Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2

Impact of Vaccination and Nonpharmaceutical Interventions With Possible COVID-19 Viral Evolutions Using an Agent-Based Simulation

Introduction

The COVID-19 pandemic continues with highly contagious variants and waning immunity. As the virus keeps evolving to be more infectious and immune evasive, some question whether the COVID-19 pandemic can be managed through sustainable public health measures.

Methods

We developed an agent-based simulation to explore the impact of COVID-19 mutations, periodic vaccinations, and nonpharmaceutical interventions on reducing COVID-19 deaths. The model is calibrated to the greater Seattle area by observing local epidemic data. We perform scenario analyses on viral mutations that change infectiousness, disease severity, and immune evasiveness from previous infections and vaccination every 6 months. The simulation is run until the end of year 2023.

Results

Variants with increased infectivity or increased immune evasion dominate previous strains. With enhanced immune protection from a pancoronavirus vaccine, the most optimistic periodic vaccination rate reduces average total deaths by 44.6% compared with the most pessimistic periodic vaccination rate. A strict threshold nonpharmaceutical intervention policy reduces average total deaths by 71.3% compared with an open society, whereas a moderate nonpharmaceutical intervention policy results in a 33.6% reduction.

Conclusions

Our findings highlight the potential benefits of pancoronavirus vaccines that offer enhanced and longer-lasting immunity. We emphasize the crucial role of nonpharmaceutical interventions in reducing COVID-19 deaths regardless of virus mutation scenarios. Owing to highly immune evasive and contagious SARS-CoV-2 variants, most scenarios in this study fail to reduce the mortality of COVID-19 to the level of influenza and pneumonia. However, our findings indicate that periodic vaccinations and a threshold nonpharmaceutical intervention policy may succeed in achieving this goal. This indicates the need for caution and vigilance in managing a continuing COVID-19 epidemic.

Model-informed health and socio-economic benefits of enhancing global equity and access to Covid-19 vaccines

We take a model-informed approach to the view that a global equitable access (GEA) to Covid-19 vaccines is the key to bring this pandemic to an end. We show that the equitable redistribution (proportional to population size) of the currently available vaccines is not sufficient to stop the pandemic, whereas a 60% increase in vaccine access (the global share of vaccinated people) would have allowed the current distribution to stop the pandemic in about a year of vaccination, saving millions of people in poor countries. We then investigate the interplay between access to vaccines and their distribution among rich and poor countries, showing that the access increase to stop the pandemic gets minimized at + 32% by the equitable distribution (- 36% in rich countries and + 60% in poor ones). To estimate the socio-economic benefits of a vaccination campaign with enhanced global equity and access (eGEA), we compare calibrated simulations of the current scenario with a hypothetical, vaccination-intensive scenario that assumes high rollouts (shown however by many rich and poor countries during the 2021-2022 vaccination campaign) and an improved equity from the current 2.5:1 to a 2:1 rich/poor-ratio of the population fractions vaccinated per day. Assuming that the corresponding + 130% of vaccine production is made possible by an Intellectual Property waiver, we show that the money saved on vaccines globally by the selected eGEA scenario overcomes the 5-year profit of the rights holders in the current situation. This justifies compensation mechanisms in exchange for the necessary licensing agreements. The good news is that the benefits of this eGEA scenario are still relevant, were we ready to implement it now.

Using the precaution adoption process model to understand decision-making about the COVID-19 booster vaccine in England

BACKGROUND

COVID-19 continues to pose a threat to public health. Booster vaccine programmes are critical to maintain population-level immunity. Stage theory models of health behaviour can help our understanding of vaccine decision-making in the context of perceived threats of COVID-19.

PURPOSE

To use the Precaution Adoption Process Model (PAPM) to understand decision-making about the COVID-19 booster vaccine (CBV) in England.

METHODS

An online, cross-sectional survey informed by the PAPM, the extended Theory of Planned Behaviour and Health Belief Model administered to people over the age of 50 residing in England, UK in October 2021. A multivariate, multinomial logistic regression model was used to examine associations with the different stages of CBV decision-making.

RESULTS

Of the total 2,004 participants: 135 (6.7%) were unengaged with the CBV programme; 262 (13.1%) were undecided as to whether to have a CBV; 31 (1.5%) had decided not to have a CBV; 1,415 (70.6%) had decided to have a CBV; and 161 (8.0%) had already had their CBV. Being unengaged was positively associated with beliefs in their immune system to protect against COVID-19, being employed, and low household income; and negatively associated with CBV knowledge, a positive COVID-19 vaccine experience, subjective norms, anticipated regret of not having a CBV, and higher academic qualifications. Being undecided was positively associated with beliefs in their immune system and having previously received the Oxford/AstraZeneca (as opposed to Pfizer/BioNTech) vaccine; and negatively associated with CBV knowledge, positive attitudes regarding CBV, a positive COVID-19 vaccine experience, anticipated regret of not having a CBV, white British ethnicity, and living in East Midlands (vs London).

CONCLUSIONS

Public health interventions promoting CBV may improve uptake through tailored messaging directed towards the specific decision stage relating to having a COVID-19 booster.

Qualitative Conceptual Content Analysis of COVID-19 Vaccine Administration Error Inquiries

The launch of the COVID-19 vaccination program was the largest vaccination campaign in U.S. history, with an unprecedented demand for vaccine and new vaccination providers, warranting significant education and communication efforts. NIP-INFO (nipinfo@cdc.gov) is the Centers for Disease Control and Prevention's (CDC's) immunization inquiry response service, and it receives inquiries for COVID-19 and routine non-COVID vaccines. A qualitative analysis of NIP-INFO's content was performed to better characterize and understand some of the knowledge gaps and reasons that COVID-19 vaccine administration errors occur. A total of 734 COVID-19 vaccine administration error inquiries were received between January 2021 and April 2022. The most frequent inquiries related to storage (n = 191; 26.0%), incorrect dosage or product (n = 190; 25.9%), unauthorized age group (n = 108; 14.7%), and schedule (n = 105; 14.3%). Training and communication strategies are imperative to ensure proper vaccine administration and build and maintain vaccine confidence.

Persistence of the Omicron variant of SARS-CoV-2 in Australia: The impact of fluctuating social distancing

We modelled emergence and spread of the Omicron variant of SARS-CoV-2 in Australia between December 2021 and June 2022. This pandemic stage exhibited a diverse epidemiological profile with emergence of co-circulating sub-lineages of Omicron, further complicated by differences in social distancing behaviour which varied over time. Our study delineated distinct phases of the Omicron-associated pandemic stage, and retrospectively quantified the adoption of social distancing measures, fluctuating over different time periods in response to the observable incidence dynamics. We also modelled the corresponding disease burden, in terms of hospitalisations, intensive care unit occupancy, and mortality. Supported by good agreement between simulated and actual health data, our study revealed that the nonlinear dynamics observed in the daily incidence and disease burden were determined not only by introduction of sub-lineages of Omicron, but also by the fluctuating adoption of social distancing measures. Our high-resolution model can be used in design and evaluation of public health interventions during future crises.

Technical challenges of modelling real-life epidemics and examples of overcoming these

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the importance of mathematical modelling in informing and advising policy decision-making. Effective practice of mathematical modelling has challenges. These can be around the technical modelling framework and how different techniques are combined, the appropriate use of mathematical formalisms or computational languages to accurately capture the intended mechanism or process being studied, in transparency and robustness of models and numerical code, in simulating the appropriate scenarios via explicitly identifying underlying assumptions about the process in nature and simplifying approximations to facilitate modelling, in correctly quantifying the uncertainty of the model parameters and projections, in taking into account the variable quality of data sources, and applying established software engineering practices to avoid duplication of effort and ensure reproducibility of numerical results. Via a collection of 16 technical papers, this special issue aims to address some of these challenges alongside showcasing the usefulness of modelling as applied in this pandemic. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs-a series of ideas, approaches and methods taken from existing visualization research and practice-deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs-a series of ideas, approaches and methods taken from existing visualization research and practice-deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.