Risk-based prediction for optimal timing of booster vaccination for COVID-19 to prevent severe disease (preprint)

While waning protection from vaccination and natural infection against SARS-CoV-2 infection is well-documented, recent analyses have also found waning of protection against severe COVID-19. This highlights a broader need to understand the optimal timing of COVID-19 booster vaccines specific to an individual to mitigate the risk of severe COVID-19, while accounting for waning of protection and differential risk by age group and immune status. Here we show that more frequent COVID-19 booster vaccination (every 6-12 months) in older age groups and the immunocompromised population would effectively mitigate the burden of severe COVID-19, while frequent boosters in the younger population may only provide modest benefit. Analyzing United States COVID-19 surveillance and seroprevalence data in a microsimulation model, we estimated that in persons 75+ years, annual and semiannual bivalent boosters would reduce annual absolute risk of severe COVID-19 by 311 (277-369) and 578 (494-671) cases, respectively, compared to a one-time bivalent booster dose. In contrast, for persons 18-49 years, the model estimated that annual and semiannual bivalent boosters would reduce annual absolute risk of severe COVID-19 by 20 (13-26) and 37 (24-50) cases per 100,000 persons, respectively, compared to a one-time bivalent booster dose. Persons with prior infection had a much lower benefit of more frequent boosting, while immunocompromised persons had larger benefit. This study underscores the benefit of customizing timing of COVID-19 booster vaccines based on individual risk.

Predicting the public health impact of bivalent vaccines and nirmatrelvir-ritonavir against COVID-19 (preprint)

Background: Uptake of COVID-19 bivalent vaccines and oral medication nirmatrelvir-ritonavir (Paxlovid) has remained low across the United States. Assessing the public health impact of increasing uptake of these interventions in key risk groups can guide further public health resources and policy. Methods: This modeling study used person-level data from the California Department of Public Health on COVID-19 cases, hospitalizations, deaths, and vaccine administration from July 23, 2022 to January 23, 2023. We modeled the impact of additional uptake of bivalent COVID-19 vaccines and nirmatrelvir-ritonavir during acute illness in different risk groups defined by age (50+, 65+, 75+ years) and vaccination status (everyone, primary series only, previously vaccinated). We predicted the number of averted COVID-19 cases, hospitalizations, and deaths and number needed to treat (NNT). Results: For both bivalent vaccines and nirmatrelvir-ritonavir, the most efficient strategy (based on NNT) for averting severe COVID-19 was targeting the 75+ years group. We predicted that perfect coverage of bivalent boosters in the 75+ years group would avert 3,920 hospitalizations (95%UI: 2,491-4,882; 7.8% total averted; NNT 387) and 1,074 deaths (95%UI: 774-1,355; 16.2% total averted; NNT 1,410). Perfect uptake of nirmatrelvir-ritonavir in the 75+ years group would avert 5,644 hospitalizations (95%UI: 3,947-6,826; 11.2% total averted; NNT 11) and 1,669 deaths (95%UI: 1,053-2,038; 25.2% total averted; NNT 35). Conclusions: These findings suggest prioritizing uptake of bivalent boosters and nirmatrelvir-ritonavir among the oldest age groups would be efficient and have substantial public health impact in reducing the burden of severe COVID-19, but would not address the entire burden of severe COVID-19.

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

Breakthrough infections in vaccinated individuals and reinfections among previously infected individuals are increasingly prevalent, especially during the Omicron wave. Here, we analyze data from SARS-CoV-2 surveillance across 35 California prisons to understand the impact of vaccination and prior infection on infectiousness of individuals with SARS-CoV-2 Omicron infections in prison settings. We estimate that vaccination, prior infection, and both vaccination and prior infection reduced an index case's risk of transmitting to close contacts by 24% (9-37%), 21% (4-36%) and 41% (23-54%), respectively. Booster vaccine doses and more recent vaccination further reduced infectiousness. These findings suggest that although vaccinated and/or previously infected individuals remain infectious upon SARS-CoV-2 Omicron infection in this prison setting, their infectiousness is reduced compared to individuals without any history of vaccination or infection.

Estimation of COVID-19 cases prevented by vaccination in California (preprint)

Importance: Despite widespread vaccination against COVID-19 in the United States, there are limited empirical data quantifying the public health impact in the population. Objective: To estimate the number of cases of COVID-19 averted due to COVID-19 vaccination Design, Setting, and Participants: The 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. Intervention: COVID-19 vaccination Main Outcomes and Measures: COVID-19 cases Results: There 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 Relevance: This 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.

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

A critical question in the COVID-19 pandemic is how to optimally allocate the first available vaccinations to maximize health impact. We used a static simulation model with detailed demographic and risk factor stratification to compare the impact of different vaccine prioritization strategies in the United States on key health outcomes, using California as a case example. We calibrated the model to demographic and location data on 28,175 COVID-19 deaths in California up to December 30, 2020, and incorporated variation in risk by occupation and comorbidity status using published estimates. We predicted the proportion of COVID-19 clinical cases, deaths and disability-adjusted life years (DALYs) averted over 6 months relative to a scenario of no vaccination for five vaccination strategies that prioritized vaccination by a single risk factor: random allocation; targeting special populations (e.g. incarcerated individuals); targeting older individuals; targeting essential workers; and targeting individuals with comorbidities. Targeting older individuals averted the highest proportion of DALYs (40% for 5 million individuals vaccinated) and deaths (65%) but the lowest proportion of cases (12%). Targeting essential workers averted the lowest proportion of DALYs (25%) and deaths (33%). Allocating vaccinations simultaneously by age and location or by age, sex, race/ethnicity, location, occupation, and comorbidity status averted a significantly higher proportion of DALYs (48% and 56%) than any strategy prioritizing by a single risk factor. Our results corroborate findings of other studies that age targeting is the best single-risk-factor prioritization strategy for averting DALYs, and suggest that targeting by multiple risk factors would provide additional benefit.

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

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 (preprint)

Background: Multiple COVID-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. Objective: To estimate the probability of averting outbreaks in homeless shelters under different infection control strategies. Design: Microsimulation model of COVID-19 transmission in a representative homeless shelter over 30 days under different infection control strategies, including daily symptom-based screening, twice-weekly polymerase-chain-reaction (PCR) testing and universal mask wearing. Setting: A shelter of 250 residents and 50 staff. Patients: Residents and staff of homeless shelters in the US. Model calibrated to data from cross-sectional PCR surveys during COVID-19 outbreaks in five shelters in three US cities. Measurements: Probability of averting a COVID-19 outbreak ([≥]3 infections in 14 days). Results: Basic reproduction number (R0) estimates for the observed outbreaks ranged from 2.9 to 6.2. The probability of averting an outbreak diminished with higher transmissibility (R0) within the simulated shelter and increasing transmission intensity in the local community. With moderate transmission intensity in the local community, 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: 0.33, 0.11 and 0.03 for daily symptom-based screening; 0.52, 0.27, and 0.04 for twice-weekly PCR testing; 0.47, 0.20 and 0.06 for universal masking; and 0.68, 0.40 and 0.08 for these strategies combined. Limitations: R0 values calibrated to reported outbreaks may be higher than for average shelter due to smaller outbreaks going unreported. Conclusion: In high-risk homeless shelter environments and locations with high community incidence of COVID-19 most infection control strategies are unlikely to prevent outbreaks. In lower-risk environments, combined interventions should be adopted to reduce outbreak risk. Primary Funding Source: University of California, San Francisco; UCSF Benioff Homelessness and Housing Initiative.

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

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 (preprint)

Background: School 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. Methods: We 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. Results: At 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% to 7.2% 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. 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. Conclusions: School 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.