean rank score will then be compared between groups. In this scheme, a lower mean rank score indicates greater overall stability for participants. Secondary endpoints : The key secondary endpoints are the individual components of the primary components and include the following: death, transfer to ICU primarily for invasive ventilation, transfer to ICU for other indication, non-fatal MACE ( any of following, MI, stroke, acute HF, new onset Afib), length of stay > 4 days, development of acute kidney injury ( > 40% decline in eGFR or doubling of serum creatinine), urgent intravenous treatment for high blood pressure, 30% increase in baseline high sensitivity troponin, 30% increase in baseline BNP, increase in CRP to > 30% in 48 hours and lymphocyte count drop> 30%. We will also look at the World Health Organization (WHO) ordinal scale for clinical improvement (in COVID-19) in our data. In this scale death will be assigned the highest score of 8. Patients with no limitation of activity will be assigned a score of 1 which indicates overall more stability (3). Additionally, we will evaluate the potential effects of discontinuing RAAS inhibition on alternative schedules (longer/shorter than 7 days, intermittent discontinuation) using a mechanistic mathematical model of COVID-19 immunopathology calibrated to data collected from our patient cohort. In particular, we will assess the impact of alternative schedules on primary and secondary endpoints including increases to baseline CRP and lymphocyte counts. RANDOMIZATION: Participants will be randomized in a 1:1 ratio. Randomization will be performed within an electronic database system at the time of enrolment using a random number generator, an approach that has been successfully used in other clinical trials. Neither participant, study team, or treating team will be blinded to the intervention arm. BLINDING: This is an open label study with no blinding. NUMBERS TO BE RANDOMISED (SAMPLE SIZE): The approximate number of participants required for this trial is 40 patients (randomized 1:1 to continuation versus discontinuation of RAAS inhibitors). This number was calculated based on previous rates of outcomes for COVID-19 in the literature (e.g. death, ICU transfer) and statistical power calculations. TRIAL STATUS: Protocol number: MP-37-2021-6641, Version 4: 01-10-2020. Trial start date September 1st 2020 and currently enrolling participants. Estimated end date for recruitment of participants : July 2021. Estimated end date for study completion: September 1st 2021. TRIAL REGISTRATION: Trial registration: ClincalTrials.gov : NCT04508985 , date of registration: August 11th , 2020 FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.

Leveraging Computational Modeling to Understand Infectious Diseases.

PURPOSE OF REVIEW: Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation. RECENT FINDINGS: Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps. SUMMARY: Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.