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1. AI-Driven Agent-Based Models to Study the Role of Vaccine Acceptance in Controlling COVID-19 Spread in the US

   https://doi.org/10.1109/BigData52589.2021.9671811  

   Bhattacharya, Parantapa; Machi, Dustin; Chen, Jiangzhuo; Hoops, Stefan; Lewis, Bryan; Mortveit, Henning; Venkatramanan, Srinivasan; Wilson, Mandy L.; Marathe, Achla; Porebski, Przemyslaw; et al (December 2021, IEEE International Conference on Big Data)

   We study the role of vaccine acceptance in controlling the spread of COVID-19 in the US using AI-driven agent-based models. Our study uses a 288 million node social contact network spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12.59 billion daily interactions. The highly-resolved agent-based models use realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Developing a national model at this resolution that is driven by realistic data requires a complex scalable workflow, model calibration, simulation, and analytics components. Our workflow optimizes the total execution time and helps in improving overall human productivity.This work develops a pipeline that can execute US-scale models and associated workflows that typically present significant big data challenges. Our 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 nationwide. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. Improving vaccine acceptance by 10% in all states increases averted infections from 4.5M to 4.7M (a 4.4% improvement) and total deaths from 28.2K to 29.9K (a 6% increase) nationwide. The analysis also reveals interesting spatio-temporal differences in COVID-19 dynamics as a result of vaccine acceptance. To our knowledge, this is the first national-scale analysis of the effect of vaccine acceptance on the spread of COVID-19, using detailed and realistic agent-based models. 

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2. Differential effects of intervention timing on COVID-19 spread in the United States

   https://doi.org/10.1126/sciadv.abd6370  

   Pei, Sen; Kandula, Sasikiran; Shaman, Jeffrey (December 2020, Science Advances)

   null (Ed.)

   Assessing the effects of early nonpharmaceutical interventions on coronavirus disease 2019 (COVID-19) spread is crucial for understanding and planning future control measures to combat the pandemic. We use observations of reported infections and deaths, human mobility data, and a metapopulation transmission model to quantify changes in disease transmission rates in U.S. counties from 15 March to 3 May 2020. We find that marked, asynchronous reductions of the basic reproductive number occurred throughout the United States in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same measures been implemented 1 to 2 weeks earlier, substantial cases and deaths could have been averted and that delayed responses to future increased incidence will facilitate a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive control in combatting the COVID-19 pandemic. 

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3. Reimagining COVID vaccine distribution: reflecting on waste and equity

   https://doi.org/10.1186/s12963-026-00460-x  

   Greenhalgh, Scott; Alva, Maria L (February 2026, Population Health Metrics)

   Background: Efficient distribution and administration of vaccines are critical to preventing unnecessary morbidity and mortality. We assess the distribution, uptake, and wastage of COVID-19 vaccine doses across the U.S., providing insights for optimizing future vaccination distribution strategies. We quantify the impact of limiting vaccine wastage and illustrate incidence and deaths averted under two targets set by the Global Alliance for Vaccines and Immunization (GAVI). Methods: We obtained COVID-19 vaccine doses administered by location and wastage data from jurisdictions, pharmacies, and federal entities from the Centers for Disease Control and Prevention through a Freedom of Information Act. From this data, along with county-level data on COVID-19 vaccine hesitancy, we conducted a retrospective analysis covering the period from December 2020 to October 2022 involving 761 million vaccine doses distributed across all counties and states in the U.S. We use GAVI targets of 25% and 15% vaccine waste to serve as benchmarks for assessing the impact of potential improvements in vaccine distribution and acceptance at the county and state levels in the U.S. Results: We estimate the proportion of vaccines wasted, and then incidence and deaths averted had adherence to GAVI waste targets occurred to inform on the quality of the national vaccination effort and identify potential regions for improvement. Among the 761 million distributed COVID-19 vaccine doses, only 600 million were administered, resulting in a national average of 1.8 doses per capita. Substantial regional disparities were observed, with the District of Columbia reaching 2.5 doses per capita and Alabama lagging at 1.3 doses per capita. Thirty states exceeded the GAVI 15% vaccine waste target, corresponding to 64.2 million unused doses. Meeting the 15% target would have averted 36.1 million incidences and 7.8 thousand deaths. Conclusions: Addressing the causes of county-level variations and targeting states with below-average vaccine hesitancy and above-target vaccine waste would likely maximize future vaccine distribution efforts and minimize wastage-related losses. This strategy highlights an avenue for improving future vaccine distribution policy. 

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4. Vaccination and three non-pharmaceutical interventions determine the dynamics of COVID-19 in the US

   https://doi.org/10.1057/s41599-022-01142-3  

   Zhong, Lu; Diagne, Mamadou; Wang, Qi; Gao, Jianxi (December 2022, Humanities and Social Sciences Communications)

   Abstract The rapid rollout of the COVID-19 vaccine raises the question of whether and when the ongoing pandemic could be eliminated with vaccination and non-pharmaceutical interventions (NPIs). Despite advances in the impact of NPIs and the conceptual belief that NPIs and vaccination control COVID-19 infections, we lack evidence to employ control theory in real-world social human dynamics in the context of disease spreading. We bridge the gap by developing a new analytical framework that treats COVID-19 as a feedback control system with the NPIs and vaccination as the controllers and a computational model that maps human social behaviors into input signals. This approach enables us to effectively predict the epidemic spreading in 381 Metropolitan statistical areas (MSAs) in the US by learning our model parameters utilizing the time series NPIs (i.e., the stay-at-home order, face-mask wearing, and testing) data. This model allows us to optimally identify three NPIs to predict infections accurately in 381 MSAs and avoid over-fitting. Our numerical results demonstrate our approach’s excellent predictive power with R 2  > 0.9 for all the MSAs regardless of their sizes, locations, and demographic status. Our methodology allows us to estimate the needed vaccine coverage and NPIs for achieving R e to a manageable level and how the variants of concern diminish the likelihood for disease elimination at each location. Our analytical results provide insights into the debates surrounding the elimination of COVID-19. NPIs, if tailored to the MSAs, can drive the pandemic to an easily containable level and suppress future recurrences of epidemic cycles. 

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5. Universal healthcare as pandemic preparedness: The lives and costs that could have been saved during the COVID-19 pandemic

   Alison P Galvani; Alyssa S Parpia; Abhishek Pandey; Pratha Sah; Kenneth Colon; Gerald Friedman; Travis Campbell; James G Kahn; Burton H Singer; Meagan C Fitzpatrick (June 2022, Proceedings of the National Academy of Sciences of the United States of America)

   The fragmented and inefficient healthcare system in the United States leads to many preventable deaths and unnecessary costs every year. During a pandemic, the lives saved and economic benefits of a single-payer universal healthcare system relative to the status quo would be even greater. For Americans who are uninsured and underinsured, financial barriers to COVID-19 care delayed diagnosis and exacerbated transmission. Concurrently, deaths beyond COVID-19 accrued from the background rate of uninsurance. Universal healthcare would alleviate the mortality caused by the confluence of these factors. To evaluate the repercussions of incomplete insurance coverage in 2020, we calculated the elevated mortality attributable to the loss of employer-sponsored insurance and to background rates of uninsurance, summing with the increased COVID-19 mortality due to low insurance coverage. Incorporating the demography of the uninsured with age-specific COVID-19 and nonpandemic mortality, we estimated that a single-payer universal healthcare system would have saved about 212,000 lives in 2020 alone. We also calculated that US$105.6 billion of medical expenses associated with COVID-19 hospitalization could have been averted by a single-payer universal healthcare system over the course of the pandemic. These economic benefits are in addition to US$438 billion expected to be saved by single-payer universal healthcare during a nonpandemic year. 

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