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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.

Nursing homes (NH) were among the first settings to receive COVID-19 vaccines in the United States, but staff vaccination coverage remains low at an average of 64%. Using an agent-based model, we examined the impact of community prevalence, the Delta variant, staff vaccination coverage, and boosters for residents on outbreak dynamics in nursing homes. We found that increased staff primary series coverage and high booster vaccine effectiveness (VE) in residents leads to fewer infections and that the cumulative incidence is highly dependent on community transmission. Despite high VE, high community transmission resulted in continued symptomatic infections in NHs.

Objectives Universities have turned to SARS-CoV-2 models to examine campus reopening strategies. While these studies have explored a variety of modeling techniques, none have used empirical data. Methods In this study, we use an empirical proximity network of college freshmen obtained using smartphone Bluetooth to simulate the spread of the virus. We investigate the role of immunization, testing, isolation, mask wearing, and social distancing in the presence of implementation challenges and imperfect compliance. Results We show that frequent testing could drastically reduce the spread of the virus if levels of immunity are low, but its effects are limited if immunity is more ubiquitous. Furthermore, moderate levels of mask wearing and social distancing could lead to additional reductions in cumulative incidence, but their benefit decreases rapidly as immunity and testing frequency increase. However, if immunity from vaccination is imperfect or declines over time, scenarios not studied here, frequent testing and other interventions may play more central roles. Conclusions Our findings suggest that although regular testing and isolation are powerful tools, they have limited benefit if immunity is high or other interventions are widely adopted. If universities can attain even moderate levels of vaccination, masking, and social distancing, they may be able to relax the frequency of testing to once every four weeks.

This article, which forms the second part of a two-part article in this volume, reflects on what we argue to be the most crucial element of any Linked Data enterprise: its community. The first section discusses a variety of funded and unfunded activities conducted under the aegis of Pelagios, and specifically two ‘small grant’ programmes that dramatically expanded its scope and reach. The second section describes the evolution of Pelagios as a ‘community of practice’, in which the work of the initiative, conceived of in terms of both method and tool development, has been facilitated by, and coordinated with, individuals and groups external to its investigative teams. This growing and maturing community has ultimately been realized in the foundation of a formal voluntary organization: the Pelagios Network Association. The paper concludes with brief reflections on some interlinked sister initiatives that have emerged from the Pelagios community and continue to support scholars and practitioners working in related fields.

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.

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.

In this article, we examine the making of research infrastructures for digital research. In line with many scholars in this field, we understand research infrastructures as deeply relational and adaptive systems that are embedded in research practice. Our aim was to identify the relevant context factors, actor constellations, organizational settings, and strategies which contribute to the evolution of a basic service into an actual infrastructure. To this end, we conducted thirty-three case studies of non-commercial and commercial research services along the research life cycle. By examining how these services emerge, we hope to gain a better understanding of the conditions and strategies to transform a service into an infrastructure. We are able to identify competitive disadvantages for publicly financed infrastructure projects with regard to the mode of implementation and the resources invested in development and marketing. We suggest that the results of this study are of practical relevance, especially for individuals, communities, and organizations wanting to create research infrastructures, as well as for funders and policy makers wanting to support innovative and sustainable infrastructures.

This paper is concerned with the ethical aspects of museum metadata. These are not always immediately evident when working with the metadata related to museum objects, although, I will argue, they are embedded in the object, accumulated at each phase of its journey into the institution; and continue to accumulate while it is part of a collection. This takes place against a backdrop of new development and possibilities afforded by digital technologies for building connections between and across heritage collections online, which can result in these complicated metadata potentially entering the data ecosystem. This eventuality, I will argue, has ethical and technical implications which need to be considered and understood through the theoretical lenses of critical data studies, museum informatics and the growing calls from museum scholars and others to decolonisation of museum collections. Using a small collection of drawings from the Pitt Rivers Museum of Anthropology and World Archaeology at the University of Oxford, I will demonstrate how difficult museum metadata can be buried deep in museum documentation, and how this data, once brought to the surface by digitisation, can expose the trauma of a collection’s origins. I will go on to ask whether the current models used to share heritage data online are appropriate mechanisms for materials with such sensitive histories, and ask how best to handle them in the increasingly digital future.

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.

Objectives To reduce childhood mortality from severe malaria by implementing the World Health Organization's standardized malarial treatment protocol. Design Observational study comparing the mortality rate from malaria before and after the intervention. Setting Inpatient pediatric ward in a district referral hospital of Sierra Leone. Participants A total of 1298 pediatric patients (ages 0-13 years, male and female) received the intervention, representing 100% of the pediatric patients admitted with severe malaria during the dates of implementation (there were no exclusion criteria). Interventions We implemented the World Health Organization's standardized malarial protocol on September 30, 2015. Based on monthly run reports of mortality and root cause analysis, we adapted the malaria protocol by adding sublingual glucose as a treatment to target hypoglycemia complications in March 2016. Main Outcome Measures The primary outcome was a change in monthly percent mortality from severe malaria, and the secondary outcome was the percent of mortality attributed to hypoglycemia. Results The monthly average percent mortality from severe malaria dropped from 9% to 3.6% after the intervention, which was borderline statistically significant (p 0.06, CI 95% 1.5 to 5.6). The secondary outcome, percent of malarial deaths attributable to hypoglycemia via chart reviews, dropped from 83% to 44% across the study period. There was an increase in the average number of admissions for severe malaria from 71 to 153 children per month in the second half of the year (range from 49-212 per month). Conclusion Implementing the WHO malaria treatment protocol with bedside tracking of protocol steps reduced malaria mortality and improved our ward's efficiency without adding any human or medical resources.

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.

Nursing homes (NH) were among the first settings to receive COVID-19 vaccines in the United States, but staff vaccination coverage remains low at an average of 64%. Using an agent-based model, we examined the impact of community prevalence, the Delta variant, staff vaccination coverage, and boosters for residents on outbreak dynamics in nursing homes. We found that increased staff primary series coverage and high booster vaccine effectiveness (VE) in residents leads to fewer infections and that the cumulative incidence is highly dependent on community transmission. Despite high VE, high community transmission resulted in continued symptomatic infections in NHs.

This article provides a short history of the methods and tools developed by the Pelagios initiative: a series of seven projects dedicated to linking digital historical resources based on the geographic places to which they relate and refer. The first section of the article situates the work within the wider field of semantic and geospatial technologies. It then describes the initial development of the Pelagios methodology through an open consortium of ancient Mediterranean data resource providers, in association with the Pleiades Gazetteer of the Ancient World. The second section details how this approach was expanded for application in other historical and archaeological periods and domains, with a special focus on tool development. In particular, we reflect on the development and application of Recogito, an online semantic geoannotation platform that allows anyone to identify place references within historical texts, imagery or datasets.

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: 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.

BackgroundIn clinical trials, several SARS-CoV-2 vaccines were shown to reduce risk of severe COVID-19 illness. Local, population-level, real-world evidence of vaccine effectiveness is accumulating. We assessed vaccine effectiveness for community-dwelling New York City (NYC) residents using a quasi-experimental, regression discontinuity design, leveraging a period (January 12–March 9, 2021) when ≥65-year-olds were vaccine-eligible but younger persons, excluding essential workers, were not.MethodsWe constructed segmented, negative binomial regression models of age-specific COVID-19 hospitalization rates among 45–84-year-old NYC residents during a post-vaccination program implementation period (February 21–April 17, 2021), with a discontinuity at age 65 years. The relationship between age and hospitalization rates in an unvaccinated population was incorporated using a pre-implementation period (December 20, 2020–February 13, 2021). We calculated the rate ratio (RR) and 95% confidence interval (CI) for the interaction between implementation period (pre or post) and age-based eligibility (45–64 or 65–84 years). Analyses were stratified by race/ethnicity and borough of residence. Similar analyses were conducted for COVID-19 deaths.ResultsHospitalization rates among 65–84-year-olds decreased from pre- to post-implementation periods (RR 0.85, 95% CI: 0.74–0.97), controlling for trends among 45–64-year-olds. Accordingly, an estimated 721 (95% CI: 126–1,241) hospitalizations were averted. Residents just above the eligibility threshold (65–66-year-olds) had lower hospitalization rates than those below (63–64-year-olds). Racial/ethnic groups and boroughs with higher vaccine coverage generally experienced greater reductions in RR point estimates. Uncertainty was greater for the decrease in COVID-19 death rates (RR 0.85, 95% CI: 0.66–1.10).ConclusionThe vaccination program in NYC reduced COVID-19 hospitalizations among the initially age-eligible ≥65-year-old population by approximately 15%. The real-world evidence of vaccine effectiveness makes it more imperative to improve vaccine access and uptake to reduce inequities in COVID-19 outcomes.

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.

A statistical framework was developed to jointly estimate the distribution of generation time and incubation period from human-to-human transmission pairs of COVID-19, accounting for sampling biases due to exponential growth of the epidemic during the data collection period., Background Coronavirus disease 2019 (COVID-19) has caused a heavy disease burden globally. The impact of process and timing of data collection on the accuracy of estimation of key epidemiological distributions are unclear. Because infection times are typically unobserved, there are relatively few estimates of generation time distribution. Methods We developed a statistical framework to jointly estimate generation time and incubation period from human-to-human transmission pairs, accounting for sampling biases. We applied the framework on 80 laboratory-confirmed human-to-human transmission pairs in China. We further inferred the infectiousness profile, serial interval distribution, proportions of presymptomatic transmission, and basic reproduction number (R0) for COVID-19. Results The estimated mean incubation period was 4.8 days (95% confidence interval [CI], 4.1–5.6), and mean generation time was 5.7 days (95% CI, 4.8–6.5). The estimated R0 based on the estimated generation time was 2.2 (95% CI, 1.9–2.4). A simulation study suggested that our approach could provide unbiased estimates, insensitive to the width of exposure windows. Conclusions Properly accounting for the timing and process of data collection is critical to have correct estimates of generation time and incubation period. R0 can be biased when it is derived based on serial interval as the proxy of generation time.

Disasters, such as cyclones, create conditions that increase the risk of infectious disease outbreaks. Epidemic forecasts can be valuable for targeting highest risk populations before an outbreak. The two main barriers to routine use of real-time forecasts include scientific and operational challenges. First, accuracy may be limited by availability of data and the uncertainty associated with the inherently stochastic processes that determine when and where outbreaks happen and spread. Second, even if data are available, the appropriate channels of communication may prevent their use for decision making.In April 2019, only six weeks after Cyclone Idai devastated Mozambique’s central region and sparked a cholera outbreak, Cyclone Kenneth severely damaged northern areas of the country. By June 10, a total of 267 cases of cholera were confirmed, sparking a vaccination campaign. Prior to Kenneth’s landfall, a team of academic researchers, humanitarian responders, and health agencies developed a simple model to forecast areas at highest risk of a cholera outbreak. The model created risk indices for each district using combinations of four metrics: (1) flooding data; (2) previous annual cholera incidence; (3) sensitivity of previous outbreaks to the El Niño-Southern Oscillation cycle; and (4) a diffusion (gravity) model to simulate movement of infected travelers. As information on cases became available, the risk model was continuously updated. A web-based tool was produced, which identified highest risk populations prior to the cyclone and the districts at-risk following the start of the outbreak.The model prior to Kenneth’s arrival using the metrics of previous incidence, projected flood, and El Niño sensitivity accurately predicted areas at highest risk for cholera. Despite this success, not all data were available at the scale at which the vaccination campaign took place, limiting the model’s utility, and the extent to which the forecasts were used remains unclear. Here, the science behind these forecasts and the organizational structure of this collaborative effort are discussed. The barriers to the routine use of forecasts in crisis settings are highlighted, as well as the potential for flexible teams to rapidly produce actionable insights for decision making using simple modeling tools, both before and during an outbreak.

Introduction The Ebola virus disease epidemic was devastating to the West African region, particularly for pregnant women. Prior to the epidemic, maternal mortality in this region was among the highest in the world. Throughout the region, screening of patients with Ebola was difficult, as the symptoms of malaria or typhoid mimicked Ebola, but even more difficult for pregnant women, because of the large overlap between Ebola symptoms and pregnancy-related complications. In November 2014, the world's first maternity-specific isolation and screening system, to our knowledge, was created at the Princess Christian Maternity Hospital in Freetown to meet the emergent needs of the population of pregnant women during the epidemic. Process Starting in December 2014 through June 2016, in collaboration with hospital leadership and the Ministry of Health and Sanitation, Partners In Health supported Princess Christian Maternity Hospital in creating a safer health care environment with the shared goal of improving safety and health outcomes and of addressing the unique needs of pregnant women, by focusing on improving 4 key areas: 1) screening, 2) isolation, 3) laboratory diagnostics, and 4) clinical service delivery in isolation, including human resource management and training. Outcomes The screening guidelines were adapted to include maternal health care considerations, a new screening area was constructed, the laboratory result turnaround time was reduced, and the isolation unit was improved to enhance safety and care delivery. Human resources were supported with additional staff hired and trainings on infection prevention and control, overall resulting in better preparing Princess Christian Maternity Hospital to provide care for pregnant women during outbreaks. Discussion The authors' experience at Princess Christian Maternity Hospital provides a model of screening, isolation, and care specifically for maternity patients, and directly addresses infection risk and mortality. The recommendations we provide can be used in future outbreaks.

In response to the SARS-CoV-2 pandemic, unprecedented travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns—defined as restrictions on both local movement or long distance travel—will determine how effective these kinds of interventions are. Here, we evaluate the effects of lockdowns on human mobility and simulate how these changes may affect epidemic spread by analyzing aggregated mobility data from mobile phones. We show that in 2020 following lockdown announcements but prior to their implementation, both local and long distance movement increased in multiple locations, and urban-to-rural migration was observed around the world. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. Our model shows that this increased movement has the potential to increase seeding of the epidemic in less urban areas, which could undermine the goal of the lockdown in preventing disease spread. Lockdowns play a key role in reducing contacts and controlling outbreaks, but appropriate messaging surrounding their announcement and careful evaluation of changes in mobility are needed to mitigate the possible unintended consequences.

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.HighlightsSARS-CoV-2 vaccine trials did not directly estimate vaccine efficacy against transmission.We describe an approach to estimate a lower bound of vaccine efficacy against transmission.We estimate one dose of the Moderna vaccine reduces the potential for transmission by at least 61%.We recommend separate analysis of tests triggered by symptoms vs. cross-sectional tests.

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

Abstract Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility. Supplementary Information The online version of this article (10.1007/s10654-021-00727-7) contains supplementary material, which is available to authorized users.

In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.

BackgroundNursing homes and other long term care facilities have been disproportionately impacted by the COVID-19 pandemic. Strategies are urgently needed to reduce transmission in these vulnerable populations. We aim to evaluate the reduction in transmission in nursing homes achieved through contact-targeted interventions and testing.MethodsWe developed an agent-based Susceptible–Exposed– Infectious(Asymptomatic/Symptomatic)–Recovered (SEIR) model to examine SARS-CoV-2 transmission in nursing homes. Residents and staff are modelled individually; residents are split into two cohorts based on COVID-19 diagnosis. We evaluate the effectiveness of two contact-targeted interventions. In the resident cohorting intervention, recovered residents are moved back from the COVID (infected) cohort to the non-COVID (susceptible/uninfected) cohort. In the immunity-based staffing intervention, recovered staff, who we assume have protective immunity, are assigned to work in the non-COVID cohort, while susceptible staff work in the COVID cohort and are assumed to have high levels of protection from personal protective equipment. These interventions aim to reduce the fraction of people’s contacts that are presumed susceptible (and therefore potentially infected) and replace them with recovered (immune) contacts. We further evaluate two types of screening tests conducted with varying frequency: 1) rapid antigen testing and 2) PCR testing.ResultsThe frequency and type of testing has a larger impact on the size of outbreaks than the cohorting and staffing interventions. The most effective testing strategy modeled is daily antigen testing. Under all screening testing strategies, the resident cohorting intervention and the immunity-based staffing intervention reduce the final size of the outbreak among residents, with the latter reducing it more. The efficacy of these interventions among staff varies by testing strategy and outbreak size.ConclusionsIncreasing the frequency of screening testing of all residents and staff, or even staff alone, in nursing homes has the potential to greatly reduce outbreaks in this vulnerable setting. Immunity-based staffing can further reduce spread at little or no additional cost and becomes particularly important when daily testing is not feasible.

BACKGROUND Nowcasting approaches enhance the utility of reportable disease data for trend monitoring by correcting for delays, but implementation details affect accuracy. OBJECTIVE To support real-time COVID-19 situational awareness, the New York City Department of Health and Mental Hygiene used nowcasting to account for testing and reporting delays. We conducted an evaluation to determine which implementation details would yield the most accurate estimated case counts. METHODS A time-correlated Bayesian approach called Nowcasting by Bayesian Smoothing (NobBS) was applied in real time to line lists of reportable disease surveillance data, accounting for the delay from diagnosis to reporting and the shape of the epidemic curve. We retrospectively evaluated nowcasting performance for confirmed case counts among residents diagnosed during the period from March to May 2020, a period when the median reporting delay was 2 days. RESULTS Nowcasts with a 2-week moving window and a negative binomial distribution had lower mean absolute error, lower relative root mean square error, and higher 95% prediction interval coverage than nowcasts conducted with a 3-week moving window or with a Poisson distribution. Nowcasts conducted toward the end of the week outperformed nowcasts performed earlier in the week, given fewer patients diagnosed on weekends and lack of day-of-week adjustments. When estimating case counts for weekdays only, metrics were similar across days when the nowcasts were conducted, with Mondays having the lowest mean absolute error of 183 cases in the context of an average daily weekday case count of 2914. CONCLUSIONS Nowcasting using NobBS can effectively support COVID-19 trend monitoring. Accounting for overdispersion, shortening the moving window, and suppressing diagnoses on weekends—when fewer patients submitted specimens for testing—improved the accuracy of estimated case counts. Nowcasting ensured that recent decreases in observed case counts were not overinterpreted as true declines and supported officials in anticipating the magnitude and timing of hospitalizations and deaths and allocating resources geographically.