Vaccine Allocation Strategies Research Articles

Optimal Allocation of Multi-Type Vaccines in a Two-Dose Vaccination Campaign for Epidemic Control: A Case Study of COVID-19

As a typical case of the optimal planning for the provision of restricted medical resources, widespread vaccination is considered an effective and sustainable way to prevent and control large-scale novel coronavirus disease 2019 (COVID-19) outbreaks. However, an initial supply shortage of vaccines is inevitable because of the narrow production and logistical capacity. This work focuses on the multi-type vaccine resource allocation problem in a two-dose vaccination campaign under limited supply. To address this issue, we extended an age-stratified susceptible, exposed, infectious, and recovered (SEIR) epidemiological model to incorporate a two-dose vaccination campaign involving multiple vaccine types to fully characterize the various stages of infection and vaccination. Afterward, we integrated the proposed epidemiological model into a nonlinear programming (NLP) model to determine the optimal allocation strategy under supply capacity and vaccine hesitancy constraints with the goal of minimizing the cumulative number of deaths due to the pandemic over the entire planning horizon. A case study based on real-world data from the initial mass vaccination campaign against COVID-19 in the Midlands, England, was taken to validate the applicability of our model. Then, we performed a comparative study to demonstrate the performance of the proposed method and conducted an extensive sensitivity analysis on critical model parameters. Our results indicate that prioritizing the allocation of vaccines to elderly persons is an effective strategy for reducing COVID-19-related fatalities. Furthermore, we found that vaccination alone will not be sufficient for epidemic control in the short term, and appropriate non-pharmacological interventions are still important for effective viral containment during the initial vaccine rollout. The results also showed that the relative efficacy of the first dose is a vital factor affecting the optimal interval between doses. It is always best to complete the two-dose vaccination schedule as soon as possible when the relative efficacy of the first dose is low. Conversely, delaying the second dose of a vaccine as long as possible to increase the proportion of the population vaccinated with a single dose tends to be more favorable when the relative efficacy of the first dose is high. Finally, our proposed model is general and easily extendable to the study of other infectious disease outbreaks and provides important implications for public health authorities seeking to develop effective vaccine allocation strategies for tackling possible future pandemics.

Estimating measures to reduce the transmission of SARS-CoV-2 in Australia to guide a ‘National Plan’ to reopening

The availability of COVID-19 vaccines promised a reduction in the severity of disease and relief from the strict public health and social measures (PHSMs) imposed in many countries to limit spread and burden of COVID-19. We were asked to define vaccine coverage thresholds for Australia’s transition to easing restrictions and reopening international borders. Using evidence of vaccine effectiveness against the then-circulating Delta variant, we used a mathematical model to determine coverage targets. The absence of any COVID-19 infections in many sub-national jurisdictions in Australia posed particular methodological challenges. We used a novel metric called Transmission Potential (TP) as a proxy measure of the population-level effective reproduction number. We estimated TP of the Delta variant under a range of PHSMs, test-trace-isolate-quarantine (TTIQ) efficiencies, vaccination coverage thresholds, and age-based vaccine allocation strategies. We found that high coverage across all ages (≥70%) combined with ongoing TTIQ and minimal PHSMs was sufficient to avoid lockdowns. At lesser coverage (≤60%) rapid case escalation risked overwhelming of the health sector or the need to reimpose stricter restrictions. Maintaining low case numbers was most beneficial for health and the economy, and at higher coverage levels (≥80%) further easing of restrictions was deemed possible. These results directly informed easing of COVID-19 restrictions in Australia.

The effective reproduction number: Convexity, concavity and invariance

Motivated by the question of optimal vaccine allocation strategies in heterogeneous population for epidemic models, we study various properties of the effective reproduction number . In the simplest case, given a fixed non-negative matrix K , this corresponds mathematically to the study of the spectral radius R_{e}(\eta) of the matrix product \mathrm{Diag}(\eta)K , as a function of \eta \in \mathbb{R}_{+}^{n} . The matrix K and the vector \eta can be interpreted as a next-generation operator and a vaccination strategy. This can be generalized in an infinite-dimensional case where the matrix K is replaced by a positive integral compact operator, which is composed with a multiplication by a non-negative function \eta . We give sufficient conditions for the function R_{e} to be convex or a concave. Eventually, we provide equivalence properties on models which ensure that the function R_{e} is unchanged.

Parity of esteem: A global COVID-19 vaccination approach for people with mental illnesses, based on facts from 34 countries; recommendations and solutions

Background: The coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has detrimental effects on physical and mental health. Patients with severe mental illness are at higher risk of contracting the virus due to social determinants of health. Vulnerable populations include the elderly, people with pre-existing conditions, and those exposed to SARS-CoV-2. Unfortunately, only a few countries have updated vaccination strategies to prioritize patients with mental illnesses. Therefore, we aimed to explore whether individuals with mental disorders are prioritized in vaccine allocation strategies in different world regions. They are often neglected in policymaking but are highly vulnerable to the threatening complications of COVID-19. Methods: A questionnaire was developed to record details regarding COVID-19 vaccination and prioritizations for groups of persons with non-communicable diseases (NCDs), mental disorders, and substance use disorders (SUDs). NCDs were defined according to the WHO as chronic diseases that are the result of a combination of genetic, physiological, environmental, and behavioral factors such as cardiovascular diseases, cancer, respiratory diseases, and diabetes. Results: Most countries surveyed (80%) reported healthcare delivery via a nationalized health service. It was found that 82% of the countries had set up advisory groups, but only 26% included a mental health professional. Most frequently, malignancy (68%) was prioritized followed by diabetes type 2 (62%) and type 1 (59%). Only nine countries (26%) prioritized mental health conditions. Conclusion: The spread of the coronavirus has exposed both the strengths and flaws of our healthcare systems. The most vulnerable groups suffered the most and were hit first and faced most challenges. These findings raise awareness that patients with mental illnesses have been overlooked in immunization campaigns. The range of their mortality, morbidity, and quality of life could have widened due to this delay.

Evaluation of vaccine rollout strategies for emerging infectious diseases: A model-based approach including protection attitudes

Vaccine allocation strategies become crucial during vaccine shortages, especially in the face of potential outbreaks of new infectious diseases, as witnessed during the COVID-19 pandemic. To address this, a specialized compartmental model is created, which simulates an emerging infectious disease similar to COVID-19. This model divides the population into different age groups and is used to compare various vaccine prioritisation approaches, aiming to minimize the total number of fatalities. The model is an improvement upon previous ones as it incorporates essential behavioural factors and is adapted to account for the protective effects of vaccination against both disease infection and transmission. It takes into account human behaviors such as mask-wearing and social distancing by utilizing specific parameters related to self-protection, awareness levels, and the frequency of daily person-to-person interactions within each age group. Furthermore, a novel method for dynamic vaccine prioritisation was introduced in this study. This approach is model-independent and relies on the dynamic R number. It is the first time such a method has been developed, offering a decision-making approach that is not tied to any specific model. This innovation provides a flexible and adaptable strategy for determining vaccine priorities based on real-time data and the current state of the outbreak.Our findings reveal crucial insights into vaccine allocation strategies. When the daily rollout rates are fast (0.75% or higher) and children are eligible for vaccination, prioritising groups with high daily person-to-person interactions can lead to substantial reductions in total fatalities (up to approximately 40% lower). On the other hand, if rollout rates are slower and overall vaccination coverage is high, focusing on vaccinating elders emerges as the most effective strategy, resulting in up to approximately 10% fewer fatalities. However, the scenario changes significantly when children are not eligible for vaccination, as they constitute a highly interactive population group. In this case, the differences between priority strategies become smaller. With fast daily rollout rates, prioritisation based on interactions achieves only a 7% reduction in total fatalities, while a slower rollout with vaccination of elders first leads to an approximately 11% reduction in fatalities compared to the scenario where children are eligible for vaccination. The impact of behavioural parameters is equally critical. When the self-protection levels exercised by the population are low, it significantly affects the optimal vaccine prioritisation strategy to be followed, making it essential to consider behavioural factors in decision-making.

Robust COVID-19 vaccination control in a multi-city dynamic transmission network: A novel reinforcement learning-based approach

Vaccines are a highly effective intervention in mitigating the COVID-19 pandemic, but with limited resources, an optimal vaccine allocation plan is essential for reducing the number of infections. However, most previous studies on vaccine allocation strategies have neglected the fact that the real-world virus transmission environment is a network structure with dynamically changing flows between cities. To address this, we propose a Multi-City Network Vaccination Model that incorporates a stochastic daily multi-city virus transmission network to simulate a more realistic vaccination environment. We also present a novel reinforcement learning approach based on Proximal Policy Optimization (PPO) to allocate vaccines in our Multi-City Network Vaccination Model. Our PPO-based dynamic vaccine allocation approach reduces peak infections by 8% and is more robust than two other heuristic approaches. Our framework provides a valuable tool for regional and national authorities to make better public health decisions during a pandemic.

COVID-19: Agent-based simulation-optimization to vaccine center location vaccine allocation problem

This article presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location–allocation Mixed-Integer Programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics.

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

Vaccination is an important epidemic intervention strategy. However, it is generally unclear how the outcomes of different vaccine strategies change depending on population characteristics, vaccine mechanisms and allocation objective. In this paper we develop a conceptual mathematical model to simulate strategies for pre-epidemic vaccination. We extend the SEIR model to incorporate a range of vaccine mechanisms and disease characteristics. We then compare the outcomes of optimal and suboptimal vaccination strategies for three public health objectives (total infections, total symptomatic infections and total deaths) using numerical optimisation. Our comparison shows that the difference in outcomes between vaccinating optimally and suboptimally depends on vaccine mechanisms, disease characteristics, and objective considered. Our modelling finds vaccines that impact transmission produce better outcomes as transmission is reduced for all strategies. For vaccines that impact the likelihood of symptomatic disease or dying due to infection, the improvement in outcome as we decrease these variables is dependent on the strategy implemented. Through a principled model-based process, this work highlights the importance of designing effective vaccine allocation strategies. We conclude that efficient allocation of resources can be just as crucial to the success of a vaccination strategy as the vaccine effectiveness and/or amount of vaccines available.

Evaluating vaccine allocation strategies using simulation-assisted causal modeling

We develop a model to retrospectively evaluate age-dependent counterfactual vaccine allocation strategies against the coronavirus disease 2019 (COVID-19) pandemic. To estimate the effect of allocation on the expected severe-case incidence, we employ a simulation-assisted causal modeling approach that combines a compartmental infection-dynamics simulation, a coarse-grained causal model, and literature estimates for immunity waning. We compare Israel's strategy, implemented in 2021, with counterfactual strategies such as no prioritization, prioritization of younger age groups, or a strict risk-ranked approach; we find that Israel's implemented strategy was indeed highly effective. We also study the impact of increasing vaccine uptake for given age groups. Because of its modular structure, our model can easily be adapted to study future pandemics. We demonstrate this by simulating a pandemic with characteristics of the Spanish flu. Our approach helps evaluate vaccination strategies under the complex interplay of core epidemic factors, including age-dependent risk profiles, immunity waning, vaccine availability, and spreading rates.

Vaccination against COVID-19 and Clinical correlates among a population of psychiatric outpatients

IntroductionPatients suffering from psychiatric disorders represent a population that is particularly at risk of COVID-19-related morbidity and mortality. Vaccination was the most effective strategy to prevent the severe forms of the disease.ObjectivesWe aimed in our study to determine the rate of COVID-19 vaccination and to identify its correlated factors in psychiatric outpatients.MethodsThis is a descriptive and analytical cross-sectional study conducted on 178 outpatients at the department of psychiatry (Monastir, Tunisia) over a period of one month (from March 2022 to April 2022). Data was collected via a questionnaire focused on two main attributes: (1) sociodemographic and clinical characteristics; (2) questions about the flu vaccination history and its modalities.ResultsThe mean age of our patients was 44.9±13.7 years. The majority of them (81.5%) had a chronic evolution of their psychiatric disorder (> 2 years). Psychosis was the most represented disorder with 57.3% compared to mood disorders and anxiety disorders. Among our population, 73% of the patients received vaccination against COVID-19. The majority got 2 doses (60%), were vaccinated on their own initiative (68%) and by making an appointment (71.4%). Patients with depressive disorders accessed to vaccination program in 100% of cases. The group of psychotic patients had a vaccination rate of 66%. Vaccination was significantly associated with gender (p=0.001), age (p=0.04), marital status (p<10-3), number of children (p=0.002), housing situation (0.018), diagnosis (p<10-3) and treatment (p=0.01)ConclusionsPatients with psychiatric disorders experience a distinct burden of the COVID-19 disease. They should therefore be prioritised in vaccine allocation strategies, especially among patients with psychotic disorders.Disclosure of InterestNone Declared

Fairness and efficiency considerations in COVID-19 vaccine allocation strategies: A case study comparing front-line workers and 65-74 year olds in the United States.

The COVID-19 epidemic in the United States has been characterized by two stark disparities. COVID-19 burden has been unequally distributed among racial and ethnic groups and at the same time the mortality rates have been sharply higher among older age groups. These disparities have led some to suggest that inequalities could be reduced by vaccinating front-line workers before vaccinating older individuals, as older individuals in the US are disproportionately Non-Hispanic White. We compare the performance of two distribution policies, one allocating vaccines to front-line workers and another to older individuals aged 65-74-year-old. We estimate both the number of lives saved and the number of years of life saved under each of the policies, overall and in every race/ethnicity groups, in the United States and every state. We show that prioritizing COVID-19 vaccines for 65-74-year-olds saves both more lives and more years of life than allocating vaccines front-line workers in each racial/ethnic group, in the United States as a whole and in nearly every state. When evaluating fairness of vaccine allocation policies, the overall benefit to impact of each population subgroup should be considered, not only the proportion of doses that is distributed to each subgroup. Further work can identify prioritization schemes that perform better on multiple equity metrics.

Comparative performance of between-population vaccine allocation strategies with applications for emerging pandemics

Vaccine allocation decisions during emerging pandemics have proven to be challenging due to competing ethical, practical, and political considerations. Complicating decision making, policy makers need to consider vaccine allocation strategies that balance needs both within and between populations. When vaccine stockpiles are limited, doses should be allocated in locations to maximize their impact. Using a susceptible-exposed-infectious-recovered (SEIR) model we examine optimal vaccine allocation decisions across two populations considering the impact of characteristics of the population (e.g., size, underlying immunity, heterogeneous risk structure, interaction), vaccine (e.g., vaccine efficacy), pathogen (e.g., transmissibility), and delivery (e.g., varying speed and timing of rollout). Across a wide range of characteristics considered, we find that vaccine allocation proportional to population size (i.e., pro-rata allocation) performs either better or comparably to nonproportional allocation strategies in minimizing the cumulative number of infections. These results may argue in favor of sharing of vaccines between locations in the context of an epidemic caused by an emerging pathogen, where many epidemiologic characteristics may not be known.

Optimal Covid-19 vaccine stations location and allocation strategies

PurposeThis study proposes strategies for vaccine center allocation for coronavirus disease (COVID) vaccine by determining the number of vaccination stations required for the vaccination drive, location of vaccination station, assignment of demand group to vaccination station, allocation of the scarce medical professional teams to station and number of optimal days a vaccination station to be functional in a week.Design/methodology/approachThe authors propose a mixed-integer nonlinear programming model. However, to handle nonlinearity, the authors devise a heuristic and then propose a two-stage mixed-integer linear programming (MILP) formulation to optimize the allocation of vaccination centers or stations to demand groups in the first stage and the allocation of vaccination centers to cold storage links in the second stage. The first stage optimizes the cost and average distance traveled by people to reach the vaccination center, whereas the second stage optimizes the vaccine’s holding and storage and transportation cost by efficiently allocating cold storage links to the centers.Findings The model is studied for the real-world case of Chandigarh, India. The results obtained validate that the proposed approach can immensely help government agencies and policymaking body for a successful vaccination drive. The model tries to find a tradeoff between loss due to underutilized medical teams and the distance traveled by a demand group to get the vaccination.Originality/value To the best of our knowledge, there are hardly any studies on a vaccination program at such a scale due to sudden outbreaks such as Covid-19.

How can age-based vaccine allocation strategies be optimized? A multi-objective optimization framework.

Human life is deeply influenced by infectious diseases. A vaccine, when available, is one of the most effective ways of controlling the spread of an epidemic. However, vaccine shortage and uncertain vaccine effectiveness in the early stage of vaccine production make vaccine allocation a critical issue. To tackle this issue, we propose a multi-objective framework to optimize the vaccine allocation strategy among different age groups during an epidemic under vaccine shortage in this study. Minimizing total disease onsets and total severe cases are the two objectives of this vaccine allocation optimization problem, and the multistage feature of vaccine allocation are considered in the framework. An improved Strength Pareto Evolutionary Algorithm (SPEA2) is used to solve the optimization problem. To evaluate the two objectives under different strategies, a deterministic age-stratified extended SEIR model is developed. In the proposed framework, different combinations of vaccine effectiveness and vaccine production capacity are investigated, and it is identified that for COVID-19 the optimal strategy is highly related to vaccine-related parameters. When the vaccine effectiveness is low, allocating most of vaccines to 0–19 age group or 65+ age group is a better choice under a low production capacity, while allocating most of vaccines to 20–49 age group or 50–64 age group is a better choice under a relatively high production capacity. When the vaccine effectiveness is high, a better strategy is to allocate vaccines to 65+ age group under a low production capacity, while to allocate vaccines to 20–49 age group under a relatively high production capacity.

An agent-based model to assess large-scale COVID-19 vaccination campaigns for the Italian territory: The case study of Lombardy region

BackgroundIn Italy, the administration of COVID-19 vaccines began in late 2020. In the early stages, the number of available doses was limited. To maximize the effectiveness of the vaccine campaign, the national health agency assigned priority access to at-risk individuals, such as health care workers and the elderly. Current vaccination campaign strategies do not take full advantage of the latest mathematical models, which capture many subtle nuances, allowing different territorial situations to be analyzed aiming to make context-specific decisions. ObjectivesThe main objective is the definition of an agent-based model using open data and scientific literature to assess and optimize the impact of vaccine campaigns for an Italian region. Specifically, the aim is twofold: (i) estimate the reduction in the number of infections and deaths attributable to vaccines, and (ii) assess the performances of alternative vaccine allocation strategies. MethodsThe COVID-19 Agent-based simulator Covasim has been employed to build an agent-based model by considering the Lombardy region as case study. The model has been tailored by leveraging open data and knowledge from the scientific literature. Dynamic mobility restrictions and the presence of Variant of Concern have been explicitly represented. Free parameters have been calibrated using the grid search methodology. ResultsThe model mimics the COVID-19 wave that hit Lombardy from September 2020 to April 2021. It suggests that 168,492 cumulative infections 2,990 cumulative deaths have been avoided due to the vaccination campaign in Lombardy from January 1 to April 30, 2021. Without vaccines, the number of deaths would have been 66% greater in the 80–89 age group and 114% greater for those over 90. The best vaccine allocation strategy depends on the goal. To minimize infections, the best policy is related to dose availability. If at least 1/3 of the population can be covered in 4 months, targeting at-risk individuals and the elderly first is recommended; otherwise, the youngest people should be vaccinated first. To minimize overall deaths, priority is best given to at-risk groups and the elderly in all scenarios. ConclusionsThis work proposes a methodological approach that leverages open data and scientific literature to build a model of COVID-19 capable of assessing and optimizing the impact of vaccine campaigns. This methodology can help national institutions to design regional mathematical models that can support pandemic-related decision-making processes.

Optimal control of the spatial allocation of COVID-19 vaccines: Italy as a case study.

While campaigns of vaccination against SARS-CoV-2 are underway across the world, communities face the challenge of a fair and effective distribution of a limited supply of doses. Current vaccine allocation strategies are based on criteria such as age or risk. In the light of strong spatial heterogeneities in disease history and transmission, we explore spatial allocation strategies as a complement to existing approaches. Given the practical constraints and complex epidemiological dynamics, designing effective vaccination strategies at a country scale is an intricate task. We propose a novel optimal control framework to derive the best possible vaccine allocation for given disease transmission projections and constraints on vaccine supply and distribution logistics. As a proof-of-concept, we couple our framework with an existing spatially explicit compartmental COVID-19 model tailored to the Italian geographic and epidemiological context. We optimize the vaccine allocation on scenarios of unfolding disease transmission across the 107 provinces of Italy, from January to April 2021. For each scenario, the optimal solution significantly outperforms alternative strategies that prioritize provinces based on incidence, population distribution, or prevalence of susceptibles. Our results suggest that the complex interplay between the mobility network and the spatial heterogeneities implies highly non-trivial prioritization strategies for effective vaccination campaigns. Our work demonstrates the potential of optimal control for complex and heterogeneous epidemiological landscapes at country, and possibly global, scales.

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

The vaccines are considered to be important for the prevention and control of coronavirus disease 2019 (COVID-19). However, considering the limited vaccine supply within an extended period of time in many countries where COVID-19 vaccine booster shot are taken and new vaccines are developed to suppress the mutation of virus, designing an effective vaccination strategy is extremely important to reduce the number of deaths and infections. Then, the simulations were implemented to study the relative reduction in morbidity and mortality of vaccine allocation strategies by using the proposed model and actual South Africa's epidemiological data. Our results indicated that in light of South Africa's demographics, vaccinating older age groups (>60 years) largely reduced the cumulative deaths and the “0–20 first” strategy was the most effective way to reduce confirmed cases. In addition, “21–30 first” and “31–40 first” strategies have also had a positive effect. Partial vaccination resulted in lower numbers of infections and deaths under different control measures compared with full vaccination in low-income countries. In addition, we analyzed the sensitivity of daily testing volume and infection rate, which are critical to optimize vaccine allocation. However, comprehensive reduction in infections was mainly affected by the vaccine proportion of the target age group. An increase in the proportion of vaccines given priority to “0–20” groups always had a favorable effect, and the prioritizing vaccine allocation among the “60+” age group with 60% of the total amount of vaccine consistently resulted in the greatest reduction in deaths. Meanwhile, we observed a significant distinction in the effect of COVID-19 vaccine allocation policies under varying priority strategies on relative reductions in the effective reproduction number. Our results could help evaluate to control measures performance and the improvement of vaccine allocation strategy for COVID-19 epidemic.

Comparison of health-oriented cross-regional allocation strategies for the COVID-19 vaccine: a mathematical modelling study

Background Controlling the epidemic spread and establishing the immune barrier in a short time through accurate vaccine demand prediction and optimised vaccine allocation strategy are still urgent problems to be solved under the condition of frequent virus mutations. Methods A cross-regional Susceptible-Exposed-Infected-Removed dynamic model was used for scenario simulation to systematically elaborate and compare the effects of different cross-regional vaccine allocation strategies on the future development of the epidemic in regions with different population sizes, prevention and control capabilities, and initial risk levels. Furthermore, the trajectory of the cross-regional vaccine allocation strategy, calculated using a particle swarm optimisation algorithm, was compared with the trajectories of other strategies. Results By visualising the final effect of the particle swarm optimisation vaccine allocation strategy, this study revealed the important role of prevention and control (including the level of social distancing control, the speed of tracking and isolating exposed and infected individuals, and the initial frequency of mask-wearing) in determining the allocation of vaccine resources. Most importantly, it supported the idea of prioritising control in regions with a large population and low initial risk level, which broke the general view that high initial risk needs to be given priority and proposed that outbreak risk should be firstly considered instead. Conclusions This is the first study to use a particle swarm optimisation algorithm to study the cross-regional allocation of COVID-19 vaccines. These data provide a theoretical basis for countries and regions to develop more targeted and sustainable vaccination strategies. KEY MESSAGE The innovative combination of particle swarm optimisation and cross-regional SEIR model to simulate the pandemic trajectory and predict the vaccine demand helped to speed up and stabilise the construction of the immune barrier, especially faced with new virus mutations. We proposed that priority should be given to regions where it is possible to prevent more infections rather than regions where it is at high initial risk, thus regional outbreak risk should be considered when making vaccine allocation decisions. An optimal health-oriented strategy for vaccine allocation in the COVID-19 pandemic is determined considering both pharmaceutical and non-pharmaceutical policy interventions, including speed of isolation, degree of social distancing control, and frequency of mask-wearing.

Modeling strategies for the allocation of SARS-CoV-2 vaccines in the United States

The Advisory Committee on Immunization Practices (ACIP) recommended phased allocation of SARS-CoV-2 vaccines in December 2020. To support the development of this guidance, we used a mathematical model of SARS-CoV-2 transmission to evaluate the relative impact of three vaccine allocation strategies on infections, hospitalizations, and deaths. All three strategies initially prioritized healthcare personnel (HCP) for vaccination. Strategies of subsequently prioritizing adults aged ≥65 years, or a combination of essential workers and adults aged ≥75 years, prevented the most deaths. Meanwhile, prioritizing adults with high-risk medical conditions immediately after HCP prevented the most infections. All three strategies prevented a similar fraction of hospitalizations. While no model is capable of fully capturing the complex social dynamics which shape epidemics, exercises such as this one can be a useful way for policy makers to formalize their assumptions and explore the key features of a problem before making decisions.

The allocation of COVID-19 vaccines and antivirals against emerging SARS-CoV-2 variants of concern in East Asia and Pacific region: A modelling study

SummaryBackgroundIn view of emerging variants of concern (VOCs), we aimed to evaluate the impact of various allocation strategies of COVID-19 vaccines and antiviral such that the pandemic exit strategy could be tailored to risks and preferences of jurisdictions in the East Asia and Pacific region (EAP) to improve its efficiency and effectiveness.MethodsVaccine efficacies were estimated from the titre distributions of 50% plaque reduction neutralization test (PRNT50), assuming that PRNT50 titres of primary vaccination decreased by 2-10 folds due to antibody waning and emergence of VOCs, and an additional dose of vaccine would increase PRNT50 titres by 3- or 9-fold. We then used an existing SARS-CoV-2 transmission model to assess the outcomes of vaccine allocation strategies with and without the use of antivirals for symptomatic patients in Japan, Hong Kong, and Vietnam.FindingsIncreasing primary vaccination coverage was the most important contributing factor in reducing the total and peak number of COVID-19 hospitalisations, especially when population vaccine coverage or vaccine uptake among older adults was low. Providing antivirals to 50% of symptomatic infections only further reduced total and peak hospitalisations by 10-13%. The effectiveness of an additional dose of vaccine was highly dependent on the immune escape potential of VOCs and antibody waning, but less dependent on the boosting efficacy of the additional dose.InterpretationIncreasing primary vaccination coverage should be prioritised in the design of allocation strategies of COVID-19 vaccines and antivirals against emerging VOCs, such as Omicron, in the EAP region. Heterologous vaccination with any available vaccine as the additional dose could be considered when planning pandemic exit strategies tailored to the circumstances of EAP jurisdictions.FundingHealth and Medical Research Fund, General Research Fund, AIR@InnoHK