Can vaccine prioritization reduce disparities in COVID-19 burden for historically marginalized populations?

SARS-CoV-2 vaccination strategies were designed to reduce COVID-19 mortality, morbidity, and health inequities. To assess the impact of vaccination strategies on disparities in COVID-19 burden among historically marginalized populations (HMPs), e.g. Black race and Hispanic ethnicity, we used an agent-based simulation model, populated with census-tract data from North Carolina. We projected COVID-19 deaths, hospitalizations, and cases from 2020 July 1 to 2021 December 31, and estimated racial/ethnic disparities in COVID-19 outcomes. We modeled 2-stage vaccination prioritization scenarios applied to sub-groups including essential workers, older adults (65+), adults with high-risk health conditions, HMPs, or people in low-income tracts. Additionally, we estimated the effects of maximal uptake (100% for HMP vs. 100% for everyone), and distribution to only susceptible people. We found strategies prioritizing essential workers, then older adults led to the largest mortality and case reductions compared to no prioritization. Under baseline uptake scenarios, the age-adjusted mortality for HMPs was higher (e.g. 33.3%-34.1% higher for the Black population and 13.3%-17.0% for the Hispanic population) compared to the White population. The burden on HMPs decreased only when uptake was increased to 100% in HMPs; however, the Black population still had the highest relative mortality rate even when targeted distribution strategies were employed. If prioritization schemes were not paired with increased uptake in HMPs, disparities did not improve. The vaccination strategies publicly outlined were insufficient, exacerbating disparities between racial and ethnic groups. Strategies targeted to increase vaccine uptake among HMPs are needed to ensure equitable distribution and minimize disparities in outcomes.

Flexible analytic model to inform multi-stakeholder pediatric vaccine scheduling decisions.

BACKGROUND: Pediatric immunization is important for preventing potentially life-threatening diseases in children. Over time, the number of recommended pediatric vaccines has increased and is likely to increase further as new vaccines are developed. Given the different number of doses for available vaccines and various constraints (e.g., the appropriate age for each dose of a vaccine or the time between doses), it is challenging to develop a recommended vaccination schedule or a catch-up schedule when a child falls behind on one or more vaccinations. METHODS: We developed an integer programming optimization model, enabled by Python programming and embedded into an Excel-based decision tool, to recommend childhood vaccination schedules or personalized catch-up schedules. The model recommends a vaccination schedule that balances the goal of being as close as possible to the clinically-indicated dosing schedules and the goal of minimizing clinic visits, and gives users the ability to trade off between these two goals. We illustrated the broad applicability of our proposed model with commonly-faced vaccine scheduling challenges in the United States. RESULTS: The illustrative computational case study confirms our model's ability to create personalized schedules based on each child's age and vaccination history, and to adjust appropriately when a new vaccine becomes available. CONCLUSIONS: The model presented in this paper fills the need for an easy-to-use tool to recommend vaccination schedules for de novo and catch-up purposes. It provides straightforward recommendations that can be easily used by physicians, is flexible to handle the requirements varying by region, and can be updated as new vaccines are approved for use.

The balancing role of distribution speed against varying efficacy levels of COVID-19 vaccines under variants.

During a pandemic, vaccination plays an important role in reducing the infection spread or adverse outcomes such as hospitalizations and deaths. However, a vaccine's overall public health impact depends not only on its initial efficacy, but also its efficacy against emerging variants and ease and speed of distribution. For example, mutations in SARS-CoV-2 raised concerns about diminishing vaccine effectiveness against COVID-19 caused by particular variants. Furthermore, due to supply-chain challenges, the accessibility and distribution of the vaccines have been hindered in many regions, especially in low-income countries, while the second or third wave of the COVID-19 pandemic has occurred due to the variants. Hence, we evaluated the interactions between the speed of distribution and efficacy against infection of multiple vaccines when variants emerge by utilizing a Susceptible-Infected-Recovered-Deceased model and assessing the level of infection attack rate. Our results show that speed is a key factor to a successful immunization strategy to control the pandemic even when the emerging variants may reduce the efficacy of a vaccine. Understanding the interactions between speed and efficacy and distributing vaccines that are available as quickly as possible are crucial to eradicate the pandemic before new variants spread.

A Structured Overview of Insights and Opportunities for Enhancing Supply Chain Resilience

Widespread product shortages during the COVID-19 pandemic and other emergencies have prompted several large studies of how to make supply chains more resilient. In this paper, we leverage these studies, as well as the academic literature, to provide a review of our state of knowledge about supply chain resilience. To do this, we (1) classify the failure modes of a supply chain, (2) quantitatively evaluate the level of resilience needed in a supply chain to achieve desired business or societal outcomes, (3) describe a structured framework of actions to enhance supply chain resilience, and (4) use the resulting conceptual paradigm to review the academic literature on supply chain risk and resilience. In each step, we summarize key insights from our current state of understanding, as well as gaps that present opportunities for research and practice. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Impact of Testing Capacity and Compliance With Isolation on COVID-19: A Mathematical Modeling Study.

Introduction: Diagnostic tests can play an important role in reducing the transmission of infectious respiratory diseases, particularly during a pandemic. The potential benefit of diagnostic testing depends on at least 4 factors: (1) how soon testing becomes available after the beginning of the pandemic and (2) at what capacity; (3) compliance with isolation after testing positive; and (4) compliance with isolation when experiencing symptoms, even in the absence of testing. Methods: To understand the interplay between these factors and provide further insight into policy decisions for future pandemics, we developed a compartmental model and simulated numerous scenarios using the dynamics of COVID-19 as a case study. Results: Our results quantified the significant benefits of early start of testing and high compliance with isolation. Early start of testing, even with low testing capacity over time, could significantly slow down the disease spread if compliance with isolation is high. By contrast, when the start of testing was delayed, the benefit of testing on reducing infection spread was limited, even when testing capacity was high; the additional testing capacity required increased superlinearly for each day of delay to achieve a similar infection attack rate as in starting testing earlier. Conclusions: Our study highlighted the importance of the early start of testing and public health messaging to promote isolation compliance when needed for an ongoing effective response to COVID-19 and future pandemics.

Evaluating scenarios for school reopening under COVID19.

BACKGROUND: Thousands of school systems have struggled with the decisions about how to deliver education safely and effectively amid the COVID19 pandemic. This study evaluates the public health impact of various school reopening scenarios (when, and how to return to in-person instruction) on the spread of COVID19. METHODS: An agent-based simulation model was adapted and used to project the impact of various school reopening strategies on the number of infections, hospitalizations, and deaths in the state of Georgia during the study period, i.e., February 18th-November 24th, 2020. The tested strategies include (i) schools closed, i.e., all students receive online instruction, (ii) alternating school day, i.e., half of the students receive in-person instruction on Mondays and Wednesdays and the other half on Tuesdays and Thursdays, (iii) alternating school day for children, i.e., half of the children (ages 0-9) receive in-person instruction on Mondays and Wednesdays and the other half on Tuesdays and Thursdays, (iv) children only, i.e., only children receive in-person instruction, (v) regular, i.e., all students return to in-person instruction. We also tested the impact of universal masking in schools. RESULTS: Across all scenarios, the number of COVID19-related deaths ranged from approximately 8.8 to 9.9 thousand, the number of cumulative infections ranged from 1.76 to 1.96 million for adults and 625 to 771 thousand for children and youth, and the number of COVID19-related hospitalizations ranged from approximately 71 to 80 thousand during the study period. Compared to schools reopening August 10 with a regular reopening strategy, the percentage of the population infected reduced by 13%, 11%, 9%, and 6% in the schools closed, alternating school day for children, children only, and alternating school day reopening strategies, respectively. Universal masking in schools for all students further reduced outcome measures. CONCLUSIONS: Reopening schools following a regular reopening strategy would lead to higher deaths, hospitalizations, and infections. Hybrid in-person and online reopening strategies, especially if offered as an option to families and teachers who prefer to opt-in, provide a good balance in reducing the infection spread compared to the regular reopening strategy, while ensuring access to in-person education.

Resource allocation for different types of vaccines against COVID-19: Tradeoffs and synergies between efficacy and reach.

OBJECTIVE: Vaccine shortage and supply-chain challenges have caused limited access by many resource-limited countries during the COVID-19 pandemic. One of the primary decisions for a vaccine-ordering decision-maker is how to allocate the limited resources between different types of vaccines effectively. We studied the tradeoff between efficacy and reach of the two vaccine types that become available at different times. METHODS: We extended a Susceptible-Infected-Recovered-Deceased (SIR-D) model with vaccination, ran extensive simulations with different settings, and compared the level of infection attack rate (IAR) under different reach ratios between two vaccine types under different resource allocation decisions. RESULTS: We found that when there were limited resources, allocating resources to a vaccine with high efficacy that became available earlier than a vaccine with lower efficacy did not always lead to a lower IAR, particularly if the former could vaccinate less than 42.5% of the population (with the selected study parameters) who could have received the latter. Sensitivity analyses showed that this result stayed robust under different study parameters. CONCLUSIONS: Our results showed that a vaccine with lower resource requirements (wider reach) can significantly contribute to reducing IAR, even if it becomes available later in the pandemic, compared to a higher efficacy vaccine that becomes available earlier but requires more resources. Limited resource in vaccine distribution is significant challenge in many parts of the world that needs to be addressed to improve the global access to life-saving vaccines. Understanding the tradeoffs between efficacy and reach is critical for resource allocation decisions between different vaccine types for improving health outcomes.

Significance of SARS-CoV-2 specific antibody testing during COVID-19 vaccine allocation.

OBJECTIVE: To assess the value of using SARS-CoV-2 specific antibody testing to prioritize the vaccination of susceptible individuals as part of a COVID-19 vaccine distribution plan when vaccine supply is limited. METHODS: An extended susceptible-infected-recovered (SIR) compartmental model was used to simulate COVID-19 spread when considering diagnosis, isolation, and vaccination of a cohort of 1 million individuals. The scenarios modeled represented 4 pandemic severity scenarios and various times when the vaccine becomes available during the pandemic. Eligible individuals have a probability p of receiving antibody testing prior to vaccination (p = 0, 0.25, 0.5, 0.75, and 1). The vaccine was modeled as a single dose vaccine with 90% and 70% efficacy. The value of serology testing was evaluated by comparing the infection attack rate, peak infections, peak day, and deaths. RESULTS: The use of antibody testing to prioritize the allocation of limited vaccines reduces infection attack rates and deaths. The size of the reduction depends on when the vaccine becomes available relative to the infection peak day. The largest percentage reduction in cases and deaths occurs when the vaccine is deployed before and close to the infection peak day. The reduction in the number of cases and deaths diminishes as vaccine deployment is delayed. CONCLUSIONS: Antibody testing as part of the vaccination plan is an effective method to maximize the benefit of a COVID-19 vaccine. Decision-makers need to consider relative timing between the infection peak day and when the vaccine becomes available.

Association of Simulated COVID-19 Vaccination and Nonpharmaceutical Interventions With Infections, Hospitalizations, and Mortality.

Importance: Vaccination against SARS-CoV-2 has the potential to significantly reduce transmission and COVID-19 morbidity and mortality. The relative importance of vaccination strategies and nonpharmaceutical interventions (NPIs) is not well understood. Objective: To assess the association of simulated COVID-19 vaccine efficacy and coverage scenarios with and without NPIs with infections, hospitalizations, and deaths. Design, Setting, and Participants: An established agent-based decision analytical model was used to simulate COVID-19 transmission and progression from March 24, 2020, to September 23, 2021. The model simulated COVID-19 spread in North Carolina, a US state of 10.5 million people. A network of 1 017 720 agents was constructed from US Census data to represent the statewide population. Exposures: Scenarios of vaccine efficacy (50% and 90%), vaccine coverage (25%, 50%, and 75% at the end of a 6-month distribution period), and NPIs (reduced mobility, school closings, and use of face masks) maintained and removed during vaccine distribution. Main Outcomes and Measures: Risks of infection from the start of vaccine distribution and risk differences comparing scenarios. Outcome means and SDs were calculated across replications. Results: In the worst-case vaccination scenario (50% efficacy, 25% coverage), a mean (SD) of 2 231 134 (117 867) new infections occurred after vaccination began with NPIs removed, and a mean (SD) of 799 949 (60 279) new infections occurred with NPIs maintained during 11 months. In contrast, in the best-case scenario (90% efficacy, 75% coverage), a mean (SD) of 527 409 (40 637) new infections occurred with NPIs removed and a mean (SD) of 450 575 (32 716) new infections occurred with NPIs maintained. With NPIs removed, lower efficacy (50%) and higher coverage (75%) reduced infection risk by a greater magnitude than higher efficacy (90%) and lower coverage (25%) compared with the worst-case scenario (mean [SD] absolute risk reduction, 13% [1%] and 8% [1%], respectively). Conclusions and Relevance: Simulation outcomes suggest that removing NPIs while vaccines are distributed may result in substantial increases in infections, hospitalizations, and deaths. Furthermore, as NPIs are removed, higher vaccination coverage with less efficacious vaccines can contribute to a larger reduction in risk of SARS-CoV-2 infection compared with more efficacious vaccines at lower coverage. These findings highlight the need for well-resourced and coordinated efforts to achieve high vaccine coverage and continued adherence to NPIs before many prepandemic activities can be resumed.

Homebound by COVID19: the benefits and consequences of non-pharmaceutical intervention strategies.

BACKGROUND: Recent research has been conducted by various countries and regions on the impact of non-pharmaceutical interventions (NPIs) on reducing the spread of COVID19. This study evaluates the tradeoffs between potential benefits (e.g., reduction in infection spread and deaths) of NPIs for COVID19 and being homebound (i.e., refraining from interactions outside of the household). METHODS: An agent-based simulation model, which captures the natural history of the disease at the individual level, and the infection spread via a contact network assuming heterogeneous population mixing in households, peer groups (workplaces, schools), and communities, is adapted to project the disease spread and estimate the number of homebound people and person-days under multiple scenarios, including combinations of shelter-in-place, voluntary quarantine, and school closure in Georgia from March 1 to September 1, 2020. RESULTS: Compared to no intervention, under voluntary quarantine, voluntary quarantine with school closure, and shelter-in-place with school closure scenarios 4.5, 23.1, and 200+ homebound adult-days were required to prevent one infection, with the maximum number of adults homebound on a given day in the range of 119 K-248 K, 465 K-499 K, 5388 K-5389 K, respectively. Compared to no intervention, school closure only reduced the percentage of the population infected by less than 16% while more than doubling the peak number of adults homebound. CONCLUSIONS: Voluntary quarantine combined with school closure significantly reduced the number of infections and deaths with a considerably smaller number of homebound person-days compared to shelter-in-place.

Challenging Times for the World and How Our Community Can Help

As the INFORMS community continue to follow the developments of the coronavirus outbreak, there are at least two important considerations for community, i.e., potential impact on their professional activities and what they can do to contribute to the understanding of the coronavirus disease and the intervention efforts. Here, Keskinocak suggests that historically, INFORMS community has developed and used O.R. and analytics methods to save lives, save money and solve problems, and help make the world a better place. There is probably no better time to put their expertise and capabilities to good use!

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.

The impact of social distancing on COVID19 spread: State of Georgia case study.

As the spread of COVID19 in the US continues to grow, local and state officials face difficult decisions about when and how to transition to a "new normal." The goal of this study is to project the number of COVID19 infections and resulting severe outcomes, and the need for hospital capacity under social distancing, particularly, shelter-in-place and voluntary quarantine for the State of Georgia. We developed an agent-based simulation model to project the infection spread. The model utilizes COVID19-specific parameters and data from Georgia on population interactions and demographics. The simulation study covered a seven and a half-month period, testing different social distancing scenarios, including baselines (no-intervention or school closure only) and combinations of shelter-in-place and voluntary quarantine with different timelines and compliance levels. The following outcomes are compared at the state and community levels: the number and percentage of cumulative and daily new symptomatic and asymptomatic infections, hospitalizations, and deaths; COVID19-related demand for hospital beds, ICU beds, and ventilators. The results suggest that shelter-in-place followed by voluntary quarantine reduced peak infections from approximately 180K under no intervention and 113K under school closure, respectively, to below 53K, and delayed the peak from April to July or later. Increasing shelter-in-place duration from four to five weeks yielded 2-9% and 3-11% decrease in cumulative infection and deaths, respectively. Regardless of the shelter-in-place duration, increasing voluntary quarantine compliance decreased daily new infections from almost 53K to 25K, and decreased cumulative infections by about 50%. The cumulative number of deaths ranged from 6,660 to 19,430 under different scenarios. Peak infection date varied across scenarios and counties; on average, increasing shelter-in-place duration delayed the peak day by 6 days. Overall, shelter-in-place followed by voluntary quarantine substantially reduced COVID19 infections, healthcare resource needs, and severe outcomes.