Vaccinating Children Against COVID-19 is Essential Prior to the Removal of Non-Pharmaceutical Interventions

Objectives(s)To evaluate the joint impact of childhood vaccination rates and masking policies, in schools and workplaces, on community transmission and severe outcomes due to COVID-19. Study designWe utilized a stochastic, agent-based simulation of North Carolina, to evaluate the impact of 24 health policy decisions on overall incidence of disease, COVID-19 related hospitalization, and mortality from July 1, 2021-July 1, 2023. ResultsUniversal mask removal in schools in January 2022 could lead to a 38.1-47%, 27.6-36.2%, and 15.9-19.7% increase in cumulative infections for ages 5-9, 10-19, and the total population, respectively, depending on the rate of vaccination of children relative to the adult population. Additionally, without increased vaccination uptake in the adult population, a 25% increase in child vaccination uptake from 50% to 75% uptake and from 75% to 100% uptake relative to the adult population, leads to a 22% and 18% or 28% and 33% decrease in peak hospitalizations in 2022 across scenarios when masks are removed either January 1st or March 8th 2022, respectively. Increasing vaccination uptake for the entire eligible population can reduce peak hospitalizations in 2022 by an average of 89% and 92% across all masking scenarios compared to the scenarios where no children are vaccinated. Conclusion(s)High vaccination uptake among both children and adults is necessary to mitigate the increase in infections from mask removal in schools and workplaces.

COVID-19 Projections for K12 Schools in Fall 2021: Significant Transmission without Interventions

BackgroundMillions of primary school students across the United States are about to return to in-person learning. Amidst circulation of the highly infectious Delta variant, there is danger that without the appropriate safety precautions, substantial amount of school-based spread of COVID-19 may occur. MethodsWe used an extended Susceptible-Infected-Recovered computational model to estimate the number of new infections during 1 semester among a student population under different assumptions about mask usage, routine testing, and levels of incoming protection. Our analysis considers three levels of incoming protection (30%, 40%, or 50%; denoted as "low", "mid", or "high"). Universal mask usage decreases infectivity by 50%, and weekly testing may occur among 50% of the student population; positive tests prompt quarantine until recovery, with compliance contingent on symptom status. ResultsWithout masking and testing, more than 75% of susceptible students become get infected within three months in all settings. With masking, this values decreases to 50% for "low" incoming protection settings ("mid"=35%, "high"=24%). Testing half the masked population ("testing") further drops infections to 22% (16%, 13%). ConclusionWithout interventions in place, the vast majority of susceptible students will become infected through the semester. Universal masking can reduce student infections by 26-78%, and biweekly testing along with masking reduces infections by another 50%. To prevent new infections in the community, limit school absences, and maintain in-person learning, interventions such as masking and testing must be implemented widely, especially among elementary school settings in which children are not yet eligible for the vaccine.

Can Vaccine Prioritization Reduce Disparities in Covid-19 Burden for Historically Marginalized Populations?

1.ImportanceNationally stated goals for distributing SARS-CoV-2 vaccines included to reduce COVID-19 mortality, morbidity, and inequity using prioritization groups. However, the impact of these prioritization strategies is not well understood, particularly their effect on health inequity in COVID-19 burden for historically marginalized racial and ethnic populations. ObjectiveTo assess the impact of vaccination prioritization and operational strategies on disparities in COVID-19 burden among historically marginalized populations, and on mortality and morbidity by race and ethnicity. DesignWe use an agent-based simulation model of North Carolina to project SARS-CoV-2 infections and COVID-19-associated deaths (mortality), hospitalizations (morbidity), and cases over 18 months (7/1/2020-12/31/2021) with vaccine distribution beginning 12/13/2020 to frontline medical and people 75+, assuming initial uptake similar to influenza vaccine. We study two-stage subsequent prioritization including essential workers ("essential"), adults 65+ ("age"), adults with high-risk health conditions, HMPs, or people in low income tracts, with eligibility for the general population in the third stage. For age-essential and essential-age strategies, we also simulated maximal uptake (100% for HMP or 100% for everyone), and we allowed for distribution to susceptible-only people. ResultsPrioritizing Age then Essential had the largest impact on mortality (2.5% reduction from no prioritization); Essential then Age had the lowest morbidity and reduced infections (4.2% further than Age-Essential) without significantly impacting mortality. Under each prioritization scenario, the age-adjusted mortality burden for HMPs is higher (e.g., 33.3-34.1% higher for the Black population, 13.3%-17.0% for the Hispanic population) compared to the White population, and the gap grew under some prioritizations. In the Age-Essential strategy, the burden on HMPs decreases only when uptake is increased to 100% in HMPs. However, the Black population still had the highest mortality rate even with the Susceptible-Only distribution. Conclusions and RelevanceSimulation results show that prioritization strategies have differential impact on mortality, morbidity, and disparities overall and by race and ethnicity. If prioritization schemes were not paired with increased uptake in HMPs, disparities did not improve and could worsen. Although equity was one of the tenets of vaccine distribution, the vaccination strategies publicly outlined are insufficient to remove and may exacerbate disparities between racial and ethnic groups, thus targeted strategies are needed for the future.

Use of Modeling to Inform Decision-Making During the COVID-19 Pandemic: A Qualitative Study

BackgroundThe COVID-19 Pandemic has popularized computer-based decision-support models as a tool for decision-makers to manage their organizations. It is unclear how decision-makers have considered these models to inform COVID-19-related decisions. MethodsWe interviewed decision-makers from North Carolina across diverse organizational backgrounds to assess major decision-making processes during COVID-19, including the use of modeling as an input to inform decision-making. ResultsInterviewees were aware of models during COVID-19, with some depending upon multiple models. Models were used to compare trends in disease spread across localities, allocate scarce resources, and track disease spread within small geographic areas. Decision-makers desired models to project disease spread within subpopulations and estimate where local outbreaks could occur as well as estimate the outcomes of social distancing policies, including consequences beyond typical health-related outcomes. Challenges to the use of modeling included doubts that models could reflect nuances of human behavior, concerns about the quality of data used in models, and the limited amount of modeling at the local level. ConclusionsThroughout COVID-19, decision-makers perceived modeling as valuable for understanding disease spread within their communities and to inform organization decisions, yet there were variations in organizations ability and willingness to use models for these purposes.