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

A unified framework for diagnostic test development and evaluation during outbreaks of emerging infections

Evaluating diagnostic test accuracy during epidemics is difficult due to an urgent need for test availability, changing disease prevalence and pathogen characteristics, and constantly evolving testing aims and applications. Based on lessons learned during the SARS-CoV-2 pandemic, we introduce a framework for rapid diagnostic test development, evaluation, and validation during outbreaks of emerging infections. The framework is based on the feedback loop between test accuracy evaluation, modelling studies for public health decision-making, and impact of public health interventions. We suggest that building on this feedback loop can help future diagnostic test evaluation platforms better address the requirements of both patient care and public health.

Learning from the COVID-19 pandemic: A systematic review of mathematical vaccine prioritization models

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

Cost-Effectiveness Analysis of Vaccines for COVID-19 According to Sex, Comorbidity and Socioeconomic Status: A Population Study

BACKGROUND AND OBJECTIVE

Coronavirus disease 2019 (COVID-19) vaccines are extremely effective in preventing severe disease, but their real-world cost effectiveness is still an open question. We present an analysis of the cost-effectiveness and economic impact of the initial phase of the COVID-19 vaccination rollout in the Basque Country, Spain.

METHODS

To calculate costs and quality-adjusted life years for the entire population of the Basque Country, dynamic modelling and a real-world data analysis were combined. Data on COVID-19 infection outcomes (cases, hospitalisations, intensive care unit admissions and deaths) and population characteristics (age, sex, socioeconomic status and comorbidity) during the initial phase of the vaccination rollout, from January to June of 2021, were retrieved from the Basque Health Service database. The outcomes in the alternative scenario (without vaccination) were estimated with the dynamic model used to guide public health authority policies, from February to December 2020. Individual comorbidity-adjusted life expectancy and costs were estimated.

RESULTS

By averting severe disease-related outcomes, COVID-19 vaccination resulted in monetary savings of €26.44 million for the first semester of 2021. The incremental cost-effectiveness ratio was €707/quality-adjusted life year considering official vaccine prices and dominant real prices. While the analysis by comorbidity showed that vaccines were considerably more cost effective in individuals with pre-existing health conditions, this benefit was lower in the low socioeconomic status group.

CONCLUSIONS

The incremental cost-effectiveness ratio of the vaccination programme justified the policy of prioritising high-comorbidity patients. The initial phase of COVID-19 vaccination was dominant from the perspective of the healthcare payer.

The application of simulation methods during the COVID-19 pandemic: A scoping review

With the outbreak of COVID-19 pandemic, simulation modelling approaches have become effective tools to simulate the potential effects of different intervention measures and predict the dynamic COVID-19 trends. In this scoping review, Studies published between February 2020 and May 2022 that investigated the spread of COVID-19 using four common simulation modeling methods were systematically reported and summarized. Publication trend, characteristics, software, and code availability of included articles were analyzed. Among the included 340 studies, most articles used agent-based model (ABM; n = 258; 75.9 %), followed by the models of system dynamics (n = 42; 12.4 %), discrete event simulation (n = 25; 7.4 %), and hybrid simulation (n = 15; 4.4 %). Furthermore, our review emphasized the purposes and sample time period of included articles. We classified the purpose of the 340 included studies into five categories, most studies mainly analyzed the spread of COVID-19 under policy interventions. For the sample time period analysis, most included studies analyzed the COVID-19 spread in the second wave. Our findings play a crucial role for policymakers to make evidence-based decisions in preventing the spread of COVID-19 pandemic and help in providing scientific decision-makings resilient to similar events and infectious diseases in the future.

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

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

COVID-19 vaccine distribution planning using a congested queuing system-A real case from Australia

Crisis-induced vaccine supply chain management has recently drawn attention to the importance of immediate responses to a crisis (e.g., the COVID-19 pandemic). This study develops a queuing model for a crisis-induced vaccine supply chain to ensure efficient coordination and distribution of different COVID-19 vaccine types to people with various levels of vulnerability. We define a utility function for queues to study the changes in arrival rates related to the inventory level of vaccines, the efficiency of vaccines, and a risk aversion coefficient for vaccinees. A multi-period queuing model considering congestion in the vaccination process is proposed to minimise two contradictory objectives: (i) the expected average wait time of vaccinees and (ii) the total investment in the holding and ordering of vaccines. To develop the bi-objective non-linear programming model, the goal attainment algorithm and the non-dominated sorting genetic algorithm (NSGA-II) are employed for small- to large-scale problems. Several solution repairs are also implemented in the classic NSGA-II algorithm to improve its efficiency. Four standard performance metrics are used to investigate the algorithm. The non-parametric Friedman and Wilcoxon signed-rank tests are applied on several numerical examples to ensure the privilege of the improved algorithm. The NSGA-II algorithm surveys an authentic case study in Australia, and several scenarios are created to provide insights for an efficient vaccination program.

Hamster models of COVID-19 pneumonia reviewed: How human can they be?

The dramatic global consequences of the coronavirus disease 2019 (COVID-19) pandemic soon fueled quests for a suitable model that would facilitate the development and testing of therapies and vaccines. In contrast to other rodents, hamsters are naturally susceptible to infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the Syrian hamster (Mesocricetus auratus) rapidly developed into a popular model. It recapitulates many characteristic features as seen in patients with a moderate, self-limiting course of the disease such as specific patterns of respiratory tract inflammation, vascular endothelialitis, and age dependence. Among 4 other hamster species examined, the Roborovski dwarf hamster (Phodopus roborovskii) more closely mimics the disease in highly susceptible patients with frequent lethal outcome, including devastating diffuse alveolar damage and coagulopathy. Thus, different hamster species are available to mimic different courses of the wide spectrum of COVID-19 manifestations in humans. On the other hand, fewer diagnostic tools and information on immune functions and molecular pathways are available than in mice, which limits mechanistic studies and inference to humans in several aspects. Still, under pandemic conditions with high pressure on progress in both basic and clinically oriented research, the Syrian hamster has turned into the leading non-transgenic model at an unprecedented pace, currently used in innumerable studies that all aim to combat the impact of the virus with its new variants of concern. As in other models, its strength rests upon a solid understanding of its similarities to and differences from the human disease, which we review here.

An iterative algorithm for optimizing COVID-19 vaccination strategies considering unknown supply

BACKGROUND AND OBJECTIVE

The distribution of the newly developed vaccines presents a great challenge in the ongoing SARS-CoV-2 pandemic. Policy makers must decide which subgroups should be vaccinated first to minimize the negative consequences of the pandemic. These decisions must be made upfront and under uncertainty regarding the amount of vaccine doses available at a given time. The objective of the present work was to develop an iterative optimization algorithm, which provides a prioritization order of predefined subgroups. The results of this algorithm should be optimal but also robust with respect to potentially limited vaccine supply.

METHODS

We present an optimization meta-heuristic which can be used in a classic simulation-optimization setting with a simulation model in a feedback loop. The meta-heuristic can be applied in combination with any epidemiological simulation model capable of depicting the effects of vaccine distribution to the modeled population, accepts a vaccine prioritization plan in a certain notation as input, and generates decision making relevant variables such as COVID-19 caused deaths or hospitalizations as output. We finally demonstrate the mechanics of the algorithm presenting the results of a case study performed with an epidemiological agent-based model.

RESULTS

We show that the developed method generates a highly robust vaccination prioritization plan which is proven to fulfill an elegant supremacy criterion: the plan is equally optimal for any quantity of vaccine doses available. The algorithm was tested on a case study in the Austrian context and it generated a vaccination plan prioritization favoring individuals age 65+, followed by vulnerable groups, to minimize COVID-19 related burden.

DISCUSSION

The results of the case study coincide with the international policy recommendations which strengthen the applicability of the approach. We conclude that the path-dependent optimum optimum provided by the algorithm is well suited for real world applications, in which decision makers need to develop strategies upfront under high levels of uncertainty.

A 5-year look-back at the notification and management of vaccine supply shortages in Germany

BackgroundUnavailability of vaccines endangers the overall goal to protect individuals and whole populations against infections.MethodsThe German notification system includes the publication of vaccine supply shortages reported by marketing authorisation holders (MAH), information on the availability of alternative vaccine products, guidance for physicians providing vaccinations and an unavailability reporting tool to monitor regional distribution issues.AimThis study provides a retrospective analysis of supply issues and measures in the context of European and global vaccine supply constraints.Resultsbetween October 2015 and December 2020, the 250 notifications concerned all types of vaccines (54 products). Most shortages were caused by increased demand associated with immigration in Germany in 2015 and 2016, new or extended vaccine recommendations, increased awareness, or changes in global immunisation programmes. Shortages of a duration up to 30 days were mitigated using existing storage capacities. Longer shortages, triggered by high demand on a national level, were mitigated using alternative products and re-allocation; in a few cases, vaccines were imported. However, for long lasting supply shortages associated with increased global demand, often occurring in combination with manufacturing issues, few compensatory mechanisms were available. Nevertheless, only few critical incidents were identified: (i) shortage of hexavalent vaccines endangering neonatal immunisation programmes in 2015;(ii) distribution issues with influenza vaccines in 2018; and (iii) unmet demand for pneumococcal and influenza vaccines during the coronavirus disease (COVID)-19 pandemic.ConclusionVaccine product shortages in Germany resemble those present in neighbouring EU states and often reflect increased global demand not matched by manufacturing capacities.

On the role of data, statistics and decisions in a pandemic

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

You are only as safe as your riskiest contact: Effective COVID-19 vaccine distribution using local network information

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Using simulation to evaluate nomination of most popular contacts for vaccination.

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Simulating spread of COVID-19 across two contact networks among high-schoolers.

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Targeting in this way can reduce spread to the susceptible population by 20% or more.

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Results are robust in a synthetic network replicating spread in a small town.

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Results are robust across a wide range of infectiousness, and mistaken nomination.

When vaccines are limited, prior research has suggested it is most protective to distribute vaccines to the most central individuals – those who are most likely to spread the disease. But surveying the population’s social network is a costly and time-consuming endeavour, often not completed before vaccination must begin. This paper validates a local targeting method for distributing vaccines. That is, ask randomly chosen individuals to nominate for vaccination the person they are in contact with who has the most disease-spreading contacts. Even better, ask that person to nominate the next person for vaccination, and so on. To validate this approach, we simulate the spread of COVID-19 along empirical contact networks collected in two high schools, in the United States and France, pre-COVID. These weighted networks are built by recording whenever students are in close spatial proximity and facing one another. We show here that nomination of most popular contacts performs significantly better than random vaccination, and on par with strategies which assume a full survey of the population. These results are robust over a range of realistic disease-spread parameters, as well as a larger synthetic contact network of 3000 individuals.

Unresolved COVID Controversies: 'Normal science' and potential non-scientific influences

ABSTRACTThe COVID-19 health crisis has so far involved enormous consequences in human pain, suffering and death. While biomedical science responded early, its response has been marked by several controversies between what appeared to be mainstream perspectives, and diverse alternative views; far from leading to productive debate, controversies often preceded polarisation and, allegedly, exclusion and even censorship of alternative views, followed by the pretense of scientific consensus. This paper describes and discusses the main controversies in the production of COVID biomedical knowledge and derived control measures, to establish if alternative positions are also legitimate from a 'normal science' perspective (rather than comparing them for superiority); explores potential non-scientific explanations of the alleged exclusion of certain views; and analyzes ethical issues implied. The operation of non-scientific factors in scientific and regulatory processes (e.g. various forms of subtle corruption) has been documented in the past; the intervention of such influences in the mishandling of controversies (i.e. on early management, non-pharmacological prevention and vaccination) cannot be ruled out and deserves further investigation. Some of these controversies, increasingly visible in the public domain, also involve ethical challenges that need urgent attention. Polarisation, censorship and dogma are foreign to true science and must be left behind.

When Do We Need Massive Computations to Perform Detailed COVID-19 Simulations?

The COVID-19 pandemic has infected over 250 million people worldwide and killed more than 5 million as of November 2021. Many intervention strategies are utilized (e.g., masks, social distancing, vaccinations), but officials making decisions have a limited time to act. Computer simulations can aid them by predicting future disease outcomes, but they also require significant processing power or time. It is examined whether a machine learning model can be trained on a small subset of simulation runs to inexpensively predict future disease trajectories resembling the original simulation results. Using four previously published agent-based models (ABMs) for COVID-19, a decision tree regression for each ABM is built and its predictions are compared to the corresponding ABM. Accurate machine learning meta-models are generated from ABMs without strong interventions (e.g., vaccines, lockdowns) using small amounts of simulation data: the root-mean-square error (RMSE) with 25% of the data is close to the RMSE for the full dataset (0.15 vs 0.14 in one model; 0.07 vs 0.06 in another). However, meta-models for ABMs employing strong interventions require much more training data (at least 60%) to achieve a similar accuracy. In conclusion, machine learning meta-models can be used in some scenarios to assist in faster decision-making.

Austria's Digital Vaccination Registry: Stakeholder Views and Implications for Governance

In this study, we explore the recent setup of a digital vaccination record in Austria. Working from a social-scientific perspective, we find that the introduction of the electronic vaccination pass was substantially accelerated by the COVID-19 pandemic. Our interviews with key stakeholders (n = 16) indicated that three main factors drove this acceleration. The pandemic (1) sidelined historical conflicts regarding data ownership and invoked a shared sense of the value of data, (2) accentuated the need for enhanced administrative efficiency in an institutionally fragmented system, and (3) helped invoke the national vaccination registry as an indispensable infrastructure for public health governance with the potential to innovate its healthcare system in the long term.

Modelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden

Against the COVID-19 pandemic, non-pharmaceutical interventions have been widely applied and vaccinations have taken off. The upcoming question is how the interplay between vaccinations and social measures will shape infections and hospitalizations. Hence, we extend the Susceptible-Exposed-Infectious-Removed (SEIR) model including these elements. We calibrate it to data of Luxembourg, Austria and Sweden until 15 December 2020. Sweden results having the highest fraction of undetected, Luxembourg of infected and all three being far from herd immunity in December. We quantify the level of social interaction, showing that a level around 1/3 of before the pandemic was still required in December to keep the effective reproduction number Refft below 1, for all three countries. Aiming to vaccinate the whole population within 1 year at constant rate would require on average 1,700 fully vaccinated people/day in Luxembourg, 24,000 in Austria and 28,000 in Sweden, and could lead to herd immunity only by mid summer. Herd immunity might not be reached in 2021 if too slow vaccines rollout speeds are employed. The model thus estimates which vaccination rates are too low to allow reaching herd immunity in 2021, depending on social interactions. Vaccination will considerably, but not immediately, help to curb the infection; thus limiting social interactions remains crucial for the months to come.

Modeling and Forecasting Cases of RSV Using Artificial Neural Networks

In this paper, we study and present a mathematical modeling approach based on artificial neural networks to forecast the number of cases of respiratory syncytial virus (RSV). The number of RSV-positive cases in most of the countries around the world present a seasonal-type behavior. We constructed and developed several multilayer perceptron (MLP) models that intend to appropriately forecast the number of cases of RSV, based on previous history. We compared our mathematical modeling approach with a classical statistical technique for the time-series, and we concluded that our results are more accurate. The dataset collected during 2005 to 2010 consisting of 312 weeks belongs to Bogotá D.C., Colombia. The adjusted MLP network that we constructed has a fairly high forecast accuracy. Finally, based on these computations, we recommend training the selected MLP model using 70% of the historical data of RSV-positive cases for training and 20% for validation in order to obtain more accurate results. These results are useful and provide scientific information for health authorities of Colombia to design suitable public health policies related to RSV.

Efficiency in COVID-19 Vaccination Campaigns-A Comparison across Germany's Federal States

Vaccination programs are considered a central pillar of the efforts to stop COVID-19. However, vaccine doses are scarce and several organizational and logistical obstacles, such as the timing of and reserves for second shots and delivery failures, apparently slow down vaccination roll-outs in several countries. Moreover, it is an open question as to where vaccines are administered as efficiently as possible (vaccination centers, hospitals, doctor's offices, pharmacists, etc.). The first aim of our study was to systematically evaluate the efficiency of a country's vaccination campaign. The second aim was to analyze how the integration of doctors' offices into a campaign that formerly relied only on vaccination centers affected the speed of that campaign. Using data on vaccine deliveries and vaccinations given in Germany, we find considerable differences across federal states in terms of efficiency, defined as the ability to administer the most vaccinations out of a given number of available doses. Back-of-the-envelope calculations for January to May 2021 show that vaccinations would have been 3.4-6.9% higher if all federal states had adopted a similar ratio between vaccinations given and vaccines stored, as the most efficient states did. This corresponds to 1.7-3.3% of Germany's total population. In terms of our second research goal, we find evidence that the integration of doctors' offices into the vaccination campaign significantly increased the ratio of vaccinations administered out of a given stock of vaccine doses. On average, there appears to be a structural break in this ratio after doctors' offices were integrated into the vaccination campaign on 5 April 2021. On average, an additional 11.6 out of 100 available doses were administered each week compared to the period prior to that date. We conclude that there are considerable regional differences in the efficiency of the vaccination roll-out. Systematic efficiency analyses are one step to detecting inefficiencies and to identify best practices that can be adopted to eventually speed up the vaccination roll-out in a country.