Epidemiological Impact of SARS-CoV-2 Vaccination: Mathematical Modeling Analyses

Learning from the COVID-19 pandemic: A systematic review of mathematical vaccine prioritization models

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

Estimating protection afforded by prior infection in preventing reinfection: applying the test-negative study design

The COVID-19 pandemic has highlighted the need to use infection testing databases to rapidly estimate effectiveness of prior infection in preventing reinfection ($P{E}_S$) by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Mathematical modeling was used to demonstrate a theoretical foundation for applicability of the test-negative, case-control study design to derive $P{E}_S$. Apart from the very early phase of an epidemic, the difference between the test-negative estimate for $P{E}_S$ and true value of $P{E}_S$ was minimal and became negligible as the epidemic progressed. The test-negative design provided robust estimation of $P{E}_S$ and its waning. Assuming that only 25% of prior infections are documented, misclassification of prior infection status underestimated $P{E}_S$, but the underestimate was considerable only when > 50% of the population was ever infected. Misclassification of latent infection, misclassification of current active infection, and scale-up of vaccination all resulted in negligible bias in estimated $P{E}_S$. The test-negative design was applied to national-level testing data in Qatar to estimate $P{E}_S$ for SARS-CoV-2. $P{E}_S$ against SARS-CoV-2 Alpha and Beta variants was estimated at 97.0% (95% CI, 93.6-98.6) and 85.5% (95% CI, 82.4-88.1), respectively. These estimates were validated using a cohort study design. The test-negative design offers a feasible, robust method to estimate protection from prior infection in preventing reinfection.

An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

BACKGROUND

The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).

METHODOLOGY

PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.

FINDINGS

In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.

INTERPRETATION

The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

Mathematical analysis and optimal control of an epidemic model with vaccination and different infectivity

This paper aims to explore the complex dynamics and impact of vaccinations on controlling epidemic outbreaks. An epidemic transmission model which considers vaccinations and two different infection statuses with different infectivity is developed. In terms of a dynamic analysis, we calculate the basic reproduction number and control reproduction number and discuss the stability of the disease-free equilibrium. Additionally, a numerical simulation is performed to explore the effects of vaccination rate, immune waning rate and vaccine ineffective rate on the epidemic transmission. Finally, a sensitivity analysis revealed three factors that can influence the threshold: transmission rate, vaccination rate, and the hospitalized rate. In terms of optimal control, the following three time-related control variables are introduced to reconstruct the corresponding control problem: reducing social distance, enhancing vaccination rates, and enhancing the hospitalized rates. Moreover, the characteristic expression of optimal control problem. Four different control combinations are designed, and comparative studies on control effectiveness and cost effectiveness are conducted by numerical simulations. The results showed that Strategy C (including all the three controls) is the most effective strategy to reduce the number of symptomatic infections and Strategy A (including reducing social distance and enhancing vaccination rate) is the most cost-effective among the three strategies.

Severity and Vaccine Effectiveness in Patients With the Omicron Variant of COVID-19 in Suzhou: A Retrospective Single-Center Study

Background The Omicron variant of the coronavirus disease 2019 (COVID-19) virus has spread rapidly worldwide, even in areas with high vaccination rates. Consequently, it has further exacerbated the current global pandemic. In this study, we aimed to characterize the clinical severity of patients with the COVID-19 variant Omicron and analyze vaccine effectiveness in predicting clinical severity. Methodology A total of 142 patients who contracted the COVID-19 virus in the Omicron era were retrospectively studied, and differences in their clinical severity were analyzed. They were stratified as follows: unvaccinated vs. vaccinated, unvaccinated vs. one to two vaccine doses vs. three vaccine doses, and cycle threshold (CT) values ≤ 28 vs. CT > 28. Results Of the 142 patients, 27 were asymptomatic, 83 had mild disease, and 32 had moderate disease. The median age was 32 years for asymptomatic patients vs. 31 years for those with mild disease vs. 59 years for those with moderate disease (P＜0.05), and the direct medical hospitalization costs were ¥4901 for asymptomatic patients vs. ¥5259 for those with mild disease vs. ¥8378 for those with moderate disease (P＜0.05). Of the 142 patients, 112 (78.8%) were vaccinated, 11 (7.7%) had one vaccine dose, 63 (44.4%) had two vaccine doses, and 38 (26.7%) received three vaccine doses. The median direct medical cost in the vaccinated group was significantly lower than that in the unvaccinated group (¥5470.5 vs. ¥7535.5, P＜0.05). For ORF1ab and N genes, hospital stay length and direct medical cost significantly decreased in the group with CT values > 28 compared with those in the group with CT values ≤ 28 (P＜0.05). Multiple regression analysis showed that being ≥ 60 years old could be a predictor of moderate disease severity in patients, and three vaccine doses could be effective against moderate COVID-19. Conclusion Mild infection is the main clinical manifestation of the Omicron variant. Vaccination can significantly decrease direct Omicron-associated medical costs. Although vaccination cannot provide protection against severe disease caused by this variant, three vaccine doses are highly effective in preventing moderate COVID-19.

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in a population-level SEIR model. We demonstrated that the effect of temperature on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020-Jan 25, 2021) in the Greater Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 6-16 days and 8.3-10.2 folds (R = 0.93). This work showcases a simple yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.

Neighborhood Condition Prevalence Rates Correlate With COVID-19 Mortality in Milwaukee County, Wisconsin

Purpose

We sought to determine if census tract-level (ie, neighborhood) COVID-19 death rates in Milwaukee County correlated with the census tract-level condition prevalence rates (CPRs) for individual COVID-19 mortality risk.

Methods

This study used Milwaukee County-reported COVID-19 death rates per 100,000 lives for the 296 census tracts within the county to perform a linear regression with individual COVID-19 mortality risk CPR, mean age, racial composition of census tract (by percentage of non-White residents), and poverty (by percentage within census tract), followed by multiple regression with all 7 CPRs as well as the 7 CPRs combined with the additional demographic variables. CPR estimates were accessed from the Centers for Disease Control and Prevention 500 Cities Project. Demographics were accessed from the U.S. Census. The Milwaukee County Medical Examiner's office identified 898 deaths from COVID-19 in Milwaukee County from March 2020 to June 2021.

Results

Among the variables included, crude death rate demonstrated a statistically significant association with the 7 COVID-19 mortality risk CPRs (as analyzed collectively), census tract mean age, and several of the CPRs individually. The addition of census tract age, race, and poverty in multiple regression did not improve the association of the 7 CPRs with crude death rate.

Conclusions

Results from this population-level study indicated that census tracts with high COVID-19 mortality correlated with high-risk condition prevalence estimates within those census tracts, illustrating how health data collection and analysis at a census tract level could be helpful when planning pandemic-mitigating public health efforts.

Effects of vaccination on mitigating COVID-19 outbreaks: a conceptual modeling approach

This paper is devoted to investigating the impact of vaccination on mitigating COVID-19 outbreaks. In this work, we propose a compartmental epidemic ordinary differential equation model, which extends the previous so-called SEIRD model ^[1,2,3,4] by incorporating the birth and death of the population, disease-induced mortality and waning immunity, and adding a vaccinated compartment to account for vaccination. Firstly, we perform a mathematical analysis for this model in a special case where the disease transmission is homogeneous and vaccination program is periodic in time. In particular, we define the basic reproduction number $ \mathcal{R}_0 $ for this system and establish a threshold type of result on the global dynamics in terms of $ \mathcal{R}_0 $. Secondly, we fit our model into multiple COVID-19 waves in four locations including Hong Kong, Singapore, Japan, and South Korea and then forecast the trend of COVID-19 by the end of 2022. Finally, we study the effects of vaccination again the ongoing pandemic by numerically computing the basic reproduction number $ \mathcal{R}_0 $ under different vaccination programs. Our findings indicate that the fourth dose among the high-risk group is likely needed by the end of the year.

State estimation of the time-space propagation of COVID-19 using a distributed parameter observer based on a SEIR-type model

The real-time prediction and estimation of the spread of diseases, such as COVID-19 is of paramount importance as evidenced by the recent pandemic. This work is concerned with the distributed parameter estimation of the time-space propagation of such diseases using a diffusion-reaction epidemiological model of the susceptible-exposed-infected-recovered (SEIR) type. State estimation is based on continuous measurements of the number of infections and deaths per unit of time and of the host spatial domain. The observer design method is based on positive definite matrices to parameterize a class of Lyapunov functionals, in order to stabilize the estimation error dynamics. Thus, the stability conditions can be expressed as a set of matrix inequality constraints which can be solved numerically using sum of squares (SOS) and standard semi-definite programming (SDP) tools. The observer performance is analyzed based on a simplified case study corresponding to the situation in France in March 2020 and shows promising results.

Pandemic control - do's and don'ts from a control theory perspective

Managing a pandemic is a difficult task. Pandemics are part of the dynamics of nonlinear systems with multiple different interactive features that co-adapt to each other (such as humans, animals, and pathogens). The target of controlling such a nonlinear system is best achieved using the control system theory developed in engineering and applied in systems biology. But is this theory and its principles actually used in controlling the current coronavirus disease-19 pandemic? We review the evidence for applying principles in different aspects of pandemic control related to different goals such as disease eradication, disease containment, and short- or long-term economic loss minimization. Successful policies implement multiple measures in concordance with control theory to achieve a robust response. In contrast, unsuccessful policies have numerous failures in different measures or focus only on a single measure (only testing, vaccines, etc.). Successful approaches rely on predictions instead of reactions to compensate for the costs of time delay, on knowledge-based analysis instead of trial-and-error, to control complex nonlinear systems, and on risk assessment instead of waiting for more evidence. Iran is an example of the effects of delayed response due to waiting for evidence to arrive instead of a proper risk analytical approach. New Zealand, Australia, and China are examples of appropriate application of basic control theoretic principles and focusing on long-term adaptive strategies, updating measures with the evolution of the pandemic.

Public Preferences for Policies to Promote COVID-19 Vaccination Uptake: A Discrete Choice Experiment in The Netherlands

OBJECTIVES

The COVID-19 pandemic forms an unprecedented public health, economic, and social crisis. Uptake of vaccination is critical for controlling the pandemic. Nevertheless, vaccination hesitancy is considerable, requiring policies to promote uptake. We investigate Dutch citizens' preferences for policies that aim to promote vaccination through facilitating choice of vaccination, profiling it as the norm, making vaccination more attractive through rewards, or punishing people who reject vaccination.

METHODS

We conducted a discrete choice experiment in which 747 respondents were asked to choose between policies to promote vaccination uptake and their impacts on the number of deaths, people with permanent health problems, households with income loss, and a tax increase.

RESULTS

Respondents generally had a negative preference for policies that promote vaccination. They particularly disliked policies that punish those who reject the vaccine and were more favorable toward policies that reward vaccination, such as awarding additional rights to vaccinated individuals through vaccination passports. Respondents who reject vaccination were in general much more negative about the policy options than respondents who consider accepting the vaccine. Nevertheless, vaccination passports are supported by both respondents who accept the vaccine, those who reject vaccination, and those who are unsure about vaccination.

CONCLUSIONS

This study provides concrete directions for governments attempting to increase the vaccination uptake in ways that are supported by the public. Our results could encourage policy makers to focus on policy options that make vaccination easier and reward people who take the vaccine, as especially the implementation of vaccination passports was supported.

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in population-level SEIR model. We demonstrated that the temperature effect on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020 â€" Jan 25, 2021) in the Great Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 16 days and 8.6 folds ( R = 0.93), respectively. This work showcases a simple, yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.

Duration of mRNA vaccine protection against SARS-CoV-2 Omicron BA.1 and BA.2 subvariants in Qatar

SARS-CoV-2 Omicron BA.1 and BA.2 subvariants are genetically divergent. We conducted a matched, test-negative, case-control study to estimate duration of protection of the second and third/booster doses of mRNA COVID-19 vaccines against BA.1 and BA.2 infections in Qatar. BNT162b2 effectiveness was highest at 46.6% (95% CI: 33.4-57.2%) against symptomatic BA.1 and at 51.7% (95% CI: 43.2-58.9%) against symptomatic BA.2 infections in the first three months after the second dose, but declined to ~10% or below thereafter. Effectiveness rebounded to 59.9% (95% CI: 51.2-67.0%) and 43.7% (95% CI: 36.5-50.0%), respectively, in the first month after the booster dose, before declining again. Effectiveness against COVID-19 hospitalization and death was 70-80% after the second dose and >90% after the booster dose. mRNA-1273 vaccine protection showed similar patterns. mRNA vaccines provide comparable, moderate, and short-lived protection against symptomatic BA.1 and BA.2 Omicron infections, but strong and durable protection against COVID-19 hospitalization and death.

Modeling the COVID-19 pandemic: a primer and overview of mathematical epidemiology

Since the start of the still ongoing COVID-19 pandemic, there have been many modeling efforts to assess several issues of importance to public health. In this work, we review the theory behind some important mathematical models that have been used to answer questions raised by the development of the pandemic. We start revisiting the basic properties of simple Kermack-McKendrick type models. Then, we discuss extensions of such models and important epidemiological quantities applied to investigate the role of heterogeneity in disease transmission e.g. mixing functions and superspreading events, the impact of non-pharmaceutical interventions in the control of the pandemic, vaccine deployment, herd-immunity, viral evolution and the possibility of vaccine escape. From the perspective of mathematical epidemiology, we highlight the important properties, findings, and, of course, deficiencies, that all these models have.

Analyzing inherent biases in SARS-CoV-2 PCR and serological epidemiologic metrics

BACKGROUND

Prospective observational data show that infected persons with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain polymerase chain reaction (PCR) positive for a prolonged duration, and that detectable antibodies develop slowly with time. We aimed to analyze how these effects can bias key epidemiological metrics used to track and monitor SARS-CoV-2 epidemics.

METHODS

An age-structured mathematical model was constructed to simulate progression of SARS-CoV-2 epidemics in populations. PCR testing to diagnose infection and cross-sectional surveys to measure seroprevalence were also simulated. Analyses were conducted on simulated outcomes assuming a natural epidemic time course and an epidemic in presence of interventions.

RESULTS

The prolonged PCR positivity biased the epidemiological measures. There was a lag of 10 days between the true epidemic peak and the actually-observed peak. Prior to epidemic peak, PCR positivity rate was twofold higher than that based only on current active infection, and half of those tested positive by PCR were in the prolonged PCR positivity stage after infection clearance. Post epidemic peak, PCR positivity rate poorly predicted true trend in active infection. Meanwhile, the prolonged PCR positivity did not appreciably bias estimation of the basic reproduction number R0. The time delay in development of detectable antibodies biased measured seroprevalence. The actually-observed seroprevalence substantially underestimated true prevalence of ever infection, with the underestimation being most pronounced around epidemic peak.

CONCLUSIONS

Caution is warranted in interpreting PCR and serological testing data, and any drawn inferences need to factor the effects of the investigated biases for an accurate assessment of epidemic dynamics.

Population-Level Effectiveness of COVID-19 Vaccination Program in the United States: Causal Analysis Based on Structural Nested Mean Model

Though COVID-19 vaccines have shown high efficacy, real-world effectiveness at the population level remains unclear. Based on the longitudinal data on vaccination coverage and daily infection cases from fifty states in the United States from March to May 2021, causal analyses were conducted using structural nested mean models to estimate the population-level effectiveness of the COVID-19 vaccination program against infection with the original strain. We found that in the US, every 1% increase of vaccination coverage rate reduced the weekly growth rate of COVID-19 confirmed cases by 1.02% (95% CI: 0.26%, 1.69%), and the estimated population-level effectiveness of the COVID-19 program was 63.9% (95% CI: 18.0%, 87.5%). In comparison to a no-vaccination scenario, the COVID-19 vaccination campaign averted 8.05 million infections through the study period. Scenario analyses show that a vaccination program with doubled vaccination speed or with more rapid vaccination speed at the early stages of the campaign would avert more infections and increase vaccine effectiveness. The COVID-19 vaccination program demonstrated a high population-level effectiveness and significantly reduced the disease burden in the US. Accelerating vaccine rollout, especially at an early stage of the campaign, is crucial for reducing COVID-19 infections.

Timing the race of vaccination, new variants, and relaxing restrictions during COVID-19 pandemic

Late in 2019, China identified a new type of coronavirus, SARS-CoV-2, and due to its fast spread, the World Health Organisation (WHO) declared a pandemic named COVID-19. Some variants of this virus were detected, including the Delta, which caused new waves of infections. This work uses an extended version of a SIRD model that includes vaccination effects to measure the impact of the Delta variant in three countries: Germany, Israel and Brazil. The calibrated models were able to reproduce the dynamics of the above countries. In addition, hypothetical scenarios were simulated to quantify the impact of vaccination and mitigation policies during the Delta wave. The results showed that the model could reproduce the complex dynamics observed in the different countries. The estimated increase of transmission rate due to the Delta variant was highest in Israel (7.9), followed by Germany (2.7) and Brazil (1.5). These values may support the hypothesis that people immunised against COVID-19 may lose their defensive antibodies with time since Israel, Germany, and Brazil fully vaccinated half of the population in March, July, and October. The scenario to study the impact of vaccination revealed relative reductions in the total number of deaths between 30% and 250%; an absolute reduction of 300 thousand deaths in Brazil due to vaccination during the Delta wave. The second hypothetical scenario revealed that mitigation policies saved up to 300 thousand Brazilians; relative reductions in the total number of deaths between 24% and 120% in the three analysed countries. Therefore, the results suggest that both vaccination and mitigation policies were crucial in decreasing the spread and the number of deaths during the Delta wave.

Characterisation of Omicron Variant during COVID-19 Pandemic and the Impact of Vaccination, Transmission Rate, Mortality, and Reinfection in South Africa, Germany, and Brazil

Several variants of SARS-CoV-2 have been identified in different parts of the world, including Gamma, detected in Brazil, Delta, detected in India, and the recent Omicron variant, detected in South Africa. The emergence of a new variant is a cause of great concern. This work considers an extended version of an SIRD model capable of incorporating the effects of vaccination, time-dependent transmissibility rates, mortality, and even potential reinfections during the pandemic. We use this model to characterise the Omicron wave in Brazil, South Africa, and Germany. During Omicron, the transmissibility increased by five for Brazil and Germany and eight for South Africa, whereas the estimated mortality was reduced by three-fold. We estimated that the reported cases accounted for less than 25% of the actual cases during Omicron. The mortality among the nonvaccinated population in these countries is, on average, three to four times higher than the mortality among the fully vaccinated. Finally, we could only reproduce the observed dynamics after introducing a new parameter that accounts for the percentage of the population that can be reinfected. Reinfection was as high as 40% in South Africa, which has only 29% of its population fully vaccinated and as low as 13% in Brazil, which has over 70% and 80% of its population fully vaccinated and with at least one dose, respectively. The calibrated models were able to estimate essential features of the complex virus and vaccination dynamics and stand as valuable tools for quantifying the impact of protocols and decisions in different populations.

Comprehensive narrative review of real-world COVID-19 vaccines: viewpoints and opportunities

Currently, people all over the world have been affected by coronavirus disease 2019 (COVID-19). Fighting against COVID-19 is the top priority for all the countries and nations. The development of a safe and effective COVID-19 vaccine is considered the optimal way of ending the pandemic. Three hundred and 44 vaccines were in development, with 149 undergoing clinical research and 35 authorized for emergency use as to March 15 of 2022. Many studies have shown the effective role of COVID-19 vaccines in preventing SARS-CoV-2 infections as well as serious and fatal COVID-19 cases. However, tough challenges have arisen regarding COVID-19 vaccines, including long-term immunity, emerging COVID-19 variants, and vaccine inequalities. A systematic review was performed of recent COVID-19 vaccine studies, with a focus on vaccine type, efficacy and effectiveness, and protection against SARS-CoV-2 variants, breakthrough infections, safety, deployment and vaccine strategies used in the real-world. Ultimately, there is a need to establish a unified evaluation standard of vaccine effectiveness, monitor vaccine safety and effectiveness, along with the virological characteristics of SARS-CoV-2 variants; and determine the most useful booster schedule. These aspects must be coordinated to ensure timely responses to beneficial or detrimental situations. In the future, global efforts should be directed toward effective and immediate vaccine allocations, improving vaccine coverage, SARS-CoV-2 new variants tracking, and vaccine booster development.

Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmission

Different COVID-19 treatment candidates are under development, and some are becoming available including two promising drugs from Merck and Pfizer. This study provides conceptual frameworks for the effects of three types of treatments, both therapeutic and prophylactic, and to investigate their population-level impact, to inform drug development, licensure, decision-making, and implementation. Different drug efficacies were assessed using an age-structured mathematical model describing SARS-CoV-2 transmission and disease progression, with application to the United States as an illustrative example. Severe and critical infection treatment reduces progression to COVID-19 severe and critical disease and death with small number of treatments needed to avert one disease or death. Post-exposure prophylaxis treatment had a large impact on flattening the epidemic curve, with large reductions in infection, disease, and death, but the impact was strongly age dependent. Pre-exposure prophylaxis treatment had the best impact and effectiveness, with immense reductions in infection, disease, and death, driven by the robust control of infection transmission. Effectiveness of both pre-exposure and post-exposure prophylaxis treatments was disproportionally larger when a larger segment of the population was targeted than a specific age group. Additional downstream potential effects of treatment, beyond the primary outcome, enhance the population-level impact of both treatments. COVID-19 treatments are an important modality in controlling SARS-CoV-2 disease burden. Different types of treatment act synergistically for a larger impact, for these treatments and vaccination.

Modeling the Impact of the Imperfect Vaccination of the COVID-19 with Optimal Containment Strategy

Since the beginning of the COVID-19 pandemic, vaccination has been the main strategy to contain the spread of the coronavirus. However, with the administration of many types of vaccines and the constant mutation of viruses, the issue of how effective these vaccines are in protecting the population is raised. This work aimed to present a mathematical model that investigates the imperfect vaccine and finds the additional measures needed to help reduce the burden of disease. We determine the R0 threshold of disease spread and use stability analysis to determine the condition that will result in disease eradication. We also fitted our model to COVID-19 data from Morocco to estimate the parameters of the model. The sensitivity analysis of the basic reproduction number, with respect to the parameters of the model, is simulated for the four possible scenarios of the disease progress. Finally, we investigate the optimal containment measures that could be implemented with vaccination. To illustrate our results, we perform the numerical simulations of optimal control.

Risk assessment of COVID-19 epidemic resurgence in relation to SARS-CoV-2 variants and vaccination passes

The introduction of COVID-19 vaccination passes (VPs) by many countries coincided with the Delta variant fast becoming dominant across Europe. A thorough assessment of their impact on epidemic dynamics is still lacking. Here, we propose the VAP-SIRS model that considers possibly lower restrictions for the VP holders than for the rest of the population, imperfect vaccination effectiveness against infection, rates of (re-)vaccination and waning immunity, fraction of never-vaccinated, and the increased transmissibility of the Delta variant. Some predicted epidemic scenarios for realistic parameter values yield new COVID-19 infection waves within two years, and high daily case numbers in the endemic state, even without introducing VPs and granting more freedom to their holders. Still, suitable adaptive policies can avoid unfavorable outcomes. While VP holders could initially be allowed more freedom, the lack of full vaccine effectiveness and increased transmissibility will require accelerated (re-)vaccination, wide-spread immunity surveillance, and/or minimal long-term common restrictions.

Assessing the impact of vaccines, other public health measures, and declining immunity on SARS-CoV-2 control is challenging. This is particularly true in the context of vaccination passes, whereby vaccinated individuals have more freedom of making contacts than unvaccinated ones. Here, we use a mathematical model to simulate various scenarios and investigate the likelihood of containing COVID-19 outbreaks in example European countries. We demonstrate that both Alpha and Delta SARS-CoV-2 variants inevitably lead to recurring outbreaks when measures are lifted for vaccination pass holders. High re-vaccination rates and a lowered fraction of the unvaccinated population increase the benefit of vaccination passes. These observations are important for policy making, highlighting the need for continued vigilance, even where the epidemic is under control, especially when new variants of concern emerge.

Krueger, Gogolewski, and Bodych et al. assess the risk of COVID-19 epidemic resurgence in relation to SARS-CoV-2 variants and vaccination passes. Their model predicts that new COVID-19 infection waves within two years from the onset of the vaccination program are possible but that suitable adaptive policies can help to avoid unfavorable outcomes.

Characterizing the effective reproduction number during the COVID-19 pandemic: Insights from Qatar’s experience

Background

The effective reproduction number, R_t, is a tool to track and understand pandemic dynamics. This investigation of R_t estimations was conducted to guide the national COVID-19 response in Qatar, from the onset of the pandemic until August 18, 2021.

Methods

Real-time “empirical” R_t^Empirical was estimated using five methods, including the Robert Koch Institute, Cislaghi, Systrom-Bettencourt and Ribeiro, Wallinga and Teunis, and Cori et al. methods. R_t was also estimated using a transmission dynamics model (R_t^Model-based). Uncertainty and sensitivity analyses were conducted. Correlations between different R_t estimates were assessed by calculating correlation coefficients, and agreements between these estimates were assessed through Bland-Altman plots.

Results

R_t^Empirical captured the evolution of the pandemic through three waves, public health response landmarks, effects of major social events, transient fluctuations coinciding with significant clusters of infection, and introduction and expansion of the Alpha (B.1.1.7) variant. The various estimation methods produced consistent and overall comparable R_t^Empirical estimates with generally large correlation coefficients. The Wallinga and Teunis method was the fastest at detecting changes in pandemic dynamics. R_t^Empirical estimates were consistent whether using time series of symptomatic PCR-confirmed cases, all PCR-confirmed cases, acute-care hospital admissions, or ICU-care hospital admissions, to proxy trends in true infection incidence. R_t^Model-based correlated strongly with R_t^Empirical and provided an average R_t^Empirical.

Conclusions

R_t estimations were robust and generated consistent results regardless of the data source or the method of estimation. Findings affirmed an influential role for R_t estimations in guiding national responses to the COVID-19 pandemic, even in resource-limited settings.

Relative infectiousness of SARS-CoV-2 vaccine breakthrough infections, reinfections, and primary infections

SARS-CoV-2 breakthrough infections in vaccinated individuals and in those who had a prior infection have been observed globally, but the transmission potential of these infections is unknown. The RT-qPCR cycle threshold (Ct) value is inversely correlated with viral load and culturable virus. Here, we investigate differences in RT-qPCR Ct values across Qatar's national cohorts of primary infections, reinfections, BNT162b2 (Pfizer-BioNTech) breakthrough infections, and mRNA-1273 (Moderna) breakthrough infections. Our matched-cohort analyses of the randomly diagnosed infections show higher mean Ct value in all cohorts of breakthrough infections compared to the cohort of primary infections in unvaccinated individuals. The Ct value is 1.3 (95% CI: 0.9-1.8) cycles higher for BNT162b2 breakthrough infections, 3.2 (95% CI: 1.9-4.5) cycles higher for mRNA-1273 breakthrough infections, and 4.0 (95% CI: 3.5-4.5) cycles higher for reinfections in unvaccinated individuals. Since Ct value correlates inversely with SARS-CoV-2 infectiousness, these differences imply that vaccine breakthrough infections and reinfections are less infectious than primary infections in unvaccinated individuals. Public health benefits of vaccination may have been underestimated, as COVID-19 vaccines not only protect against acquisition of infection, but also appear to protect against transmission of infection.

Targeted vaccination and the speed of SARS-CoV-2 adaptation

The limited supply of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) raises the question of targeted vaccination. Many countries have opted to vaccinate older and more sensitive hosts first to minimize the disease burden. However, what are the evolutionary consequences of targeted vaccination? We clarify the consequences of different vaccination strategies through the analysis of the speed of viral adaptation measured as the rate of change of the frequency of a vaccine-adapted variant. We show that such a variant is expected to spread faster if vaccination targets individuals who are likely to be involved in a higher number of contacts. We also discuss the pros and cons of dose-sparing strategies. Because delaying the second dose increases the proportion of the population vaccinated with a single dose, this strategy can both speed up the spread of the vaccine-adapted variant and reduce the cumulative number of deaths. Hence, strategies that are most effective at slowing viral adaptation may not always be epidemiologically optimal. A careful assessment of both the epidemiological and evolutionary consequences of alternative vaccination strategies is required to determine which individuals should be vaccinated first.

Immunity in the ABM-DSGE Framework for Preventing and Controlling Epidemics-Validation of Results

The COVID-19 pandemic has raised many questions on how to manage an epidemiological and economic crisis around the world. Since the beginning of the COVID-19 pandemic, scientists and policy makers have been asking how effective lockdowns are in preventing and controlling the spread of the virus. In the absence of vaccines, the regulators lacked any plausible alternatives. Nevertheless, after the introduction of vaccinations, to what extent the conclusions of these analyses are still valid should be considered. In this paper, we present a study on the effect of vaccinations within the dynamic stochastic general equilibrium model with an agent-based epidemic component. Thus, we validated the results regarding the need to use lockdowns as an efficient tool for preventing and controlling epidemics that were obtained in November 2020.

Vaccination Schedule under Conditions of Limited Vaccine Production Rate

The paper is devoted to optimal vaccination scheduling during a pandemic to minimize the probability of infection. The recent COVID-19 pandemic showed that the international community is not properly prepared to manage a crisis of this scale. Just after the vaccines had been approved by medical agencies, the policymakers needed to decide on the distribution strategy. To successfully fight the pandemic, the key is to find the equilibrium between the vaccine distribution schedule and the available supplies caused by limited production capacity. This is why society needs to be divided into stratified groups whose access to vaccines is prioritized. Herein, we present the problem of distributing protective actions (i.e., vaccines) and formulate two mixed-integer programs to solve it. The problem of distributing protective actions (PDPA) aims at finding an optimal schedule for a given set of social groups with a constant probability of infection. The problem of distributing protective actions with a herd immunity threshold (PDPAHIT) also includes a variable probability of infection, i.e., the situation when herd immunity is obtained. The results of computational experiments are reported and the potential of the models is illustrated with examples.

COVID-19 phase 4 vaccine candidates, effectiveness on SARS-CoV-2 variants, neutralizing antibody, rare side effects, traditional and nano-based vaccine platforms: a review

The COVID-19 pandemic has endangered world health and the economy. As the number of cases is increasing, different companies have started developing potential vaccines using both traditional and nano-based platforms to overcome the pandemic. Several countries have approved a few vaccine candidates for emergency use authorization (EUA), showing significant effectiveness and inducing a robust immune response. Oxford-AstraZeneca, Pfizer-BioNTech's BNT162, Moderna's mRNA-1273, Sinovac's CoronaVac, Johnson & Johnson, Sputnik-V, and Sinopharm's vaccine candidates are leading the race. However, the SARS-CoV-2 is constantly mutating, making the vaccines less effective, possibly by escaping immune response for some variants. Besides, some EUA vaccines have been reported to induce rare side effects such as blood clots, cardiac injury, anaphylaxis, and some neurological effects. Although the COVID-19 vaccine candidates promise to overcome the pandemic, a more significant and clear understanding is needed. In this review, we brief about the clinical trial of some leading candidates, their effectiveness, and their neutralizing effect on SARS-CoV-2 variants. Further, we have discussed the rare side effects, different traditional and nano-based platforms to understand the scope of future development.

Concerns and Challenges Related to Sputnik V Vaccination Against the Novel COVID-19 Infection in the Russian Federation: The Role of Mental Health, and Personal and Social Issues as Targets for Future Psychosocial Interventions

BACKGROUND

Vaccine hesitancy causes serious difficulties in vaccination campaigns in many countries. The study of the population's attitude toward vaccination and detection of the predictive important individual psychological and social factors defining the vaccination necessity perception will allow elaborating promoting vaccination adherence measures.

OBJECTIVES

The aim of this research was to study COVID-19 threat appraisal, fear of COVID-19, trust in COVID-19 information sources, COVID-19 conspiracy beliefs, and the relationship of sociodemographic variables to COVID-19 preventive behavior.

METHODS

We carried out a cohort cross-sectional study of the population's attitude toward vaccination against the novel COVID-19 coronavirus infection, using a specially designed questionnaire for an online survey. Totally, there were 4,977 respondents, ranging in age from 18 to 81 years. Statistical assessment was carried out using the SPSS-11 program.

RESULTS

There were different attitudes toward vaccination. Among respondents, 34.2% considered vaccination to be useful, 31.1% doubted its effectiveness, and 9.9% considered vaccination unnecessary. The survey indicated that 7.4% of respondents were indifferent to the vaccine, while 12.2% deemed it to be dangerous. Nearly one-third (32.3%) of respondents indicated that they did not plan to be vaccinated, while another third (34.0%) would postpone their decision until more comprehensive data on the results and effectiveness of vaccination were available. Only 11.6% of the respondents were vaccinated at the time of the study. Young people were less focused on vaccination compared to middle-aged and elderly people. Receiving information concerning COVID-19 vaccination from healthcare workers and scientific experts was associated with greater vaccination acceptance.

CONCLUSION

The study results showed that vaccination attitudes interacted with individuals' mental health and various sociodemographic factors. Insofar as reports of physicians and experts are essential for shaping attitudes to vaccination, the study results inform the selection of target groups in need of particular psychosocial interventions to overcome their vaccine hesitancy.

Waning of BNT162b2 Vaccine Protection against SARS-CoV-2 Infection in Qatar

BACKGROUND

Waning of vaccine protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or coronavirus disease 2019 (Covid-19) is a concern. The persistence of BNT162b2 (Pfizer-BioNTech) vaccine effectiveness against infection and disease in Qatar, where the B.1.351 (or beta) and B.1.617.2 (or delta) variants have dominated incidence and polymerase-chain-reaction testing is done on a mass scale, is unclear.

METHODS

We used a matched test-negative, case-control study design to estimate vaccine effectiveness against any SARS-CoV-2 infection and against any severe, critical, or fatal case of Covid-19, from January 1 to September 5, 2021.

RESULTS

Estimated BNT162b2 effectiveness against any SARS-CoV-2 infection was negligible in the first 2 weeks after the first dose. It increased to 36.8% (95% confidence interval [CI], 33.2 to 40.2) in the third week after the first dose and reached its peak at 77.5% (95% CI, 76.4 to 78.6) in the first month after the second dose. Effectiveness declined gradually thereafter, with the decline accelerating after the fourth month to reach approximately 20% in months 5 through 7 after the second dose. Effectiveness against symptomatic infection was higher than effectiveness against asymptomatic infection but waned similarly. Variant-specific effectiveness waned in the same pattern. Effectiveness against any severe, critical, or fatal case of Covid-19 increased rapidly to 66.1% (95% CI, 56.8 to 73.5) by the third week after the first dose and reached 96% or higher in the first 2 months after the second dose; effectiveness persisted at approximately this level for 6 months.

CONCLUSIONS

BNT162b2-induced protection against SARS-CoV-2 infection appeared to wane rapidly following its peak after the second dose, but protection against hospitalization and death persisted at a robust level for 6 months after the second dose. (Funded by Weill Cornell Medicine-Qatar and others.).

The Hard Lessons and Shifting Modeling Trends of COVID-19 Dynamics: Multiresolution Modeling Approach

The COVID-19 pandemic has placed epidemiologists, modelers, and policy makers at the forefront of the global discussion of how to control the spread of coronavirus. The main challenges confronting modelling approaches include real-time projections of changes in the numbers of cases, hospitalizations, and fatalities, the consequences of public health policy, the understanding of how best to implement varied non-pharmaceutical interventions and potential vaccination strategies, now that vaccines are available for distribution. Here, we: (i) review carefully selected literature on COVID-19 modeling to identify challenges associated with developing appropriate models along with collecting the fine-tuned data, (ii) use the identified challenges to suggest prospective modeling frameworks through which adaptive interventions such as vaccine strategies and the uses of diagnostic tests can be evaluated, and (iii) provide a novel Multiresolution Modeling Framework which constructs a multi-objective optimization problem by considering relevant stakeholders’ participatory perspective to carry out epidemic nowcasting and future prediction. Consolidating our understanding of model approaches to COVID-19 will assist policy makers in designing interventions that are not only maximally effective but also economically beneficial.

BNT162b2 and mRNA-1273 COVID-19 vaccine effectiveness against the SARS-CoV-2 Delta variant in Qatar

With the global expansion of the highly transmissible SARS-CoV-2 Delta (B.1.617.2) variant, we conducted a matched test-negative case-control study to assess the real-world effectiveness of COVID-19 messenger RNA vaccines against infection with Delta in Qatar's population. BNT162b2 effectiveness against any, symptomatic or asymptomatic, Delta infection was 45.3% (95% CI, 22.0-61.6%) ≥14 d after the first vaccine dose, but only 51.9% (95% CI, 47.0-56.4%) ≥14 d after the second dose, with 50% of fully vaccinated individuals receiving their second dose before 11 May 2021. Corresponding mRNA-1273 effectiveness ≥14 d after the first or second dose was 73.7% (95% CI, 58.1-83.5%) and 73.1% (95% CI, 67.5-77.8%), respectively. Notably, effectiveness against Delta-induced severe, critical or fatal disease was 93.4% (95% CI, 85.4-97.0%) for BNT162b2 and 96.1% (95% CI, 71.6-99.5%) for mRNA-1273 ≥ 14 d after the second dose. Our findings show robust effectiveness for both BNT162b2 and mRNA-1273 in preventing Delta hospitalization and death in Qatar's population, despite lower effectiveness in preventing infection, particularly for the BNT162b2 vaccine.

Assessment of the Risk of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Reinfection in an Intense Reexposure Setting

BACKGROUND

Risk of reinfection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unknown. We assessed the risk and incidence rate of documented SARS-CoV-2 reinfection in a cohort of laboratory-confirmed cases in Qatar.

METHODS

All SARS-CoV-2 laboratory-confirmed cases with at least 1 polymerase chain reaction-positive swab that was ≥45 days after a first positive swab were individually investigated for evidence of reinfection. Viral genome sequencing of the paired first positive and reinfection viral specimens was conducted to confirm reinfection.

RESULTS

Out of 133 266 laboratory-confirmed SARS-CoV-2 cases, 243 persons (0.18%) had at least 1 subsequent positive swab ≥45 days after the first positive swab. Of these, 54 cases (22.2%) had strong or good evidence for reinfection. Median time between the first swab and reinfection swab was 64.5 days (range, 45-129). Twenty-three of the 54 cases (42.6%) were diagnosed at a health facility, suggesting presence of symptoms, while 31 (57.4%) were identified incidentally through random testing campaigns/surveys or contact tracing. Only 1 person was hospitalized at the time of reinfection but was discharged the next day. No deaths were recorded. Viral genome sequencing confirmed 4 reinfections of 12 cases with available genetic evidence. Reinfection risk was estimated at 0.02% (95% confidence interval [CI], .01%-.02%), and reinfection incidence rate was 0.36 (95% CI, .28-.47) per 10 000 person-weeks.

CONCLUSIONS

SARS-CoV-2 reinfection can occur but is a rare phenomenon suggestive of protective immunity against reinfection that lasts for at least a few months post primary infection.

SARS-CoV-2 (Covid-19) vaccines structure, mechanisms and effectiveness: A review

The world has been suffering from COVID-19 disease for more than a year, and it still has a high mortality rate. In addition to the need to minimize transmission of the virus through non-pharmacological measures such as the use of masks and social distance, many efforts are being made to develop a variety of vaccines to prevent the disease worldwide. So far, several vaccines have reached the final stages of safety and efficacy in various phases of clinical trials, and some, such as Moderna/NIAID and BioNTech/Pfizer, have reported very high safety and protection. The important point is that comparing different vaccines is not easy because there is no set standard for measuring neutralization. In this study, we have reviewed the common platforms of COVID-19 vaccines and tried to present the latest reports on the effectiveness of these vaccines.

SARS-CoV-2 infection hospitalization, severity, criticality, and fatality rates in Qatar

The SARS-CoV-2 pandemic resulted in considerable morbidity and mortality as well as severe economic and societal disruptions. Despite scientific progress, true infection severity, factoring both diagnosed and undiagnosed infections, remains poorly understood. This study aimed to estimate SARS-CoV-2 age-stratified and overall morbidity and mortality rates based on analysis of extensive epidemiological data for the pervasive epidemic in Qatar, a country where < 9% of the population are ≥ 50 years. We show that SARS-CoV-2 severity and fatality demonstrate a striking age dependence with low values for those aged < 50 years, but rapidly growing rates for those ≥ 50 years. Age dependence was particularly pronounced for infection criticality rate and infection fatality rate. With Qatar's young population, overall SARS-CoV-2 severity and fatality were not high with < 4 infections in every 1000 being severe or critical and < 2 in every 10,000 being fatal. Only 13 infections in every 1000 received any hospitalization in acute-care-unit beds and < 2 in every 1000 were hospitalized in intensive-care-unit beds. However, we show that these rates would have been much higher if Qatar's population had the demographic structure of Europe or the United States. Epidemic expansion in nations with young populations may lead to considerably lower disease burden than currently believed.

A Mathematical Model of COVID-19 with Vaccination and Treatment

We formulate and theoretically analyze a mathematical model of COVID-19 transmission mechanism incorporating vital dynamics of the disease and two key therapeutic measures-vaccination of susceptible individuals and recovery/treatment of infected individuals. Both the disease-free and endemic equilibrium are globally asymptotically stable when the effective reproduction number R 0(v) is, respectively, less or greater than unity. The derived critical vaccination threshold is dependent on the vaccine efficacy for disease eradication whenever R 0(v) > 1, even if vaccine coverage is high. Pontryagin's maximum principle is applied to establish the existence of the optimal control problem and to derive the necessary conditions to optimally mitigate the spread of the disease. The model is fitted with cumulative daily Senegal data, with a basic reproduction number R 0 = 1.31 at the onset of the epidemic. Simulation results suggest that despite the effectiveness of COVID-19 vaccination and treatment to mitigate the spread of COVID-19, when R 0(v) > 1, additional efforts such as nonpharmaceutical public health interventions should continue to be implemented. Using partial rank correlation coefficients and Latin hypercube sampling, sensitivity analysis is carried out to determine the relative importance of model parameters to disease transmission. Results shown graphically could help to inform the process of prioritizing public health intervention measures to be implemented and which model parameter to focus on in order to mitigate the spread of the disease. The effective contact rate b, the vaccine efficacy ε, the vaccination rate v, the fraction of exposed individuals who develop symptoms, and, respectively, the exit rates from the exposed and the asymptomatic classes σ and ϕ are the most impactful parameters.

Efficacy and landscape of Covid-19 vaccines: a review article

INTRODUCTION

The rapid advance of Coronavirus disease 2019 (Covid-19) has led to the incessant search for therapeutic and prophylactic measures to fight the pandemic. Because it is a viral infection, the safest long-term prophylactic form, in addition to social distance and hygiene, is the vaccine.

OBJECTIVE

Thus, this study aimed at conducting a review of the efficacy and landscape of Covid-19 vaccines.

METHODS

The following electronic databases were used MEDLINE via PubMed, SCIELO, LILACS, NEJM, and Clinical Trials. Our study includes the 7 vaccines (phase 3) that reported an efficacy rate for Covid-19, including characteristics inherent to each one of them.

RESULTS

Preliminary studies have shown that, although an efficacy ≥70% is necessary to eliminate the infection, a prophylactic vaccine with efficacy <70% will still have an important impact and can contribute to the elimination of the virus, provided that appropriate measures of social distancing remain.

CONCLUSIONS

The effectiveness of the vaccines obtained in this study varied between 50.38 and 95%, data that may represent a reduction in serious cases, hospitalizations, sequels, and deaths caused by Covid-19, respecting the panorama presented in this article.

A review of mathematical model-based scenario analysis and interventions for COVID-19

Mathematical model-based analysis has proven its potential as a critical tool in the battle against COVID-19 by enabling better understanding of the disease transmission dynamics, deeper analysis of the cost-effectiveness of various scenarios, and more accurate forecast of the trends with and without interventions. However, due to the outpouring of information and disparity between reported mathematical models, there exists a need for a more concise and unified discussion pertaining to the mathematical modeling of COVID-19 to overcome related skepticism. Towards this goal, this paper presents a review of mathematical model-based scenario analysis and interventions for COVID-19 with the main objectives of (1) including a brief overview of the existing reviews on mathematical models, (2) providing an integrated framework to unify models, (3) investigating various mitigation strategies and model parameters that reflect the effect of interventions, (4) discussing different mathematical models used to conduct scenario-based analysis, and (5) surveying active control methods used to combat COVID-19.

COVID-19 vaccines that reduce symptoms but do not block infection need higher coverage and faster rollout to achieve population impact

Trial results for two COVID-19 vaccines suggest at least 90% efficacy against symptomatic disease (VEDIS). It remains unknown whether this efficacy is mediated by lowering SARS-CoV-2 infection susceptibility (VESUSC) or development of symptoms after infection (VESYMP). We aim to assess and compare the population impact of vaccines with different efficacy profiles (VESYMP and VESUSC) satisfying licensure criteria. We developed a mathematical model of SARS-CoV-2 transmission, calibrated to data from King County, Washington. Rollout scenarios starting December 2020 were simulated with combinations of VESUSC and VESYMP resulting in up to 100% VEDIS. We assumed no reduction of infectivity upon infection conditional on presence of symptoms. Proportions of cumulative infections, hospitalizations and deaths prevented over 1 year from vaccination start are reported. Rollouts of 1 M vaccinations (5000 daily) using vaccines with 50% VEDIS are projected to prevent 23-46% of infections and 31-46% of deaths over 1 year. In comparison, vaccines with 90% VEDIS are projected to prevent 37-64% of infections and 46-64% of deaths over 1 year. In both cases, there is a greater reduction if VEDIS is mediated mostly by VESUSC. The use of a "symptom reducing" vaccine will require twice as many people vaccinated than a "susceptibility reducing" vaccine with the same 90% VEDIS to prevent 50% of the infections and death over 1 year. Delaying the start of the vaccination by 3 months decreases the expected population impact by more than 50%. Vaccines which prevent COVID-19 disease but not SARS-CoV-2 infection, and thereby shift symptomatic infections to asymptomatic infections, will prevent fewer infections and require larger and faster vaccination rollouts to have population impact, compared to vaccines that reduce susceptibility to infection. If uncontrolled transmission across the U.S. continues, then expected vaccination in Spring 2021 will provide only limited benefit.

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

Vaccines are thought to be the best available solution for controlling the ongoing SARS-CoV-2 pandemic. However, the emergence of vaccine-resistant strains may come too rapidly for current vaccine developments to alleviate the health, economic and social consequences of the pandemic. To quantify and characterize the risk of such a scenario, we created a SIR-derived model with initial stochastic dynamics of the vaccine-resistant strain to study the probability of its emergence and establishment. Using parameters realistically resembling SARS-CoV-2 transmission, we model a wave-like pattern of the pandemic and consider the impact of the rate of vaccination and the strength of non-pharmaceutical intervention measures on the probability of emergence of a resistant strain. As expected, we found that a fast rate of vaccination decreases the probability of emergence of a resistant strain. Counterintuitively, when a relaxation of non-pharmaceutical interventions happened at a time when most individuals of the population have already been vaccinated the probability of emergence of a resistant strain was greatly increased. Consequently, we show that a period of transmission reduction close to the end of the vaccination campaign can substantially reduce the probability of resistant strain establishment. Our results suggest that policymakers and individuals should consider maintaining non-pharmaceutical interventions and transmission-reducing behaviours throughout the entire vaccination period.

Progress and challenges in the comprehensive management of chronic viral hepatitis: Key ways to achieve the elimination

Chronic viral hepatitis is a significant health problem throughout the world, which already represents high annual mortality. By 2040, chronic viral hepatitis due to virus B and virus C and their complications cirrhosis and hepatocellular carcinoma will be more deadly than malaria, vitellogenesis-inhibiting hormone, and tuberculosis altogether. In this review, we analyze the global impact of chronic viral hepatitis with a focus on the most vulnerable groups, the goals set by the World Health Organization for the year 2030, and the key points to achieve them, such as timely access to antiviral treatment of direct-acting antiviral, which represents the key to achieving hepatitis C virus elimination. Likewise, we review the strategies to prevent transmission and achieve control of hepatitis B virus. Finally, we address the impact that the coronavirus disease 2019 pandemic has had on implementing elimination strategies and the advantages of implementing telemedicine programs.

Modelling SARS-CoV-2 unreported cases in Italy: Analysis of serological survey and vaccination scenarios

OBJECTIVES

Aim of the present paper is the study of the large unreported component, characterizing the SARS-CoV-2 epidemic event in Italy, taking advantage of the Istat survey. Particular attention is devoted to the sensitivity and specificity of the serological test and their effects.

METHODS

The model satisfactory reproduces the data of the Italian survey showing a relevant predictive power and relegating in a secondary position models which do not include, in the simulation, the presence of asymptomatic groups. The corrections due to the serological test sensitivity (in particular those ones depending on the symptoms onset) are crucial for a realistic analysis of the unreported (and asymptomatic) components.

RESULTS

The relevant presence of an unreported component during the second pandemic wave in Italy is confirmed and the ratio of reported to unreported cases is predicted to be roughly 1:4 in the last months of year 2020. A method to correct the serological data on the basis of the antibody sensitivity is suggested and systematically applied. The asymptomatic component is also studied in some detail and its amount quantified. A model analyses of the vaccination scenarios is performed confirming the relevance of a massive campaign (at least 80000 immunized per day) during the first six months of the year 2021, to obtain important immunization effects within August/September 2021.

COVID-19 optimal vaccination policies: A modeling study on efficacy, natural and vaccine-induced immunity responses

About a year into the pandemic, COVID-19 accumulates more than two million deaths worldwide. Despite non-pharmaceutical interventions such as social distance, mask-wearing, and restrictive lockdown, the daily confirmed cases remain growing. Vaccine developments from Pfizer, Moderna, and Gamaleya Institute reach more than 90% efficacy and sustain the vaccination campaigns in multiple countries. However, natural and vaccine-induced immunity responses remain poorly understood. There are great expectations, but the new SARS-CoV-2 variants demand to inquire if the vaccines will be highly protective or induce permanent immunity. Further, in the first quarter of 2021, vaccine supply is scarce. Consequently, some countries that are applying the Pfizer vaccine will delay its second required dose. Likewise, logistic supply, economic and political implications impose a set of grand challenges to develop vaccination policies. Therefore, health decision-makers require tools to evaluate hypothetical scenarios and evaluate admissible responses. Following some of the WHO-SAGE recommendations, we formulate an optimal control problem with mixed constraints to describe vaccination schedules. Our solution identifies vaccination policies that minimize the burden of COVID-19 quantified by the number of disability-adjusted years of life lost. These optimal policies ensure the vaccination coverage of a prescribed population fraction in a given time horizon and preserve hospitalization occupancy below a risk level. We explore "via simulation" plausible scenarios regarding efficacy, coverage, vaccine-induced, and natural immunity. Our simulations suggest that response regarding vaccine-induced immunity and reinfection periods would play a dominant role in mitigating COVID-19.

Modeling the Impact of COVID-19 Vaccination in Lebanon: A Call to Speed-Up Vaccine Roll Out

Four months into the SARS-CoV-2 vaccination campaign, only 10.7% of the Lebanese population have received at least one dose, raising serious concerns over the speed of vaccine roll-out and its impact in the event of a future surge. Using mathematical modeling, we assessed the short-term impact of various vaccine roll-out scenarios on SARS-CoV-2 epidemic course in Lebanon. At current population immunity levels, estimated by the model at 40% on 15 April 2021, a large epidemic wave is predicted if all social distancing restrictions are gradually eased and variants of concern are introduced. Reaching 80% vaccine coverage by the end of 2021 will flatten the epidemic curve and will result in a 37% and 34% decrease in the peak daily numbers of severe/critical disease cases and deaths, respectively; while reaching intermediate coverage of 40% will result in only a 10-11% decrease in each. Reaching 80% vaccine coverage by August would prevent twice as many severe/critical disease cases and deaths than if it were reached by December. Easing restrictions over a longer duration resulted in more favorable vaccination impact. In conclusion, for vaccination to have impact in the short-term, scale-up has to be rapid and reach high coverage (at least 70%), while sustaining social distancing measures during roll-out. At current vaccination pace, this is unlikely to be achieved. Concerted efforts need to be made to overcome local challenges and substantially scale up vaccination to avoid a surge that the country, with its multiple crises and limited health-care capacity, is largely unprepared for.

Herd Immunity against Severe Acute Respiratory Syndrome Coronavirus 2 Infection in 10 Communities, Qatar

We investigated what proportion of the population acquired severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whether the herd immunity threshold has been reached in 10 communities in Qatar. The study included 4,970 participants during June 21-September 9, 2020. Antibodies against SARS-CoV-2 were detected by using an electrochemiluminescence immunoassay. Seropositivity ranged from 54.9% (95% CI 50.2%-59.4%) to 83.8% (95% CI 79.1%-87.7%) across communities and showed a pooled mean of 66.1% (95% CI 61.5%-70.6%). A range of other epidemiologic measures indicated that active infection is rare, with limited if any sustainable infection transmission for clusters to occur. Only 5 infections were ever severe and 1 was critical in these young communities; infection severity rate of 0.2% (95% CI 0.1%-0.4%). Specific communities in Qatar have or nearly reached herd immunity for SARS-CoV-2 infection: 65%-70% of the population has been infected.

Controlling the pandemic during the SARS-CoV-2 vaccination rollout

There is a consensus that mass vaccination against SARS-CoV-2 will ultimately end the COVID-19 pandemic. However, it is not clear when and which control measures can be relaxed during the rollout of vaccination programmes. We investigate relaxation scenarios using an age-structured transmission model that has been fitted to age-specific seroprevalence data, hospital admissions, and projected vaccination coverage for Portugal. Our analyses suggest that the pressing need to restart socioeconomic activities could lead to new pandemic waves, and that substantial control efforts prove necessary throughout 2021. Using knowledge on control measures introduced in 2020, we anticipate that relaxing measures completely or to the extent as in autumn 2020 could launch a wave starting in April 2021. Additional waves could be prevented altogether if measures are relaxed as in summer 2020 or in a step-wise manner throughout 2021. We discuss at which point the control of COVID-19 would be achieved for each scenario.

Potential impact of introducing vaccines against COVID-19 under supply and uptake constraints in France: A modelling study

BACKGROUND

The accelerated vaccine development in response to the COVID-19 pandemic should lead to a vaccine being available early 2021, albeit in limited supply and possibly without full vaccine acceptance. We assessed the short-term impact of a COVID-19 immunization program with varying constraints on population health and non-pharmaceutical interventions (NPIs) needs.

METHODS

A SARS-CoV-2 transmission model was calibrated to French epidemiological data. We defined several vaccine implementation scenarios starting in January 2021 based on timing of discontinuation of NPIs, supply and uptake constraints, and their relaxation. We assessed the number of COVID-19 hospitalizations averted, the need for and number of days with NPIs in place over the 2021-2022 period.

RESULTS

An immunisation program under constraints could reduce the burden of COVID-19 hospitalizations by 9-40% if the vaccine prevents against infections. Relaxation of constraints not only reduces further COVID-19 hospitalizations (30-39% incremental reduction), it also allows for NPIs to be discontinued post-2021 (0 days with NPIs in 2022 versus 11 to 125 days for vaccination programs under constraints and 327 in the absence of vaccination).

CONCLUSION

For 2021, COVID-19 control is expected to rely on a combination of NPIs and the outcome of early immunisation programs. The ability to overcome supply and uptake constraints will help prevent the need for further NPIs post-2021. As the programs expand, efficiency assessments will be needed to ensure optimisation of control policies post-emergency use.