Background Following widespread exposure to Omicron variants, COVID-19 has transitioned to endemic cir- culation. Populations now have diverse infection and vaccination histories, resulting in heterogeneous immune landscapes. Careful consideration of vaccination is required through the post-Omicron phase of COVID-19 management to minimise disease burden. We assess the impact and cost-effectiveness of targeted COVID-19 vaccination strategies to support global vaccination recommendations. Methods We integrated immunological, transmission, clinical and cost-effectiveness models, and simulated populations with different characteristics and immune landscapes. We calculated the expected number of infections, hospitalisations and deaths for different vaccine scenarios. Costs (from a healthcare perspective) were estimated for exemplar country income level groupings in the Western Pacific Region. Results are reported as incremental costs and disability-adjusted life years averted compared to no additional vaccination. Parameter and stochastic uncertainty were captured through scenario and sensitivity analysis. Findings Across different population demographics and income levels, we consistently found that annual elder-targeted boosting strategies are most likely to be cost-effective or cost-saving, while paediatric programs are unlikely to be cost-effective. Results remained consistent while accounting for uncertain- ties in the epidemiological and economic models. Half-yearly boosting may only be cost-effective in higher income settings with older population demographics and higher cost-effectiveness thresholds. Interpretation These results demonstrate the value of continued booster vaccinations to protect against severe COVID-19 disease outcomes across high and middle-income settings and show that the biggest health gains relative to vaccine costs are achieved by targeting older age-groups. Funding Funded by the World Health Organization.

Cost-effective boosting allocations in the post-Omicron era of COVID-19 management

Since the emergence of SARS-CoV-2 (COVID-19), there have been multiple waves of infection and multiple rounds of vaccination rollouts. Both prior infection and vaccination can prevent future infection and reduce severity of outcomes, combining to form hybrid immunity against COVID-19 at the individual and population level. Here, we explore how different combinations of hybrid immunity affect the size and severity of near-future Omicron waves. To investigate the role of hybrid immunity, we use an agent-based model of COVID-19 transmission with waning immunity to simulate outbreaks in populations with varied past attack rates and past vaccine coverages, basing the demographics and past histories on the World Health Organization (WHO) Western Pacific Region (WPR). We find that if the past infection immunity is high but vaccination levels are low, then the secondary outbreak with the same variant can occur within a few months after the first outbreak; meanwhile, high vaccination levels can suppress near-term outbreaks and delay the second wave. Additionally, hybrid immunity has limited impact on future COVID-19 waves with immune-escape variants. Enhanced understanding of the interplay between infection and vaccine exposure can aid anticipation of future epidemic activity due to current and emergent variants, including the likely impact of responsive vaccine interventions.

/pdf/modelling-the-impact-of-hybrid-immunity-on-future-covid-19-ff841fia.pdf

Modelling the impact of hybrid immunity on future COVID-19 epidemic waves

Abstract Background Since the emergence of SARS-CoV-2 (COVID-19), there have been multiple waves of infection and multiple rounds of vaccination rollouts. Both prior infection and vaccination can prevent future infection and reduce severity of outcomes, combining to form hybrid immunity against COVID-19 at the individual and population level. Here, we explore how different combinations of hybrid immunity affect the size and severity of near-future Omicron waves. Methods To investigate the role of hybrid immunity, we use an agent-based model of COVID-19 transmission with waning immunity to simulate outbreaks in populations with varied past attack rates and past vaccine coverages, basing the demographics and past histories on the World Health Organization Western Pacific Region. Results We find that if the past infection immunity is high but vaccination levels are low, then the secondary outbreak with the same variant can occur within a few months after the first outbreak; meanwhile, high vaccination levels can suppress near-term outbreaks and delay the second wave. Additionally, hybrid immunity has limited impact on future COVID-19 waves with immune-escape variants. Conclusions Enhanced understanding of the interplay between infection and vaccine exposure can aid anticipation of future epidemic activity due to current and emergent variants, including the likely impact of responsive vaccine interventions. 

pdf/modelling-the-impact-of-hybrid-immunity-on-future-covid-19-4o3wijtz0p.pdf

Interventions to mitigate the spread of infectious diseases, while succeeding in their goal, have economic and social costs associated with them. These limit the duration and intensity of the interventions. We study a class of interventions which reduce the reproduction number and find the optimal strength of the intervention which minimises the attack rate of an immunity inducing infection. The intervention works by eliminating the overshoot part of an epidemic, and avoids a second-wave of infections. We extend the framework by considering a heterogeneous population and find that the optimal intervention can pose an ethical dilemma for decision and policy makers. This ethical dilemma is shown to be analogous to the trolley problem and we discuss how the dilemma can be avoided.

/pdf/which-lockdowns-are-the-best-lockdowns-optimal-interventions-2ccpxcvw.pdf

Which lockdowns are the best lockdowns? Optimal interventions and the trolley problem in heterogeneous populations

We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of "what-if" scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity. As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10K CPU cores. Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4K to 28.2K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2K to 29.9K (a 6% improvement) nationwide. 

Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread

https://doi.org/10.1016/j.idm.2023.05.003

Understanding the impact of disease and vaccine mechanisms on the importance of optimal vaccine allocation

Early case detection is critical to preventing onward transmission of COVID-19 by enabling prompt isolation of index infections, and identification and quarantining of contacts. Timeliness and completeness of ascertainment depend on the surveillance strategy employed. We use rapid prototype modelling to quickly investigate the effectiveness of testing strategies, to aid decision making. Models are developed with a focus on providing relevant results to policy makers, and these models are continually updated and improved as new questions are posed. The implementation of testing strategies in high risk settings in Australia was supported using models to explore the effects of test frequency and sensitivity on outbreak detection. An exponential growth model is firstly used to demonstrate how outbreak detection changes with varying growth rate, test frequency and sensitivity. From this model we see that low sensitivity tests can be compensated for by high frequency testing. This model is then updated to an Agent Based Model, which was used to test the robustness of the results from the exponential model, and to extend it to include intermittent workplace scheduling. These models help our fundamental understanding of disease detectability through routine surveillance in workplaces and evaluate the impact of testing strategies and workplace characteristics on the effectiveness of surveillance. This analysis highlights the risks of particular work patterns while also identifying key testing strategies to best improve outbreak detection in high risk workplaces.

/pdf/rapid-prototyping-of-models-for-covid-19-outbreak-detection-19j61cjg.pdf

Rapid prototyping of models for COVID-19 outbreak detection in workplaces

Estimating the impact of invasive pests and diseases on ecosystem services: modelling carbon sequestration loss due to myrtle rust (Austropuccinia psidii exotic strains) in Australia

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