The origins of viruses: evolutionary dynamics of the escape hypothesis

This study examines the interactions between healthy target cells, infected target cells, virus particles, and immune cells within an HIV model. The model exhibits two equilibrium points: an infection-free equilibrium and an infection equilibrium. Stability analysis shows that the infection-free equilibrium is locally asymptotically stable when R0<1. Further, it is unstable when R0>1. The infection equilibrium is locally asymptotically stable when R0>1. The structural and practical identifiabilities of the within-host model for HIV infection dynamics were investigated using differential algebra techniques and Monte Carlo simulations. The HIV model was structurally identifiable by observing the total uninfected and infected target cells, immune cells, and viral load. Monte Carlo simulations assessed the practical identifiability of parameters. The production rate of target cells (λ), the death rate of healthy target cells (d), the death rate of infected target cells (δ), and the viral production rate by infected cells (π) were practically identifiable. The rate of infection of target cells by the virus (β), the death rate of infected cells by immune cells (Ψ), and antigen-driven proliferation rate of immune cells (b) were not practically identifiable. Practical identifiability was constrained by the noise and sparsity of the data. Analysis shows that increasing the frequency of data collection can significantly improve the identifiability of all parameters. This highlights the importance of optimal data sampling in HIV clinical studies, as it determines the best time points, frequency, and the number of sample points required to accurately capture the dynamics of the HIV infection within a host.

Mathematical modelling of SARS-CoV-2 infection of human and animal host cells reveals differences in the infection rates and delays in viral particle production by infected cells

The pre-clinical development of antiviral agents involves experimental trials in animals and ferrets as an animal model for the study of SARS-CoV-2. Here, we used mathematical models and experimental data to characterize the within-host infection dynamics of SARS-CoV-2 in ferrets. We also performed a global sensitivity analysis of model parameters impacting the characteristics of the viral infection. We provide estimates of the viral dynamic parameters in ferrets, such as the infection rate, the virus production rate, the infectious virus proportion, the infected cell death rate, the virus clearance rate, as well as other related characteristics, including the basic reproduction number, pre-peak infectious viral growth rate, post-peak infectious viral decay rate, pre-peak infectious viral doubling time, post-peak infectious virus half-life, and the target cell loss in the respiratory tract. These parameters and indices are not significantly different between animals infected with viral strains isolated from the environment and isolated from human hosts, indicating a potential for transmission from fomites. While the infection period in ferrets is relatively short, the similarity observed between our results and previous results in humans supports that ferrets can be an appropriate animal model for SARS-CoV-2 dynamics-related studies, and our estimates provide helpful information for such studies.

In previous work Bayesian Markov-Chain Monte Carlo techniques were used to identify HIV dynamic parameters from patient data. This study used viral load data from HIV patients during interrupted cycles of antiretroviral drugs. The data were fit to a well-established single-compartment model of HIV dynamics. Experimental evidence supports the use of a new compartmental model that includes the re-circulation of T-cells between anatomical reservoirs. If the infection dynamics are indeed compartmentalized, it is not clear how this would affect the estimated values of the dynamic parameters. In this study, we identify parameters of the simple, one-compartment model using data generated by the complex spatial model, and investigate the bias in parameter values introduced by this method. Multiple instances of simulated noisy data was generated from the spatial model using parameter estimates from previous studies. Markov-Chain Monte Carlo methods were used to identify parameter values for the simple model from the simulated data, and the identified values were compared with the true values to determine the existence of bias. The maximum likelihood bias in the median estimates of the proliferation, death and infection rate parameters for target T-cells and virion production rates were on average smaller than one standard deviation of the reported parameter uncertainties for the simple model. The median estimates of the death rate of infected cells and the efficacy of the drug exhibited an average positive bias (the posteriors were larger than the priors) that was larger than one-standard deviation of the prior for all patients. Neglecting the spatial dynamics does not seem to significantly affect the estimation of the proliferation, death, and infection rate parameters for target T-Cells. Conversely, the values of the infected cell death rates and drug efficacies exhibited a consistent bias when estimated using the simplified model. Neglecting spatial dynamics will result in a consistent overestimation of the values of these parameters.

Influence of delayed viral production on viral dynamics in HIV-1 infected patients.

The USA struggled in responding to the COVID-19 pandemic, but not all states struggled equally. Identifying the factors associated with cross-state variation in infection and mortality rates could help to improve responses to this and future pandemics. We sought to answer five key policy-relevant questions regarding the following: 1) what roles social, economic, and racial inequities had in interstate variation in COVID-19 outcomes; 2) whether states with greater health-care and public health capacity had better outcomes; 3) how politics influenced the results; 4) whether states that imposed more policy mandates and sustained them longer had better outcomes; and 5) whether there were trade-offs between a state having fewer cumulative SARS-CoV-2 infections and total COVID-19 deaths and its economic and educational outcomes. Data disaggregated by US state were extracted from public databases, including COVID-19 infection and mortality estimates from the Institute for Health Metrics and Evaluation's (IHME) COVID-19 database; Bureau of Economic Analysis data on state gross domestic product (GDP); Federal Reserve economic data on employment rates; National Center for Education Statistics data on student standardised test scores; and US Census Bureau data on race and ethnicity by state. We standardised infection rates for population density and death rates for age and the prevalence of major comorbidities to facilitate comparison of states' successes in mitigating the effects of COVID-19. We regressed these health outcomes on prepandemic state characteristics (such as educational attainment and health spending per capita), policies adopted by states during the pandemic (such as mask mandates and business closures), and population-level behavioural responses (such as vaccine coverage and mobility). We explored potential mechanisms connecting state-level factors to individual-level behaviours using linear regression. We quantified reductions in state GDP, employment, and student test scores during the pandemic to identify policy and behavioural responses associated with these outcomes and to assess trade-offs between these outcomes and COVID-19 outcomes. Significance was defined as p<0·05. Standardised cumulative COVID-19 death rates for the period from Jan 1, 2020, to July 31, 2022 varied across the USA (national rate 372 deaths per 100 000 population [95% uncertainty interval [UI] 364-379]), with the lowest standardised rates in Hawaii (147 deaths per 100 000 [127-196]) and New Hampshire (215 per 100 000 [183-271]) and the highest in Arizona (581 per 100 000 [509-672]) and Washington, DC (526 per 100 000 [425-631]). A lower poverty rate, higher mean number of years of education, and a greater proportion of people expressing interpersonal trust were statistically associated with lower infection and death rates, and states where larger percentages of the population identify as Black (non-Hispanic) or Hispanic were associated with higher cumulative death rates. Access to quality health care (measured by the IHME's Healthcare Access and Quality Index) was associated with fewer total COVID-19 deaths and SARS-CoV-2 infections, but higher public health spending and more public health personnel per capita were not, at the state level. The political affiliation of the state governor was not associated with lower SARS-CoV-2 infection or COVID-19 death rates, but worse COVID-19 outcomes were associated with the proportion of a state's voters who voted for the 2020 Republican presidential candidate. State governments' uses of protective mandates were associated with lower infection rates, as were mask use, lower mobility, and higher vaccination rate, while vaccination rates were associated with lower death rates. State GDP and student reading test scores were not associated with state COVD-19 policy responses, infection rates, or death rates. Employment, however, had a statistically significant relationship with restaurant closures and greater infections and deaths: on average, 1574 (95% UI 884-7107) additional infections per 10 000 population were associated in states with a one percentage point increase in employment rate. Several policy mandates and protective behaviours were associated with lower fourth-grade mathematics test scores, but our study results did not find a link to state-level estimates of school closures. COVID-19 magnified the polarisation and persistent social, economic, and racial inequities that already existed across US society, but the next pandemic threat need not do the same. US states that mitigated those structural inequalities, deployed science-based interventions such as vaccination and targeted vaccine mandates, and promoted their adoption across society were able to match the best-performing nations in minimising COVID-19 death rates. These findings could contribute to the design and targeting of clinical and policy interventions to facilitate better health outcomes in future crises. Bill & Melinda Gates Foundation, J Stanton, T Gillespie, J and E Nordstrom, and Bloomberg Philanthropies.

Efficacy of SARS-CoV-2 (COVID-19) vaccines was assessed by comparing infection and death rates in the eight most- and eight least-fully-vaccinated German states. Infection and death rates were measured on three dates in fall 2021. Less-vaccinated states had substantially higher infection and death rates than more-vaccinated states. These effects occurred in the context of a time main effect and a vaccination rate x time interaction: Infection and death rates increased substantially over time, particularly in less-vaccinated states. These increased infection and death rates over time may be due to Germany having few measures other than vaccination in place for controlling the spread of COVID-19, despite the high community prevalence of the Delta variant. The results are discussed in the context of anti-vax rhetoric, policy, strategies for quelling the pandemic, the lack of a synergistic research relationship between the social and medical sciences, and issues related to vaccine equity.

Mathematical models of HIV-1 infection can help interpret drug treatment experiments and improve our understanding of the interplay between HIV-1 and the immune system. We develop and analyze an age- structured model of HIV-1 infection that allows for variations in the death rate of productively infected T cells and the production rate of viral particles as a function of the length of time a T cell has been infected. We show that this model is a generalization of the standard differential equation and of delay models previously used to describe HIV-1 infection, and provides a means for exploring fundamental issues of viral production and death. We show that the model has uninfected and infected steady states, linked by a transcritical bifurcation. We perform a local stability analysis of the nontrivial equilibrium solution and provide a general stability condition for models with age structure. We then use numerical methods to study solutions of our model focusing on the analysis of primary HIV infection. We show that the time to reach peak viral levels in the blood depends not only on initial conditions but also on the way in which viral production ramps up. If viral production ramps up slowly, we find that the time to peak viral load is delayed compared to results obtained using the standard (constant viral production) model of HIV infection. We find that data on viral load changing over time is insufficient to identify the functions specifying the dependence of the viral production rate or infected cell death rate on infected cell age. These functions must be determined through new quantitative experiments.

In order to develop a better understanding of the evolutionary dynamics of HIV drug resistance, it is necessary to quantify accurately the in vivo fitness costs of resistance mutations. However, the reliable estimation of such fitness costs is riddled with both theoretical and experimental difficulties. Experimental fitness assays typically suffer from the shortcoming that they are based on in vitro data. Fitness estimates based on the mathematical analysis of in vivo data, however, are often questionable because the underlying assumptions are not fulfilled. In particular, the assumption that the replication rate of the virus population is constant in time is frequently grossly violated. By extending recent work of Marée and colleagues, we present here a new approach that corrects for time-dependent viral replication in time-series data for growth competition of mutants. This approach allows a reliable estimation of the relative replicative capacity (with confidence intervals) of two competing virus variants growing within the same patient, using longitudinal data for the total plasma virus load, the relative frequency of the two variants and the death rate of infected cells. We assess the accuracy of our method using computer-generated data. An implementation of the developed method is freely accessible on the Web (http://www.eco.ethz.ch/fitness.html).

Effects of diffusion and delayed immune response on dynamic behavior in a viral model

Understanding the dynamics of acute HIV infection can offer valuable insights into the early stages of viral behavior, potentially helping uncover various aspects of HIV pathogenesis. The standard viral dynamics model explains HIV viral dynamics during acute infection reasonably well. However, the model makes simplifying assumptions, neglecting some aspects of HIV infection. For instance, in the standard model, target cells are infected by a single HIV virion. Yet, cellular multiplicity of infection (MOI) may have considerable effects in pathogenesis and viral evolution. Further, when using the standard model, we take constant infected cell death rates, simplifying the dynamic immune responses. Here, we use four models—1) the standard viral dynamics model, 2) an alternate model incorporating cellular MOI, 3) a model assuming density-dependent death rate of infected cells and 4) a model combining (2) and (3)—to investigate acute infection dynamics in 43 people living with HIV very early after HIV exposure. We find that all models qualitatively describe the data, but none of the tested models is by itself the best to capture different kinds of heterogeneity. Instead, different models describe differing features of the dynamics more accurately. For example, while the standard viral dynamics model may be the most parsimonious across study participants by the corrected Akaike Information Criterion (AICc), we find that viral peaks are better explained by a model allowing for cellular MOI, using a linear regression analysis as analyzed by R2. These results suggest that heterogeneity in within-host viral dynamics cannot be captured by a single model. Depending on the specific aspect of interest, a corresponding model should be employed.

Impact of complete lockdown on total infection and death rates: A hierarchical cluster analysis

PurposeThis study aims to explore the efficacy of government policy directions in mitigating the effects of the COVID-19 pandemic by employing a panel of 22 countries throughout the 2020-second quarter of 2022.Design/methodology/approachThe panel autoregressive distributed lag (ARDL) model is employed to examine this phenomenon and to investigate the long-run effects of government policy decisions on infection and mortality rates from the pandemic.FindingsThe study reveals the following key findings: (1) Income support and debt relief facilities and stringent standards of governments are associated with reduced infection and death rates. (2) The response of governments has resulted in decreased mortality rates while simultaneously leading to an unexpected increase in infection rates. (3) Containment and healthcare practices have led to a decrease in infection rates but an increase in mortality rates, presenting another counterintuitive outcome. Despite the expectation that robust government responses would decrease infection rates and that healthcare containment practices would reduce mortality, these results highlight a lack of health equity and the challenge of achieving high vaccination rates across countries.Research limitations/implicationsTo effectively combat the spread of COVID-19, it is crucial to implement containment health practices in conjunction with tracing and individual-level quarantine. Simply implementing containment health measures without these interconnected strategies would be ineffective. Therefore, policy implications derived from containment health measures should be accompanied by targeted, aggressive, and rapid containment strategies aimed at significantly reducing the number of individuals infected with COVID-19.Practical implicationsThis study concludes by suggesting the importance of implementing economic support in terms of income, and debt relief has played a crucial role in mitigating the spread of COVID-19 infections and reducing fatality rates.Social implicationsTo effectively combat the spread of COVID-19, it is crucial to implement containment health practices in conjunction with tracing and individual-level quarantine. Simply implementing containment health measures without these interconnected strategies would be ineffective. Therefore, policy implications derived from containment health measures should be accompanied by targeted, aggressive, and rapid containment strategies aimed at significantly reducing the number of individuals infected with COVID-19.Originality/valueThis research makes a unique contribution to the existing literature by investigating the impact of government responses on reducing COVID-19 infections and fatalities, specifically focusing on the period before COVID-19 vaccinations became available.

Evaluation of the rate of COVID-19 infection, hospitalization and death among Iranian patients with multiple sclerosis

Bacterial infection is a frequent, morbid, and mortal complication of liver transplantation. Selective bowel decontamination (SBD) has been reported to reduce the rate of bacterial infection after liver transplantation in uncontrolled trials, but benefits of this intervention have been less clear in controlled studies. Eighty candidates for liver transplantation were randomly assigned in a double-blinded fashion to an SBD regimen consisting of gentamicin 80 mg+polymyxin E 100 mg+nystatin 2 million units (37 patients) or to nystatin alone (43 patients). Both treatments were administered orally in 10 ml (increasing to 20 ml, according to predefined criteria), four times daily, through day 21 after transplantation. Anal fecal swab cultures were performed on days 0, 4, 7, and 21. Rates of infection, death, and charges for medical care were assessed from day 0 through day 60. More than 85% of patients in both treatment groups began study treatment more than 3 days before transplantation. Rates of infection (32.4 vs. 27.9%), death (5.4 vs. 4.7%), or charges for medical care (median $194,000 vs. $163,000) were not reduced in patients assigned to SBD. On days 0, 4, 7, and 21, growth of aerobic gram-negative flora in fecal cultures of patients assigned to SBD was significantly less than that of patients taking nystatin alone; growth of aerobic gram-positive flora, anaerobes, and yeast was not significantly different. Routine use of SBD in patients undergoing liver transplantation is not associated with significant benefit.