Infectiousness of SARS-CoV-2 breakthrough infections and reinfections during the Omicron wave

SARS-CoV-2 breakthrough infections in vaccinated individuals and reinfections among previously infected individuals have become increasingly common. Such infections highlight a broader need to understand the contribution of vaccination, including booster doses, and natural immunity to the infectiousness of persons with SARS-CoV-2 infections, especially in high-risk populations with intense transmission such as prisons. Here, we show that both vaccine-derived and naturally acquired immunity independently reduce the infectiousness of persons with Omicron variant SARS-CoV-2 infections in a prison setting. Analyzing SARS-CoV-2 surveillance data from December 2021 to May 2022 across 35 California state prisons with a predominately male population, we estimate that unvaccinated Omicron cases had a 36% (95% confidence interval (CI): 31-42%) risk of transmitting infection to close contacts, as compared to 28% (25-31%) risk among vaccinated cases. In adjusted analyses, we estimated that any vaccination, prior infection alone, and both vaccination and prior infection reduced an index cases risk of transmitting infection by 22% (6-36%), 23% (3-39%) and 40% (20-55%), respectively. Receipt of booster doses and more recent vaccination further reduced infectiousness among vaccinated cases. These findings suggest that although vaccinated and/or previously infected individuals remain highly infectious upon SARS-CoV-2 infection in this prison setting, their infectiousness is reduced compared to individuals without any history of vaccination or infection, underscoring some benefit of vaccination to reduce but not eliminate transmission.

Estimation of COVID-19 cases prevented by vaccination in California

ImportanceDespite widespread vaccination against COVID-19 in the United States, there are limited empirical data quantifying the public health impact in the population. ObjectiveTo estimate the number of cases of COVID-19 averted due to COVID-19 vaccination Design, Setting, and ParticipantsThe California Department of Public Health (CDPH) provided person-level data on COVID-19 cases and COVID-19 vaccine administration. To estimate the number of COVID-19 cases that would have occurred in the vaccine era in absence of vaccination, we applied a statistical model that estimated the relationship of COVID-19 cases in the pre-vaccine era between the unvaccinated age group (<12 years) and vaccine-eligible groups ([≥]12 years) to COVID-19 case data after the start of vaccination. The primary study outcome was the difference between predicted number of COVID-19 cases in absence of vaccination and observed COVID-19 cases with vaccination. As a sensitivity analysis, we developed a second independent model that estimated the number of vaccine-averted COVID-19 cases by applying published data on vaccine effectiveness to data on COVID-19 vaccine administration and estimated risk of COVID-19 over time. InterventionCOVID-19 vaccination Main Outcomes and MeasuresCOVID-19 cases ResultsThere were 4,585,248 confirmed COVID-19 cases in California from January 1, 2020 to October 16, 2021, during which 27,164,680 vaccine-eligible individuals [≥]12 years were reported to have received at least 1 dose of a COVID-19 vaccine in the vaccine era (79.5% of the eligible population). We estimated that 1,523,500 [95% prediction interval (976,800-2,230,800)] COVID-19 cases were averted and there was a 34% [95% prediction interval (25-43)] reduction in cases due to vaccination in the primary model. Approximately 66% of total cases averted occurred after the delta variant became the dominant strain of SARS-CoV-2 circulating in California. Our alternative model identified comparable findings. Conclusions and RelevanceThis study provides robust evidence on the public health impact of COVID-19 vaccination in the United States and further supports the urgency for continued vaccination. Key PointsO_ST_ABSQuestionC_ST_ABSHow many COVID-19 cases have been prevented by COVID-19 vaccination in California? FindingsIn this empirical analysis of California using data from the Department of Public Health, we estimated that COVID-19 vaccination has prevented over 1.5 million COVID-19 cases from the introduction of vaccination through October 16, 2021. MeaningThese findings support that COVID-19 vaccination had a large public health impact in California in terms of averted cases of COVID-19 and can be generalized across the United States.

Comparison of COVID-19 vaccine prioritization strategies in the United States

A key public health question during any disease outbreak when limited vaccine is available is who should be prioritized for early vaccination. Most vaccine prioritization analyses only consider variation in risk of infection and death by a single risk factor, such as age. We provide a more granular approach with stratification by demographics, risk factors, and location. We use this approach to compare the impact of different COVID-19 vaccine prioritization strategies on COVID-19 cases, deaths and disability-adjusted life years (DALYs) over the first 6 months of vaccine rollout, using California as a case example. We estimate the proportion of cases, deaths and DALYs averted relative to no vaccination for strategies prioritizing vaccination by a single risk factor and by multiple risk factors (e.g. age, location). We find that age-based targeting averts the most deaths (62% for 5 million individuals vaccinated) and DALYs (38%) of strategies targeting by a single risk factor and targeting essential workers averts the least deaths (31%) and DALYs (24%) over the first 6 months of rollout. However, targeting by two or more risk factors simultaneously averts up to 40% more DALYs. Our findings highlight the potential value of multiple-risk-factor targeting of vaccination against COVID-19 and other infectious diseases.

Routine asymptomatic testing strategies for airline travel during the COVID-19 pandemic: a simulation analysis

BackgroundAirline travel has been significantly reduced during the COVID-19 pandemic due to concern for individual risk of SARS-CoV-2 infection and population-level transmission risk from importation. Routine viral testing strategies for COVID-19 may facilitate safe airline travel through reduction of individual and/or population-level risk, although the effectiveness and optimal design of these "test-and-travel" strategies remain unclear. MethodsWe developed a microsimulation of SARS-CoV-2 transmission in a cohort of airline travelers to evaluate the effectiveness of various testing strategies to reduce individual risk of infection and population-level risk of transmission. We evaluated five testing strategies in asymptomatic passengers: i) anterior nasal polymerase chain reaction (PCR) within 3 days of departure; ii) PCR within 3 days of departure and PCR 5 days after arrival; iii) rapid antigen test on the day of travel (assuming 90% of the sensitivity of PCR during active infection); iv) rapid antigen test on the day of travel and PCR 5 days after arrival; and v) PCR within 3 days of arrival alone. The travel period was defined as three days prior to the day of travel and two weeks following the day of travel, and we assumed passengers followed guidance on mask wearing during this period. The primary study outcome was cumulative number of infectious days in the cohort over the travel period (population-level transmission risk); the secondary outcome was the proportion of infectious persons detected on the day of travel (individual-level risk of infection). Sensitivity analyses were conducted. FindingsAssuming a community SARS-CoV-2 incidence of 50 daily infections, we estimated that in a cohort of 100,000 airline travelers followed over the travel period, there would be a total of 2,796 (95% UI: 2,031, 4,336) infectious days with 229 (95% UI: 170, 336) actively infectious passengers on the day of travel. The pre-travel PCR test (within 3 days prior to departure) reduced the number of infectious days by 35% (95% UI: 27, 42) and identified 88% (95% UI: 76, 94) of the actively infectious travelers on the day of flight; the addition of PCR 5 days after arrival reduced the number of infectious days by 79% (95% UI: 71, 84). The rapid antigen test on the day of travel reduced the number of infectious days by 32% (95% UI: 25, 39) and identified 87% (95% UI: 81, 92) of the actively infectious travelers; the addition of PCR 5 days after arrival reduced the number of infectious days by 70% (95% UI: 65, 75). The post-travel PCR test alone (within 3 days of landing) reduced the number of infectious days by 42% (95% UI: 31, 51). The ratio of true positives to false positives varied with the incidence of infection. The overall study conclusions were robust in sensitivity analysis. InterpretationRoutine asymptomatic testing for COVID-19 prior to travel can be an effective strategy to reduce individual risk of COVID-19 infection during travel, although post-travel testing with abbreviated quarantine is likely needed to reduce population-level transmission due to importation of infection when traveling from a high to low incidence setting.

Comparison of infection control strategies to reduce COVID-19 outbreaks in homeless shelters in the United States: a simulation study

BackgroundCOVID-19 outbreaks have occurred in homeless shelters across the US, highlighting an urgent need to identify the most effective infection control strategy to prevent future outbreaks. MethodsWe developed a microsimulation model of SARS-CoV-2 transmission in a homeless shelter and calibrated it to data from cross-sectional polymerase-chain-reaction (PCR) surveys conducted during COVID-19 outbreaks in five shelters in three US cities from March 28 to April 10, 2020. We estimated the probability of averting a COVID-19 outbreak when an exposed individual is introduced into a representative homeless shelter of 250 residents and 50 staff over 30 days under different infection control strategies, including daily symptom-based screening, twice-weekly PCR testing and universal mask wearing. ResultsThe proportion of PCR-positive residents and staff at the shelters with observed outbreaks ranged from 2.6% to 51.6%, which translated to basic reproduction number (R0) estimates of 2.9-6.2. The probability of averting an outbreak diminished with higher transmissibility (R0) within the simulated shelter and increasing incidence in the local community. With moderate community incidence (~30 confirmed cases/1,000,000 people/day), the estimated probabilities of averting an outbreak in a low-risk (R0=1.5), moderate-risk (R0=2.9), and high-risk (R0=6.2) shelter were, respectively: 0.35, 0.13 and 0.04 for daily symptom-based screening; 0.53, 0.20, and 0.09 for twice-weekly PCR testing; 0.62, 0.27 and 0.08 for universal masking; and 0.74, 0.42 and 0.19 for these strategies combined. ConclusionsIn high-risk homeless shelter environments and locations with high community incidence of COVID-19, even intensive infection control strategies (incorporating daily symptom-screening, frequent PCR testing and universal mask wearing) are unlikely to prevent outbreaks, suggesting a need for non-congregate housing arrangements for people experiencing homelessness. In lower-risk environments, combined interventions should be employed to reduce outbreak risk.

Frequency of routine testing for SARS-CoV-2 to reduce transmissionamong workers

Abstract Routine asymptomatic testing strategies for COVID-19 have been proposed to prevent outbreaks in high-risk healthcare environments. We used simulation modeling to evaluate the optimal frequency of viral testing. We found that routine testing substantially reduces risk of outbreaks, but may need to be as frequent as twice weekly.

Healthcare worker absenteeism, child care costs, and COVID-19 school closures: a simulation analysis

BackgroundSchool closures have been enacted as a measure of mitigation during the ongoing COVID-19 pandemic. It has been shown that school closures could cause absenteeism amongst healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness. MethodsWe provide national- and county-level simulations of school closures and unmet child care needs across the United States. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors. ResultsAt the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.5% to 8.6%, and the effectiveness of school closures to range from 3.2% (R0 = 4) to 7.2% (R0 = 2) reduction in fewer ICU beds at peak demand. At the county-level, we find substantial variations of projected unmet child care needs and school closure effects, ranging from 1.9% to 18.3% of healthcare worker households and 5.7% to 8.8% reduction in fewer ICU beds at peak demand (R0 = 2). We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p < 0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 71.1% to 98.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures. ConclusionsSchool closures are projected to reduce peak ICU bed demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible tradeoff between school closures and healthcare worker absenteeism.