Assessing Vaccination Prioritization Strategies for COVID-19 in South Africa Based on Age-Specific Compartment Model

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.

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.

LSDV-Vectored SARS-CoV-2 S and N Vaccine Protects against Severe Clinical Disease in Hamsters

The SARS-CoV-2 pandemic demonstrated the need for potent and broad-spectrum vaccines. This study reports the development and testing of a lumpy skin disease virus (LSDV)-vectored vaccine against SARS-CoV-2, utilizing stabilized spike and conserved nucleocapsid proteins as antigens to develop robust immunogenicity. Construction of the vaccine (LSDV-SARS2-S,N) was confirmed by polymerase chain reaction (PCR) amplification and sequencing. In vitro characterization confirmed that cells infected with LSDV-SARS2-S,N expressed SARS-CoV-2 spike and nucleocapsid protein. In BALB/c mice, the vaccine elicited high magnitude IFN-γ ELISpot responses (spike: 2808 SFU/10⁶ splenocytes) and neutralizing antibodies (ID₅₀ = 6552). Testing in hamsters, which emulate human COVID-19 disease progression, showed the development of high titers of neutralizing antibodies against the Wuhan and Delta SARS-CoV-2 variants (Wuhan ID₅₀ = 2905; Delta ID₅₀ = 4648). Additionally, hamsters vaccinated with LSDV-SARS2-S,N displayed significantly less weight loss, lung damage, and reduced viral RNA copies following SARS-CoV-2 infection with the Delta variant as compared to controls, demonstrating protection against disease. These data demonstrate that LSDV-vectored vaccines display promise as an effective SARS-CoV-2 vaccine and as a potential vaccine platform for communicable diseases in humans and animals. Further efficacy testing and immune response analysis, particularly in non-human primates, are warranted.

Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA

In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.

Assessing the Impact of Vaccination on the Dynamics of COVID-19 in Africa: A Mathematical Modeling Study

Several effective COVID-19 vaccines are administered to combat the COVID-19 pandemic globally. In most African countries, there is a comparatively limited deployment of vaccination programs. In this work, we develop a mathematical compartmental model to assess the impact of vaccination programs on curtailing the burden of COVID-19 in eight African countries considering SARS-CoV-2 cumulative case data for each country for the third wave of the COVID-19 pandemic. The model stratifies the total population into two subgroups based on individual vaccination status. We use the detection and death rates ratios between vaccinated and unvaccinated individuals to quantify the vaccine's effectiveness in reducing new COVID-19 infections and death, respectively. Additionally, we perform a numerical sensitivity analysis to assess the combined impact of vaccination and reduction in the SARS-CoV-2 transmission due to control measures on the control reproduction number (Rc). Our results reveal that on average, at least 60% of the population in each considered African country should be vaccinated to curtail the pandemic (lower the Rc below one). Moreover, lower values of Rc are possible even when there is a low (10%) or moderate (30%) reduction in the SARS-CoV-2 transmission rate due to NPIs. Combining vaccination programs with various levels of reduction in the transmission rate due to NPI aids in curtailing the pandemic. Additionally, this study shows that vaccination significantly reduces the severity of the disease and death rates despite low efficacy against COVID-19 infections. The African governments need to design vaccination strategies that increase vaccine uptake, such as an incentive-based approach.

Does pre-COVID impulsive behaviour predict adherence to hygiene and social distancing measures in youths following the COVID-19 pandemic onset? Evidence from a South African longitudinal study

BACKGROUND

Engagement in protective behaviours relating to the COVID-19 pandemic has been proposed to be key to infection control. This is particularly the case for youths as key drivers of infections. A range of factors influencing adherence have been identified, including impulsivity and risk taking. We assessed the association between pre-COVID impulsivity levels and engagement in preventative measures during the COVID-19 pandemic in a longitudinal South African sample, in order to inform future pandemic planning.

METHODS

Data were collected from N = 214 youths (mean age at baseline: M = 17.81 (SD = .71), 55.6% female) living in a South African peri-urban settlement characterised by high poverty and deprivation. Baseline assessments were taken in 2018/19 and the COVID follow-up was conducted in June-October 2020 via remote data collection. Impulsivity was assessed using the Balloon Analogue Task (BART), while hygiene and social distancing behaviours were captured through self-report. Stepwise hierarchical regression analyses were performed to estimate effects of impulsivity on measure adherence.

RESULTS

Self-rated engagement in hygiene behaviours was high (67.1-86.1% "most of the time", except for "coughing/sneezing into one's elbow" at 33.3%), while engagement in social distancing behaviours varied (22.4-57.8% "most of the time"). Higher impulsivity predicted lower levels of hygiene (β = .14, p = .041) but not social distancing behaviours (β = -.02, p = .82). This association was retained when controlling for a range of demographic and COVID-related factors (β = .14, p = .047) and was slightly reduced when including the effects of a life-skills interventions on hygiene behaviour (β = -.13, p = .073).

CONCLUSIONS

Our data indicate that impulsivity may predict adolescent engagement in hygiene behaviours post COVID-19 pandemic onset in a high risk, sub-Saharan African setting, albeit with a small effect size. For future pandemics, it is important to understand predictors of engagement, particularly in the context of adversity, where adherence may be challenging. Limitations include a small sample size and potential measure shortcomings.

Beyond beauty: A qualitative exploration of authenticity and its impacts on Chinese consumers' purchase intention in live commerce

Live commerce is a phenomenally innovative form of social commerce in China. In this paper, the authors aim to explore the authenticity of live commerce. By employing a qualitative approach using in-depth interviews and grounded theory, 21 initial categories are classified into six core categories. Among them, authenticity-associated concepts are classified into explicit concepts and implicit concepts. Explicit concepts of authenticity are associated with objectively authentic cues, while implicit concepts of authenticity are associated with subjectively authentic experiences. Moreover, the study explores the relationship between explicit concepts of authenticity and product commitment, as well as the relationship between implicit concepts of authenticity and affective commitment. Both of these paths are found to influence consumers' shopping-related behaviors. Although consumers can more easily perceive explicitly authentic cues than implicitly authentic experiences, this study suggests that the latter may be more effective in inducing shopping behaviors. In addition, the effect of streamer attractiveness on opinion leader building is addressed, while authenticity is found to be an alternative approach to attract consumers both for attractive and nonattractive streamers. Finally, the study addresses theoretical implications and practical implications as well as suggestions for future research.

Usage of Compartmental Models in Predicting COVID-19 Outbreaks

Accurately predicting the spread of the SARS-CoV-2, the cause of the COVID-19 pandemic, is of great value for global regulatory authorities to overcome a number of challenges including medication shortage, outcome of vaccination, and control strategies planning. Modeling methods that are used to simulate and predict the spread of COVID-19 include compartmental model, structured metapopulations, agent-based networks, deep learning, and complex network, with compartmental modeling as one of the most widely used methods. Compartmental model has two noteworthy features, a flexible framework that allows users to easily customize the model structure and its high adaptivity that allows well-matured approaches (e.g., Bayesian inference and mixed-effects modeling) to improve parameter estimation. We retrospectively evaluated the prediction performances of the compartmental models on the CDC COVID-19 Mathematical Modeling webpage based on data collected between August 2020 and February 2021, and subsequently discussed in detail their corresponding model enhancement. Finally, we presented examples using the compartmental models to assist policymaking. By evaluating all models in parallel, we systemically evaluated the performance and evolution of using compartmental models for COVID-19 pandemic prediction. In summary, as a 100-year-old epidemic approach, the compartmental model presents a powerful tool that is extremely adaptive and can be readily customized and implemented to address new data or emerging needs during a pandemic.