Infectious disease surveillance needs for the United States: lessons from COVID-19 (preprint)

The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity.

Increased vaccine sensitivity of an emerging SARS-CoV-2 variant (preprint)

Host immune responses are a key source of selective pressure driving pathogen evolution. Emergence of many SARS-CoV-2 lineages has been associated with improvements in their ability to evade population immunity resulting from both vaccination and infection. Here we show diverging trends of escape from vaccine-derived and infection-derived immunity for the emerging XBB/XBB.1.5 Omicron lineage. Among 31,739 patients tested in ambulatory settings in Southern California from December, 2022 to February, 2023, adjusted odds of prior receipt of 2, 3, 4, and [≥]5 COVID-19 vaccine doses were 10% (95% confidence interval: 1-18%), 11% (3-19%), 13% (3-21%), and 25% (15-34%) lower, respectively, among cases infected with XBB/XBB.1.5 than among cases infected with other co-circulating lineages. Similarly, prior vaccination was associated with greater protection against progression to hospitalization among cases with XBB/XBB.1.5 than among non-XBB/XBB.1.5 cases (70% [30-87%] and 48% [7-71%], respectively, for recipients of [≥]4 doses). In contrast, cases infected with XBB/XBB.1.5 had 17% (11-24%) and 40% (19-65%) higher adjusted odds of having experienced 1 and [≥]2 prior documented infections, respectively, including with pre-Omicron variants. As immunity acquired from SARS-CoV-2 infection becomes increasingly widespread, fitness costs associated with enhanced vaccine sensitivity in XBB/XBB.1.5 may be offset by increased ability to evade infection-derived host responses.

Estimated preventable COVID-19-associated deaths due to non-vaccination in the United States (preprint)

While some studies have previously estimated lives saved by COVID-19 vaccination, we estimate how many deaths could have been averted by vaccination in the US but were not because of a failure to be vaccinated. We used a simple method based on a nationally representative dataset to estimate the preventable deaths among unvaccinated individuals in the U.S. from May 30, 2021 to April 30, 2022 adjusted for the effects of age and time. We estimated that at least 215,000 deaths could have been prevented among unvaccinated adults during the 11 months had they been vaccinated with at least a primary series. While more granular data on other factors causing differences in death rates between the vaccinated and unvaccinated groups could inform these estimates, this method addresses an important public health question and provides an estimate of vaccine-preventable deaths due to SARS-CoV-2. The same methods can be used during future public health emergencies.

Leveraging Serosurveillance and Postmortem Surveillance to Quantify the Impact of COVID-19 in Africa (preprint)

Background The COVID-19 pandemic has had a devastating impact on global health, the magnitude of which appears to differ intercontinentally: for example, reports suggest 271,900 per million people have been infected in Europe versus 8,800 per million people in Africa. While Africa is the second largest continent by population, its reported COVID-19 cases comprise <3% of global cases. Although social, environmental, and environmental explanations have been proposed to clarify this discrepancy, systematic infection underascertainment may be equally responsible. Methods We seek to quantify magnitude of underascertainment in COVID-19's cumulative incidence in Africa. Using serosurveillance and postmortem surveillance, we constructed multiplicative factors estimating ratios of true infections to reported cases in African nations since March 2020. Results Multiplicative factors derived from serology data - in a subset of 12 nations - suggested a range of COVID-19 reporting rates, from 1 in 630 infections reported in Kenya (May 2020) to 1 in 15 infections reported in South Africa (November 2021). The largest multiplicative factor, 3,795, corresponded to Malawi (June 2020), suggesting <0.05% of infections captured. A similar set of multiplicative factors for all nations derived from postmortem data points toward the same conclusion: reported COVID-19 cases are unrepresentative of true infections, suggesting a key reason for low case burden in many African nations is significant underdetection and underreporting. Conclusions While estimating COVID-19's exact burden is challenging, the multiplicative factors we present provide incidence curves reflecting likely-to-worst-case ranges of infection. Our results stress the need for expansive surveillance to allocate resources in areas experiencing severe discrepancies between reported cases, projected infections, and deaths.

Excess COVID-19 associated deaths among the unvaccinated population >= 18 years old in the US, May 30 through December 4, 2021 (preprint)

Vaccines against SARS-CoV-2 were authorized at the end of 2020 and are effective in preventing deaths; however, many persons remain unvaccinated. We used weekly publicly available CDC data from 26 U.S. jurisdictions, which include COVID-19 death rates by age and vaccination status, to estimate the number of excess deaths that might have been averted by vaccination from May 30 to December 4, 2021 in unvaccinated persons >= 18 years old. We subtracted the death rate in the vaccinated from rates in the unvaccinated to estimate the death rate each week that could be attributable to non-vaccination and multiplied this rate difference by the number of people in the unvaccinated group for each age group and each week, to estimate the excess mortality among the unvaccinated. Then, we extrapolated the number of deaths due to non-vaccination in the 26 jurisdictions to the whole US population using 2020 census estimates. In the 26 participating jurisdictions, there were an estimated 83,400 excess deaths among the unvaccinated populations, from May 30 to December 4, 2021. The largest number of excess deaths occurred in those 65-79 years old (n=28,900; 34.7% of total), followed by those 50-64 years old (n=25,900; 31.1%). Extrapolated to the US population we estimated approximately 135,000 excess deaths during the study period in persons >= 18 years old. Our estimates are an underestimate of all excess deaths that have occurred since vaccine became available because our analysis period was limited to May 30 to December 4, 2021 and many excess deaths occurred before and after this period. In summary, we used retrospective data to provide an estimate of the substantial number of COVID-19 deaths among the unvaccinated illustrating the importance of vaccination to prevent further unnecessary mortality during this pandemic.

Fairness and efficiency considerations in COVID-19 vaccine allocation strategies: a case study comparing front-line workers and 65-74 year olds in the United States (preprint)

1 The COVID-19 epidemic in the United States has been characterized by two stark disparities. COVID-19 burden has been unequally distributed among racial and ethnic groups and at the same time the mortality rates have been sharply higher among older age groups. These disparities have led some to suggest that inequalities could be reduced by vaccinating front-line workers before vaccinating older individuals, as older individuals in the US are disproportionately Non-Hispanic White. We compare the performance of two distribution policies, one allocating vaccines to front-line workers and another to older individuals aged 65-74-year-old. We estimate both the number of lives saved and the number of years of life saved under each of the policies, overall and in every race/ethnicity groups, in the United States and every state. We show that prioritizing COVID-19 vaccines for 65-74-year-olds saves both more lives and more years of life than allocating vaccines front-line workers in each racial/ethnic group, in the United States as a whole and in nearly every state. When evaluating fairness of vaccine allocation policies, the overall benefit to impact of each population subgroup should be considered, not only the proportion of doses that is distributed to each subgroup. Further work can identify prioritization schemes that perform better on multiple equity metrics.

Clinical outcomes among patients infected with Omicron (B.1.1.529) SARS-CoV-2 variant in southern California (preprint)

The Omicron (B.1.1.529) variant of SARS-CoV-2 rapidly achieved global dissemination following its emergence in southern Africa in November, 2021.1,2 Epidemiologic surveillance has revealed changes in COVID-19 case-to-hospitalization and case-to-mortality ratios following Omicron variant emergence,3-6 although interpretation of these changes presents challenges due to differential protection against Omicron or Delta (B.1.617.2) variant SARS-CoV-2 infections associated with prior vaccine-derived and naturally-acquired immunity, as well as longer-term changes in testing and healthcare practices.7 Here we report clinical outcomes among 222,688 cases with Omicron variant infections and 23,305 time-matched cases with Delta variant infections within the Kaiser Permanente Southern California healthcare system, who were followed longitudinally following positive outpatient tests between 15 December, 2021 and 17 January, 2022, when Omicron cases were almost exclusively BA.1 or its sublineages. Adjusted hazard ratios of progression to any hospital admission, symptomatic hospital admission, intensive care unit admission, mechanical ventilation, and death were 0.59 (95% confidence interval: 0.51-0.69), 0.59 (0.51-0.68), 0.50 (0.29-0.87), 0.36 (0.18-0.72), and 0.21 (0.10-0.44) respectively, for cases with Omicron versus Delta variant infections. In contrast, among 14,661 Omicron cases ascertained by outpatient testing between 3 February and 17 March, 2022, infection with the BA.2 or BA.1/BA.1.1 subvariants did not show evidence of differential risk of severe outcomes. Lower risk of severe clinical outcomes among cases with Omicron variant infection merits consideration in planning of healthcare capacity needs amid establishment of the Omicron variant as the dominant circulating SARS-CoV-2 lineage globally, and should inform the interpretation of both case- and hospital-based surveillance data.

Infectious disease dynamics and restrictions on social gathering size (preprint)

Social gatherings can be an important locus of transmission for many pathogens including SARS-CoV-2. During an outbreak, restricting the size of these gatherings is one of several non-pharmaceutical interventions available to policy-makers to reduce transmission. Often these restrictions take the form of prohibitions on gatherings above a certain size. While it is generally agreed that such restrictions reduce contacts, the specific size threshold separating "allowed" from "prohibited" gatherings often does not have a clear scientific basis, which leads to dramatic differences in guidance across location and time. Building on the observation that gathering size distributions are often heavy-tailed, we develop a theoretical model of transmission during gatherings and their contribution to general disease dynamics. We find that a key, but often overlooked, determinant of the optimal threshold is the distribution of gathering sizes. Using data on pre-pandemic contact patterns from several sources as well as empirical estimates of transmission parameters for SARS-CoV-2, we apply our model to better understand relationship between restriction threshold and reduction in cases. We find that, under reasonable transmission parameter ranges, restrictions may have to be set quite low to have any demonstrable effect on cases due to relative frequency of smaller gatherings. We compare our conceptual model with observed changes in reported contacts during lockdown in March of 2020.

Population Impact of SARS-CoV-2 Variants with Enhanced Transmissibility and/or Partial Immune Escape (preprint)

SARS-CoV-2 variants of concern exhibit varying degrees of transmissibility and, in some cases, escape from infection- and vaccine-induced immunity. Much effort has been devoted to measuring these phenotypes, but predicting their impact on the course of the pandemic – especially that of immune escape – remains a challenge. Here, we use a mathematical model to simulate the dynamics of wildtype and variant strains of SARS-CoV-2 in the context of vaccine rollout and nonpharmaceutical interventions. We show that variants with enhanced transmissibility easily rise to high frequency, whereas partial immune escape, on its own, often fails to do so. However, when these phenotypes are combined, enhanced transmissibility can carry the variant to high frequency, at which point partial immune escape may limit the ability of vaccination to control the epidemic. Our findings suggest that moderate immune escape poses a low risk unless combined with a substantial increase in transmissibility.Funding: MB and BPT were supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award number R01AI128344. RK, ML and WPH were supported by the U.S. National Cancer Institute SeroNet cooperative agreement U01CA261277.Declaration of Interests: RK discloses consulting fees from Partners In Health and the PanAmerican Health Organization. ML received funding through his institution from US CDC, NIH, and UK National Institute for Health Research, and Pfizer, and consulting fees or honoraria from Merck,Sanofi Pasteur, Janssen, and Bristol Myers Squibb. He is a member of the Scientific Advisory Board for CEPI, the Coalition for Epidemic Preparedness Innovations. WPH serves on the Advisory Board of Biobot Analytics and has received compensation for expert witness testimony on the course of the SARS-CoV-2 pandemic. All others have nothing to disclose.

Population impact of SARS-CoV-2 variants with enhanced transmissibility and/or partial immune escape (preprint)

SARS-CoV-2 variants of concern exhibit varying degrees of transmissibility and, in some cases, escape from infection- and vaccine-induced immunity. Much effort has been devoted to measuring these phenotypes, but predicting their impact on the course of the pandemic - especially that of immune escape - remains a challenge. Here, we use a mathematical model to simulate the dynamics of wildtype and variant strains of SARS-CoV-2 in the context of vaccine rollout and nonpharmaceutical interventions. We show that variants with enhanced transmissibility easily rise to high frequency, whereas partial immune escape, on its own, often fails to do so. However, when these phenotypes are combined, enhanced transmissibility can carry the variant to high frequency, at which point partial immune escape may limit the ability of vaccination to control the epidemic. Our findings suggest that moderate immune escape poses a low risk unless combined with a substantial increase in transmissibility.

Identifying and alleviating bias due to differential depletion of susceptible people in post-marketing evaluations of COVID-19 vaccines (preprint)

Recent studies have provided key information about SARS-CoV-2 vaccines’ efficacy and effectiveness (VE). One important question that remains is whether the protection conferred by vaccines wanes over time. However, estimates over time are subject to bias from differential depletion of susceptibles between vaccinated and unvaccinated groups. Here we examine the extent to which biases occur under different scenarios and assess whether serologic testing has the potential to correct this bias. By identifying non-vaccine antibodies, these tests could identify individuals with prior infection. We find in scenarios with high baseline VE, differential depletion of susceptibles creates minimal bias in VE estimates, suggesting that any observed declines are likely not due to spurious waning alone. However, if baseline VE is lower, the bias for leaky vaccines (that reduce individual probability of infection given contact) is larger and should be corrected by excluding individuals with past infection if the mechanism is known to be leaky. Conducting analyses both unadjusted and adjusted for past infection could give lower and upper bounds for the true VE. Studies of VE should therefore enroll individuals regardless of prior infection history but also collect information, ideally through serologic testing, on this critical variable.

Effectiveness of the BNT162b2 mRNA COVID-19 Vaccine in Pregnancy (preprint)

Background: As mass vaccination campaigns against COVID-19 accelerate worldwide, there remains only limited evidence regarding vaccine effectiveness (VE) among pregnant women. Pregnant women have been shown to be at risk for severe COVID-19, resulting in adverse obstetrics outcomes, and their immune system is known to undergo alterations during pregnancy. Phase III clinical trials of the approved mRNA COVID-19 vaccines excluded pregnant women, yet current guidelines encourage offering the vaccine to pregnant women. In this study, we examine data from Israel’s largest healthcare organization to evaluate the effectiveness of the BNT162b2 mRNA vaccine among pregnant women. Methods: : We conducted an observational cohort study of pregnant women 16 years or older, with no history of SARS-CoV-2, who were vaccinated between December 20, 2020 and June 3, 2021. Vaccinated subjects were matched to unvaccinated controls according to a set of demographic and clinical characteristics. Study outcomes included documented infection with SARS-CoV-2, symptomatic COVID-19, COVID-19-related hospitalization, severe illness and death. For each outcome, VE was estimated at several periods following vaccination as one minus the risk ratio using the Kaplan–Meier estimator. Results: : 10,861 vaccinated women were matched to an identical number of unvaccinated controls. Estimated VE from 7 through 28 days after the second dose was 97% (95% CI 91%-100%) for any documented infection, 96% (86-100%) for infections with documented symptoms, and 85% (32%-100%) for COVID-19-related hospitalization. Only one event of severe illness was observed in the unvaccinated group, and no deaths were observed in either group -- insufficient incidence for estimating VE for these outcomes. Discussion: The BNT162b2 mRNA vaccine was found to have high VE among pregnant women. Since high VE has been reported as one of the strongest predictors of COVID-19 vaccine acceptance among pregnant women, the high VE estimates found in this study have the potential to increase vaccine acceptance in this group. In addition, the present VE estimates are similar to those reported in the general population for the same variants, suggesting that it may be possible to infer the VE for pregnant women from studies in the general population for both current and future variants.

Comparative performance of between-population vaccine allocation strategies with applications for emerging pandemics (preprint)

2 Vaccine allocation decisions during emerging pandemics have proven to be challenging due to competing ethical, practical, and political considerations. Complicating decision making, policy makers need to consider vaccine allocation strategies that balance needs both within and between populations. Due to limited vaccine stockpiles, vaccine doses should be allocated in locations where their impact will be maximized. Using a susceptible-exposed-infectious-recovered (SEIR) model we examine optimal vaccine allocation decisions across two populations considering the impact of population size, underlying immunity, continuous vaccine roll-out, heterogeneous population risk structure, and differences in disease transmissibility. We find that in the context of an emerging pathogen where many epidemiologic characteristics might not be known, equal vaccine allocation between populations performs optimally in most scenarios. In the specific case considering heterogeneous population risk structure, first targeting individuals at higher risk of transmission or death due to infection leads to equal resource allocation across populations.

Infections, Hospitalizations, and Deaths Averted Via Direct Effects of the Pfizer-BioNTech BNT162b2 mRNA COVID-19 Vaccine in a Nationwide Vaccination Campaign, Israel (preprint)

Background: Israel recently initiated a rapid nationwide COVID-19 vaccination campaign to immunize persons ≥16 years of age and used exclusively Pfizer–BioNTech BNT162b2 mRNA COVID-19 vaccine according to schedule (two doses, 21 days apart). We provide the first estimates of the number of COVID-19 cases, hospitalizations, and deaths averted by a nationwide vaccination campaign. Methods: We used national surveillance data from the first 112 days (Dec 20, 2020 ‒ Apr 10, 2021) of Israel’s vaccination campaign to estimate averted burden of four outcomes: SARS-CoV-2 infections and COVID-19-related hospitalizations, severe or critical hospitalizations, and deaths. The direct effects of the immunization program were estimated for all susceptible individuals (i.e., no previous evidence of laboratory-confirmed SARS-CoV-2 infection) who were at least partially vaccinated (defined as receipt of at least one dose with ≥14 days after the first dose). Cases averted were estimated based on cumulative daily, age-specific rate differences comparing rates among unvaccinated to those who were at least partially vaccinated for each of the four outcomes and the (age-specific) size of the susceptible population and proportion that was at least partially vaccinated. Findings: Through Apr 10, 2021, we estimated that Israel’s vaccination campaign averted 158,665 (95% uncertainty range: 115,899‒201,431) SARS-CoV-2 infections, 24,597 (6,622‒42,571) hospitalizations, 17,432 (3,065‒31,799) severe and critical hospitalizations, and 5,533 (-1,146‒12,213) deaths. Of these, 66% of hospitalizations and 91% of deaths averted were among those ≥65 years of age. Seventy-three percent of SARS-CoV-2 infections and 79% of COVID-19-related hospitalizations and deaths averted stemmed from the protective effects in fully vaccinated persons. Interpretations: Without vaccination, Israel would have likely experienced triple the number of hospitalizations and deaths compared to what actually occurred during their largest wave of SARS-CoV-2 to date, which would have likely overwhelmed the healthcare system. Indirect effects and long-term benefits of the program, which could be substantial, were not included in these estimates and warrant future research. Funding: None.Declaration of Interest: John McLaughlin, Frederick Angulo, Farid Khan, Gabriel Mircus, Kaijie Pan, Jo Southern, David Swerdlow, and Luis Jodar hold stock and stock options in Pfizer Inc. Marc Lipsitch has provided advice on COVID-19 free of charge to Janssen, Astra-Zeneca, Pfizer, and COVAXX (United Biomedical), as well as to the nonprofit One Day Sooner and has received consulting income or honoraria from Merck, Bristol Meyers Squibb, and Sanofi, and institutional research support from Pfizer. All other authors report no conflicts.

Estimating vaccine efficacy against transmission via effect on viral load (preprint)

Determining policies to end the SARS-CoV-2 pandemic will require an understanding of the efficacy and effectiveness (hereafter, efficacy) of vaccines. Beyond the efficacy against severe disease and symptomatic and asymptomatic infection, understanding vaccine efficacy against transmission will help model epidemic trajectory and determine appropriate control measures. Recent studies have proposed using random virologic testing in individual randomized controlled trials to improve estimation of vaccine efficacy against infection. We propose to further use the viral load measures from these tests to estimate efficacy against transmission. This estimation requires a model of the relationship between viral load and transmissibility and assumptions about the vaccine effect on transmission and the progress of the epidemic. We describe these key assumptions, potential violations of them, and solutions that can be implemented to mitigate these violations. Assessing these assumptions and implementing this random sampling, with viral load measures, will enable better estimation of the crucial measure of vaccine efficacy against transmission.

Decreased Infectivity Following BNT162b2 Vaccination (preprint)

Background: BNT162b2 was shown to be 92% effective in preventing COVID-19. Prioritizing vaccine rollout, and achievement of herd immunity depend on SARS-CoV-2 transmission reduction. The vaccine’s effect on infectivity is thus a critical priority.Methods: In a cohort of all 9650 HCW of a large single tertiary medical center, we calculated the prevalence of positive SAR-CoV-2 qRT-PCR cases with an asymptomatic presentation, tested following known or presumed exposure and the infectious subset (N-gene-Ct-value<30) of these and the prevalence of never-symptomatic infections. Additionally, infection incidence rates were calculated for symptomatic cases and infectious (Ct<30) cases. Vaccine effectiveness within three months of vaccine rollout was measured as one minus the relative risk or rate ratio, respectively. To further assess infectiousness, we compared the mean Ct-value and the proportion of infections with a positive SARS-CoV-2 antigen test of vaccinated vs. unvaccinated. The correlation between IgG levels within the week before detection and Ct level was assessed.Findings: Reduced prevalence among fully vaccinated HCW was observed for (i) infections detected due to exposure, with asymptomatic presentation (VE(i)=65.1%, 95%CI 45-79%), (ii) the presumed infectious (Ct<30) subset of these (VE(ii)=69.6%, 95%CI 43-84%) (iii) never-symptomatic infections (VE(iii)=72.3%, 95%CI 48-86%), and (iv) the presumed infectious (Ct<30) subset (VE(iv)=83.0%, 95%CI 51-94%).Incidence of (v) symptomatic and (vi) symptomatic-infectious cases was significantly lower among fully vaccinated vs. unvaccinated individuals (VE(v)= 89.7%, 95%CI 84-94%, VE(vi)=88.1%, 95%CI 80-95%).The mean Ct-value was significantly higher in vaccinated vs. unvaccinated (27.3±1.2 vs. 22.2±1.0, p<0.001) and the proportion of positive SARS-CoV-2 antigen tests was also significantly lower among vaccinated vs. unvaccinated PCR-positive HCW (80% vs. 31%, p<0.001). Lower infectivity was correlated with higher IgG concentrations (R=0.36, p=0.01).Interpretation: These results suggest that BNT162b2 is moderately to highly effective in reducing infectivity, via preventing infection and through reducing viral shedding. Funding: Sheba Medical Center, IsraelDeclaration of Interest: All authors declare they have no competing interestsEthical Approval: The Sheba Ethical committee, reviewed the protocol and approved thestudy.

SARS-CoV-2 infection and risk of clinical sequelae during the post-acute phase: a retrospective cohort study (preprint)

Objective: Clinical sequelae have not been well characterized during the post-acute phase of SARS-CoV-2 among adults 18 to 65 years old, and this study sought to fill that gap by evaluating excess risk and relative hazards for developing incident clinical sequelae during the post-acute phase. Design: Retrospective cohort study including three propensity-matched groups. Setting: This study merged three data sources from a large United States health plan: a large national administrative claims database, an outpatient lab testing database, and an inpatient hospital admissions database. Participants: Individuals 18 to 65 years old with continuous health plan enrollment from January 2019 to date of SARS-CoV-2 diagnosis. Three comparator groups were identified and propensity-score matched to individuals infected with SARS-CoV-2: a 2020 comparator group, a historical 2019 comparator group and a historical comparator group with viral lower respiratory tract illness (vLRTI). Main outcome measures: Over 50 clinical sequelae during the post-acute phase (index date + 21 days) were ascertained using ICD-10 codes. Excess risk due to SARS-CoV-2 during the 4 months following the acute phase of illness and hazard ratios with 95% Bonferroni-corrected confidence intervals were calculated. Results: This study found 14% of adults [≤]65 years of age who were infected with SARS-CoV-2 (n=193113) had at least one new clinical sequelae that required medical attention during the post-acute phase of illness. When considering risk for specific sequelae attributable to SARS-Cov-2 infection during the post-acute phase, clinical outcomes including chronic respiratory failure, cardiac arrythmia, hypercoagulability, encephalopathy, peripheral neuropathy, amnesia (memory difficulty), diabetes, liver test abnormalities, myocarditis, anxiety and fatigue were significantly elevated compared to the three propensity-matched comparator groups (2020, 2019, vLRTI). Significant risk differences due to SARS-CoV-2 infection ranged from 0.02 to 2.26 per 100 people and hazard ratios ranged from 1.24 to 25.65 when compared to the 2020 comparator group. Conclusions: Our results confirm excess risk for developing clinical sequelae due to SARS-CoV-2 during the post-acute phase, including specific types of sequelae less commonly seen among other viral illnesses. Although individuals who were older, had pre-existing conditions, and were hospitalized due to COVID-19 were at greatest excess risk, younger adults ([≤]50 years), adults who did not have pre-existing conditions or adults who were not hospitalized due to COVID-19 were still at elevated risk for developing new clinical sequelae. The elevated risk for incident sequelae during the post-acute phase is relevant for healthcare planning.

Interpreting vaccine efficacy trial results for infection and transmission (preprint)

Randomized controlled trials (RCTs) have shown high efficacy of multiple vaccines against SARS-CoV-2 disease (COVID-19), and recent studies have shown the vaccines are also effective against infection. Evidence for the effect of each of these vaccines on ability to transmit the virus is also beginning to emerge. We describe an approach to estimate these vaccines effects on viral positivity, a prevalence measure which under the reasonable assumption that vaccinated individuals who become infected are no more infectious than unvaccinated individuals forms a lower bound on efficacy against transmission. Specifically, we recommend separate analysis of positive tests triggered by symptoms (usually the primary outcome) and cross-sectional prevalence of positive tests obtained regardless of symptoms. The odds ratio of carriage for vaccine vs. placebo provides an unbiased estimate of vaccine effectiveness against viral positivity, under certain assumptions, and we show through simulations that likely departures from these assumptions will only modestly bias this estimate. Applying this approach to published data from the RCT of the Moderna vaccine, we estimate that one dose of vaccine reduces the potential for transmission by at least 61%, possibly considerably more. We describe how these approaches can be translated into observational studies of vaccine effectiveness. HighlightsO_LISARS-CoV-2 vaccine trials did not directly estimate vaccine efficacy against transmission. C_LIO_LIWe describe an approach to estimate a lower bound of vaccine efficacy against transmission. C_LIO_LIWe estimate one dose of the Moderna vaccine reduces the potential for transmission by at least 61%. C_LIO_LIWe recommend separate analysis of tests triggered by symptoms vs. cross-sectional tests. C_LI

Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data (preprint)

Designing public health responses to outbreaks requires close monitoring of population-level health indicators in real-time. Thus an accurate estimation of the epidemic curve is critical. We propose an approach to reconstruct epidemic curves in near real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between the months of March and April 2020. We address two data collection problems that affected the reliability of the available real-time epidemiological data, namely, the frequent missing information documenting when a patient first experienced symptoms, and the frequent retrospective revision of historical information (including right censoring). This is done by using a novel back-calculating procedure based on imputing patients dates of symptom onset from reported cases, according to a dynamically-estimated backward reporting delay conditional distribution, and adjusting for right censoring using an existing package, NobBS, to estimate in real time (nowcast) cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number (Rt) in real-time. At each step, we evaluate how different assumptions affect the recovered epidemiological events and compare the proposed approach to the alternative procedure of merely using curves of case counts, by report day, to characterize the time-evolution of the outbreak. Finally, we assess how these real-time estimates compare with subsequently documented epidemiological information that is considered more reliable and complete that became available weeks to months later in time. Our approach may help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health surveillance systems in other locations.

Modeling the impact of racial and ethnic disparities on COVID-19 epidemic dynamics (preprint)

The impact of variable infection risk by race and ethnicity on the dynamics of SARS-CoV-2 spread is largely unknown. Here, we fit structured compartmental models to seroprevalence data from New York State and analyze how herd immunity thresholds (HITs), final sizes, and epidemic risk changes across groups. A simple model where interactions occur proportionally to contact rates reduced the HIT, but more realistic models of preferential mixing within groups increased the threshold toward the value observed in homogeneous populations. Across all models, the burden of infection fell disproportionately on minority populations: in a model fit to Long Island serosurvey and census data, 81% of Hispanics or Latinos were infected when the HIT was reached compared to 34% of non-Hispanic whites. Our findings, which are meant to be illustrative and not best estimates, demonstrate how racial and ethnic disparities can impact epidemic trajectories and result in unequal distributions of SARS-CoV-2 infection.