Optimizing age-specific vaccination

On value compatibility: reflections on the ethical framework for pandemic healthcare distribution

An ethical framework for pandemic healthcare distribution typically encompasses multiple ethical values. However, integrating various ethical values and distributive principles into a single framework raises concerns about their compatibility and the overall coherence of the framework. This issue of value compatibility could lead to moral inconsistencies within the ethical framework, leading to practical indetermination when facing conflicting implications. This paper offers a methodological resolution to the compatibility problem, serving as an effective tool to mitigate the impact of value conflicts where possible. It proposes four pathways: specifying values rather than balancing them, incorporating values rather than weighing them, reinforcing values rather than aggregating them, and seeking scientific evidence. By developing coherent ethical frameworks where values do not contradict each other, this approach also enhances practical ethical decision-making. Using the COVID-19 vaccine distribution as a case study, this approach demonstrates how conflicting values can yield practical prioritization strategies, such as allocating vaccines to healthcare and essential workers, addressing multiple layers of disadvantage, and assessing age-related prioritization. Reflecting on the compatibility of values within ethical frameworks offers crucial insights beyond COVID-19, contributing to the development of robust ethical frameworks for future public health crises.

Existence and stability of equilibria in infectious disease dynamics with behavioral feedback

Mathematical models have provided a general framework for understanding the dynamics and control of infectious disease. Many compartmental models are limited in that they do not account for the range of behavioral feedbacks that have been observed in the response to emerging infections. Here we expand on the SIR compartmental model framework by introducing a general class of behavioral feedbacks that encompasses both individual responses and nonpharmaceutical interventions. By linking transmission dynamics and behavior, this class of models can capture the interplay of disease incidence, behavioral response, and controls such as vaccination. We prove mathematically the existence of two endemic equilibria depending on the vaccination rate: one in the presence of low vaccination but with reduced societal activity (the "new normal"), and one with return to normal activity but with vaccination rate below that required for disease elimination. Establishing the existence and stability of these equilibria is a precursor to designing control strategies that may exploit them.

Epihiper-A high performance computational modeling framework to support epidemic science

This paper describes Epihiper, a state-of-the-art, high performance computational modeling framework for epidemic science. The Epihiper modeling framework supports custom disease models, and can simulate epidemics over dynamic, large-scale networks while supporting modulation of the epidemic evolution through a set of user-programmable interventions. The nodes and edges of the social-contact network have customizable sets of static and dynamic attributes which allow the user to specify intervention target sets at a very fine-grained level; these also permit the network to be updated in response to nonpharmaceutical interventions, such as school closures. The execution of interventions is governed by trigger conditions, which are Boolean expressions formed using any of Epihiper's primitives (e.g. the current time, transmissibility) and user-defined sets (e.g. people with work activities). Rich expressiveness, extensibility, and high-performance computing responsiveness were central design goals to ensure that the framework could effectively target realistic scenarios at the scale and detail required to support the large computational designs needed by state and federal public health policymakers in their efforts to plan and respond in the event of epidemics. The modeling framework has been used to support the CDC Scenario Modeling Hub for COVID-19 response, and was a part of a hybrid high-performance cloud system that was nominated as a finalist for the 2021 ACM Gordon Bell Special Prize for high performance computing-based COVID-19 Research.

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

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.

Comprehensive Review of COVID-19: Epidemiology, Pathogenesis, Advancement in Diagnostic and Detection Techniques, and Post-Pandemic Treatment Strategies

At present, COVID-19 remains a public health concern due to the ongoing evolution of SARS-CoV-2 and its prevalence in particular countries. This paper provides an updated overview of the epidemiology and pathogenesis of COVID-19, with a focus on the emergence of SARS-CoV-2 variants and the phenomenon known as 'long COVID'. Meanwhile, diagnostic and detection advances will be mentioned. Though many inventions have been made to combat the COVID-19 pandemic, some outstanding ones include multiplex RT-PCR, which can be used for accurate diagnosis of SARS-CoV-2 infection. ELISA-based antigen tests also appear to be potential diagnostic tools to be available in the future. This paper also discusses current treatments, vaccination strategies, as well as emerging cell-based therapies for SARS-CoV-2 infection. The ongoing evolution of SARS-CoV-2 underscores the necessity for us to continuously update scientific understanding and treatments for it.

Modelling COVID-19 Vaccination in the UK: Impact of the Autumn 2022 and Spring 2023 Booster Campaigns

INTRODUCTION

The delivery of COVID-19 vaccines was successful in reducing hospitalizations and mortality. However, emergence of the Omicron variant resulted in increased virus transmissibility. Consequently, booster vaccination programs were initiated to decrease the risk of severe disease and death among vulnerable members of the population. This study aimed to estimate the effects of the booster program and alternative vaccination strategies on morbidity and mortality due to COVID-19 in the UK.

METHOD

A Susceptible-Exposed-Infectious-Recovered (SEIR) model was used to assess the impact of several vaccination strategies on severe outcomes associated with COVID-19, including hospitalizations, mortality, National Health Service (NHS) capacity quantified by hospital general ward and intensive care unit (ICU) bed days, and patient productivity. The model accounted for age-, risk- and immunity-based stratification of the UK population. Outcomes were evaluated over a 48-week time horizon from September 2022 to August 2023 considering the actual UK autumn 2022/spring 2023 booster campaigns and six counterfactual strategies.

RESULTS

The model estimated that the autumn 2022/spring 2023 booster campaign resulted in a reduction of 18,921 hospitalizations and 1463 deaths, compared with a no booster scenario. Utilization of hospital bed days due to COVID-19 decreased after the autumn 2022/spring 2023 booster campaign. Expanding the booster eligibility criteria and improving uptake improved all outcomes, including averting twice as many ICU admissions, preventing more than 20% additional deaths, and a sevenfold reduction in long COVID, compared with the autumn 2022/spring 2023 booster campaign. The number of productive days lost was reduced by fivefold indicating that vaccinating a wider population has a beneficial impact on the morbidities associated with COVID-19.

CONCLUSION

Our modelling demonstrates that the autumn 2022/spring 2023 booster campaign reduced COVID-19-associated morbidity and mortality. Booster campaigns with alternative eligibility criteria warrant consideration in the UK, given their potential to further reduce morbidity and mortality as future variants emerge.

Exploring the future adult vaccine landscape-crowded schedules and new dynamics

Amidst the backdrop of the COVID-19 pandemic, vaccine innovation has garnered significant attention, but this field was already on the cusp of a groundbreaking renaissance. Propelling these advancements are scientific and technological breakthroughs, alongside a growing understanding of the societal and economic boons vaccines offer, particularly for non-pediatric populations like adults and the immunocompromised. In a departure from previous decades where vaccine launches could be seamlessly integrated into existing processes, we anticipate potentially than 100 novel, risk-adjusted product launches over the next 10 years in the adult vaccine market, primarily addressing new indications. However, this segment is infamous for its challenges: low uptake, funding shortfalls, and operational hurdles linked to delivery and administration. To unlock the societal benefits of this burgeoning expansion, we need to adopt a fresh perspective to steer through the dynamics sparked by the rapid growth of the global adult vaccine market. This article aims to provide that fresh perspective, offering a detailed analysis of the anticipated number of adult vaccine approvals by category and exploring how our understanding of barriers to adult vaccine uptake might evolve. We incorporated pertinent insights from external stakeholder interviews, spotlighting shifting preferences, perceptions, priorities, and decision-making criteria. Consequently, this article aspires to serve as a pivotal starting point for industry participants, equipping them with the knowledge to skillfully navigate the anticipated surge in both volume and complexity.

Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies

The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities given data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework

OBJECTIVES

To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs).

METHODS

A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake.

RESULTS

A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AI-enabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS-assisted allocation of vaccines at the local level, considering context-specific constraints and objectives.

CONCLUSIONS

We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines.

Optimality of Maximal-Effort Vaccination

It is widely acknowledged that vaccinating at maximal effort in the face of an ongoing epidemic is the best strategy to minimise infections and deaths from the disease. Despite this, no one has proved that this is guaranteed to be true if the disease follows multi-group SIR (Susceptible-Infected-Recovered) dynamics. This paper provides a novel proof of this principle for the existing SIR framework, showing that the total number of deaths or infections from an epidemic is decreasing in vaccination effort. Furthermore, it presents a novel model for vaccination which assumes that vaccines assigned to a subgroup are distributed randomly to the unvaccinated population of that subgroup. It suggests, using COVID-19 data, that this more accurately captures vaccination dynamics than the model commonly found in the literature. However, as the novel model provides a strictly larger set of possible vaccination policies, the results presented in this paper hold for both models.

Lessons Learned from a Global Perspective of Coronavirus Disease-2019

Coronavirus disease-2019 has impacted the world globally. Countries and health care organizations across the globe responded to this unprecedented public health crisis in a varied manner in terms of public health and social measures, vaccination development and rollout, the conduct of research, developments of therapeutics, sharing of information, and in how they continue to deal with the widespread aftermath. This article reviews the various elements of the global response to the pandemic, focusing on the lessons learned and strategies to consider during future pandemics.

Evaluating primary and booster vaccination prioritization strategies for COVID-19 by age and high-contact employment status using data from contact surveys

The debate around vaccine prioritization for COVID-19 has revolved around balancing the benefits from: (1) the direct protection conferred by the vaccine amongst those at highest risk of severe disease outcomes, and (2) the indirect protection through vaccinating those that are at highest risk of being infected and of transmitting the virus. While adults aged 65+ are at highest risk for severe disease and death from COVID-19, essential service and other in-person workers with greater rates of contact may be at higher risk of acquiring and transmitting SARS-CoV-2. Unfortunately, there have been relatively little data available to understand heterogeneity in contact rates and risk across these demographic groups. Here, we retrospectively analyze and evaluate vaccination prioritization strategies by age and worker status. We use a mathematical model of SARS-CoV-2 transmission and uniquely detailed contact data collected as part of the Berkeley Interpersonal Contact Survey to evaluate five vaccination prioritization strategies: (1) prioritizing only adults over age 65, (2) prioritizing only high-contact workers, (3) splitting prioritization between adults 65+ and high-contact workers, (4) tiered prioritization of adults over age 65 followed by high-contact workers, and (5) tiered prioritization of high-contact workers followed by adults 65+. We find that for the primary two-dose vaccination schedule, assuming 70% uptake, a tiered roll-out that first prioritizes adults 65+ averts the most deaths (31% fewer deaths compared to a no-vaccination scenario) while a tiered roll-out that prioritizes high contact workers averts the most number of clinical infections (14% fewer clinical infections compared to a no-vaccination scenario). We also consider prioritization strategies for booster doses during a subsequent outbreak of a hypothetical new SARS-CoV-2 variant. We find that a tiered roll-out that prioritizes adults 65+ for booster doses consistently averts the most deaths, and it may also avert the most number of clinical cases depending on the epidemiology of the SARS-CoV-2 variant and the vaccine efficacy.

A longitudinal analysis of humoral, T cellular response and influencing factors in a cohort of healthcare workers: Implications for personalized SARS-CoV-2 vaccination strategies

Introduction

SARS-CoV-2 mRNA vaccinations elicit both virus-specific humoral and T-cell responses, but a complex interplay of different influencing factors, such as natural immunity, gender, and age, guarantees host protection. The present study aims to assess the immune dynamics of humoral, T-cell response, and influencing factors to stratify individual immunization status up to 10 months after Comirnaty-vaccine administration.

Methods

To this aim, we longitudinally evaluated the magnitude and kinetics of both humoral and T-cell responses by serological tests and enzyme-linked immunospot assay at 5 time points. Furthermore, we compared the course over time of the two branches of adaptive immunity to establish an eventual correlation between adaptive responses. Lastly, we evaluated putative influencing factors collected by an anonymized survey administered to all participants through multiparametric analysis. Among 984 healthcare workers evaluated for humoral immunity, 107 individuals were further analyzed to describe SARS-CoV-2-specific T-cell responses. Participants were divided into 4 age groups: <40 and ≥40 years for men, <48 and ≥48 years for women. Furthermore, results were segregated according to SARS-CoV-2-specific serostatus at baseline.

Results

The disaggregated evaluation of humoral responses highlighted antibody levels decreased in older subjects. The humoral responses were higher in females than in males (p=0.002) and previously virus-exposed subjects compared to naïve subjects (p<0.001). The vaccination induced a robust SARS-CoV-2 specific T-cell response at early time points in seronegative subjects compared to baseline levels (p<0.0001). However, a contraction was observed 6 months after vaccination in this group (p<0.01). On the other hand, the pre-existing specific T-cell response detected in natural seropositive individuals was longer-lasting than the response of the seronegative subjects, decreasing only 10 months after vaccination. Our data suggest that T-cell reactiveness is poorly impacted by sex and age. Of note, SARS-CoV-2-specific T-cell response was not correlated to the humoral response at any time point.

Discussion

These findings suggest prospects for rescheduling vaccination strategies by considering individual immunization status, personal characteristics, and the appropriate laboratory tests to portray immunity against SARS-CoV-2 accurately. Deepening our knowledge about T and B cell dynamics might optimize the decision-making process in vaccination campaigns, tailoring it to each specific immune response.

Asymptotic Analysis of Optimal Vaccination Policies

Targeted vaccination policies can have a significant impact on the number of infections and deaths in an epidemic. However, optimising such policies is complicated, and the resultant solution may be difficult to explain to policy-makers and to the public. The key novelty of this paper is a derivation of the leading-order optimal vaccination policy under multi-group susceptible-infected-recovered dynamics in two different cases. Firstly, it considers the case of a small vulnerable subgroup in a population and shows that (in the asymptotic limit) it is optimal to vaccinate this group first, regardless of the properties of the other groups. Then, it considers the case of a small vaccine supply and transforms the optimal vaccination problem into a simple knapsack problem by linearising the final size equations. Both of these cases are then explored further through numerical examples, which show that these solutions are also directly useful for realistic parameter values. Moreover, the findings of this paper give some general principles for optimal vaccination policies which will help policy-makers and the public to understand the reasoning behind optimal vaccination programs in more generic cases.

Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support

This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; (ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; (iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; (iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.

Mixing age and risk groups for accessing COVID-19 vaccines: a modelling study

OBJECTIVE

To characterise the optimal targeting of age and risk groups for COVID-19 vaccines.

DESIGN

Motivated by policies in Japan and elsewhere, we consider rollouts that target a mix of age and risk groups when distributing the vaccines. We identify the optimal group mix for three policy objectives: reducing deaths, reducing cases and reducing severe cases.

SETTING

Japan, a country where the rollout occurred over multiple stages targeting a mix of age and risk groups in each stage.

PRIMARY OUTCOMES

We use official statistics on COVID-19 deaths to quantify the virus transmission patterns in Japan. We then search over all possible group mix across rollout stages to identify the optimal strategies under different policy objectives and virus and vaccination conditions.

RESULTS

Low-risk young adults can be targeted together with the high-risk population and the elderly to optimally reduce deaths, cases and severe cases under high virus transmissibility. Compared with targeting the elderly or the high-risk population only, applying optimal group mix can further reduce deaths and severe cases by over 60%. High-efficacy vaccines can mitigate the health loss under suboptimal targeting in the rollout.

CONCLUSIONS

Mixing age and risk groups outperforms targeting individual groups separately, and optimising the group mix can substantially increase the health benefits of vaccines. Additional policy measures boosting vaccine efficacy are necessary under outbreaks of transmissible variants.

Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies

The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities with data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

Effects of infection fatality ratio and social contact matrices on vaccine prioritization strategies

Effective strategies of vaccine prioritization are essential to mitigate the impacts of severe infectious diseases. We investigate the role of infection fatality ratio (IFR) and social contact matrices on vaccination prioritization using a compartmental epidemic model fueled by real-world data of different diseases and countries. Our study confirms that massive and early vaccination is extremely effective to reduce the disease fatality if the contagion is mitigated, but the effectiveness is increasingly reduced as vaccination beginning delays in an uncontrolled epidemiological scenario. The optimal and least effective prioritization strategies depend non-linearly on epidemiological variables. Regions of the epidemiological parameter space, in which prioritizing the most vulnerable population is more effective than the most contagious individuals, depend strongly on the IFR age profile being, for example, substantially broader for COVID-19 in comparison with seasonal influenza. Demographics and social contact matrices deform the phase diagrams but do not alter their qualitative shapes.

Strategic COVID-19 vaccine distribution can simultaneously elevate social utility and equity

Balancing social utility and equity in distributing limited vaccines is a critical policy concern for protecting against the prolonged COVID-19 pandemic and future health emergencies. What is the nature of the trade-off between maximizing collective welfare and minimizing disparities between more and less privileged communities? To evaluate vaccination strategies, we propose an epidemic model that explicitly accounts for both demographic and mobility differences among communities and their associations with heterogeneous COVID-19 risks, then calibrate it with large-scale data. Using this model, we find that social utility and equity can be simultaneously improved when vaccine access is prioritized for the most disadvantaged communities, which holds even when such communities manifest considerable vaccine reluctance. Nevertheless, equity among distinct demographic features may conflict; for example, low-income neighbourhoods might have fewer elder citizens. We design two behaviour-and-demography-aware indices, community risk and societal risk, which capture the risks communities face and those they impose on society from not being vaccinated, to inform the design of comprehensive vaccine distribution strategies. Our study provides a framework for uniting utility and equity-based considerations in vaccine distribution and sheds light on how to balance multiple ethical values in complex settings for epidemic control.

COVID-19 pandemic vaccination strategies of early 2021 based on behavioral differences between residents of Tokyo and Osaka, Japan

Background

During the fourth COVID-19 wave in Japan, marked differences became apparent in the scale of the epidemic between metropolitan Tokyo in eastern Japan and Osaka prefecture in western Japan.

Methods

Public epidemic data were analyzed, with performance of mathematical simulations using simplified SEIR models.

Results

The increase in the number of infected persons per 100,000 population during the fourth wave of expansion was greater in Osaka than in Tokyo. The basic reproduction number in Osaka was greater than in Tokyo. Particularly, the number of infected people in their 20 s increased during the fourth wave: The generation-specific reproduction number for people in their 20 s was higher than for people of other generations. Both Tokyo and Osaka were found to have strong correlation between the increase in the number of infected people and the average number of people using the main downtown stations at night. Simulations showed vaccination of people in their 60 s and older reduced the number of infected people among the high-risk elderly population in the fourth wave. However, age-specific vaccination of people in their 20 s reduced the number of infected people more than vaccination of people in their 60 s and older.

Conclusions

Differences in the epidemic between Tokyo and Osaka are explainable by different behaviors of the most socially active generation. When vaccine supplies are adequate, priority should be assigned to high-risk older adults, but if vaccine supplies are scarce, simulation results suggest consideration of vaccinating specific groups among whom the epidemic is spreading rapidly.

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.

Behavioral responses to risk promote vaccinating high-contact individuals first

How should communities prioritize COVID-19 vaccinations? Prior studies found that prioritizing the elderly and most vulnerable minimizes deaths. However, prior research has ignored how behavioral responses to risk of disease endogenously change transmission rates. We show that incorporating risk-driven behavioral responses enhances fit to data and may change prioritization to vaccinating high-contact individuals. Behavioral responses matter because deaths grow exponentially until communities are compelled to reduce contacts, with deaths stabilizing at levels that oblige higher-contact groups to sufficiently cut their interactions and slow transmissions. More lives may be saved by vaccinating and taking those high-contact groups out of transmission chains earlier because the remaining groups will take more precautions while waiting for their turn for vaccination. These findings are especially important considering the need for further vaccination in many countries, the emergence of new variants, and the expected challenge of distributing new vaccines in the coming months and years. © 2022 The Author. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

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.

Effect of vaccine efficacy on disease transmission with age-structured

Recent outbreaks of novel infectious diseases (e.g., COVID-19, H2N3) have highlighted the threat of pathogen transmission, and vaccination offers a necessary tool to relieve illness. However, vaccine efficacy is one of the barriers to eradicating the epidemic. Intuitively, vaccine efficacy is closely related to age structures, and the distribution of vaccine efficacy usually obeys a Gaussian distribution, such as with H3N2 and influenza A and B. Based on this fact, in this paper, we study the effect of vaccine efficacy on disease spread by considering different age structures and extending the traditional susceptible-infected-recovery/vaccinator(SIR/V) model with two stages to three stages, which includes the decision-making stage, epidemic stage, and birth-death stage. Extensive numerical simulations show that our model generates a higher vaccination level compared with the case of complete vaccine efficacy because the vaccinated individuals in our model can form small and numerous clusters slower than that of complete vaccine efficacy. In addition, priority vaccination for the elderly is conducive to halting the epidemic when facing population ageing. Our work is expected to provide valuable information for decision-making and the design of more effective disease control strategies.

Contact tracing-induced Allee effect in disease dynamics

Contact tracing, case isolation, quarantine, social distancing, and other non-pharmaceutical interventions (NPIs) have been a cornerstone in managing the COVID-19 pandemic. However, their effects on disease dynamics are not fully understood. Saturation of contact tracing caused by the increase of infected individuals has been recognized as a crucial variable by healthcare systems worldwide. Here, we model this saturation process with a mechanistic and a phenomenological model and show that it induces an Allee effect which could determine an infection threshold between two alternative states—containment and outbreak. This transition was considered elsewhere as a response to the strength of NPIs, but here we show that they may be also determined by the number of infected individuals. As a consequence, timing of NPIs implementation and relaxation after containment is critical to their effectiveness. Containment strategies such as vaccination or mobility restriction may interact with contact tracing-induced Allee effect. Each strategy in isolation tends to show diminishing returns, with a less than proportional effect of the intervention on disease containment. However, when combined, their suppressing potential is enhanced. Relaxation of NPIs after disease containment--e.g. because vaccination--have to be performed in attention to avoid crossing the infection threshold required to a novel outbreak. The recognition of a contact tracing-induced Allee effect, its interaction with other NPIs and vaccination, and the existence of tipping points contributes to the understanding of several features of disease dynamics and its response to containment interventions. This knowledge may be of relevance for explaining the dynamics of diseases in different regions and, more importantly, as input for guiding the use of NPIs, vaccination campaigns, and its combination for the management of epidemic outbreaks.

Modelling vaccination strategies for COVID-19

Mathematical models have been used to guide strategies for COVID-19 vaccine distribution. But with the emergence of new SARS-CoV-2 variants and waning immunity, how should vaccine distribution be prioritized?

Comparing the impact of vaccination strategies on the spread of COVID-19, including a novel household-targeted vaccination strategy

With limited availability of vaccines, an efficient use of the limited supply of vaccines in order to achieve herd immunity will be an important tool to combat the wide-spread prevalence of COVID-19. Here, we compare a selection of strategies for vaccine distribution, including a novel targeted vaccination approach (EHR) that provides a noticeable increase in vaccine impact on disease spread compared to age-prioritized and random selection vaccination schemes. Using high-fidelity individual-based computer simulations with Oslo, Norway as an example, we find that for a community reproductive number in a setting where the base pre-vaccination reproduction number R = 2.1 without population immunity, the EHR method reaches herd immunity at 48% of the population vaccinated with 90% efficiency, whereas the common age-prioritized approach needs 89%, and a population-wide random selection approach requires 61%. We find that age-based strategies have a substantially weaker impact on epidemic spread and struggle to achieve herd immunity under the majority of conditions. Furthermore, the vaccination of minors is essential to achieving herd immunity, even for ideal vaccines providing 100% protection.

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.

Analyzing the demographic, spatial, and temporal factors influencing social contact patterns in U.S. and implications for infectious disease spread

Background

Diseases such as COVID-19 are spread through social contact. Reducing social contacts is required to stop disease spread in pandemics for which vaccines have not yet been developed. However, existing data on social contact patterns in the United States (U.S.) is limited.

Method

We use American Time Use Survey data from 2003–2018 to describe and quantify the age-pattern of disease-relevant social contacts. For within-household contacts, we construct age-structured contact duration matrices (who spends time with whom, by age). For both within-household and non-household contacts, we also estimate the mean number and duration of contact by location. We estimate and test for differences in the age-pattern of social contacts based on demographic, temporal, and spatial characteristics.

Results

The mean number and duration of social contacts vary by age. The biggest gender differences in the age-pattern of social contacts are at home and at work; the former appears to be driven by caretaking responsibilities.

Non-Hispanic Blacks have a shorter duration of contact and fewer social contacts than non-Hispanic Whites. This difference is largely driven by fewer and shorter contacts at home. Pre-pandemic, non-Hispanic Blacks have shorter durations of work contacts. Their jobs are more likely to require close physical proximity, so their contacts are riskier than those of non-Hispanic Whites. Hispanics have the highest number of household contacts and are also more likely to work in jobs requiring close physical proximity than non-Hispanic Whites.

With the exceptions of work and school contacts, the duration of social contact is higher on weekends than on weekdays. Seasonal differences in the total duration of social contacts are driven by school-aged respondents who have significantly shorter contacts during the summer months. Contact patterns did not differ by metro status. Age patterns of social contacts were similar across regions.

Conclusion

Social contact patterns differ by age, race and ethnicity, and gender. Other factors besides contact patterns may be driving seasonal variation in disease incidence if school-aged individuals are not an important source of transmission. Pre-pandemic, there were no spatial differences in social contacts, but this finding has likely changed during the pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06610-w.

COVID-19: vaccination problems

This minireview addresses problems of financing the vaccine development, regulatory questions, the ethics and efficacy of vaccine prioritization strategies and the coverage of variant viruses by current vaccines. Serious adverse effects observed with adenovirus vectored vaccines and mRNA vaccines in mass vaccination campaigns are reported. The ethical problems of continuing with placebo controlled vaccine trials and alternative clinical trial protocols are discussed as well as concrete vaccination issues such as the splitting of doses, the delaying of the second dose, the immunization with two different vaccine types and the need of vaccinating seropositive subjects. Strategies to increase vaccine acceptance in the population are shortly mentioned.

Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities-An Agent-Based Modeling Evaluation

(1) Background: The Austrian supply of COVID-19 vaccine is limited for now. We aim to provide evidence-based guidance to the authorities in order to minimize COVID-19-related hospitalizations and deaths in Austria. (2) Methods: We used a dynamic agent-based population model to compare different vaccination strategies targeted to the elderly (65 ≥ years), middle aged (45-64 years), younger (15-44 years), vulnerable (risk of severe disease due to comorbidities), and healthcare workers (HCW). First, outcomes were optimized for an initially available vaccine batch for 200,000 individuals. Second, stepwise optimization was performed deriving a prioritization sequence for 2.45 million individuals, maximizing the reduction in total hospitalizations and deaths compared to no vaccination. We considered sterilizing and non-sterilizing immunity, assuming a 70% effectiveness. (3) Results: Maximum reduction of hospitalizations and deaths was achieved by starting vaccination with the elderly and vulnerable followed by middle-aged, HCW, and younger individuals. Optimizations for vaccinating 2.45 million individuals yielded the same prioritization and avoided approximately one third of deaths and hospitalizations. Starting vaccination with HCW leads to slightly smaller reductions but maximizes occupational safety. (4) Conclusion: To minimize COVID-19-related hospitalizations and deaths, our study shows that elderly and vulnerable persons should be prioritized for vaccination until further vaccines are available.

Dynamic prioritization of COVID-19 vaccines when social distancing is limited for essential workers

COVID-19 vaccines have been authorized in multiple countries, and more are under rapid development. Careful design of a vaccine prioritization strategy across sociodemographic groups is a crucial public policy challenge given that 1) vaccine supply will be constrained for the first several months of the vaccination campaign, 2) there are stark differences in transmission and severity of impacts from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across groups, and 3) SARS-CoV-2 differs markedly from previous pandemic viruses. We assess the optimal allocation of a limited vaccine supply in the United States across groups differentiated by age and essential worker status, which constrains opportunities for social distancing. We model transmission dynamics using a compartmental model parameterized to capture current understanding of the epidemiological characteristics of COVID-19, including key sources of group heterogeneity (susceptibility, severity, and contact rates). We investigate three alternative policy objectives (minimizing infections, years of life lost, or deaths) and model a dynamic strategy that evolves with the population epidemiological status. We find that this temporal flexibility contributes substantially to public health goals. Older essential workers are typically targeted first. However, depending on the objective, younger essential workers are prioritized to control spread or seniors to directly control mortality. When the objective is minimizing deaths, relative to an untargeted approach, prioritization averts deaths on a range between 20,000 (when nonpharmaceutical interventions are strong) and 300,000 (when these interventions are weak). We illustrate how optimal prioritization is sensitive to several factors, most notably, vaccine effectiveness and supply, rate of transmission, and the magnitude of initial infections.

Are We Ready for the Arrival of the New COVID-19 Vaccinations? Great Promises and Unknown Challenges Still to Come

While the SARS-CoV-2 pandemic continues to strike and collect its death toll throughout the globe, as of 31 January 2021, the vaccine candidates worldwide were 292, of which 70 were in clinical testing. Several vaccines have been approved worldwide, and in particular, three have been so far authorized for use in the EU. Vaccination can be, in fact, an efficient way to mitigate the devastating effect of the pandemic and offer protection to some vulnerable strata of the population (i.e., the elderly) and reduce the social and economic burden of the current crisis. Regardless, a question is still open: after vaccination availability for the public, will vaccination campaigns be effective in reaching all the strata and a sufficient number of people in order to guarantee herd immunity? In other words: after we have it, will we be able to use it? Following the trends in vaccine hesitancy in recent years, there is a growing distrust of COVID-19 vaccinations. In addition, the online context and competition between pro- and anti-vaxxers show a trend in which anti-vaccination movements tend to capture the attention of those who are hesitant. Describing this context and analyzing its possible causes, what interventions or strategies could be effective to reduce COVID-19 vaccine hesitancy? Will social media trend analysis be helpful in trying to solve this complex issue? Are there perspectives for an efficient implementation of COVID-19 vaccination coverage as well as for all the other vaccinations?