Effects of COVID-19 Vaccination Timing and Risk Prioritization on Mortality Rates, United States.

During rollout of coronavirus disease vaccination, policymakers have faced critical trade-offs. Using a mathematical model of transmission, we found that timing of vaccination rollout would be expected to have a substantially greater effect on mortality rate than risk-based prioritization and uptake and that prioritizing first doses over second doses may be lifesaving.

The impacts of COVID-19 vaccine timing, number of doses, and risk prioritization on mortality in the US.

As COVID-19 vaccination begins worldwide, policymakers face critical trade-offs. Using a mathematical model of COVID-19 transmission, we find that timing of the rollout is expected to have a substantially greater impact on mortality than risk-based prioritization and uptake and that prioritizing first doses over second doses may be life saving.

Estimated Association of Construction Work With Risks of COVID-19 Infection and Hospitalization in Texas.

Importance: Policy makers have relaxed restrictions for certain nonessential industries, including construction, jeopardizing the effectiveness of social distancing measures and putting already at-risk populations at greater risk of coronavirus disease 2019 (COVID-19) infection. In Texas, Latinx populations are overly represented among construction workers, and thus have elevated rates of exposure that are compounded by prevalent high-risk comorbidities and lack of access to health care. Objective: To assess the association between construction work during the COVID-19 pandemic and hospitalization rates for construction workers and the surrounding community. Design, Setting, and Participants: This decision analytical model used a mathematical model of COVID-19 transmission, stratified by age and risk group, with construction workers modeled explicitly. The model was based on residents of the Austin-Round Rock metropolitan statistical area, with a population of 2.17 million. Based on 500 stochastic simulations for each of 15 scenarios that varied the size of the construction workforce and level of worksite transmission risk, the association between continued construction work and hospitalizations was estimated and then compared with anonymized line-list hospitalization data from central Texas through August 20, 2020. Exposures: Social distancing interventions, size of construction workforce, and level of disease transmission at construction worksites. Main Outcomes and Measures: For each scenario, the total number of COVID-19 hospitalizations and the relative risk of hospitalization among construction workers was projected and then compared with relative risks estimated from reported hospitalization data. Results: Allowing unrestricted construction work was associated with an increase of COVID-19 hospitalization rates through mid-August 2020 from 0.38 per 1000 residents to 1.5 per 1000 residents and from 0.22 per 1000 construction workers to 9.3 per 1000 construction workers. This increased risk was estimated to be offset by safety measures (such as thorough cleaning of equipment between uses, wearing of protective equipment, limits on the number of workers at a worksite, and increased health surveillance) that were associated with a 50% decrease in transmission. The observed relative risk of hospitalization among construction workers compared with other occupational categories among adults aged 18 to 64 years was 4.9 (95% CI, 3.8-6.2). Conclusions and Relevance: The findings of this study suggest that unrestricted work in high-contact industries, such as construction, is associated with a higher level of community transmission, increased risks to at-risk workers, and larger health disparities among members of racial and ethnic minority groups.

Effects of Cocooning on Coronavirus Disease Rates after Relaxing Social Distancing.

As coronavirus disease spreads throughout the United States, policymakers are contemplating reinstatement and relaxation of shelter-in-place orders. By using a model capturing high-risk populations and transmission rates estimated from hospitalization data, we found that postponing relaxation will only delay future disease waves. Cocooning vulnerable populations can prevent overwhelming medical surges.

Impact of Social Distancing Measures on Coronavirus Disease Healthcare Demand, Central Texas, USA.

Social distancing orders have been enacted worldwide to slow the coronavirus disease (COVID-19) pandemic, reduce strain on healthcare systems, and prevent deaths. To estimate the impact of the timing and intensity of such measures, we built a mathematical model of COVID-19 transmission that incorporates age-stratified risks and contact patterns and projects numbers of hospitalizations, patients in intensive care units, ventilator needs, and deaths within US cities. Focusing on the Austin metropolitan area of Texas, we found that immediate and extensive social distancing measures were required to ensure that COVID-19 cases did not exceed local hospital capacity by early May 2020. School closures alone hardly changed the epidemic curve. A 2-week delay in implementation was projected to accelerate the timing of peak healthcare needs by 4 weeks and cause a bed shortage in intensive care units. This analysis informed the Stay Home-Work Safe order enacted by Austin on March 24, 2020.

Cocooning is essential to relaxing social distancing.

As the first wave of COVID-19 recedes, policymakers are contemplating the relaxation of shelter-in-place orders. Using a model capturing high-risk populations and transmission rates estimated from hospitalization data, we find that postponing relaxation will only delay a second wave and cocooning vulnerable populations is needed to prevent overwhelming medical surges.