Changes in population immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States between December 2021 and November 2022

ImportanceWhile a substantial fraction of the US population was infected with SARS-CoV-2 during December 2021 - February 2022, the subsequent evolution of population immunity against SARS-CoV-2 Omicron variants reflects the competing influences of waning protection over time and acquisition or restoration of immunity through additional infections and vaccinations. ObjectiveTo estimate changes in population immunity against infection and severe disease due to circulating SARS-CoV-2 Omicron variants in the United States from December 2021 to November 2022, and to quantify the protection against a potential 2022-2023 winter SARS-CoV-2 wave. Design, setting, participantsBayesian evidence synthesis of reported COVID-19 data (diagnoses, hospitalizations), vaccinations, and waning patterns for vaccine- and infection-acquired immunity, using a mathematical model of COVID-19 natural history. Main Outcomes and MeasuresPopulation immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States, by location (national, state, county) and week. ResultsBy November 9, 2022, 94% (95% CrI, 79%-99%) of the US population were estimated to have been infected by SARS-CoV-2 at least once. Combined with vaccination, 97% (95%-99%) were estimated to have some prior immunological exposure to SARS-CoV-2. Between December 1, 2021 and November 9, 2022, protection against a new Omicron infection rose from 22% (21%-23%) to 63% (51%-75%) nationally, and protection against an Omicron infection leading to severe disease increased from 61% (59%-64%) to 89% (83%-92%). Increasing first booster uptake to 55% in all states (current US coverage: 34%) and second booster uptake to 22% (current US coverage: 11%) would increase protection against infection by 4.5 percentage points (2.4-7.2) and protection against severe disease by 1.1 percentage points (1.0-1.5). Conclusions and RelevanceEffective protection against SARS-CoV-2 infection and severe disease in November 2022 was substantially higher than in December 2021. Despite this high level of protection, a more transmissible or immune evading (sub)variant, changes in behavior, or ongoing waning of immunity could lead to a new SARS-CoV-2 wave. Key pointsO_ST_ABSQuestionC_ST_ABSHow did population immunity against SARS-CoV-2 infection and subsequent severe disease change between December 2021, and November 2022? FindingsOn November 9, 2022, the protection against a SARS-CoV-2 infection with the Omicron variant was estimated to be 63% (51%-75%) in the US, and the protection against severe disease was 89% (83%-92%). MeaningAs most of the newly acquired immunity has been accumulated in the December 2021-February 2022 Omicron wave, risk of reinfection and subsequent severe disease remains present at the beginning of the 2022-2023 winter, despite high levels of protection.

Time trends between vaccination coverage and voting patterns before and during the COVID-19 pandemic: analysis of COVID-19 and flu surveys in the United States

BackgroundWe assessed the relationship between vaccination coverage and voting patterns: how has the association between COVID-19 vaccination and voting patterns changed during the pandemic, how does it compare to the association between flu vaccination coverage and voting patterns, and what can the time trends between flu vaccination and voting patterns tell us about the broader relationship between vaccination coverage and voting patterns. MethodsWe analyzed survey data on flu and COVID-19 vaccination coverage utilizing National Immunization Surveys for flu (NIS-FLU; years 2010-2021) and for COVID (NIS-ACM; 2021-2022), CDC surveillance of COVID-19 vaccination coverage (2021-2022) and US COVID-19 Trends and Impact Survey (CTIS; 2021-2022). We described the association between state-level COVID-19 and flu vaccination coverage and state-level voting patterns using Pearson correlation coefficient. We examined individual-level characteristics of people vaccinated for COVID-19 and for flu using logistic regression among responses in CTIS during April-June 2022. We analyzed flu vaccination coverage by age in NIS-FLU between 2010-2021, and its relationship with voting patterns to see whether there has been a departure from the secular pre-pandemic trend during the pandemic. ResultsBetween May 2021 - June 2022 there was a strong and consistent correlation between state-level COVID-19 vaccination coverage and voting patterns for the Democratic party in the 2020 presidential elections. Pearson correlation coefficient was around 0.8 in NIS-ACM, CTIS and CDC surveillance with a range of 0.76-0.92. COVID-19 vaccination coverage in June 2022 was higher than flu vaccination coverage in all states and it had a stronger correlation with voting patterns (R=0.90 vs. R=0.60 in CTIS). There was a small reduction in the flu vaccination coverage between 2020-2021 and 2021-2022 flu seasons. In the individual-level logistic regression, vaccinated people were more likely to be living in a county where the majority voted for the Democratic candidate in 2020 elections both for COVID-19 (aOR .18, 95%CI 2.12-2.24) and for flu (aOR 1.38, 95%CI 1.36-1.41). We demonstrate a longstanding correlation between voting patterns and flu vaccination coverage. It varied by age with the strongest correlation in the youngest age groups. During the 2020-2021 flu season, all age groups, except for 5-12 years old, had a stronger correlation coefficient with voting patterns than in the previous years. However, the observed and predicted vaccination coverage show relatively modest differences in their correlation with vote share. ConclusionsThere are existing pre-pandemic patterns between vaccination coverage and voting patterns as demonstrated by the flu vaccination coverage for 2010-2021. During the pandemic COVID-19 vaccination has been more strongly correlated with vote share than the correlation observed for flu during and before the pandemic. The findings align with other research that has identified an association between adverse health outcomes and the political environment in the United States.

Protection against Omicron conferred by mRNA primary vaccine series, boosters, and prior infection

BO_SCPLOWACKGROUNDC_SCPLOWPrisons and jails are high-risk settings for Covid-19 transmission, morbidity, and mortality. We evaluate protection conferred by prior infection and vaccination against the SARS-CoV-2 Omicron variant within the California state prison system. MO_SCPLOWETHODSC_SCPLOWWe employed a test-negative design to match resident and staff cases during the Omicron wave (December 24, 2021--April 14, 2022) to controls according to a cases test-week as well as demographic, clinical, and carceral characteristics. We estimated protection against infection using conditional logistic regression, with exposure status defined by vaccination, stratified by number of mRNA doses received, and prior infection, stratified by periods before or during Delta variant predominance. RO_SCPLOWESULTSC_SCPLOWWe matched 15,783 resident and 8,539 staff cases to 180,169 resident and 90,409 staff controls. Among cases, 29.7% and 2.2% were infected before or during the emergence of the Delta variant, respectively; 30.6% and 36.3% were vaccinated with two or three doses, respectively. Estimated protection from Omicron infection for two and three doses were 14.9% (95% Confidence Interval [CI], 12.3--19.7%) and 43.2% (42.2--47.4%) for those without known prior infections, 47.8% (95% CI, 46.6--52.8%) and 61.3% (95% CI, 60.7--64.8%) for those infected before the emergence of Delta, and 73.1% (95% CI, 69.8--80.1%) and 86.8% (95% CI, 82.1--92.7) for those infected during the period of Delta predominance. CO_SCPLOWONCLUSIONC_SCPLOWA third mRNA dose provided significant, additional protection over two doses, including among individuals with prior infection. Our findings suggest that vaccination should remain a priority--even in settings with high levels of transmission and prior infection.

Bias-adjusted predictions of county-level vaccination coverage from the COVID-19 Trends and Impact Survey

The potential for bias in non-representative, large-scale, low-cost survey data can limit their utility for population health measurement and public health decision-making. We developed a multi-step regression framework to bias-adjust vaccination coverage predictions from the large-scale US COVID-19 Trends and Impact Survey that included post-stratification to the American Community Survey and secondary normalization to an unbiased reference indicator. As a case study, we applied this framework to generate county-level predictions of long-run vaccination coverage among children ages 5 to 11 years. Our vaccination coverage predictions suggest a low ceiling on long-term national coverage (46%), detect substantial geographic heterogeneity (ranging from 11% to 91% across counties in the US), and highlight widespread disparities in the pace of scale-up in the three months following Emergency Use Authorization of COVID-19 vaccination for 5 to 11 year-olds. Generally, our analysis demonstrates an approach to leverage differing strengths of multiple sources of information to produce estimates on the time-scale and geographic-scale necessary for proactive decision-making. The utility of large-scale, low-cost survey data for improving population health measurement is amplified when these data are combined with other representative sources of data.

Model-based estimates of deaths averted and cost per life saved by scaling-up mRNA COVID-19 vaccination in low and lower-middle income countries in the COVID-19 Omicron variant era

BackgroundWhile almost 60% of the world has received at least one dose of COVID-19 vaccine, the global distribution of vaccination has not been equitable. Only 4% of the population of low-income countries has received a full primary vaccine series, compared to over 70% of the population of high-income nations. MethodsWe used economic and epidemiologic models, parameterized with public data on global vaccination and COVID-19 deaths, to estimate the potential benefits of scaling up vaccination programs in low and lower-middle income countries (LIC/LMIC) in 2022 in the context of global spread of the Omicron variant of SARS-CoV2. Outcomes were expressed as number of avertable deaths through vaccination, costs of scale-up, and cost per death averted. We conducted sensitivity analyses over a wide range of parameter estimates to account for uncertainty around key inputs. FindingsGlobal scale up of vaccination to provide two doses of mRNA vaccine to everyone in LIC/LMIC would cost $35.5 billion and avert 1.3 million deaths from COVID-19, at a cost of $26,900 per death averted. Scaling up vaccination to provide three doses of mRNA vaccine to everyone in LIC/LMIC would cost $61.2 billion and avert 1.5 million deaths from COVID-19 at a cost of $40,800 per death averted. Lower estimated infection fatality ratios, higher cost-per-dose, and lower vaccine effectiveness or uptake lead to higher cost-per-death averted estimates in the analysis. InterpretationScaling up COVID-19 global vaccination would avert millions of COVID-19 deaths and represents a reasonable investment in the context of the value of a statistical life (VSL). Given the magnitude of expected mortality facing LIC/LMIC without vaccination, this effort should be an urgent priority.

Waning of Vaccine-Conferred Protection against SARS-CoV-2 Infection: Matched Case-Control Test-Negative Design Study in Two High-Risk Populations

To distinguish waning of vaccine responses from differential variant protection, we performed a test-negative case-control analysis during a Delta variant-dominant period in Californias prisons. We found that infection odds increased each 28-day period post-vaccination, reaching 3.4-fold (residents) to 4.7-fold (staff) increased odds of infection after 180 days.

Population immunity to SARS-CoV-2 in US states and counties due to infection and vaccination, January 2020-November 2021

Prior infection and vaccination both contribute to population-level SARS-CoV-2 immunity. We used a Bayesian model to synthesize evidence and estimate population immunity to prevalent SARS-CoV-2 variants in the United States over the course of the epidemic until December 1, 2021, and how this changed with the introduction of the Omicron variant. We used daily SARS-CoV-2 infection estimates and vaccination coverage data for each US state and county. We estimated relative rates of vaccination conditional on previous infection status using the Census Bureaus Household Pulse Survey. We used published evidence on natural and vaccine-induced immunity, including waning and immune escape. The estimated percentage of the US population with a history of SARS-CoV-2 infection or vaccination as of December 1, 2021, was 88.2% (95%CrI: 83.6%-93.5%), compared to 24.9% (95%CrI: 18.5%-34.1%) on January 1, 2021. State-level estimates for December 1, 2021, ranged between 76.9% (95%CrI: 67.6%-87.6%, West Virginia) and 94.4% (95%CrI: 91.2%-97.3%, New Mexico). Accounting for waning and immune escape, the effective protection against the Omicron variant on December 1, 2021, was 21.8% (95%CrI: 20.7%-23.4%) nationally and ranged between 14.4% (95%CrI: 13.2%-15.8%, West Virginia), to 26.4% (95%CrI: 25.3%-27.8%, Colorado). Effective protection against severe disease from Omicron was 61.2% (95%CrI: 59.1%-64.0%) nationally and ranged between 53.0% (95%CrI: 47.3%-60.0%, Vermont) and 65.8% (95%CrI: 64.9%-66.7%, Colorado). While over three-quarters of the US population had prior immunological exposure to SARS-CoV-2 via vaccination or infection on December 1, 2021, only a fifth of the population was estimated to have effective protection to infection with the immune-evading Omicron variant. SignificanceBoth SARS-CoV-2 infection and COVID-19 vaccination contribute to population-level immunity against SARS-CoV-2. This study estimates the immunity and effective protection against future SARS-CoV-2 infection in each US state and county over 2020-2021. The estimated percentage of the US population with a history of SARS-CoV-2 infection or vaccination as of December 1, 2021, was 88.2% (95%CrI: 83.6%-93.5%). Accounting for waning and immune escape, protection against the Omicron variant was 21.8% (95%CrI: 20.7%-23.4%). Protection against infection with the Omicron variant ranged between 14.4% (95%CrI: 13.2%-15.8%%, West Virginia) and 26.4% (95%CrI: 25.3%-27.8%, Colorado) across US states. The introduction of the immune-evading Omicron variant resulted in an effective absolute increase of approximately 30 percentage points in the fraction of the population susceptible to infection.

Simple decision rules to predict local surges in COVID-19 hospitalizations during the winter and spring of 2022

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes leave many U.S. communities at risk for surges of COVID-19 during the winter and spring of 2022 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations during this period are expected to differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop simple decision rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. These decision rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We showed that these decision rules present reasonable accuracy, sensitivity, and specificity (all [≥]80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19 during the winter and spring of 2022. Our proposed decision rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations. Significance StatementIn many U.S. communities, the risk of exceeding local healthcare capacity during the winter and spring of 2022 remains substantial since COVID-19 hospitalizations may rise due to seasonal changes, low vaccination coverage, and the emergence of new variants of SARS-CoV-2, such as the omicron variant. Here, we provide simple and easy-to-communicate decision rules to predict whether local hospital occupancy is expected to exceed capacity within a 4- or 8-week period if no additional mitigating measures are implemented. These decision rules can serve as an alert system for local policymakers to respond proactively to mitigate future surges in the COVID-19 hospitalization and minimize risk of overwhelming local healthcare capacity.

Uptake of Covid-19 vaccines among frontline workers in California state prisons

BackgroundPrisons are high-risk environments for Covid-19. Vaccination levels among prison staff remain troublingly low - lower than levels among residents and members of the surrounding community. The situation is troubling because prison staff are a key vector for Covid-19 transmission. ObjectiveTo assess patterns and timing of staff vaccination in California state prisons and identify individual- and community-level factors associated with being unvaccinated. DesignWe calculated fractions of prison staff and incarcerated residents in California state prisons who remained unvaccinated. Adjusted analyses identified demographic, community, and peer factors associated with vaccination uptake among staff. SettingCalifornia Department of Corrections and Rehabilitation prisons. ParticipantsCustody and healthcare staff who worked in direct contact with residents. Main Outcomes and MeasuresRemaining unvaccinated through June 30, 2021. ResultsA total of 26% of custody staff and 52% of healthcare staff took [≥]1 dose in the first two months of vaccine offer; uptake stagnated thereafter. By June 30, 2021, 61% of custody and 37% of healthcare staff remained unvaccinated. Remaining unvaccinated was positively associated with younger age, prior Covid-19, residing in a community with relatively low vaccination rates, and sharing shifts with co-workers who had relatively low vaccination rates. Conclusions and RelevanceVaccine uptake among prison staff in California in regular contact with incarcerated residents has plateaued at levels that pose ongoing risks--both of further outbreaks in the prisons and transmission into surrounding communities. Staff decisions to forego vaccination appear to be complex and multifactorial. Achieving safe levels of vaccine protection among frontline staff may necessitate requiring vaccination as condition of employment.

Effectiveness of COVID-19 Vaccines among Incarcerated People in California State Prisons: A Retrospective Cohort Study

BackgroundPrisons and jails are high-risk settings for COVID-19 transmission, morbidity, and mortality. COVID-19 vaccines may substantially reduce these risks, but evidence is needed of their effectiveness for incarcerated people, who are confined in large, risky congregate settings. MethodsWe conducted a retrospective cohort study to estimate effectiveness of mRNA vaccines, BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna), against confirmed SARS-CoV-2 infections among incarcerated people in California prisons from December 22, 2020 through March 1, 2021. The California Department of Corrections and Rehabilitation provided daily data for all prison residents including demographic, clinical, and carceral characteristics, as well as COVID-19 testing, vaccination status, and outcomes. We estimated vaccine effectiveness using multivariable Cox models with time-varying covariates that adjusted for resident characteristics and infection rates across prisons. FindingsAmong 60,707 residents in the cohort, 49% received at least one BNT162b2 or mRNA-1273 dose during the study period. Estimated vaccine effectiveness was 74% (95% confidence interval [CI], 64-82%) from day 14 after first dose until receipt of second dose and 97% (95% CI, 88-99%) from day 14 after second dose. Effectiveness was similar among the subset of residents who were medically vulnerable (74% [95% CI, 62-82%] and 92% [95% CI, 74-98%] from 14 days after first and second doses, respectively), as well as among the subset of residents who received the mRNA-1273 vaccine (71% [95% CI, 58-80%] and 96% [95% CI, 67-99%]). ConclusionsConsistent with results from randomized trials and observational studies in other populations, mRNA vaccines were highly effective in preventing SARS-CoV-2 infections among incarcerated people. Prioritizing incarcerated people for vaccination, redoubling efforts to boost vaccination and continuing other ongoing mitigation practices are essential in preventing COVID-19 in this disproportionately affected population. FundingHorowitz Family Foundation, National Institute on Drug Abuse, Centers for Disease Control and Prevention, National Science Foundation, Open Society Foundation, Advanced Micro Devices.

The U.S. COVID-19 Trends and Impact Survey, 2020-2021: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing and vaccination

The U.S. COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, Internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey - over 20 million responses in its first year of operation - allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems. Significance statementThe U.S. COVID-19 Trends and Impact Survey (CTIS) has operated continuously since April 6, 2020, collecting over 20 million responses. The largest public health survey ever conducted in the United States, CTIS was designed to facilitate detailed demographic and geographic analyses, track trends over time, and accommodate rapid response to emerging priorities. Using examples of CTIS results illuminating trends in symptoms, risks, mitigating behaviors, testing and vaccination in relation to evolving high-priority policy questions over 12 months of the pandemic, we illustrate the value of online surveys for tracking patterns and trends in COVID outcomes as an adjunct to official reporting, and showcase unique insights that would not be visible through traditional public health reporting.

Outbreaks of Covid-19 Variants in Prisons: A Mathematical Modeling Analysis of Vaccination and Re-Opening Policies

BackgroundResidents of correctional facilities have experienced disproportionately higher rates of SARS-CoV-2 infection and Covid-19-related mortality. To protect against outbreaks, many prisons and jails imposed heavy restrictions on in-person activities, which are now beginning to lift. Uncertainty surrounds the safety of these moves. Methods and FindingsWe obtained system-wide resident-day level data for the California state prison system, the nations third largest. We used the data to develop a transmission-dynamic stochastic microsimulation model that projects the impact of various policy scenarios on risks of SARS-CoV-2 infections and related hospitalization among residents after an initial infection is introduced to a prison. The policy scenarios vary according to levels of vaccine coverage, baseline immunity, resumption of activities, and use of non-pharmaceutical interventions (e.g., masking, physical distancing). The analyses were conducted across 5 types of prisons that differed in their residential layouts, security levels, and resident demographics. If a viral variant is introduced into a prison that has resumed pre-2020 contact levels, has moderate vaccine coverage, and has no baseline immunity, 23-74% of residents are expected to be infected over 200 days. High vaccination coverage coupled with use of non-pharmaceutical measures reduces cumulative infections to 2-54% of residents. In prisons consisting mostly of dormitory housing, even with high vaccine coverage and non-pharmaceutical interventions, resumption of in-person activities is associated with substantial risk, unless there is high baseline immunity (e.g., [≥]50%) from prior outbreaks. In prisons consisting mostly of cell housing, <10% of residents are expected to become infected, even with no baseline immunity. However, hospitalization risks are substantial in prisons that house medically vulnerable populations, even for prisons consisting mostly of cells. Risks of large outbreaks are substantially higher if there is continued introduction of infections into a prison. Some findings may not be transportable to other carceral settings, and our assumptions regarding viral variants will not be accurate for all variants. ConclusionsBalancing the benefits of resuming normal in-person activities against the risks of Covid-19 outbreaks is a difficult challenge for correctional systems. The policy choices are not strictly binary. To protect against viral variants, prisons should focus on achieving both high vaccine coverage and maintaining widespread use of non-pharmaceutical interventions. With both in place, some prisons, especially those with lower room occupancy that have already had large outbreaks, could safely resume in-person activities, while continuing testing and measures to protect the medically-vulnerable.

Asymptomatic COVID-19 screening tests to facilitate full-time school attendance: model-based analysis of cost and impact

BackgroundIn March 2021, the Biden administration allocated $10 billion for COVID-19 testing in schools. We evaluate the costs and benefits of testing strategies to reduce the infection risks of full-time in-person K-8 education at different levels of community incidence. MethodsWe used an agent-based network model to simulate transmission in elementary and middle school communities, parameterized to a US school structure and assuming dominance of the delta COVID-19 variant. We assess the value of different strategies for testing students and faculty/staff, including expanded diagnostic testing ("test to stay" policies that take the place of isolation for symptomatic students or quarantine for exposed classrooms); screening (routinely testing asymptomatic individuals to identify infections and contain transmission); and surveillance (testing a random sample of students to signaling undetected transmission and trigger additional investigation or interventions). Main outcome measuresWe project 30-day cumulative incidence of SARS-CoV-2 infection; proportion of cases detected; proportion of planned and unplanned days out of school; and the cost of testing programs and of childcare costs associated with different strategies. For screening policies, we further estimate cost per SARS-CoV-2 infection averted in students and staff, and for surveillance, probability of correctly or falsely triggering an outbreak response at different incidence and attack rates. ResultsAccounting for programmatic and childcare costs, "test to stay" policies achieve similar model-projected transmission to quarantine policies, with reduced overall costs. Weekly universal screening prevents approximately 50% of in-school transmission, with a lower projected societal cost than hybrid or remote schooling. The cost per infection averted in students and staff by weekly screening is lower for older students and schools with higher mitigation and declines as community transmission rises. In settings where local student incidence is unknown or rapidly changing, surveillance may trigger detection of moderate-to-large in-school outbreaks with fewer resources compared to screening. Conclusions"Test to stay" policies and/or screening tests can facilitate consistent in-person school attendance with low transmission risk across a range of community incidence. Surveillance may be a useful reduced-cost option for detecting outbreaks and identifying school environments that may benefit from increased mitigation.

Addressing racial/ethnic disparities in the COVID-19 vaccination campaign

BackgroundAs of April 19, all adults aged 16 years and older are eligible for COVID-19 vaccination. Unequal vaccination rates across racial/ethnic groups may compound existing disparities in cases, hospitalizations, and deaths among Black, Indigenous, and Hispanic communities. MethodsFrom state websites, we extracted shares of people receiving [≥]1 vaccine dose, stratified by age and separately by race/ethnicity, through March 31, 2021. Combining these data with demographic data from the American Community Survey, we estimated relative uptake rates by race/ethnicity within each state as the observed share of vaccinations for a racial/ethnic group, divided by the expected share if uptake across racial/ethnic groups within each age group were proportional to population size, an approach that allowed us to control for historical age-based eligibility. We modeled vaccination scale-up within each census tract in a state under three scenarios: 1) a scenario in which unequal uptake rates persist, 2) a scenario in which uptake rates are equalized across race/ethnicity groups over six weeks, and 3) a scenario in which uptake is equalized and states employ place-based allocation strategies that prioritizes disadvantaged census tracts. ResultsWhite adults received a disproportionate share of vaccinations compared to Black and Hispanic adults through March 31, 2021. Across states, relative uptake rates, adjusted for eligible population size, were a median 1.3 (IQR, 1.2-1.4) times higher for White compared to Black adults, and a median 1.4 (IQR, 1.2-1.8) times higher for White compared to Hispanic adults. Projecting vaccination coverage under persistence of current disparities in uptake, we found that Black and Hispanic populations would reach 75% coverage among adults almost one month later than White populations. In alternative scenarios, we found that interventions to equalize uptake rates across racial/ethnic groups could narrow but not erase these gaps, and that geographic targeting of vaccine doses to disadvantaged communities may be needed to produce a more equitable convergence of coverage by July. DiscussionInterventions are urgently needed to eliminate disparities in COVID-19 vaccination rates. Eliminating access barriers and increasing vaccine confidence among marginalized populations can narrow gaps in coverage. Combining these interventions with place-based allocation strategies can accelerate vaccination in disadvantaged communities, who have borne a disproportionate burden from COVID-19.

Covid-19 in the California State Prison System: An Observational Study of Decarceration, Ongoing Risks, and Risk Factors

BackgroundCorrectional institutions nationwide are seeking to mitigate Covid-19-related risks. ObjectiveTo quantify changes to Californias prison population since the pandemic began and identify risk factors for Covid-19 infection. DesignWe described residents demographic characteristics, health status, Covid-19 risk scores, room occupancy, and labor participation. We used Cox proportional hazard models to estimate the association between rates of Covid-19 infection and room occupancy and out-of-room labor, respectively. SettingCalifornia state prisons (March 1-October 10, 2020). ParticipantsResidents of California state prisons. MeasurementsChanges in the incarcerated populations size, composition, housing, and activities. For the risk factor analysis, the exposure variables were room type (cells vs dormitories) and labor participation (any room occupant participating in the prior 2 weeks) and the outcome variable was incident Covid-19 case rates. ResultsThe incarcerated population decreased 19.1% (119,401 to 96,623) during the study period.On October 10, 2020, 11.5% of residents were aged [≥]60, 18.3% had high Covid-19 risk scores, 31.0% participated in out-of-room labor, and 14.8% lived in rooms with [≥]10 occupants. Nearly 40% of residents with high Covid-19 risk scores lived in dormitories. In 9 prisons with major outbreaks (6,928 rooms; 21,750 residents), dormitory residents had higher infection rates than cell residents (adjusted hazard ratio [AHR], 2.51 95%CI, 2.25-2.80) and residents of rooms with labor participation had higher rates than residents of other rooms (AHR, 1.56; 95%CI, 1.39-1.74). LimitationsInability to measure density of residents living conditions or contact networks among residents and staff. ConclusionDespite reductions in room occupancy and mixing, California prisons still house many medically vulnerable residents in risky settings. Reducing risks further requires a combination of strategies, including rehousing, decarceration, and vaccination. Funding SourcesHorowitz Family Foundation; National Institute on Drug Abuse; National Science Foundation Graduate Research Fellowship; Open Society Foundations.

Passing the Test: A model-based analysis of safe school-reopening strategies

BackgroundThe COVID-19 pandemic has induced historic educational disruptions. In December 2020, at least two-thirds of US public school students were not attending full-time in-person education. The Biden Administration has expressed that reopening schools is a priority. ObjectiveTo compare risks of SARS-COV-2 transmission in schools across different school-based prevention strategies and levels of community transmission. DesignWe developed an agent-based network model to simulate transmission in elementary and high school communities, including home, school, and inter-household interactions. SettingWe parameterized school structure based on average US classrooms, with elementary schools of 638 students and high schools of 1,451 students. We varied daily community incidence from 1 to 100 cases per 100,000 population. Patients (or Participants)We simulated students, faculty/staff, and adult household members. InterventionsWe evaluated isolation of symptomatic individuals, quarantine of an infected individuals contacts, reduced class sizes, alternative schedules, staff vaccination, and weekly asymptomatic screening. MeasurementsWe projected transmission among students, staff and families during one month following introduction of a single infection into a school. We also calculated the number of infections expected for a typical 8-week quarter, contingent on community incidence rate. ResultsSchool transmission risk varies according to student age and community incidence and is substantially reduced with effective, consistent mitigation measures. Nevertheless, when transmission occurs, it may be difficult to detect without regular, frequent testing due to the subclinical nature of most infections in children. Teacher vaccination can reduce transmission to staff, while asymptomatic screening both improves understanding of local circumstances and reduces transmission, facilitating five-day schedules at full classroom capacity. LimitationsThere is uncertainty about susceptibility and infectiousness of children and low precision regarding the effectiveness of specific prevention measures, particularly with emergence of new variants. ConclusionWith controlled community transmission and moderate school-based prevention measures, elementary schools can open with few in-school transmissions, while high schools require more intensive mitigation. Asymptomatic screening should be a key component of school reopenings, allowing reopening at higher community incidence while still minimizing transmission risk.

How do Covid-19 policy options depend on end-of-year holiday contacts in Mexico City Metropolitan Area? A Modeling Study

BackgroundWith more than 20 million residents, Mexico City Metropolitan Area (MCMA) has the largest number of Covid-19 cases in Mexico and is at risk of exceeding its hospital capacity in late December 2020. MethodsWe used SC-COSMO, a dynamic compartmental Covid-19 model, to evaluate scenarios considering combinations of increased contacts during the holiday season, intensification of social distancing, and school reopening. Model parameters were derived from primary data from MCMA, published literature, and calibrated to time-series of incident confirmed cases, deaths, and hospital occupancy. Outcomes included projected confirmed cases and deaths, hospital demand, and magnitude of hospital capacity exceedance. FindingsFollowing high levels of holiday contacts even with no in-person schooling, we predict that MCMA will have 1{middle dot}0 million (95% prediction interval 0{middle dot}5 - 1{middle dot}7) additional Covid-19 cases between December 7, 2020 and March 7, 2021 and that hospitalizations will peak at 35,000 (14,700 - 67,500) on January 27, 2021, with a >99% chance of exceeding Covid-19-specific capacity (9,667 beds). If holiday contacts can be controlled, MCMA can reopen in-person schools provided social distancing is increased with 0{middle dot}5 million (0{middle dot}2 - 1{middle dot}0) additional cases and hospitalizations peaking at 14,900 (5,600 - 32,000) on January 23, 2021 (77% chance of exceedance). InterpretationMCMA must substantially increase Covid-19 hospital capacity under all scenarios considered. MCMAs ability to reopen schools in mid-January 2021 depends on sustaining social distancing and that contacts during the end-of-year holiday were well controlled. FundingSociety for Medical Decision Making, Gordon and Betty Moore Foundation, and Wadhwani Institute for Artificial Intelligence Foundation. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAs of mid-December 2020, Mexico has the twelfth highest incidence of confirmed cases of Covid-19 worldwide and its epidemic is currently growing. Mexicos case fatality ratio (CFR) - 9{middle dot}1% - is the second highest in the world. With more than 20 million residents, Mexico City Metropolitan Area (MCMA) has the highest number and incidence rate of Covid-19 confirmed cases in Mexico and a CFR of 8{middle dot}1%. MCMA is nearing its current hospital capacity even as it faces the prospect of increased social contacts during the 2020 end-of-year holidays. There is limited Mexico-specific evidence available on epidemic, such as parameters governing time-dependent mortality, hospitalization and transmission. Literature searches required supplementation through primary data analysis and model calibration to support the first realistic model-based Covid-19 policy evaluation for Mexico, which makes this analysis relevant and timely. Added value of this studyStudy strengths include the use of detailed primary data provided by MCMA; the Bayesian model calibration to enable evaluation of projections and their uncertainty; and consideration of both epidemic and health system outcomes. The model projects that failure to limit social contacts during the end-of-year holidays will substantially accelerate MCMAs epidemic (1{middle dot}0 million (95% prediction interval 0{middle dot}5 - 1{middle dot}7) additional cases by early March 2021). Hospitalization demand could reach 35,000 (14,700 - 67,500), with a >99% chance of exceeding current capacity (9,667 beds). Controlling social contacts during the holidays could enable MCMA to reopen in-person schooling without greatly exacerbating the epidemic provided social distancing in both schools and the community were maintained. Under all scenarios and policies, current hospital capacity appears insufficient, highlighting the need for rapid capacity expansion. Implications of all the available evidenceMCMA officials should prioritize rapid hospital capacity expansion. MCMAs ability to reopen schools in mid-January 2021 depends on sustaining social distancing and that contacts during the end-of-year holiday were well controlled.

Bayesian nowcasting with adjustment for delayed and incomplete reporting to estimate COVID-19 infections in the United States

Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID- 19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 400,718 COVID-19 deaths by the end of 2020, and that 27% of the US population had been infected. The results also demonstrate wide county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers.

Adaptive policies for use of physical distancing interventions during the COVID-19 pandemic

Policymakers need decision tools to determine when to use physical distancing interventions to maximize the control of COVID-19 while minimizing the economic and social costs of these interventions. We develop a pragmatic decision tool to characterize adaptive policies that combine real-time surveillance data with clear decision rules to guide when to trigger, continue, or stop physical distancing interventions during the current pandemic. In model-based experiments, we find that adaptive policies characterized by our proposed approach prevent more deaths and require a shorter overall duration of physical distancing than alternative physical distancing policies. Our proposed approach can readily be extended to more complex models and interventions. One-sentence summariesAdaptive physical distancing policies save more lives with fewer weeks of intervention than policies which prespecify the length and timing of interventions.

Contact tracing strategies for COVID-19 containment with attenuated physical distancing

Contact tracing has been recommended as a critical component of containment strategies for COVID-19. We used a simple epidemic model to evaluate how contact tracing might enable modification of current physical distancing restrictions. Testing and tracing coverage need to exceed 50% to see substantial gains; if both are below 50%, contact tracing does not reduce transmission by more than 10%. With 90% testing and tracing as well as high isolation and quarantine efficacy, contact tracing could reduce overall transmission by >45%, which would allow for partial loosening of physical distancing measures. Benefits of contact tracing could be enhanced by testing all contacts rather than only those with symptoms and by policies to support high adherence to voluntary isolation and quarantine.