Rates of SARS-CoV-2 transmission and vaccination impact the fate of vaccine-resistant strains

Acceptance-Hesitancy of COVID-19 Vaccination and Factors Affecting It in Adults: Systematic Review Study

BACKGROUND

Despite the advent of vaccines against COVID-19, there is considerable variation in the acceptance and hesitancy towards the vaccination program across different countries. The objective of this study was to ascertain the prevalence of hesitancy and acceptance regarding the use of the vaccine against the novel coronavirus, also known as COVID-19, and to identify the factors that influence these attitudes.

MATERIALS AND METHODS

All the cross-sectional studies were retrieved from the PubMed databases, the Web of Science ISI, Scopus, and the Cochrane Library. Papers published in English between 2 November 2019 and 23 May 2023 were subjected to further assessment based on their title, abstract, and main text, with a view to ensuring their relevance to the present study.

RESULTS

Following an exhaustive investigation, 59 studies were selected for screening in this systematic review. The most frequently employed method of data collection was the online survey. The study sample comprised 59.12% women and 40.88% men, with ages ranging from 16 to 78 years. The proportion of individuals accepting the vaccine ranged from 13% to 96%, while the proportion of those exhibiting hesitancy ranged from 0% to 57.5%. The primary reasons for accepting the COIVD-19 vaccine were a heightened perception of risk associated with the virus and a general trust in the healthcare system. The most frequently cited reasons for vaccine hesitancy in the context of the ongoing pandemic include concerns about the potential dangers of the vaccines, the rapid pace of their development, the possibility of adverse effects (such as infertility or death), and the assumption that they have been designed to inject microchips.

DISCUSSION

A variety of socio-demographic factors are implicated in determining the rate of vaccine acceptance. A number of socio-demographic factors have been identified as influencing vaccine acceptance. These include high income, male gender, older age, marriage, the presence of older children who have been vaccinated and do not have chronic diseases, high education, and health insurance coverage.

CONCLUSION

Eliminating vaccine hesitancy or increasing vaccine acceptance is a crucial factor that should be addressed through various means and in collaboration with regulatory and healthcare organizations.

Complex vaccination strategies prevent the emergence of vaccine resistance

Vaccination is the most effective tool to control infectious diseases. However, the evolution of vaccine resistance, exemplified by vaccine resistance in SARS-CoV-2, remains a concern. Here, we model complex vaccination strategies against a pathogen with multiple epitopes-molecules targeted by the vaccine. We found that a vaccine targeting one epitope was ineffective in preventing vaccine escape. Vaccine resistance in highly infectious pathogens was prevented by the full-epitope vaccine, that is, one targeting all available epitopes, but only when the rate of pathogen evolution was low. Strikingly, a bet-hedging strategy of random administration of vaccines targeting different epitopes was the most effective in preventing vaccine resistance in pathogens with the low rate of infection and high rate of evolution. Thus, complex vaccination strategies, when biologically feasible, may be preferable to the currently used single-vaccine approaches for long-term control of disease outbreaks, especially when applied to livestock with near 100% vaccination rates.

Theoretical Investigation of the Green-Synthesized Carbon-Based Nanomaterial Potential as Inhibitors of ACE2 for Blocking SARS-CoV-2 Binding

Since the emergence of SARS-CoV-2 in 2020, the world has faced a global pandemic, emphasizing the urgent need for effective treatments to combat COVID-19. This study explores the use of green-synthesized carbon-based nanomaterials as potential inhibitors of ACE2, a critical receptor for SARS-CoV-2 entry into host cells. Specifically, the study examines four carbon-based nanomaterials, namely, CD1, CD2, CD3, and CD4 in amino, graphitic, pyridinic, and pyrrolic forms, respectively, synthesized from curcumin, to investigate their binding affinity with ACE2. Molecular docking studies revealed that CD3 (pyridinic form) exhibited the highest binding affinity with ACE2, surpassing that of the control compound, curcumin. Notably, CD3 formed hydrophobic interactions and hydrogen bonds with key ACE2 residues, suggesting its potential to block the binding of SARS-CoV-2 to human cells. Moreover, molecular dynamics simulations demonstrated the stability of these ligand-ACE2 complexes, further supporting the promise of CD3 as an inhibitor. Quantum chemical analyses, including frontier molecular orbitals, natural bond orbital analysis, and the quantum theory of atoms in molecules, unveiled valuable insights into the reactivity and interaction strengths of these ligands. CD3 exhibited desirable chemical properties, signifying its suitability for therapeutic development. The study's findings suggest that green-synthesized carbon-based nanomaterials, particularly CD3, have the potential to serve as effective inhibitors of ACE2, offering a promising avenue for the development of treatments against COVID-19. Further experimental validation is warranted to advance these findings and establish new therapies for the ongoing global pandemic.

Riding the waves from epidemic to endemic: Viral mutations, immunological change and policy responses

Nonpharmaceutical interventions (NPI) are an important tool for countering pandemics such as COVID-19. Some are cheap; others disrupt economic, educational, and social activity. The latter force governments to balance the health benefits of reduced infection and death against broader lockdown-induced societal costs. A literature has developed modeling how to optimally adjust lockdown intensity as an epidemic evolves. This paper extends that literature by augmenting the classic SIR model with additional states and flows capturing decay over time in vaccine-conferred immunity, the possibility that mutations create variants that erode immunity, and that protection against infection erodes faster than protecting against severe illness. As in past models, we find that small changes in parameter values can tip the optimal response between very different solutions, but the extensions considered here create new types of solutions. In some instances, it can be optimal to incur perpetual epidemic waves even if the uncontrolled infection prevalence would settle down to a stable intermediate level.

A computational model of epidemic process with three variants on a synthesized human interaction network

Virus mutations give rise to new variants that cause multiple waves of pandemics and escalate the infected number of individuals. In this paper, we develop both a simple random network that we define as a synthesized human interaction network and an epidemiological model based on the microscopic process of disease spreading to describe the epidemic process with three variants in a population with some features of social structure. The features of social structure we take into account in the model are the average number of degrees and the frequency of contacts. This paper shows many computational results from several scenarios both in varying network structures and epidemiological parameters that cannot be obtained numerically by using the compartmental model.

The mRNA-LNP vaccines - the good, the bad and the ugly?

The mRNA-LNP vaccine has received much attention during the COVID-19 pandemic since it served as the basis of the most widely used SARS-CoV-2 vaccines in Western countries. Based on early clinical trial data, these vaccines were deemed safe and effective for all demographics. However, the latest data raise serious concerns about the safety and effectiveness of these vaccines. Here, we review some of the safety and efficacy concerns identified to date. We also discuss the potential mechanism of observed adverse events related to the use of these vaccines and whether they can be mitigated by alterations of this vaccine mechanism approach.

Policy makers believe money motivates more than it does

To motivate contributions to public goods, should policy makers employ financial incentives like taxes, fines, subsidies, and rewards? While these are widely considered as the classic policy approach, a substantial academic literature suggests the impact of financial incentives is not always positive; they can sometimes fail or even backfire. To test whether policy makers are overly bullish about financial incentives, we asked county heads, mayors, and municipal government representatives of medium-to-large towns in Germany to predict the effects of a financial incentive on COVID-19 vaccination, and tested the exact same incentive in a field experiment involving all 41,548 inhabitants (clustered in 10,032 addresses) of the German town of Ravensburg. Whereas policy makers overwhelmingly predict that the financial incentive will increase vaccination-by 15.3 percentage points on average-the same financial incentive yielded a precisely estimated null effect on vaccination. We discuss when financial incentives are most likely to fail, and conclude that it is critical to educate policy makers on the potential pitfalls of employing financial incentives to promote contributions to public goods.

The effectiveness of vaccination, testing, and lockdown strategies against COVID-19

The ability of various policy activities to reduce the reproduction rate of the COVID-19 disease is widely discussed. Using a stringency index that comprises a variety of lockdown levels, such as school and workplace closures, we analyze the effectiveness of government restrictions. At the same time, we investigate the capacity of a range of lockdown measures to lower the reproduction rate by considering vaccination rates and testing strategies. By including all three components in an SIR (Susceptible, Infected, Recovery) model, we show that a general and comprehensive test strategy is instrumental in reducing the spread of COVID-19. The empirical study demonstrates that testing and isolation represent a highly effective and preferable approach towards overcoming the pandemic, in particular until vaccination rates have risen to the point of herd immunity.

Population immunity predicts evolutionary trajectories of SARS-CoV-2

The large-scale evolution of the SARS-CoV-2 virus has been marked by rapid turnover of genetic clades. New variants show intrinsic changes, notably increased transmissibility, and antigenic changes that reduce cross-immunity induced by previous infections or vaccinations. How this functional variation shapes global evolution has remained unclear. Here, we establish a predictive fitness model for SARS-CoV-2 that integrates antigenic and intrinsic selection. The model is informed by tracking of time-resolved sequence data, epidemiological records, and cross-neutralization data of viral variants. Our inference shows that immune pressure, including contributions of vaccinations and previous infections, has become the dominant force driving the recent evolution of SARS-CoV-2. The fitness model can serve continued surveillance in two ways. First, it successfully predicts the short-term evolution of circulating strains and flags emerging variants likely to displace the previously predominant variant. Second, it predicts likely antigenic profiles of successful escape variants prior to their emergence.

Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm

Analyzing the COVID-19 pandemic is a critical factor in developing effective policies to deal with similar challenges in the future. However, many parameters (e.g., the actual number of infected people, the effectiveness of vaccination) are still subject to considerable debate because they are unobservable. To model a pandemic and estimate unobserved parameters, researchers use compartmental models. Most often, in such models, the transition rates are considered as constants, which allows simulating only one epidemiological wave. However, multiple waves have been reported for COVID-19 caused by different strains of the virus. This paper presents an approach based on the reconstruction of real distributions of transition rates using genetic algorithms, which makes it possible to create a model that describes several pandemic peaks. The model is fitted on registered COVID-19 cases in four countries with different pandemic control strategies (Germany, Sweden, UK, and US). Mean absolute percentage error (MAPE) was chosen as the objective function, the MAPE values of 2.168%, 2.096%, 1.208% and 1.703% were achieved for the listed countries, respectively. Simulation results are consistent with the empirical statistics of medical studies, which confirms the quality of the model. In addition to observables such as registered infected, the output of the model contains variables that cannot be measured directly. Among them are the proportion of the population protected by vaccines, the size of the exposed compartment, and the number of unregistered cases of COVID-19. According to the results, at the peak of the pandemic, between 14% (Sweden) and 25% (the UK) of the population were infected. At the same time, the number of unregistered cases exceeds the number of registered cases by 17 and 3.4 times, respectively. The average duration of the vaccine induced immune period is shorter than claimed by vaccine manufacturers, and the effectiveness of vaccination has declined sharply since the appearance of the Delta and Omicron strains. However, on average, vaccination reduces the risk of infection by about 65-70%.

COVID-19 activity risk calculator as a gamified public health intervention tool

The Coronavirus disease 2019 (COVID-19) pandemic, caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has impacted over 200 countries leading to hospitalizations and deaths of millions of people. Public health interventions, such as risk estimators, can reduce the spread of pandemics and epidemics through influencing behavior, which impacts risk of exposure and infection. Current publicly available COVID-19 risk estimation tools have had variable effectiveness during the pandemic due to their dependency on rapidly evolving factors such as community transmission levels and variants. There has also been confusion surrounding certain personal protective strategies such as risk reduction by mask-wearing and vaccination. In order to create a simple easy-to-use tool for estimating different individual risks associated with carrying out daily-life activity, we developed COVID-19 Activity Risk Calculator (CovARC). CovARC is a gamified public health intervention as users can "play with" how different risks associated with COVID-19 can change depending on several different factors when carrying out routine daily activities. Empowering the public to make informed, data-driven decisions about safely engaging in activities may help to reduce COVID-19 levels in the community. In this study, we demonstrate a streamlined, scalable and accurate COVID-19 risk calculation system. Our study also demonstrates the quantitative impact of vaccination and mask-wearing during periods of high case counts. Validation of this impact could inform and support policy decisions regarding case thresholds for mask mandates, and other public health interventions.

Synergistic effects of vaccination and virus testing on the transmission of an infectious disease

Under the background that asymptomatic virus carriers have infectivity for an infectious disease, we establish a difference equations model with vaccination and virus testing in this paper. Assuming that the vaccine is 100% effective for susceptible people but cannot stop the infectivity of asymptomatic virus carriers, we study how to combine vaccination and virus testing at the beginning of an epidemic to effectively block the spread of infectious disease in different population sizes. By considering the daily processing capacity of the vaccine and daily proportion of testing, the corresponding numerical simulation results are obtained. It is shown that when vaccine availability and virus testing capacity are insufficient, a reasonable combination of the above two measures can slow down or even block the spread of infectious disease. Single virus testing or vaccination can also block the spread of infectious disease, but this requires a lot of manpower, material and financial resources. When the daily proportion of virus testing is fixed, the ratio of the minimum daily processing capacity of vaccines used to block the spread of infectious disease to the corresponding population size is rather stable. It demonstrates that effective protective measures of the same infectious disease in countries and regions with different population sizes can be used as a reference. These results also provide a certain reference for decision makers on how to coordinate vaccines and virus testing resources to curb the spread of such an infectious disease in a certain population size.

Vaccine hesitancy promotes emergence of new SARS-CoV-2 variants

The successive emergence of SARS-CoV-2 mutations has led to an unprecedented increase in COVID-19 incidence worldwide. Currently, vaccination is considered to be the best available solution to control the ongoing COVID-19 pandemic. However, public opposition to vaccination persists in many countries, which can lead to increased COVID-19 caseloads and hence greater opportunities for vaccine-evasive mutant strains to arise. To determine the extent that public opinion regarding vaccination can induce or hamper the emergence of new variants, we develop a model that couples a compartmental disease transmission framework featuring two strains of SARS-CoV-2 with game theoretical dynamics on whether or not to vaccinate. We combine semi-stochastic and deterministic simulations to explore the effect of mutation probability, perceived cost of receiving vaccines, and perceived risks of infection on the emergence and spread of mutant SARS-CoV-2 strains. We find that decreasing the perceived costs of being vaccinated and increasing the perceived risks of infection (that is, decreasing vaccine hesitation) will decrease the possibility of vaccine-resistant mutant strains becoming established by about fourfold for intermediate mutation rates. Conversely, we find increasing vaccine hesitation to cause both higher probability of mutant strains emerging and more wild-type cases after the mutant strain has appeared. We also find that once a new variant has emerged, perceived risk of being infected by the original variant plays a much larger role than perceptions of the new variant in determining future outbreak characteristics. Furthermore, we find that rapid vaccination under non-pharmaceutical interventions is a highly effective strategy for preventing new variant emergence, due to interaction effects between non-pharmaceutical interventions and public support for vaccination. Our findings indicate that policies that combine combating vaccine-related misinformation with non-pharmaceutical interventions (such as reducing social contact) will be the most effective for avoiding the establishment of harmful new variants.

Novel Approach for Identification of Basic and Effective Reproduction Numbers Illustrated with COVID-19

This paper presents a novel numerical technique for the identification of effective and basic reproduction numbers, Re and R0, for long-term epidemics, using an inverse problem approach. The method is based on the direct integration of the SIR (Susceptible-Infectious-Removed) system of ordinary differential equations and the least-squares method. Simulations were conducted using official COVID-19 data for the United States and Canada, and for the states of Georgia, Texas, and Louisiana, for a period of two years and ten months. The results demonstrate the applicability of the method in simulating the dynamics of the epidemic and reveal an interesting relationship between the number of currently infectious individuals and the effective reproduction number, which is a useful tool for predicting the epidemic dynamics. For all conducted experiments, the results show that the local maximum (and minimum) values of the time-dependent effective reproduction number occur approximately three weeks before the local maximum (and minimum) values of the number of currently infectious individuals. This work provides a novel and efficient approach for the identification of time-dependent epidemics parameters.

A fractional-order mathematical model based on vaccinated and infected compartments of SARS-CoV-2 with a real case study during the last stages of the epidemiological event

In 2020 the world faced with a pandemic spread that affected almost everything of humans' social and health life. Regulations to decrease the epidemiological spread and studies to produce the vaccine of SARS-CoV-2 were on one side a hope to return back to the regular life, but on the other side there were also notable criticism about the vaccines itself. In this study, we established a fractional order differential equations system incorporating the vaccinated and re-infected compartments to a S I R frame to consider the expanded and detailed form as an S V I I v R model. We considered in the model some essential parameters, such as the protection rate of the vaccines, the vaccination rate, and the vaccine's lost efficacy after a certain period. We obtained the local stability of the disease-free and co-existing equilibrium points under specific conditions using the Routh-Hurwitz Criterion and the global stability in using a suitable Lyapunov function. For the numerical solutions we applied the Euler's method. The data for the simulations were taken from the World Health Organization (WHO) to illustrate numerically some scenarios that happened.

A Cross-Sectional Study and Observational Assessment of Shoppers' COVID-19 Prevention Behaviors in Southwestern Ontario, Canada

OBJECTIVE

The aim of this study was to observe the level of alcohol-based sanitizer, mask use, and physical distancing across indoor community settings in Guelph, ON, Canada, and to identify potential barriers to practicing these behaviors.

METHODS

Shoppers were observed in June 2022 across 21 establishments. Discrete in-person observations were conducted and electronically recorded using smartphones. Multilevel logistic regression models were fitted to identify possible covariates for the 3 behavioral outcomes.

RESULTS

Of 946 observed shoppers, 69% shopped alone, 72% had at least 1 hand occupied, 26% touched their face, 29% physically distanced ≥ 2 m, 6% used hand sanitizer, and 29% wore masks. Sanitizer use was more commonly observed among people who wore masks and in establishments with coronavirus disease (COVID-19) signage posted at the entrance. Mask use was more commonly observed during days without precipitation and in establishments with some or all touch-free entrances. Shoppers more commonly physically distanced ≥ 2 m when they were shopping alone.

CONCLUSIONS

This supports evidence for environmental context influencing COVID-19 preventive behaviors. Intervention efforts aimed at visible signage, tailored messaging, and redesigning spaces to facilitate preventive behaviors may be effective at increasing adherence during outbreaks.

The importance of vaccinated individuals to population-level evolution of pathogens

Virus evolution shapes the epidemiological patterns of infectious disease, particularly via evasion of population immunity. At the individual level, host immunity itself may drive viral evolution towards antigenic escape. Using compartmental SIR-style models with imperfect vaccination, we allow the probability of immune escape to differ in vaccinated and unvaccinated hosts. As the relative contribution to selection in these different hosts varies, the overall effect of vaccination on the antigenic escape pressure at the population level changes. We find that this relative contribution to escape is important for understanding the effects of vaccination on the escape pressure and we draw out some fairly general patterns. If vaccinated hosts do not contribute much more than unvaccinated hosts to the escape pressure, then increasing vaccination always reduces the overall escape pressure. In contrast, if vaccinated hosts contribute significantly more than unvaccinated hosts to the population level escape pressure, then the escape pressure is maximised for intermediate vaccination levels. Past studies find only that the escape pressure is maximal for intermediate levels with fixed extreme assumptions about this relative contribution. Here we show that this result does not hold across the range of plausible assumptions for the relative contribution to escape from vaccinated and unvaccinated hosts. We also find that these results depend on the vaccine efficacy against transmission, particularly through the partial protection against infection. This work highlights the potential value of understanding better how the contribution to antigenic escape pressure depends on individual host immunity.

Efficacy of the neutralizing antibodies after the booster dose on SARS-CoV-2 Omicron variant and a two-year longitudinal antibody study on Wild Type convalescents

Objectives

Waning vaccine-induced immunity and emergence of new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants which may lead to immune escape, pose a major threat to the COVID-19 pandemic. Currently, enhanced efficacy of the neutralization antibodies (NAb) produced after the booster dose of vaccinations against the Omicron variant is the main focus of vaccine strategy research. In this study we have analyzed the potency of the NAbs and IgGs produced after the third vaccine dose in patients infected with Omicron variant and wild-type (WT) SARS-CoV-2.

Methods

We enrolled 75 patients with Omicron variant breakthrough infections, and 87 patients with WT infections. We recorded the clinical characteristics and vaccination information of all patients and measured the NAb and anti-S1 (spike protein)+N (nucleocapsid protein) IgG-binding antibodies against SARS-CoV-2 in serum samples of Omicron variant-infected patients at admission, and patients with WT COVID-19 infection from the time of admission and discharge, and one-year to two-years follow-ups.

Results

Our results demonstrated higher NAb levels, fewer clinical symptoms, and faster viral shedding in Omicron variant infected patients vaccinated with the booster dose. Hybrid immunity (natural infection plus vaccination) induces higher NAb levels than vaccine-only immunity. NAb and IgG levels decreased significantly at one-year follow-up in WT convalescents with natural infection. The NAb and IgG levels in booster-vaccinated COVID-19 patients were higher than those in two-dose-vaccinated patients.

Conclusion

Our results suggest that booster vaccinations are required to improve the level of protective NAbs. Moreover, our data provide important evidence for vaccination strategies based on existing vaccines.

Upper respiratory tract mucosal immunity for SARS-CoV-2 vaccines

SARS-CoV-2 vaccination significantly reduces morbidity and mortality, but has less impact on viral transmission rates, thus aiding viral evolution, and the longevity of vaccine-induced immunity rapidly declines. Immune responses in respiratory tract mucosal tissues are crucial for early control of infection, and can generate long-term antigen-specific protection with prompt recall responses. However, currently approved SARS-CoV-2 vaccines are not amenable to adequate respiratory mucosal delivery, particularly in the upper airways, which could account for the high vaccine breakthrough infection rates and limited duration of vaccine-mediated protection. In view of these drawbacks, we outline a strategy that has the potential to enhance both the efficacy and durability of existing SARS-CoV-2 vaccines, by inducing robust memory responses in the upper respiratory tract (URT) mucosa.

A mathematical model for COVID-19 considering waning immunity, vaccination and control measures

In this work we define a modified SEIR model that accounts for the spread of infection during the latent period, infections from asymptomatic or pauci-symptomatic infected individuals, potential loss of acquired immunity, people's increasing awareness of social distancing and the use of vaccination as well as non-pharmaceutical interventions like social confinement. We estimate model parameters in three different scenarios-in Italy, where there is a growing number of cases and re-emergence of the epidemic, in India, where there are significant number of cases post confinement period and in Victoria, Australia where a re-emergence has been controlled with severe social confinement program. Our result shows the benefit of long term confinement of 50% or above population and extensive testing. With respect to loss of acquired immunity, our model suggests higher impact for Italy. We also show that a reasonably effective vaccine with mass vaccination program are successful measures in significantly controlling the size of infected population. We show that for a country like India, a reduction in contact rate by 50% compared to a reduction of 10% reduces death from 0.0268 to 0.0141% of population. Similarly, for a country like Italy we show that reducing contact rate by half can reduce a potential peak infection of 15% population to less than 1.5% of population, and potential deaths from 0.48 to 0.04%. With respect to vaccination, we show that even a 75% efficient vaccine administered to 50% population can reduce the peak number of infected population by nearly 50% in Italy. Similarly, for India, a 0.056% of population would die without vaccination, while 93.75% efficient vaccine given to 30% population would bring this down to 0.036% of population, and 93.75% efficient vaccine given to 70% population would bring this down to 0.034%.

Is there a role for RDTs as we live with COVID-19? An assessment of different strategies

INTRODUCTION

By 2022, high levels of past COVID-19 infections, combined with substantial levels of vaccination and the development of Omicron, have shifted country strategies towards burden reduction policies. SARS-CoV-2 rapid antigen tests (rapid diagnostic tests (RDTs)) could contribute to these policies by helping rapidly detect, isolate and/or treat infections in different settings. However, the evidence to inform RDT policy choices in low and middle-income countries (LMICs) is limited.

METHOD

We provide an overview of the potential impact of several RDT use cases (surveillance; testing, tracing and isolation without and with surveillance; hospital-based screening to reduce nosocomial COVID-19; and testing to enable earlier/expanded treatment) for a range of country settings. We use conceptual models and literature review to identify which use cases are likely to bring benefits and how these may change with outbreak characteristics. Impacts are measured through multiple outcomes related to gaining time, reducing the burden on the health system and reducing deaths.

RESULTS

In an optimal scenario in terms of resources and capacity and with baseline parameters, we find marginal time gains of 4 days or more through surveillance and testing tracing and isolation with surveillance, a reduction in peak intensive care unit (ICU) or ICU admissions by 5% or more (hospital-based screening; testing, tracing and isolation) and reductions in COVID-19 deaths by over 6% (hospital-based screening; test and treat). Time gains may be used to strengthen ICU capacity and/or boost vulnerable individuals, though only a small minority of at-risk individuals could be reached in the time available. The impact of RDTs declines with lower country resources and capacity, more transmissible or immune-escaping variants and reduced test sensitivity.

CONCLUSION

RDTs alone are unlikely to dramatically reduce the burden of COVID-19 in LMICs, though they may have an important role alongside other interventions such as vaccination, therapeutic drugs, improved healthcare capacity and non-pharmaceutical measures.

Lima FW, Magno L, Soares F, Ferraz D, Grangeiro A, Zucchi EM, Préau M, Mabire X, Matos HRSS, Dourado I. Seroprevalence of anti-SARS-CoV-2 antibodies and factors associated with infection among adolescent men who have sex with men and transgender women in Salvador, Brazil

BACKGROUND

Brazil was strongly affected by the COVID-19 pandemic and the impact of the pandemic on sexual and gender minorities' youth remains unknown. This study aimed to estimate the seroprevalence of SARS-CoV-2 antibodies and associated factors among adolescent men who have sex with men (AMSM) and transgender women (ATGW) participants of a human immunodeficiency virus (HIV) pre-exposure prophylaxis cohort study (PrEP1519).

METHODS

This is a cross-sectional design conducted between June and October 2020 in Salvador, Brazil. Serum samples were collected from AMSM and ATGW aged 16-21 years between June-October 2020. IgG and IgM anti-SARS-CoV-2 were detected by chemiluminescence immunoassay, and data were collected through a socio-behavioral questionnaire.

RESULTS

Among the 137 participants, the seroprevalence of anti-SARS-CoV-2 IgG and IgM was 20.4%; 16.8% of the participants were positive for IgG, and 11.7% for IgM. In the multivariable analysis, the seroprevalence was two times higher among those who never wore masks (OR= 2.22; 95% CI: 1.08-4.57) and among those who believed that they could be easily cured of the disease (OR= 2.05; 95% CI: 1.05-4.01).

CONCLUSIONS

The high seroprevalence of SARS-CoV-2 antibodies among gender and sexual minority youth seems to be informed by behaviors and attitudes that contrast with public health measures and the potential severity of the disease when vaccination was still not available.

Mathematical assessment of the role of waning and boosting immunity against the BA.1 Omicron variant in the United States

Three safe and effective vaccines against SARS-CoV-2 have played a major role in combating COVID-19 in the United States. However, the effectiveness of these vaccines and vaccination programs has been challenged by the emergence of new SARS-CoV-2 variants of concern. A new mathematical model is formulated to assess the impact of waning and boosting of immunity against the Omicron variant in the United States. To account for gradual waning of vaccine-derived immunity, we considered three vaccination classes that represent high, moderate and low levels of immunity. We showed that the disease-free equilibrium of the model is globally-asymptotically, for two special cases, if the associated reproduction number is less than unity. Simulations of the model showed that vaccine-derived herd immunity can be achieved in the United States via a vaccination-boosting strategy which entails fully vaccinating at least 59% of the susceptible populace followed by the boosting of about 72% of the fully-vaccinated individuals whose vaccine-derived immunity has waned to moderate or low level. In the absence of boosting, waning of immunity only causes a marginal increase in the average number of new cases at the peak of the pandemic, while boosting at baseline could result in a dramatic reduction in the average number of new daily cases at the peak. Specifically, for the fast immunity waning scenario (where both vaccine-derived and natural immunity are assumed to wane within three months), boosting vaccine-derived immunity at baseline reduces the average number of daily cases at the peak by about 90% (in comparison to the corresponding scenario without boosting of the vaccine-derived immunity), whereas boosting of natural immunity (at baseline) only reduced the corresponding peak daily cases (in comparison to the corresponding scenario without boosting of natural immunity) by approximately 62%. Furthermore, boosting of vaccine-derived immunity is more beneficial (in reducing the burden of the pandemic) than boosting of natural immunity. Finally, boosting vaccine-derived immunity increased the prospects of altering the trajectory of COVID-19 from persistence to possible elimination.

Drivers of COVID-19 policy stringency in 175 countries and territories: COVID-19 cases and deaths, gross domestic products per capita, and health expenditures

Background

New data on COVID-19 may influence the stringency of containment policies, but these potential effect are not understood. We aimed to understand the associations of new COVID-19 cases and deaths with policy stringency globally and regionally.

Methods

We modelled the marginal effects of new COVID-19 cases and deaths on policy stringency (scored 0-100) in 175 countries and territories, adjusting for gross domestic product (GDP) per capita and health expenditure (% of GDP), and public expenditure on health. The time periods examined were March to August 2020, September 2020 to February 2021, and March to August 2021.

Results

Policy response to new cases and deaths was faster and more stringent early in the COVID-19 pandemic (March to August 2020) compared to subsequent periods. New deaths were more strongly associated with stringent policies than new cases. In an average week, one new death per 100 000 people was associated with a stringency increase of 2.1 units in the March to August 2020 period, 1.3 units in the September 2020 to February 2021 period, and 0.7 units in the March to August 2021 period. New deaths in Africa and the Western Pacific were associated with more stringency than in other regions. Higher health expenditure as a percentage of GDP was associated with less stringent policies. Similarly, higher public expenditure on health by governments was mostly associated with less stringency across all three periods. GDP per capita did not have consistent patterns of associations with stringency.

Conclusions

The stringency of COVID-19 policies was more strongly associated with new deaths than new cases. Our findings demonstrate the need for enhanced mortality surveillance to ensure policy alignment during health emergencies. Countries that invest less in health or have a lower public expenditure on health may be inclined to enact more stringent policies. This new empirical understanding of COVID-19 policy drivers can help public health officials anticipate and shape policy responses in future health emergencies.

The correlation between serum 25-hydroxy-vitamin D levels and anti-SARS-CoV-2 S-RBD IgG and neutralizing antibody levels among cancer patients receiving COVID-19 vaccines

Introduction

During the coronavirus disease 2019 (COVID-19) pandemic, vitamin D has been established as an immune-modulator that reduces pro-inflammatory damage which effectively diminish the severity of COVID-19. Vitamin D also has a significant effect against influenza and dengue and increase the seroconversion following influenza vaccination. To date, the role of vitamin D in optimizing the efficacy of COVID-19 vaccines remains unclear. This study aimed to analyze the correlation between serum 25-hydroxy-cholecalciferol or 25(OH)D levels and anti-SARS-CoV-2 S-RBD IgG and neutralizing antibody levels among cancer patients.

Methodology

A multicenter cross-sectional study was conducted among solid and hematologic cancer patients who were vaccinated with two doses of the same types of COVID-19 vaccines (either mRNA, non-replicating viral vector, or inactivated) within 6 months.

Result

The median serum 25(OH)D level in 119 cancer patients was 36.36 [IQR = 30.30] ng/mL. The seropositivity of S-RBD IgG and NAb reached 93.3 and 94.1%, respectively. The S-RBD IgG level was significantly higher in the sufficient group (median = 414.07 [1,441.83] AU/mL) than in the deficient group (median = 91.56 [652.00] AU/mL) (p-value = 0.049). Among non-chemotherapy subjects, the anti-SARS-CoV-2 S-RBD IgG levels had a significant positive correlation with 25(OH)D levels (p-value = 0.03; R = 0.588). The NAb levels also showed significantly positive correlation with 25(OH)D level (p-value = 0.005; R = 0.561). The 25(OH)D levels were positively correlated with S-RBD IgG levels among subjects younger than 60 years old (p-value = 0.047; R = 0.136). However, serum 25 (OH)D levels showed no such correlation with S-RBD IgG levels among subjects older than 60 years old (p-value = 0.933; R = 0.136).

Conclusion

Both anti-SARS-CoV-2 S-RBD IgG and NAb levels developed moderate correlation with 25(OH)D levels among subjects treated without chemotherapy. The S-RBD IgG levels also had positive correlation with 25(OH)D levels among subjects younger than 60 years old. Thus, we recommended cancer patients to maintain serum 25(OH)D levels above 30 ng/mL (75 nmol/L) to enhance the efficacy of COVID-19 vaccines.

Bioinformatics Designing and Molecular Modelling of a Universal mRNA Vaccine for SARS-CoV-2 Infection

At this present stage of COVID-19 re-emergence, designing an effective candidate vaccine for different variants of SARS-CoV-2 is a study worthy of consideration. This research used bioinformatics tools to design an mRNA vaccine that captures all the circulating variants and lineages of the virus in its construct. Sequences of these viruses were retrieved across the six continents and analyzed using different tools to screen for the preferable CD8⁺ T lymphocytes (CTL), CD4⁺ T lymphocytes (HTL), and B-cell epitopes. These epitopes were used to design the vaccine. In addition, several other co-translational residues were added to the construct of an mRNA vaccine whose molecular weight is 285.29686 kDa with an estimated pI of 9.2 and has no cross affinity with the human genome with an estimated over 68% to cover the world population. It is relatively stable, with minimal deformability in its interaction with the human innate immune receptor, which includes TLR 3 and TLR 9. The overall result has proven that the designed candidate vaccine is capable of modulating cell-mediated immune responses by activating the actions of CD4⁺ T cells, natural killer cells, and macrophages, and displayed an increased memory T cell and B cell activities, which may further be validated via in vivo and in vitro techniques.

Strain-stream model of epidemic spread in application to COVID-19

ABSTRACT

The recently developed model of the epidemic spread of two virus strains in a closed population is generalized to the situation typical for the couple of strains delta and omicron, when there is a high probability of omicron infection soon enough after recovering from delta infection. This model can be considered as a kind of combination of SIR and SIS models for the case of competition of two strains of the same virus with different contagiousness in a population. The obtained equations and results can be directly implemented for practical calculations of the replacement of strains of the SARS-CoV-2 virus. A comparison between the estimated replacement time and the corresponding statistics shows reasonable agreement.

Effectiveness of Bacille Calmette-Guerin vaccination policies in reducing infection and mortality of COVID-19: a systematic review

BACKGROUND

COVID-19 vaccination has been advocated as the most effective way to curb the pandemic. But with its inequitable distribution and slow rollout, especially in low- to middle- income countries, it will still take a long time before herd immunity is achieved. Alternative measures must therefore be explored to bolster current COVID-19 vaccination efforts. In particular, the Bacille Calmette-Guerin vaccine has been studied extensively as to its proposed conferment of non-specific immunity against different infections, including COVID-19. The aim of this study, therefore, is to evaluate the current evidence on the effectiveness of national BCG vaccination policies in reducing infection and mortality of COVID-19.

METHODS

A systematic review was conducted between April to August 2021 following the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA-P) guidelines. Literature was retrieved from PubMed, Cochrane, HERDIN, Web of Science, EBSCO, and Western Pacific Region Index Medicus (WPRIM). Studies conducted from January 2020 to August 2021 that fell within Level 1A to 2C of the Oxford Center for Evidence-Based Medicine were included in the review. Quality assessment was performed using the appropriate Joanna Briggs Institute critical appraisal tool and a quality assessment checklist for ecological studies adapted from Betran et al. RESULTS: A total of 13 studies were included in this review. Nine studies reported significant association between BCG vaccination policies and COVID-19 outcomes, even when controlling for confounding variables. In addition, among other mandated vaccines, such as pneumococcal, influenza, diphtheria-tetanus-pertussis, and measles, only BCG vaccination showed significant association with decreased COVID-19 adverse outcomes. However, other factors also showed positive association with COVID-19 outcomes, particularly markers of high economic status of countries, higher median age, and greater population densities.

CONCLUSION

The lower incidence and mortality of COVID-19 in countries with mandated BCG vaccination may not solely be attributable to BCG vaccination policies, but there is still some evidence that demonstrates a possible protective effect. Clinical trials must be continued before recommendations of BCG vaccinations are to be used as an alternative or booster vaccine against COVID-19.

The Deltacron conundrum: Its origin and potential health risks

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), since its outbreak in December 2019, has been capable of continuing the pandemic by mutating itself into different variants. Mass vaccinations, antibiotic treatment therapy, herd immunity, and preventive measures have reduced the disease's severity from the emerging variants. However, the virus is undergoing recombination among the current two variants: Delta and Omicron, resulting in a new variant, informally known as "Deltacron," which was controversial as it might be a product of lab contamination between Omicron and Delta samples. However, the proclamation was proved wrong, and the experts are putting more effort into better understanding the variant's epidemiological characteristics to control potential outbreaks. This review has discussed the potential mutations in the novel variant and prospective risk factors and therapeutic options in the context of this new variant. This study could be used as a guide for implementing appropriate controls in a sudden outbreak of this new variant.

Epidemic spreading under mutually independent intra- and inter-host pathogen evolution

The dynamics of epidemic spreading is often reduced to the single control parameter R0 (reproduction-rate), whose value, above or below unity, determines the state of the contagion. If, however, the pathogen evolves as it spreads, R0 may change over time, potentially leading to a mutation-driven spread, in which an initially sub-pandemic pathogen undergoes a breakthrough mutation. To predict the boundaries of this pandemic phase, we introduce here a modeling framework to couple the inter-host network spreading patterns with the intra-host evolutionary dynamics. We find that even in the extreme case when these two process are driven by mutually independent selection forces, mutations can still fundamentally alter the pandemic phase-diagram. The pandemic transitions, we show, are now shaped, not just by R0, but also by the balance between the epidemic and the evolutionary timescales. If mutations are too slow, the pathogen prevalence decays prior to the appearance of a critical mutation. On the other hand, if mutations are too rapid, the pathogen evolution becomes volatile and, once again, it fails to spread. Between these two extremes, however, we identify a broad range of conditions in which an initially sub-pandemic pathogen can breakthrough to gain widespread prevalence.

Structural insights into Nirmatrelvir (PF-07321332)-3C-like SARS-CoV-2 protease complexation: a ligand Gaussian accelerated molecular dynamics study

Coronavirus 3C-like protease (3CLpro) is found in SARS-CoV-2 virus, which causes COVID-19. 3CLpro controls virus replication and is a major target for target-based antiviral discovery. As reported by Pfizer, Nirmatrelvir (PF-07321332) is a competitive protein inhibitor and a clinical candidate for orally delivered medication. However, the binding mechanisms between Nirmatrelvir and 3CLpro complex structures remain unknown. This study incorporated ligand Gaussian accelerated molecular dynamics, the one-dimensional and two-dimensional potential of mean force, normal molecular dynamics, and Kramers' rate theory to determine the binding and dissociation rate constants (k_off and k_on) associated with the binding of the 3CLpro protein to the Nirmatrelvir inhibitor. The proposed approach addresses the challenges in designing small-molecule antiviral drugs.

Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19

An Adaptive Susceptible-Infected-Removed-Vaccinated (A-SIRV) epidemic model with time-dependent transmission and removal rates is constructed for investigating the dynamics of an epidemic disease such as the COVID-19 pandemic. Real data of COVID-19 spread is used for the simultaneous identification of the unknown time-dependent rates and functions participating in the A-SIRV system. The inverse problem is formulated and solved numerically using the Method of Variational Imbedding, which reduces the inverse problem to a problem for minimizing a properly constructed functional for obtaining the sought values. To illustrate and validate the proposed solution approach, the present study used available public data for several countries with diverse population and vaccination dynamics—the World, Israel, The United States of America, and Japan.

Update on COVID-19 and Effectiveness of a Vaccination Campaign in a Global Context

The COVID-19 pandemic caused by SARS-CoV-2 remains a significant issue for global health, the economy, and society. When SARS-CoV-2 began to spread, the most recent serious infectious disease of this century around the world, with its high morbidity and mortality rates, it is understandable why such infections have generally been spread in the past, mainly from international travel movements. This perspective review aimed to provide an update for clinicians on the recent developments related to the microbiological perspectives in pandemics, diagnostics, prevention (such as the spread of a virus), vaccination campaigns, treatment options, and health consequences for COVID-19 based on the current literature. In this way, the authors attempt to raise awareness on the transversal nature of these challenges by identifying the main risk/vulnerability factors that the scientific community must face including our current knowledge on the virus capacity of the mechanism of entry into the cells, the current classifications of viral variants, the knowledge of the mathematical model on the spread of viruses (the possible routes of transmission), and the effectiveness of vaccination campaigns in a global context of pandemic, particularly from COVID-19, with a look at new or future vaccines.

The SARS-CoV-2 differential genomic adaptation in response to varying UVindex reveals potential genomic resources for better COVID-19 diagnosis and prevention

Coronavirus disease 2019 (COVID-19) has been a pandemic disease reported in almost every country and causes life-threatening, severe respiratory symptoms. Recent studies showed that various environmental selection pressures challenge the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infectivity and, in response, the virus engenders new mutations, leading to the emergence of more virulent strains of WHO concern. Advance prediction of the forthcoming virulent SARS-CoV-2 strains in response to the principal environmental selection pressures like temperature and solar UV radiation is indispensable to overcome COVID-19. To discover the UV-solar radiation-driven genomic adaption of SARS-CoV-2, a curated dataset of 2,500 full-grade genomes from five different UVindex regions (25 countries) was subjected to in-depth downstream genome-wide analysis. The recurrent variants that best respond to UV-solar radiations were extracted and extensively annotated to determine their possible effects and impacts on gene functions. This study revealed 515 recurrent single nucleotide variants (rcntSNVs) as SARS-CoV-2 genomic responses to UV-solar radiation, of which 380 were found to be distinct. For all discovered rcntSNVs, 596 functional effects (rcntEffs) were detected, containing 290 missense, 194 synonymous, 81 regulatory, and 31 in the intergenic region. The highest counts of missense rcntSNVs in spike (27) and nucleocapsid (26) genes explain the SARS-CoV-2 genomic adjustment to escape immunity and prevent UV-induced DNA damage, respectively. Among all, the most commonly observed rcntEffs were four missenses (RdRp-Pro327Leu, N-Arg203Lys, N-Gly204Arg, and Spike-Asp614Gly) and one synonymous (ORF1ab-Phe924Phe) functional effects. The highest number of rcntSNVs found distinct and were uniquely attributed to the specific UVindex regions, proposing solar-UV radiation as one of the driving forces for SARS-CoV-2 differential genomic adaptation. The phylogenetic relationship indicated the high UVindex region populating SARS-CoV-2 as the recent progenitor of all included samples. Altogether, these results provide baseline genomic data that may need to be included for preparing UVindex region-specific future diagnostic and vaccine formulations.

An integrated analysis and comparison of serum, saliva and sebum for COVID-19 metabolomics

The majority of metabolomics studies to date have utilised blood serum or plasma, biofluids that do not necessarily address the full range of patient pathologies. Here, correlations between serum metabolites, salivary metabolites and sebum lipids are studied for the first time. 83 COVID-19 positive and negative hospitalised participants provided blood serum alongside saliva and sebum samples for analysis by liquid chromatography mass spectrometry. Widespread alterations to serum-sebum lipid relationships were observed in COVID-19 positive participants versus negative controls. There was also a marked correlation between sebum lipids and the immunostimulatory hormone dehydroepiandrosterone sulphate in the COVID-19 positive cohort. The biofluids analysed herein were also compared in terms of their ability to differentiate COVID-19 positive participants from controls; serum performed best by multivariate analysis (sensitivity and specificity of 0.97), with the dominant changes in triglyceride and bile acid levels, concordant with other studies identifying dyslipidemia as a hallmark of COVID-19 infection. Sebum performed well (sensitivity 0.92; specificity 0.84), with saliva performing worst (sensitivity 0.78; specificity 0.83). These findings show that alterations to skin lipid profiles coincide with dyslipidaemia in serum. The work also signposts the potential for integrated biofluid analyses to provide insight into the whole-body atlas of pathophysiological conditions.

Cellular automata in the light of COVID-19

Currently, the world has been facing the brunt of a pandemic due to a disease called COVID-19 for the last 2 years. To study the spread of such infectious diseases it is important to not only understand their temporal evolution but also the spatial evolution. In this work, the spread of this disease has been studied with a cellular automata (CA) model to find the temporal and the spatial behavior of it. Here, we have proposed a neighborhood criteria which will help us to measure the social confinement at the time of the disease spread. The two main parameters of our model are (i) disease transmission probability (q) which helps us to measure the infectivity of a disease and (ii) exponent (n) which helps us to measure the degree of the social confinement. Here, we have studied various spatial growths of the disease by simulating this CA model. Finally we have tried to fit our model with the COVID-19 data of India for various waves and have attempted to match our model predictions with regards to each wave to see how the different parameters vary with respect to infectivity and restrictions in social interaction.

Retrospective Cohort Study of the Effectiveness of the Sputnik V and EpiVacCorona Vaccines against the SARS-CoV-2 Delta Variant in Moscow (June-July 2021)

The goal of this study was to evaluate the epidemiological effectiveness of the Sputnik V and EpiVacCorona vaccines against COVID-19. This work is a retrospective cohort study of COVID-19 patients. The cohort created by the Moscow Health Department included more than 300,000 infected people who sought medical care in June and July 2021. Analysis of data revealed a tendency for the increase in the Sputnik V vaccine effectiveness (VE) as the severity of the disease increased. Protection was the lowest for mild disease, and it was more pronounced for severe disease. We also observed a decrease in VE with increasing age. For the youngest group (18-50 years old), the estimated VE in preventing death in June 2021 was 95% (95% CI 64-100), and for the older group (50+ years old), it was 74% (95% CI 67-87). The estimated protection against a severe form of the disease in the 18-50-year-old group was above 81% (CI 95% 72-93), and in the 50+ years-old group, it was above 68% (CI 95% 65-82). According to our analysis, EpiVacCorona proved to be an ineffective vaccine and therefore cannot protect against COVID-19.

Antibody- and T Cell-Dependent Responses Elicited by a SARS-CoV-2 Adenoviral-Based Vaccine in a Socially Vulnerable Cohort of Elderly Individuals

BACKGROUND

In spite of compelling evidence demonstrating safety and immunogenicity of adenoviral-based SARS-CoV-2 vaccines in the general population, its effects in socially vulnerable elderly individuals are poorly understood. Here we aimed to investigate the efficacy of two doses of combined vector vaccine, the Gam-COVID-Vac (Sputnik-V vaccine), at 14, 42, and 180 days after immunization, in a nursing home for underprivileged population and homeless individuals.

METHODS

A phase 3, open-label clinical trial involving administration of two adenoviral vectors (Ad26-Ad5) vaccine, in elderly individuals over the ages of 60 years was performed. SARS-CoV-2 Spike RBD-specific IgG antibodies at days 21-, 42- and 180 post-vaccination was analyzed in sera of individuals receiving two doses of the Sputnik-V vaccine with an interval of 21 days. SARS-CoV-2-specific CD8+ T cell responses, measured by intracellular tumor necrosis factor (TNF) was determined by flow cytometry following antigen-specific cultures.

RESULTS

A total of 72 elderly adults with a mean age of 72.6 ± 9.5 years-old was selected after applying the inclusion criteria, all corresponding to an underprivileged population. Two-doses vaccination with Sputnik-V vaccine elicited an antibody-mediated immune response (revealed by quantitative detection of SARS-CoV-2-specific IgG antibodies, CMIA) 70% at day 21, 90% at day 42, and 66.1% at day 180. Fully vaccinated individuals had robust SARS-CoV-2-specific T cell responses, evidenced by TNF production in CD4+ and CD8+ T cells in all time periods analyzed.

CONCLUSION

Six months after receipt of the second dose of the Gam-COVID-Vac vaccine, SARS-CoV-2-specific IgG levels declined substantially among the tested population, whereas CD4+ and CD8+ T-cell-mediated immunity remained at high levels. These data suggest that two doses of combined adenoviral-based vaccine elicits a considerable level of SARS-CoV-2 immune responses in elderly individuals, highlighting its safety and immunogenicity in this highly vulnerable population.

Vaccination strategies when vaccines are scarce: on conflicts between reducing the burden and avoiding the evolution of escape mutants

When vaccine supply is limited but population immunization urgent, the allocation of the available doses needs to be carefully considered. One aspect of dose allocation is the time interval between the first and the second injections in two-dose vaccines. By stretching this interval, more individuals can be vaccinated with the first dose more quickly, which can be beneficial in reducing case numbers, provided a single dose is sufficiently effective. On the other hand, there has been concern that intermediate levels of immunity in partially vaccinated individuals may favour the evolution of vaccine escape mutants. In that case, a large fraction of half-vaccinated individuals would pose a risk-but only if they encounter the virus. This raises the question whether there is a conflict between reducing the burden and the risk of vaccine escape evolution or not. We develop an SIR-type model to assess the population-level effects of the timing of the second dose. Trade-offs can occur both if vaccine escape evolution is more likely or if it is less likely in half-vaccinated than in unvaccinated individuals. Their presence or absence depends on the efficacies for susceptibility and transmissibility elicited by a single dose.

VacSIM: Learning effective strategies for COVID-19 vaccine distribution using reinforcement learning

A COVID-19 vaccine is our best bet for mitigating the ongoing onslaught of the pandemic. However, vaccine is also expected to be a limited resource. An optimal allocation strategy, especially in countries with access inequities and temporal separation of hot-spots, might be an effective way of halting the disease spread. We approach this problem by proposing a novel pipeline VacSIM that dovetails Deep Reinforcement Learning models into a Contextual Bandits approach for optimizing the distribution of COVID-19 vaccine. Whereas the Reinforcement Learning models suggest better actions and rewards, Contextual Bandits allow online modifications that may need to be implemented on a day-to-day basis in the real world scenario. We evaluate this framework against a naive allocation approach of distributing vaccine proportional to the incidence of COVID-19 cases in five different States across India (Assam, Delhi, Jharkhand, Maharashtra and Nagaland) and demonstrate up to 9039 potential infections prevented and a significant increase in the efficacy of limiting the spread over a period of 45 days through the VacSIM approach. Our models and the platform are extensible to all states of India and potentially across the globe. We also propose novel evaluation strategies including standard compartmental model-based projections and a causality-preserving evaluation of our model. Since all models carry assumptions that may need to be tested in various contexts, we open source our model VacSIM and contribute a new reinforcement learning environment compatible with OpenAI gym to make it extensible for real-world applications across the globe. 2

A Stochastic Multi-Strain SIR Model with Two-Dose Vaccination Rate

Infectious diseases remain a substantial public health concern as they are among the leading causes of death. Immunization by vaccination can reduce the infectious diseases-related risk of suffering and death. Many countries have developed COVID-19 vaccines in the past two years to control the COVID-19 pandemic. Due to an urgent need for COVID-19 vaccines, the vaccine administration of COVID-19 is in the mode of emergency use authorization to facilitate the availability and use of vaccines. Therefore, the vaccine development time is extraordinarily short, but administering two doses is generally recommended within a specific time to achieve sufficient protection. However, it may be essential to identify an appropriate interval between two vaccinations. We constructed a stochastic multi-strain SIR model for a two-dose vaccine administration to address this issue. We introduced randomness into this model mainly through the transmission rate parameters. We discussed the uniqueness of the positive solution to the model and presented the conditions for the extinction and persistence of disease. In addition, we explored the optimal cost to improve the epidemic based on two cost functions. The numerical simulations showed that the administration rate of both vaccine doses had a significant effect on disease transmission.

Fear of COVID-19 predicts vaccination willingness 14 months later

Vaccines are an important tool for governments and health agencies to contain and curb the Coronavirus Disease-19 (COVID-19) pandemic. However, despite their effectiveness and safeness, a substantial portion of the population worldwide is hesitant to get vaccinated. In the current study, we examined whether fear of COVID-19 predicts vaccination willingness. In a longitudinal study (N = 938), fear for COVID-19 was assessed in April 2020 and vaccination willingness was measured in June 2021. Approximately 11% of our sample indicated that they were not willing to get vaccinated. Results of a logistic regression showed that increased fear of COVID-19 predicts vaccination willingness 14 months later, even when controlling for several anxious personality traits, infection control perceptions, risks for loved ones, self-rated health, previous infection, media use, and demographic variables. These results show that fear of COVID-19 is a relevant construct to consider for predicting and possibly influencing vaccination willingness. Nonetheless, sensitivity and specificity of fear of COVID-19 to predict vaccination willingness were quite low and only became slightly better when fear of COVID-19 was measured concurrently. This indicates that other potential factors, such as perceived risks of the vaccines, probably also play a role in explaining vaccination willingness.

Would New SARS-CoV-2 Variants Change the War against COVID-19?

The scientific, private, and industrial sectors use a wide variety of technological platforms available to achieve protection against SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), including vaccines. However, the virus evolves continually into new highly virulent variants, which might overcome the protection provided by vaccines and may re-expose the population to infections. Mass vaccinations should be continued in combination with more or less mandatory non-pharmaceutical interventions. Therefore, the key questions to be answered are: (i) How to identify the primary and secondary infections of SARS-CoV-2? (ii) Why are neutralizing antibodies not long-lasting in both cases of natural infections and post-vaccinations? (iii) Which are the factors responsible for this decay in neutralizing antibodies? (iv) What strategy could be adapted to develop long-term herd immunity? (v) Is the Spike protein the only vaccine target or is a vaccine cocktail better?

Evolution of resistance to COVID-19 vaccination with dynamic social distancing

The greatest hope for a return to normalcy following the COVID-19 pandemic is worldwide vaccination. Yet, a relaxation of social distancing that allows increased transmissibility, coupled with selection pressure due to vaccination, will probably lead to the emergence of vaccine resistance. We analyse the evolutionary dynamics of COVID-19 in the presence of dynamic contact reduction and in response to vaccination. We use infection and vaccination data from six different countries. We show that under slow vaccination, resistance is very likely to appear even if social distancing is maintained. Under fast vaccination, the emergence of mutants can be prevented if social distancing is maintained during vaccination. We analyse multiple human factors that affect the evolutionary potential of the virus, including the extent of dynamic social distancing, vaccination campaigns, vaccine design, boosters and vaccine hesitancy. We provide guidelines for policies that aim to minimize the probability of emergence of vaccine-resistant variants.

Genomic Diversity of SARS-CoV-2 in Algeria and North African Countries: What We Know So Far and What We Expect?

Here, we report a first comprehensive genomic analysis of SARS-CoV-2 variants circulating in North African countries, including Algeria, Egypt, Libya, Morocco, Sudan and Tunisia, with respect to genomic clades and mutational patterns. As of December 2021, a total of 1669 high-coverage whole-genome sequences submitted to EpiCoV GISAID database were analyzed to infer clades and mutation annotation compared with the wild-type variant Wuhan-Hu-1. Phylogenetic analysis of SARS-CoV-2 genomes revealed the existence of eleven GISAID clades with GR (variant of the spike protein S-D614G and nucleocapsid protein N-G204R), GH (variant of the ORF3a coding protein ORF3a-Q57H) and GK (variant S-T478K) being the most common with 25.9%, 19.9%, and 19.6%, respectively, followed by their parent clade G (variant S-D614G) (10.3%). Lower prevalence was noted for GRY (variant S-N501Y) (5.1%), S (variant ORF8-L84S) (3.1%) and GV (variant of the ORF3a coding protein NS3-G251V) (2.0%). Interestingly, 1.5% of total genomes were assigned as GRA (Omicron), the newly emerged clade. Across the North African countries, 108 SARS-CoV-2 lineages using the Pangolin assignment were identified, whereby most genomes fell within six major lineages and variants of concern (VOC) including B.1, the Delta variants (AY.X, B.1.617.2), C.36, B.1.1.7 and B.1.1. The effect of mutations in SAR-CoV-2 genomes highlighted similar profiles with D614G spike (S) and ORF1b-P314L variants as the most changes found in 95.3% and 87.9% of total sequences, respectively. In addition, mutations affecting other viral proteins appeared frequently including; N:RG203KR, N:G212V, NSP3:T428I, ORF3a:Q57H, S:N501Y, M:I82T and E:V5F. These findings highlight the importance of genomic surveillance for understanding the SARS-CoV-2 genetic diversity and its spread patterns, leading to a better guiding of public health intervention measures. The know-how analysis of the present work could be implemented worldwide in order to overcome this health crisis through harmonized approaches.

Unresolved COVID Controversies: 'Normal science' and potential non-scientific influences

ABSTRACTThe COVID-19 health crisis has so far involved enormous consequences in human pain, suffering and death. While biomedical science responded early, its response has been marked by several controversies between what appeared to be mainstream perspectives, and diverse alternative views; far from leading to productive debate, controversies often preceded polarisation and, allegedly, exclusion and even censorship of alternative views, followed by the pretense of scientific consensus. This paper describes and discusses the main controversies in the production of COVID biomedical knowledge and derived control measures, to establish if alternative positions are also legitimate from a 'normal science' perspective (rather than comparing them for superiority); explores potential non-scientific explanations of the alleged exclusion of certain views; and analyzes ethical issues implied. The operation of non-scientific factors in scientific and regulatory processes (e.g. various forms of subtle corruption) has been documented in the past; the intervention of such influences in the mishandling of controversies (i.e. on early management, non-pharmacological prevention and vaccination) cannot be ruled out and deserves further investigation. Some of these controversies, increasingly visible in the public domain, also involve ethical challenges that need urgent attention. Polarisation, censorship and dogma are foreign to true science and must be left behind.

Projecting the impact of Covid-19 variants and vaccination strategies in disease transmission using a multilayer network model in Costa Rica

For countries starting to receive steady supplies of vaccines against SARS-CoV-2, the course of Covid-19 for the following months will be determined by the emergence of new variants and successful roll-out of vaccination campaigns. To anticipate this scenario, we used a multilayer network model developed to forecast the transmission dynamics of Covid-19 in Costa Rica, and to estimate the impact of the introduction of the Delta variant in the country, under two plausible vaccination scenarios, one sustaining Costa Rica's July 2021 vaccination pace of 30,000 doses per day and with high acceptance from the population and another with declining vaccination pace to 13,000 doses per day and with lower acceptance. Results suggest that the introduction and gradual dominance of the Delta variant would increase Covid-19 hospitalizations and ICU admissions by [Formula: see text] and [Formula: see text], respectively, from August 2021 to December 2021, depending on vaccine administration and acceptance. In the presence of the Delta variant, new Covid-19 hospitalizations and ICU admissions are estimated to increase around [Formula: see text] and [Formula: see text], respectively, in the same period if the vaccination pace drops. Our results can help decision-makers better prepare for the Covid-19 pandemic in the months to come.

Highlight of potential impact of new viral genotypes of SARS-CoV-2 on vaccines and anti-viral therapeutics

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of the coronavirus disease (COVID-19) pandemic, has infected millions of people globally. Genetic variation and selective pressures lead to the accumulation of single nucleotide polymorphism (SNP) within the viral genome that may affect virulence, transmission rate, viral recognition and the efficacy of prophylactic and interventional measures. To address these concerns at the genomic level, we assessed the phylogeny and SNPs of the SARS-CoV-2 mutant population collected to date in Iran in relation to globally reported variants. Phylogenetic analysis of mutant strains revealed the occurrence of the variants known as B.1.1.7 (Alpha), B.1.525 (Eta), and B.1.617 (Delta) that appear to have delineated independently in Iran. SNP analysis of the Iranian sequences revealed that the mutations were predominantly positioned within the S protein-coding region, with most SNPs localizing to the S1 subunit. Seventeen S1-localizing SNPs occurred in the RNA binding domain that interacts with ACE2 of the host cell. Importantly, many of these SNPs are predicted to influence the binding of antibodies and anti-viral therapeutics, indicating that the adaptive host response appears to be imposing a selective pressure that is driving the evolution of the virus in this closed population through enhancing virulence. The SNPs detected within these mutant cohorts are addressed with respect to current prophylactic measures and therapeutic interventions.