Quantifying and explaining accessibility with application to the 2009 H1N1 vaccination campaign

The role of hesitancy and infrastructure in the equity and efficiency of COVID-19 vaccine administration

After the first COVID-19 vaccines received emergency use authorization from the U.S. FDA in December 2020, U.S. states employed vaccine eligibility and administration plans (VEAPs) that determined when subgroups of residents would become eligible to receive the vaccine while the vaccine supply was still limited. During the implementation of these plans, public concern grew over whether the VEAPs and vaccine allocations from the federal government were resulting in an equitable and efficient vaccine distribution. In this study, we collected data on five states' VEAPs, federal vaccine allocations, vaccine administration, and vaccine hesitancy to assess the equity of vaccine access and vaccine administration efficiency that manifested during the campaign. Our results suggest that residents in states which opened eligibility to the vaccine sooner had more competition among residents to receive the vaccine than occurred in other states. Regardless of states' VEAPs, there was a consistent inefficiency in vaccine administration among all five states that could be attributed to both state and federal infrastructure deficits. A closer examination revealed a misalignment between federal vaccine allocations and the total eligible population in the states throughout the campaign, even when accounting for hesitancy. We conclude that in order to maximize the efficiency of future mass-vaccination campaigns, the federal and state governments should design adaptable allocation policies and eligibility plans that better match the true, real-time supply and demand for vaccines by accounting for vaccine hesitancy and manufacturing capacity. Further, we discuss the challenges of implementing such strategies.

Evaluating spatial accessibility to COVID-19 vaccine resources in diversely populated counties in the United States

This study examines the accessibility to COVID-19 vaccination resources in two counties surrounding Newark, NJ in the New York Metropolitan Area, United States. The study area represents diverse population makeups. COVID-19 vaccines were made available by different types of vaccination sites including county mass vaccination sites, medical facilities and pharmacies, and a FEMA community vaccination center in spring 2021. We used the two-step floating catchment area method to measure accessibility and calculated the average accessibility scores of different population groups. We examined the patterns and tested the significance of the differences in accessibility across population groups. The results showed clear spatial heterogeneity in the accessibility to vaccine resources with the existing infrastructure (medical/pharmacy vaccine sites). Accessibility patterns changed with the introduction of county mass sites and the FEMA community site. The county mass vaccination sites in one county greatly increased accessibilities for populations of minority and poverty. The FEMA community site in the other county accomplished the same. Both the local health department and the federal government played an important role in mitigating pre-existing inequalities during the vaccination campaign. Our study shows that social determinants of health need to be addressed and taken into explicit consideration when planning resource distribution during the pandemic.

Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review

This review presents and reviews various solved and open problems in developing, analyzing, and mitigating epidemic spreading processes under human decision-making. We provide a review of a range of epidemic models and explain the pros and cons of different epidemic models. We exhibit the art of coupling between epidemic models and decision models in the existing literature. More specifically, we provide answers to fundamental questions in human decision-making amid epidemics, including what interventions to take to combat the disease, who are decision-makers, and when and how to take interventions, and how to make interventions. Among many decision models, game-theoretic models have become increasingly crucial in modeling human responses or behavior amid epidemics in the last decade. In this review, we motivate the game-theoretic approach to human decision-making amid epidemics. This review provides an overview of the existing literature by developing a multi-dimensional taxonomy, which categorizes existing literature based on multiple dimensions, including (1) types of games, such as differential games, stochastic games, evolutionary games, and static games; (2) types of interventions, such as social distancing, vaccination, quarantine, and taking antidotes; (3) the types of decision-makers, such as individuals, adversaries, and central authorities at different hierarchical levels. A fine-grained dynamic game framework is proposed to capture the essence of game-theoretic decision-making amid epidemics. We showcase three representative frameworks with unique ways of integrating game-theoretic decision-making into the epidemic models from a vast body of literature. Each of the three frameworks has their unique way of modeling and analyzing and develops results from different angles. In the end, we identify several main open problems and research gaps left to be addressed and filled.

Reflecting on prediction strategies for epidemics: Preparedness and public health response

Objective

To provide an overview of the literature on respiratory infectious disease epidemic prediction, preparedness, and response (including pharmaceutical and nonpharmaceutical interventions) and their impact on public health, with a focus on respiratory conditions such as asthma.

Data Sources

Published literature obtained through PubMed database searches.

Study Selections

Studies relevant to infectious epidemics, asthma, modeling approaches, health care access, and data analytics related to intervention strategies.

Results

Prediction, prevention, and response strategies for infectious disease epidemics use extensive data sources and analytics, addressing many areas including testing and early diagnosis, identifying populations at risk of severe outcomes such as hospitalizations or deaths, monitoring and understanding transmission and spread patterns by age group, social interactions geographically and over time, evaluating the effectiveness of pharmaceutical and nonpharmaceutical interventions, and understanding prioritization of and access to treatment or preventive measures (eg, vaccination, masks), given limited resources and system constraints.

Conclusion

Previous epidemics and pandemics have revealed the importance of effective preparedness and response. Further research and implementation need to be performed to emphasize timely and actionable strategies, including for populations with particular health conditions (eg, chronic respiratory diseases) at risk for severe outcomes.