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Background The coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact on global health, the magnitude of which appears to differ intercontinentally: For example, reports suggest that 271 900 per million people have been infected in Europe versus 8800 per million people in Africa. While Africa is the second-largest continent by population, its reported COVID-19 cases comprise Methods We sought to quantify magnitudes 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 Africa since March 2020. Results Multiplicative factors derived from serology data (subset of 12 nations) suggested a range of COVID-19 reporting rates, from 1 in 2 infections reported in Cape Verde (July 2020) to 1 in 3795 infections reported in Malawi (June 2020). 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 that a key reason for low case burden in many African nations is significant underdetection and underreporting. Conclusions While estimating the exact burden of COVID-19 is challenging, the multiplicative factors we present furnish incidence estimates reflecting likely-to-worst-case ranges of infection. Our results stress the need for expansive surveillance to allocate resources in areas experiencing discrepancies between reported cases, projected infections, and deaths.

Postexposure vaccination has the potential to prevent or modify the course of clinical disease among those exposed to a pathogen. However, due to logistical constraints, postexposure vaccine trials have been difficult to implement in practice. In place of trials, investigators have used observational data to estimate the efficacy or optimal timing window for postexposure vaccines, but the relationship between these analyses and those that would be conducted in a trial is often unclear. Here, we define several possible target trials for postexposure vaccination and show how, under certain conditions, they can be emulated using observational data. We emphasize the importance of the incubation period and the timing of vaccination in trial design and emulation. As an example, we specify a protocol for postexposure vaccination against mpox and provide a step-by-step description of how to emulate it using data from a healthcare database or contact tracing program. We further illustrate some of the benefits of the target trial approach through simulation.

Policy reset and convergence on governance are needed

Nirmatrelvir/ritonavir (Paxlovid) is a combination protease inhibitor that blocks replication of SARS-CoV-2 (the virus that causes COVID-19) and has been shown to reduce the risk for hospitalization and death among patients with mild to moderate COVID-19 who are at risk for progression to severe disease* (1). In December 2021, the Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for early treatment with Paxlovid among persons with mild to moderate cases of COVID-19 who are at high risk for progression to severe disease (2). FDA and a small number of published case reports have documented recurrence of COVID-19 symptoms or a positive viral test result (COVID-19 rebound) 2-8 days after recovery or a negative SARS-CoV-2 test result among patients treated with Paxlovid (3-7); however, large-scale studies investigating severe illness after Paxlovid treatment are limited. This study used electronic health record (EHR) data from a large integrated health care system in California (Kaiser Permanente Southern California [KPSC]) to describe hospital admissions and emergency department (ED) encounters related to SARS-CoV-2 infections during the 5-15 days after pharmacy dispensation of a 5-day treatment course of Paxlovid. Among 5,287 persons aged ≥12 years who received Paxlovid during December 31, 2021-May 26, 2022, 73% had received ≥3 doses of COVID-19 vaccine

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.

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 vaccinate. We used a simple method based on a nationally representative dataset to estimate the preventable deaths among unvaccinated individuals in the US from May 30, 2021 to September 3, 2022 adjusted for the effects of age and time. We estimated that at least 232,000 deaths could have been prevented among unvaccinated adults during the 15 months had they been vaccinated with at least a primary series. While uncertainties exist regarding the exact number of preventable deaths and more granular data are needed on other factors causing differences in death rates between the vaccinated and unvaccinated groups to inform these estimates, this method is a rapid assessment on vaccine-preventable deaths due to SARS-CoV-2 that has crucial public health implications. The same rapid method can be used for future public health emergencies.

Wolff Sagy et al.1report that a single dose of MVA-BN mpox vaccine was 86% effective in preventing incidence of diagnosed mpox. In their cohort study, all eligible men were enrolled on July 31, 2022 and followed until they tested positive for mpox or December 25, 2022, whichever was earlier. Individuals vaccinated after July 31 were considered unvaccinated from Day 0 through their date of vaccination; they were considered vaccinated from their date of vaccination, which was set as Day 0 for their follow up as a vaccinated person. Incidence was compared using Cox regression on this time scale, with typically different calendar days being Day 0 for vaccinated and unvaccinated persons. The study estimates a vaccine effectiveness of 86%. In this communication we estimate the magnitude of confounding by calendar time in the analysis1and argue that the true vaccine effectiveness was likely considerably lower than estimated during the study period.

Breakthrough infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in fully vaccinated individuals are receiving intense scrutiny because of their importance in determining how long restrictions to control virus transmission will need to remain in place in highly vaccinated populations as well as in determining the need for additional vaccine doses or changes to the vaccine formulations and/or dosing intervals. Measurement of breakthrough infections is challenging outside of randomized, placebo-controlled, double-blind field trials. However, laboratory and observational studies are necessary to understand the impact of waning immunity, viral variants and other determinants of changing vaccine effectiveness against various levels of coronavirus disease 2019 (COVID-19) severity. Here, we describe the approaches being used to measure vaccine effectiveness and provide a synthesis of the burgeoning literature on the determinants of vaccine effectiveness and breakthrough rates. We argue that, rather than trying to tease apart the contributions of factors such as age, viral variants and time since vaccination, the rates of breakthrough infection are best seen as a consequence of the level of immunity at any moment in an individual, the variant to which that individual is exposed and the severity of disease being considered. We also address key open questions concerning the transition to endemicity, the potential need for altered vaccine formulations to track viral variants, the need to identify immune correlates of protection, and the public health challenges of using various tools to counter breakthrough infections, including boosters in an era of global vaccine shortages., Here, Lipsitch and colleagues assess the impact of breakthrough SARS-CoV-2 infections that occur in individuals who have been vaccinated against COVID-19. The authors explain how the rate of breakthrough infections can be measured, what the causes of these infections are and discuss other key questions that need to be considered in light of these infections.

Background Despite the high efficacy of the BNT162b2 messenger RNA vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), rare breakthrough infections have been reported, including infections among health care workers. Data are needed to characterize these infections and define correlates of breakthrough and infectivity. Methods At the largest medical center in Israel, we identified breakthrough infections by performing extensive evaluations of health care workers who were symptomatic (including mild symptoms) or had known infection exposure. These evaluations included epidemiologic investigations, repeat reverse-transcriptase–polymerase-chain-reaction (RT-PCR) assays, antigen-detecting rapid diagnostic testing (Ag-RDT), serologic assays, and genomic sequencing. Correlates of breakthrough infection were assessed in a case–control analysis. We matched patients with breakthrough infection who had antibody titers obtained within a week before SARS-CoV-2 detection (peri-infection period) with four to five uninfected controls and used generalized estimating equations to predict the geometric mean titers among cases and controls and the ratio between the titers in the two groups. We also assessed the correlation between neutralizing antibody titers and N gene cycle threshold (Ct) values with respect to infectivity. Results Among 1497 fully vaccinated health care workers for whom RT-PCR data were available, 39 SARS-CoV-2 breakthrough infections were documented. Neutralizing antibody titers in case patients during the peri-infection period were lower than those in matched uninfected controls (case-to-control ratio, 0.361; 95% confidence interval, 0.165 to 0.787). Higher peri-infection neutralizing antibody titers were associated with lower infectivity (higher Ct values). Most breakthrough cases were mild or asymptomatic, although 19% had persistent symptoms (>6 weeks). The B.1.1.7 (alpha) variant was found in 85% of samples tested. A total of 74% of case patients had a high viral load (Ct value

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 virus transmission, including efficacy against transmission of different viral variants, 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.

BNT162b2 Effectiveness against Delta Variant in Adolescents A study involving more than 94,000 vaccinated and unvaccinated adolescents between the ages of 12 and 18 years in Israel showed increasin...

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 the 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.

BackgroundThe 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 MethodsWe 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.ResultsMultiplicative 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 ConclusionsWhile 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.SummaryHere we present a range of estimates quantifying the extent of underascertainment of COVID-19 cumulative incidence in Africa. These estimates, constructed from serology and mortality data, suggest that systematic underdetection and underreporting may be contributing to the seemingly low burden of COVID-19 reported in Africa.

WHO recommends mass drug administration (MDA) of the antibiotic azithromycin for children aged 1-11 months in areas with high rates of infant and child mortality. Notwithstanding the substantial potential benefits of lowering childhood mortality, MDA raises understandable concerns about exacerbating antibiotic resistance.In this study, we aimed to evaluate the use of MDA using both quantitative and ethical considerations.We performed a series of literature searches between July 2019 and June 2022.We first compared MDA with other uses of antibiotics using the standard metric of 'number needed to treat', and five additional criteria: (1) other widely accepted uses of anti-infectives (2) absolute use (i.e. total number), of antibiotics, (3) risk-benefit trade-off, (4) availability of short-term alternatives, and (5) the precedent for implementing similar interventions. We found that MDA falls well within a justifiable range when compared with widely accepted uses of antibiotics in terms of the number needed to treat. The other five criteria we considered provided further support for the use of MDA to prevent childhood mortality.Although better data on antibiotic use and resistance are needed, efforts to reduce antibiotic use and resistance should not start with halting MDA of azithromycin in the areas with the highest rates of childhood mortality. Improving data to inform this decision is critical. However, on the basis of the best evidence available, we believe that concerns regarding resistance should not thwart MDA; instead, MDA should be accompanied by robust plans to monitor its efficacy and changes in resistance levels. Similar considerations could be included in a framework for evaluating the benefits of antibiotics against the risk of resistance in other contexts.

There is limited information on the volume of antibiotic prescribing that is influenza-associated, resulting from influenza infections and their complications (such as streptococcal pharyngitis and otitis media). Here, we estimated age/diagnosis-specific proportions of antibiotic prescriptions (fills) for the Kaiser Permanente Northern California population during 2010–2018 that were influenza-associated. The proportion of influenza-associated antibiotic prescribing among all antibiotic prescribing was higher in children aged 5–17 years compared to children aged under 5 years, ranging from 1.4% [95% CI (0.7–2.1)] in aged

Background A vaccine trial with a conventional challenge design can be very fast once it starts, but it requires a long prior process, in part to grow and standardize challenge virus in the laboratory. This detracts somewhat from its overall promise for accelerated efficacy testing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine candidates, and from the ability of developing countries and small companies to conduct it. Aims We set out to identify a challenge design that avoids this part of the long prior process. Sources Literature in trial design (including a proof of concept flu challenge trial by B. Killingley et al.), vaccinology, medical ethics, and various aspects of COVID response. Content A challenge design with deliberate natural viral exposure avoids the need to grow culture. This new design is described and compared both to a conventional challenge design and to a conventional phase III field trial. In comparison, the proposed design has ethical, scientific, and feasibility strengths. Implications The proposed new design should be considered for future vaccine trials.

Background Empirical antibiotic use is common in the hospital. Here, we characterize patterns of antibiotic use, infectious diagnoses, and microbiological laboratory results among hospitalized patients and aim to quantify the proportion of antibiotic use that is potentially attributable to specific bacterial pathogens. Methods We conducted an observational study using electronic health records from acute care facilities in the US Veterans Affairs Healthcare System. From October 2017 to September 2018, 482 381 hospitalizations for 332 657 unique patients that met all criteria were included. At least 1 antibiotic was administered at 202 037 (41.9%) of included hospital stays. We measured frequency of antibiotic use, microbiological specimen collection, and bacterial isolation by diagnosis category and antibiotic group. A tiered system based on specimen collection sites and diagnoses was used to attribute antibiotic use to presumptive causative organisms. Results Specimens were collected at 130 012 (64.4%) hospitalizations with any antibiotic use, and at least 1 bacterial organism was isolated at 35.1% of these stays. Frequency of bacterial isolation varied widely by diagnosis category and antibiotic group. Under increasingly lenient criteria, 10.2%–31.4% of 974 733 antibiotic days of therapy could be linked to a potential bacterial pathogen. Conclusions Overall, the vast majority of antibiotic use could be linked to either an infectious diagnosis or microbiological specimen. Nearly one-half of antibiotic use occurred when there was a specimen collected but no bacterial organism identified, underscoring the need for rapid and improved diagnostics to optimize antibiotic use.

To evaluate the effectiveness of the BNT162b2 messenger RNA vaccine in pregnant women, we conducted an observational cohort study of pregnant women aged 16 years or older, with no history of SARS-CoV-2, who were vaccinated between 20 December 2020 and 3 June 2021. A total of 10,861 vaccinated pregnant women were matched to 10,861 unvaccinated pregnant controls using demographic and clinical characteristics. Study outcomes included documented infection with SARS-CoV-2, symptomatic COVID-19, COVID-19-related hospitalization, severe illness and death. Estimated vaccine effectiveness from 7 through to 56 d after the second dose was 96% (95% confidence interval 89-100%) for any documented infection, 97% (91-100%) for infections with documented symptoms and 89% (43-100%) 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. In summary, the BNT162b2 mRNA vaccine was estimated to have high vaccine effectiveness in pregnant women, which is similar to the effectiveness estimated in the general population.

BackgroundStaphylococcus aureusis a leading cause of healthcare- and community-associated infections and can be difficult to treat due to antimicrobial resistance. About 30% of individuals carryS. aureusasymptomatically in their nares, a risk factor for later infection, and interactions with other species in the nasal microbiome likely modulate its carriage. It is thus important to identify ecological or functional genetic elements within the maternal or infant nasal microbiomes that influenceS. aureusacquisition and retention in early life.ResultsWe recruited 36 mother-infant pairs and profiled a subset of monthly longitudinal nasal samples from the first year after birth using shotgun metagenomic sequencing. The infant nasal microbiome is highly variable, particularly within the first 2 months. It is weakly influenced by maternal nasal microbiome composition, but primarily shaped by developmental and external factors, such as daycare. Infants display distinctive patterns ofS. aureuscarriage, positively associated withAcinetobacterspecies,Streptococcus parasanguinis,Streptococcus salivarius, andVeillonellaspecies and inversely associated with maternalDolosigranulum pigrum. Furthermore, we identify a gene family, likely acting as a taxonomic marker for an unclassified species, that is significantly anti-correlated withS. aureusin infants and mothers. In gene content-based strain profiling, infantS. aureusstrains are more similar to maternal strains.ConclusionsThis improved understanding ofS. aureuscolonization is an important first step toward the development of novel, ecological therapies for controllingS. aureuscarriage.

BackgroundMass distribution of azithromycin to preschool children twice yearly for 2 years has been shown to reduce childhood mortality in sub-Saharan Africa but at the cost of amplifying macrolide resistance. The effects on the gut resistome, a reservoir of antimicrobial resistance genes in the body, of twice-yearly administration of azithromycin for a longer period are unclear.MethodsWe investigated the gut resistome of children after they received twice-yearly distributions of azithromycin for 4 years. In the Niger site of the MORDOR trial, we enrolled 30 villages in a concurrent trial in which they were randomly assigned to receive mass distribution of either azithromycin or placebo, offered to all children 1 to 59 months of age every 6 months for 4 years. Rectal swabs were collected at baseline, 36 months, and 48 months for analysis of the participants' gut resistome. The primary outcome was the ratio of macrolide-resistance determinants in the azithromycin group to those in the placebo group at 48 months.ResultsOver the entire 48-month period, the mean (±SD) coverage was 86.6±12% in the villages that received placebo and 83.2±16.4% in the villages that received azithromycin. A total of 3232 samples were collected during the entire trial period; of the samples obtained at the 48-month monitoring visit, 546 samples from 15 villages that received placebo and 504 from 14 villages that received azithromycin were analyzed. Determinants of macrolide resistance were higher in the azithromycin group than in the placebo group: 7.4 times as high (95% confidence interval [CI], 4.0 to 16.7) at 36 months and 7.5 times as high (95% CI, 3.8 to 23.1) at 48 months. Continued mass azithromycin distributions also selected for determinants of nonmacrolide resistance, including resistance to beta-lactam antibiotics, an antibiotic class prescribed frequently in this region of Africa.ConclusionsAmong villages assigned to receive mass distributions of azithromycin or placebo twice yearly for 4 years, antibiotic resistance was more common in the villages that received azithromycin than in those that received placebo. This trial showed that mass azithromycin distributions may propagate antibiotic resistance. (Funded by the Bill and Melinda Gates Foundation and others; ClinicalTrials.gov number, NCT02047981.).

Background Global efforts are needed to elucidate the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the underlying cause of coronavirus disease 2019 (COVID-19), including seroprevalence, risk factors, and long-term sequelae, as well as immune responses after vaccination across populations and the social dimensions of prevention and treatment strategies. Methods In the United States, the National Cancer Institute in partnership with the National Institute of Allergy and Infectious Diseases, established the SARS-CoV-2 Serological Sciences Network (SeroNet) as the nation’s largest coordinated effort to study coronavirus disease 2019. The network comprises multidisciplinary researchers bridging gaps and fostering collaborations among immunologists, epidemiologists, virologists, clinicians and clinical laboratories, social and behavioral scientists, policymakers, data scientists, and community members. In total, 49 institutions form the SeroNet consortium to study individuals with cancer, autoimmune disease, inflammatory bowel diseases, cardiovascular diseases, human immunodeficiency virus, transplant recipients, as well as otherwise healthy pregnant women, children, college students, and high-risk occupational workers (including healthcare workers and first responders). Results Several studies focus on underrepresented populations, including ethnic minorities and rural communities. To support integrative data analyses across SeroNet studies, efforts are underway to define common data elements for standardized serology measurements, cellular and molecular assays, self-reported data, treatment, and clinical outcomes. Conclusions In this paper, we discuss the overarching framework for SeroNet epidemiology studies, critical research questions under investigation, and data accessibility for the worldwide scientific community. Lessons learned will help inform preparedness and responsiveness to future emerging diseases.

With large waves of infection driven by the B.1.1.529 (omicron) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), alongside evidence of waning immunity after the booster dose of coronavirus disease 2019 (Covid-19) vaccine, several countries have begun giving at-risk persons a fourth vaccine dose.To evaluate the early effectiveness of a fourth dose of the BNT162b2 vaccine for the prevention of Covid-19-related outcomes, we analyzed data recorded by the largest health care organization in Israel from January 3 to February 18, 2022. We evaluated the relative effectiveness of a fourth vaccine dose as compared with that of a third dose given at least 4 months earlier among persons 60 years of age or older. We compared outcomes in persons who had received a fourth dose with those in persons who had not, individually matching persons from these two groups with respect to multiple sociodemographic and clinical variables. A sensitivity analysis was performed with the use of parametric Poisson regression.The primary analysis included 182,122 matched pairs. Relative vaccine effectiveness in days 7 to 30 after the fourth dose was estimated to be 45% (95% confidence interval [CI], 44 to 47) against polymerase-chain-reaction-confirmed SARS-CoV-2 infection, 55% (95% CI, 53 to 58) against symptomatic Covid-19, 68% (95% CI, 59 to 74) against Covid-19-related hospitalization, 62% (95% CI, 50 to 74) against severe Covid-19, and 74% (95% CI, 50 to 90) against Covid-19-related death. The corresponding estimates in days 14 to 30 after the fourth dose were 52% (95% CI, 49 to 54), 61% (95% CI, 58 to 64), 72% (95% CI, 63 to 79), 64% (95% CI, 48 to 77), and 76% (95% CI, 48 to 91). In days 7 to 30 after a fourth vaccine dose, the difference in the absolute risk (three doses vs. four doses) was 180.1 cases per 100,000 persons (95% CI, 142.8 to 211.9) for Covid-19-related hospitalization and 68.8 cases per 100,000 persons (95% CI, 48.5 to 91.9) for severe Covid-19. In sensitivity analyses, estimates of relative effectiveness against documented infection were similar to those in the primary analysis.A fourth dose of the BNT162b2 vaccine was effective in reducing the short-term risk of Covid-19-related outcomes among persons who had received a third dose at least 4 months earlier. (Funded by the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute.).

When responding to infectious disease outbreaks, rapid and accurate estimation of the epidemic trajectory is critical. However, two common data collection problems affect the reliability of the epidemiological data in real time: missing information on the time of first symptoms, and retrospective revision of historical information, including right censoring. Here, we propose an approach to construct epidemic curves in near real time that addresses these two challenges by 1) imputation of dates of symptom onset for reported cases using a dynamically-estimated "backward" reporting delay conditional distribution, and 2) adjustment for right censoring using the NobBS software package to 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. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between March and April 2020. We evaluate how these real-time estimates compare with more complete epidemiological data that became available later. We explore the impact of the different assumptions on the estimates, and compare our estimates with those obtained from commonly used surveillance approaches. Our framework can help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health systems in other locations. PMD was supported by the fellowship Ramón Areces Foundation. JAH was funded by the National Institute of General Medical Sciences, Award U54GM088558, and the National Institutes of Health Director’s Early Independence, Award DP5-OD028145. ML was supported by the Morris-Singer Fund and by a subcontract from the Carnegie Mellon University under an award from the US Centers for Disease Control and Prevention, Award U01IP001121). MS was supported by the National Institute Of General Medical Sciences, Award R01GM130668-02. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Sí

Vaccines against SARS-CoV-2 were authorized at the end of 2020 and are effective in preventing deaths; however, many persons remain unvaccinated. Using weekly publicly available CDC data of COVID-19-associated death rates by age and vaccination status from 26 US jurisdictions, we estimated the number of excess deaths that might have been averted by vaccination among unvaccinated persons ≥ 18 years old from May 30 to December 4, 2021. 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 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 estimate the substantial number of COVID-19-associated deaths among the unvaccinated illustrating the importance of vaccination to prevent further unnecessary mortality during this pandemic.

ObjectiveTo characterize the risk of persistent and new clinical sequelae in adults aged ≥65 years after the acute phase of SARS-CoV-2 infection.DesignRetrospective cohort study.SettingUnitedHealth Group Clinical Research Database: deidentified administrative claims and outpatient laboratory test results.ParticipantsIndividuals aged ≥65 years who were continuously enrolled in a Medicare Advantage plan with coverage of prescription drugs from January 2019 to the date of diagnosis of SARS-CoV-2 infection, matched by propensity score to three comparison groups that did not have covid-19: 2020 comparison group (n=87 337), historical 2019 comparison group (n=88 070), and historical comparison group with viral lower respiratory tract illness (n=73 490).Main outcome measuresThe presence of persistent and new sequelae at 21 or more days after a diagnosis of covid-19 was determined with ICD-10 (international classification of diseases, 10threvision) codes. Excess risk for sequelae caused by infection with SARS-CoV-2 was estimated for the 120 days after the acute phase of the illness with risk difference and hazard ratios, calculated with 95% Bonferroni corrected confidence intervals. The incidence of sequelae after the acute infection was analyzed by age, race, sex, and whether patients were admitted to hospital for covid-19.ResultsAmong individuals who were diagnosed with SARS-CoV-2, 32% (27 698 of 87 337) sought medical attention in the post-acute period for one or more new or persistent clinical sequelae, which was 11% higher than the 2020 comparison group. Respiratory failure (risk difference 7.55, 95% confidence interval 7.18 to 8.01), fatigue (5.66, 5.03 to 6.27), hypertension (4.43, 2.27 to 6.37), memory difficulties (2.63, 2.23 to 3.13), kidney injury (2.59, 2.03 to 3.12), mental health diagnoses (2.50, 2.04 to 3.04), hypercoagulability 1.47 (1.2 to 1.73), and cardiac rhythm disorders (2.19, 1.76 to 2.57) had the greatest risk differences compared with the 2020 comparison group, with similar findings to the 2019 comparison group. Compared with the group with viral lower respiratory tract illness, however, only respiratory failure, dementia, and post-viral fatigue had increased risk differences of 2.39 (95% confidence interval 1.79 to 2.94), 0.71 (0.3 to 1.08), and 0.18 (0.11 to 0.26) per 100 patients, respectively. Individuals with severe covid-19 disease requiring admission to hospital had a markedly increased risk for most but not all clinical sequelae.ConclusionsThe results confirm an excess risk for persistent and new sequelae in adults aged ≥65 years after acute infection with SARS-CoV-2. Other than respiratory failure, dementia, and post-viral fatigue, the sequelae resembled those of viral lower respiratory tract illness in older adults. These findings further highlight the wide range of important sequelae after acute infection with the SARS-CoV-2 virus.

Immunity is a multifaceted phenomenon. For T cell-mediated memory responses to SARS-CoV-2, it is relevant to consider their impact both on COVID-19 disease severity and on viral spread in a population. Here, we reflect on the immunological and epidemiological aspects and implications of pre-existing cross-reactive immune memory to SARS-CoV-2, which largely originates from previous exposure to circulating common cold coronaviruses. We propose four immunological scenarios for the impact of cross-reactive CD4+ memory T cells on COVID-19 severity and viral transmission. For each scenario, we discuss its implications for the dynamics of herd immunity and on projections of the global impact of SARS-CoV-2 on the human population, and assess its plausibility. In sum, we argue that key potential impacts of cross-reactive T cell memory are already incorporated into epidemiological models based on data of transmission dynamics, particularly with regard to their implications for herd immunity. The implications of immunological processes on other aspects of SARS-CoV-2 epidemiology are worthy of future study., How does the discovery of SARS-CoV-2 cross-reactive T cells in unexposed individuals change our understanding of the COVID-19 pandemic? In this Perspective, the authors provide a thought experiment to explain why the discovery of cross-reactive T cells may affect disease severity in individuals, but is unlikely to change our estimate of the herd immunity threshold.

Summary Background Voluntary individual quarantine and voluntary active monitoring of contacts are core disease control strategies for emerging infectious diseases such as COVID-19. Given the impact of quarantine on resources and individual liberty, it is vital to assess under what conditions individual quarantine can more effectively control COVID-19 than active monitoring. As an epidemic grows, it is also important to consider when these interventions are no longer feasible and broader mitigation measures must be implemented. Methods To estimate the comparative efficacy of individual quarantine and active monitoring of contacts to control severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we fit a stochastic branching model to reported parameters for the dynamics of the disease. Specifically, we fit a model to the incubation period distribution (mean 5·2 days) and to two estimates of the serial interval distribution: a shorter one with a mean serial interval of 4·8 days and a longer one with a mean of 7·5 days. To assess variable resource settings, we considered two feasibility settings: a high-feasibility setting with 90% of contacts traced, a half-day average delay in tracing and symptom recognition, and 90% effective isolation; and a low-feasibility setting with 50% of contacts traced, a 2-day average delay, and 50% effective isolation. Findings Model fitting by sequential Monte Carlo resulted in a mean time of infectiousness onset before symptom onset of 0·77 days (95% CI −1·98 to 0·29) for the shorter serial interval, and for the longer serial interval it resulted in a mean time of infectiousness onset after symptom onset of 0·51 days (95% CI −0·77 to 1·50). Individual quarantine in high-feasibility settings, where at least 75% of infected contacts are individually quarantined, contains an outbreak of SARS-CoV-2 with a short serial interval (4·8 days) 84% of the time. However, in settings where the outbreak continues to grow (eg, low-feasibility settings), so too will the burden of the number of contacts traced for active monitoring or quarantine, particularly uninfected contacts (who never develop symptoms). When resources are prioritised for scalable interventions such as physical distancing, we show active monitoring or individual quarantine of high-risk contacts can contribute synergistically to mitigation efforts. Even under the shorter serial interval, if physical distancing reduces the reproductive number to 1·25, active monitoring of 50% of contacts can result in overall outbreak control (ie, effective reproductive number Interpretation Our model highlights the urgent need for more data on the serial interval and the extent of presymptomatic transmission to make data-driven policy decisions regarding the cost–benefit comparisons of individual quarantine versus active monitoring of contacts. To the extent that these interventions can be implemented, they can help mitigate the spread of SARS-CoV-2. Funding National Institute of General Medical Sciences, National Institutes of Health.

As of 29 February 2020 there were 79,394 confirmed cases and 2,838 deaths from COVID-19 in mainland China. Of these, 48,557 cases and 2,169 deaths occurred in the epicenter, Wuhan. A key public health priority during the emergence of a novel pathogen is estimating clinical severity, which requires properly adjusting for the case ascertainment rate and the delay between symptoms onset and death. Using public and published information, we estimate that the overall symptomatic case fatality risk (the probability of dying after developing symptoms) of COVID-19 in Wuhan was 1.4% (0.9–2.1%), which is substantially lower than both the corresponding crude or naive confirmed case fatality risk (2,169/48,557 = 4.5%) and the approximator1 of deaths/deaths + recoveries (2,169/2,169 + 17,572 = 11%) as of 29 February 2020. Compared to those aged 30–59 years, those aged below 30 and above 59 years were 0.6 (0.3–1.1) and 5.1 (4.2–6.1) times more likely to die after developing symptoms. The risk of symptomatic infection increased with age (for example, at ~4% per year among adults aged 30–60 years). An estimation of the clinical severity of COVID-19, based on the data available so far, can help to inform the public health response during the ongoing SARS-CoV-2 pandemic.

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.

BACKGROUND: There is uncertainty about the burden of hospitalization associated with respiratory syncytial virus (RSV) and influenza in children, including those with underlying medical conditions. METHODS: We applied previously developed methodology to HealthCare Cost and Utilization Project (HCUP) hospitalization data and additional data related to asthma diagnosis/previous history in hospitalized children to estimate RSV and influenza-associated hospitalization rates in different subpopulations of US children between 2003–2010. RESULTS: The estimated average annual rates (per 100,000 children) of RSV-associated hospitalization with a respiratory cause (ICD-9 codes 460–519) present anywhere in the discharge diagnosis were 2381 (95% CI(2252,2515)) in children less than a year of age; 710.6(609.1,809.2) (age 1y); 395(327.7,462.4) (age 2y); 211.3(154.6,266.8) (age 3y); 111.1(62.4,160.1) (age 4y); 72.3(29.3,116.4) (ages 5–6y); 35.6(9.9,62.2) (ages 7–11y); and 39(17.5,60.6) (ages 12–17y). The corresponding rates of influenza-associated hospitalization were lower, ranging from 181(142.5,220.3) in age

Summary Background Many countries are experiencing a resurgence of COVID-19, driven predominantly by the delta (B.1.617.2) variant of SARS-CoV-2. In response, these countries are considering the administration of a third dose of mRNA COVID-19 vaccine as a booster dose to address potential waning immunity over time and reduced effectiveness against the delta variant. We aimed to use the data repositories of Israel's largest health-care organisation to evaluate the effectiveness of a third dose of the BNT162b2 mRNA vaccine for preventing severe COVID-19 outcomes. Methods Using data from Clalit Health Services, which provides mandatory health-care coverage for over half of the Israeli population, individuals receiving a third vaccine dose between July 30, 2020, and Sept 23, 2021, were matched (1:1) to demographically and clinically similar controls who did not receive a third dose. Eligible participants had received the second vaccine dose at least 5 months before the recruitment date, had no previous documented SARS-CoV-2 infection, and had no contact with the health-care system in the 3 days before recruitment. Individuals who are health-care workers, live in long-term care facilities, or are medically confined to their homes were excluded. Primary outcomes were COVID-19-related admission to hospital, severe disease, and COVID-19-related death. The third dose effectiveness for each outcome was estimated as 1 – risk ratio using the Kaplan-Meier estimator. Findings 1 158 269 individuals were eligible to be included in the third dose group. Following matching, the third dose and control groups each included 728 321 individuals. Participants had a median age of 52 years (IQR 37–68) and 51% were female. The median follow-up time was 13 days (IQR 6–21) in both groups. Vaccine effectiveness evaluated at least 7 days after receipt of the third dose, compared with receiving only two doses at least 5 months ago, was estimated to be 93% (231 events for two doses vs 29 events for three doses; 95% CI 88–97) for admission to hospital, 92% (157 vs 17 events; 82–97) for severe disease, and 81% (44 vs seven events; 59–97) for COVID-19-related death. Interpretation Our findings suggest that a third dose of the BNT162b2 mRNA vaccine is effective in protecting individuals against severe COVID-19-related outcomes, compared with receiving only two doses at least 5 months ago. Funding The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute.

SARS-CoV-2 variants of concern exhibit varying degrees of transmissibility and, in some cases, escape from acquired immunity. Much effort has been devoted to measuring these phenotypes, but understanding their impact on the course of the pandemic – especially that of immune escape – has remained 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 frequently increase epidemic severity, whereas those with partial immune escape either fail to spread widely, or primarily cause reinfections and breakthrough infections. However, when these phenotypes are combined, a variant can continue spreading even as immunity builds up in the population, limiting the impact of vaccination and exacerbating the epidemic. These findings help explain the trajectories of past and present SARS-CoV-2 variants and may inform variant assessment and response in the future., A modeling approach looking at the impact of SARS-CoV-2 variants based on escape from immunity or different degrees of transmissibility in the context of vaccination as well as pharmaceutical interventions suggests that epidemic severity is linked more strongly to variants with enhanced transmissibility.

Background Preapproval trials showed that messenger RNA (mRNA)–based vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had a good safety profile, yet these trials were subject to size and patient-mix limitations. An evaluation of the safety of the BNT162b2 mRNA vaccine with respect to a broad range of potential adverse events is needed. Methods We used data from the largest health care organization in Israel to evaluate the safety of the BNT162b2 mRNA vaccine. For each potential adverse event, in a population of persons with no previous diagnosis of that event, we individually matched vaccinated persons to unvaccinated persons according to sociodemographic and clinical variables. Risk ratios and risk differences at 42 days after vaccination were derived with the use of the Kaplan–Meier estimator. To place these results in context, we performed a similar analysis involving SARS-CoV-2–infected persons matched to uninfected persons. The same adverse events were studied in the vaccination and SARS-CoV-2 infection analyses. Results In the vaccination analysis, the vaccinated and control groups each included a mean of 884,828 persons. Vaccination was most strongly associated with an elevated risk of myocarditis (risk ratio, 3.24; 95% confidence interval [CI], 1.55 to 12.44; risk difference, 2.7 events per 100,000 persons; 95% CI, 1.0 to 4.6), lymphadenopathy (risk ratio, 2.43; 95% CI, 2.05 to 2.78; risk difference, 78.4 events per 100,000 persons; 95% CI, 64.1 to 89.3), appendicitis (risk ratio, 1.40; 95% CI, 1.02 to 2.01; risk difference, 5.0 events per 100,000 persons; 95% CI, 0.3 to 9.9), and herpes zoster infection (risk ratio, 1.43; 95% CI, 1.20 to 1.73; risk difference, 15.8 events per 100,000 persons; 95% CI, 8.2 to 24.2). SARS-CoV-2 infection was associated with a substantially increased risk of myocarditis (risk ratio, 18.28; 95% CI, 3.95 to 25.12; risk difference, 11.0 events per 100,000 persons; 95% CI, 5.6 to 15.8) and of additional serious adverse events, including pericarditis, arrhythmia, deep-vein thrombosis, pulmonary embolism, myocardial infarction, intracranial hemorrhage, and thrombocytopenia. Conclusions In this study in a nationwide mass vaccination setting, the BNT162b2 vaccine was not associated with an elevated risk of most of the adverse events examined. The vaccine was associated with an excess risk of myocarditis (1 to 5 events per 100,000 persons). The risk of this potentially serious adverse event and of many other serious adverse events was substantially increased after SARS-CoV-2 infection. (Funded by the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute.)

BackgroundNipah virus (NiV) is an emerging, bat-borne pathogen that can be transmitted from person-to-person. Vaccines are currently being developed for NiV, and studies funded to evaluate their safety and immunogenicity, so that they could possibly be used to contain outbreaks. An important unanswered question is whether it will be possible to evaluate the efficacy of vaccine candidates in phase III clinical trials in a context where spillovers from the zoonotic reservoir are infrequent and associated with small outbreaks. The objective of this study was to investigate the feasibility of conducting a phase III vaccine trial in Bangladesh, the only country reporting regularly NiV cases.MethodsWe used simulations based on previously observed NiV cases from Bangladesh, an assumed vaccine efficacy of 90% and other NiV vaccine target characteristics, to compare three vaccination study designs: (i) cluster randomized ring vaccination, (ii) cluster randomized mass vaccination, and (iii) an observational case-control study design.ResultsThe simulations showed that, assuming a ramp-up period of 10 days and a mean hospitalization delay of 4 days, it would take 516 years and over 163,000 vaccine doses to run a ring vaccination trial under current epidemic conditions. A cluster-randomized trial in the two most affected districts would take 81 years and 2.3 million vaccine doses. An observational case-control design in these two districts would require seven years and 2.5 million vaccine doses.DiscussionWithout a change in the epidemiology of NiV, ring vaccination or cluster-randomized trials are unlikely to be completed within a reasonable time window. In this light, the remaining options are: (i) not conducting a phase III trial until the epidemiology of NiV changes, (ii) identifying alternative ways to licensure such as observational studies or controlled studies in animals such as in the US Food and Drug Administration’s (FDA) Animal Rule.

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: Among all 9650 HCW of a large tertiary medical center in Israel, we calculated the prevalence of positive SARS-CoV-2 qRT-PCR cases with asymptomatic presentation, tested following known or presumed exposure and the infectious subset (N-gene-Ct-value

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.

Understanding how antibiotic use drives resistance is crucial for guiding effective strategies to limit the spread of resistance, but the use–resistance relationship across pathogens and antibiotics remains unclear. We applied sinusoidal models to evaluate the seasonal use–resistance relationship across 3 species (Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae) and 5 antibiotic classes (penicillins, macrolides, quinolones, tetracyclines, and nitrofurans) in Boston, Massachusetts. Outpatient use of all 5 classes and resistance in inpatient and outpatient isolates in 9 of 15 species–antibiotic combinations showed statistically significant amplitudes of seasonality (false discovery rate (FDR) < 0.05). While seasonal peaks in use varied by class, resistance in all 9 species–antibiotic combinations peaked in the winter and spring. The correlations between seasonal use and resistance thus varied widely, with resistance to all antibiotic classes being most positively correlated with use of the winter peaking classes (penicillins and macrolides). These findings challenge the simple model of antibiotic use independently selecting for resistance and suggest that stewardship strategies will not be equally effective across all species and antibiotics. Rather, seasonal selection for resistance across multiple antibiotic classes may be dominated by use of the most highly prescribed antibiotic classes, penicillins and macrolides.

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.

Test-negative designs (TNDs) can be used to estimate vaccine effectiveness by comparing the relative rates of the target disease and symptomatically similar diseases among vaccinated and unvaccinated populations. However, the diagnostic tests used to identify the target disease typically suffer from imperfect sensitivity and specificity, leading to biased vaccine effectiveness estimates. Here we present a solution to this problem via a Bayesian statistical model which can either incorporate point estimates of test sensitivity and specificity, or can jointly infer them directly from laboratory validation data. This approach enables uncertainties in the performance characteristics of the diagnostic test to be correctly propagated to estimates, avoiding both bias and false precision in vaccine effectiveness. By further incorporating individual covariates of study participants, and by allowing data streams from multiple diagnostic test types to be rigorously combined, our approach provides a flexible model for the analysis of TNDs with explicitly stated assumptions.

Mathematical models predict that community-wide access to HIV testing-and-treatment can rapidly and substantially reduce new HIV infections. Yet several large universal test-and-treat HIV prevention trials in high-prevalence epidemics demonstrated variable reduction in population-level incidence. To elucidate patterns of HIV spread in universal test-and-treat trials we quantified the contribution of geographic-location, gender, age and randomized-HIV- intervention to HIV transmissions in the 30-community Ya Tsie trial in Botswana (estimated trial population: 175,664). Deep-sequence phylogenetic analysis revealed that most inferred HIV transmissions within the trial occurred within the same or between neighboring communities, and between similarly-aged partners. Transmissions into intervention communities from control communities were more common than the reverse post-baseline (30% [12.2 – 56.7] versus 3% [0.1 – 27.3]) than at baseline (7% [1.5 – 25.3] versus 5% [0.9 – 22.9]) compatible with a benefit from treatment-as-prevention. Our findings suggest that population mobility patterns are fundamental to HIV transmission dynamics and to the impact of HIV control strategies.

2AbstractVaccine 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.

Phase 3 randomized-controlled trials have provided promising results of COVID-19 vaccine efficacy, ranging from 50 to 95% against symptomatic disease as the primary endpoints, resulting in emergency use authorization/listing for several vaccines. However, given the short duration of follow-up during the clinical trials, strict eligibility criteria, emerging variants of concern, and the changing epidemiology of the pandemic, many questions still remain unanswered regarding vaccine performance. Post-introduction vaccine effectiveness evaluations can help us to understand the vaccine's effect on reducing infection and disease when used in real-world conditions. They can also address important questions that were either not studied or were incompletely studied in the trials and that will inform evolving vaccine policy, including assessment of the duration of effectiveness; effectiveness in key subpopulations, such as the very old or immunocompromised; against severe disease and death due to COVID-19; against emerging SARS-CoV-2 variants of concern; and with different vaccination schedules, such as number of doses and varying dosing intervals. WHO convened an expert panel to develop interim best practice guidance for COVID-19 vaccine effectiveness evaluations. We present a summary of the interim guidance, including discussion of different study designs, priority outcomes to evaluate, potential biases, existing surveillance platforms that can be used, and recommendations for reporting results.

This article informally reviews key research ethics guidelines and regulations, academic scholarship, and research studies and finds wide variety in how they consider risk to bystanders in medical research (namely, non-participants whom studies nevertheless place at risk). Some of these key sources give no or very little consideration to bystanders, while others offer them the utmost protection (greater than they offer study participants). This unsettled frontier would benefit from a deeper investigation of the ethics of protecting research bystanders.