Estimated Incidence of Coronavirus Disease 2019 (COVID-19) Illness and Hospitalization-United States, February-September 2020

Participatory, Virologic, and Wastewater Surveillance Data to Assess Underestimation of COVID-19 Incidence, Germany, 2020-2024

Using participatory, virologic, and wastewater surveillance systems, we estimated when and to what extent reported data of adult COVID-19 cases underestimated COVID-19 incidence in Germany. We also examined how case underestimation evolved over time. Our findings highlight how community-based surveillance systems can complement official notification systems for respiratory disease dynamics.

Multisystemic inflammatory syndrome in children and the BNT162b2 vaccine: a nationwide cohort study

Multisystemic inflammatory syndrome in children (MIS-C) is a rare, severe, post-infectious hyperinflammatory condition that occurs after COVID-19 infection. In this study, we aimed to demonstrate the risk reduction of MIS-C and severe MIS-C after Pfizer-BioNTech BNT162b2 mRNA COVID-19 vaccination. This nationwide cohort study included 526,685 PCR-confirmed COVID-19 cases (age < 19 years), of whom 14,118 were fully vaccinated prior to COVID-19 infection. MIS-C cases were collected from all hospitals in Israel from April 2020 through November 2021. The MIS-C rates were calculated among two COVID-19 populations: positive PCR confirmed cases and estimated COVID-19 cases (PCR confirmed and presumed). Vaccination status was determined from Ministry of Health (MoH) records. The MIS-C risk difference (RD) and 95% confidence intervals (95%CI) between vaccinated and unvaccinated patients are presented. Overall, 233 MIS-C cases under the age of 19 years were diagnosed and hospitalized in Israel during the study period. Among the estimated COVID-19 cases, MIS-C RD realistically ranged between 2.1 [95%CI 0.7-3.4] and 1.0 [95%CI 0.4-1.7] per 10,000 COVID-19 cases. For severe MIS-C, RD realistically ranged between 1.6 [95%CI 1.3-1.9] and 0.8 [95%CI 0.7-1.0], per 10,000 COVID-19 cases. Sensitivity analysis was performed on a wide range of presumed COVID-19 rates, demonstrating significant RD for each of these rates.

CONCLUSION

This research demonstrates that vaccinating children and adolescents against COVID-19 has reduced the risk of MIS-C during the study period.

WHAT IS KNOWN

• Most of the published literature regarding vaccine effectiveness is based on case-control studies, which are limited due to small sample sizes and the inability to fully estimate the risk of MIS-C among vaccinated and unvaccinated children and adolescents. • The known underestimation of COVID-19 diagnosis among children and adolescents is challenging, as they often have few to no symptoms.

WHAT IS NEW

• Significant risk difference was found in favor of the vaccinated group, even after including extreme assumptions regarding the underdiagnosed COVID-19 rate. • During this nationwide study period, it was found that vaccinating children and adolescents reduced the risk of MIS-C and its complications.

The role of catchment population size, data normalization, and chronology of public health interventions on wastewater-based COVID-19 viral trends

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic presented the most challenging global crisis in recent times. A pandemic caused by a novel pathogen such as SARS-CoV-2 necessitated the development of innovative techniques for the monitoring and surveillance of COVID-19 infections within communities. Wastewater surveillance (WWS) is recognized as a non-invasive, cost-effective, and valuable epidemiological tool to monitor the prevalence of COVID-19 infections in communities. Seven municipal wastewater sampling sites representing distinct sewershed communities were selected for the surveillance of the SARS-CoV-2 virus in Durham Region, Ontario, Canada over 8 months from March 2021 to October 2021. Viral RNA fragments of SARS-CoV-2 and the normalization target pepper mild mottle virus (PMMoV) were concentrated from wastewater influent using the PEG/NaCl superspeed centrifugation method and quantified using RT-qPCR. Strong significant correlations (Spearman's rs = 0.749 to 0.862, P < 0.001) were observed between SARS-CoV-2 gene copies/mL of wastewater and clinical cases reported in each delineated sewershed by onset date. Although raw wastewater offered higher correlation coefficients with clinical cases by onset date compared to PMMoV normalized data, only one site had a statistically significantly higher Spearman's correlation coefficient value for raw data than normalized data. Implementation of community stay-at-home orders and vaccinations over the course of the study period in 2021 were found to strongly correspond to decreasing SARS-CoV-2 wastewater trends in the wastewater treatment plants and upstream pumping stations.

Responding to the Return of Influenza in the United States by Applying Centers for Disease Control and Prevention Surveillance, Analysis, and Modeling to Inform Understanding of Seasonal Influenza

We reviewed the tools that have been developed to characterize and communicate seasonal influenza activity in the United States. Here we focus on systematic surveillance and applied analytics, including seasonal burden and disease severity estimation, short-term forecasting, and longer-term modeling efforts. For each set of activities, we describe the challenges and opportunities that have arisen because of the COVID-19 pandemic. In conclusion, we highlight how collaboration and communication have been and will continue to be key components of reliable and actionable influenza monitoring, forecasting, and modeling activities.

Changing COVID-19 cases and deaths detection in Florida

Epidemic data are often difficult to interpret due to inconsistent detection and reporting. As these data are critically relied upon to inform policy and epidemic projections, understanding reporting trends is similarly important. Early reporting of the COVID-19 pandemic in particular is complicated, due to changing diagnostic and testing protocols. An internal audit by the State of Florida, USA found numerous specific examples of irregularities in COVID-19 case and death reports. Using case, hospitalization, and death data from the the first year of the COVID-19 pandemic in Florida, we present approaches that can be used to identify the timing, direction, and magnitude of some reporting changes. Specifically, by establishing a baseline of detection probabilities from the first (spring) wave, we show that transmission trends among all age groups were similar, with the exception of the second summer wave, when younger people became infected earlier than seniors, by approximately 2 weeks. We also found a substantial drop in case-fatality risk (CFR) among all age groups over the three waves during the first year of the pandemic, with the most drastic changes seen in the 0 to 39 age group. The CFR trends provide useful insights into infection detection that would not be possible by relying on the number of tests alone. During the third wave, for which we have reliable hospitalization data, the CFR was remarkably stable across all age groups. In contrast, the hospitalization-to-case ratio varied inversely with cases while the death-to-hospitalization ratio varied proportionally. Although specific trends are likely to vary between locales, the approaches we present here offer a generic way to understand the substantial changes that occurred in the relationships among the key epidemic indicators.

Public interest trends for COVID-19 and pandemic trajectory: A time-series analysis of US state-level data

Google Trends provides spatiotemporal data for user-specific terms scaled from less than 1 (lowest relative popularity) to 100 (highest relative popularity) as a proxy for the public interest. Here we use US state-level data for COVID-19 to examine popularity trends during the pandemic evolution. We used "coronavirus" and "covid" search terms and set the period up from January 1st, 2020, to November 12, 2022. We measured the agreement on web rankings between states using the nonparametric Kendall's W (0 for no concordance to 1 for perfect agreement). We compiled state-level weekly data on COVID-19 incidence and mortality and scaled state curves from 0 to 100 through a min-max normalization process. We used a dynamic time-warping algorithm to calculate similarities between the popularity, mortality, and incidence of COVID-19. The methodology is a pattern recognition process between time series by distance optimization. The similarity was mapped from 0 to 1, with 1 indicating perfect similarity and 0 indicating no similarity. The peak in popularity was in March 2020, succeeded by a decline and a prolonged period of fluctuation around 20%. Public interest rose briefly at the end of 2021, to fall to a low activity of around 10%. This pattern was remarkably consistent across states (Kendal's W 0.94, p < 0.001). Web search trends were an impression of contagion growth: Overall, popularity-mortality trajectories yielded higher similarity indices (median 0.78; interquartile range 0.75-0.82) compared to popularity-incidence trajectories (median 0.74; interquartile range 0.72-0.76, Wilcoxon's exact p<0.001). The popularity-mortality trajectories had a very strong similarity (>0.80) in 19/51 (37%) regions, as opposed to only 4/51 (8%) for popularity-incidence trajectories. State-level data show a fading public concern about COVID-19, and web-search popularity patterns may reflect the COVID-19 trajectory in terms of cases and mortality.

Home testing for SARS-CoV-2 and impact on surveillance in New York State

PURPOSE

To determine the distribution of diagnosed SARS-CoV-2 infections by testing modality (at-home rapid antigen [home tests] versus laboratory-based tests in clinical settings [clinical tests]), assess factors associated with clinical testing, and estimate the true total number of diagnosed infections in New York State (NYS).

METHODS

We conducted an online survey among NYS residents and analyzed data from 1012 adults and 246 children with diagnosed infection July 13-December 7, 2022. Weighted descriptive and logistic regression model analyses were conducted. Weighted percentages and prevalence ratios by testing modality were generated. The percent of infections diagnosed by clinical tests via survey data were synthesized with daily lab-reported results to estimate the total number of diagnosed SARS-CoV-2 infections in NYS July 1-December 31, 2022.

RESULTS

Over 70% of SARS-CoV-2 infections in NYS during the study period were diagnosed exclusively with home tests. Diagnosis with a clinical test was associated with age, race/ethnicity, and region among adults, and sex, age, and education among children. We estimate 4.1 million NYS residents had diagnosed SARS-CoV-2 infection July 1-December 31, 2022, compared to 1.1 million infections reported over the same period.

CONCLUSIONS

Most SARS-CoV-2 infections in NYS were diagnosed exclusively with home tests. Surveillance metrics using laboratory-based reporting data underestimate diagnosed infections.

Influence of Seasonality and Public-Health Interventions on the COVID-19 Pandemic in Northern Europe

BACKGROUND

Most government efforts to control the COVID-19 pandemic revolved around non-pharmaceutical interventions (NPIs) and vaccination. However, many respiratory diseases show distinctive seasonal trends. In this manuscript, we examined the contribution of these three factors to the progression of the COVID-19 pandemic.

METHODS

Pearson correlation coefficients and time-lagged analysis were used to examine the relationship between NPIs, vaccinations and seasonality (using the average incidence of endemic human beta-coronaviruses in Sweden over a 10-year period as a proxy) and the progression of the COVID-19 pandemic as tracked by deaths; cases; hospitalisations; intensive care unit occupancy and testing positivity rates in six Northern European countries (population 99.12 million) using a population-based, observational, ecological study method.

FINDINGS

The waves of the pandemic correlated well with the seasonality of human beta-coronaviruses (HCoV-OC43 and HCoV-HKU1). In contrast, we could not find clear or consistent evidence that the stringency of NPIs or vaccination reduced the progression of the pandemic. However, these results are correlations and not causations.

IMPLICATIONS

We hypothesise that the apparent influence of NPIs and vaccines might instead be an effect of coronavirus seasonality. We suggest that policymakers consider these results when assessing policy options for future pandemics.

LIMITATIONS

The study is limited to six temperate Northern European countries with spatial and temporal variations in metrics used to track the progression of the COVID-19 pandemic. Caution should be exercised when extrapolating these findings.

Seroincidence of SARS-CoV-2 infection prior to and during the rollout of vaccines in a community-based prospective cohort of U.S. adults

This study used repeat serologic testing to estimate infection rates and risk factors in two overlapping cohorts of SARS-CoV-2 N protein seronegative U.S. adults. One mostly unvaccinated sub-cohort was tracked from April 2020 to March 2021 (pre-vaccine/wild-type era, n = 3421), and the other, mostly vaccinated cohort, from March 2021 to June 2022 (vaccine/variant era, n = 2735). Vaccine uptake was 0.53% and 91.3% in the pre-vaccine and vaccine/variant cohorts, respectively. Corresponding seroconversion rates were 9.6 and 25.7 per 100 person-years. In both cohorts, sociodemographic and epidemiologic risk factors for infection were similar, though new risk factors emerged in the vaccine/variant era, such as having a child in the household. Despite higher incidence rates in the vaccine/variant cohort, vaccine boosters, masking, and social distancing were associated with substantially reduced infection risk, even through major variant surges.

Detection of SARS-CoV-2 RNA in wastewater from dormitory buildings in a university campus: comparison with individual testing results

Wastewater-based epidemiology (WBE) for monitoring COVID-19 has been largely used to detect the spread of the disease at the community level. From February to December 2022, we collected 24-h composite sewage samples from dormitory buildings in George Mason University (Fairfax, Virginia, USA) housing approximately 5,200 resident students. SARS-CoV-2 RNA extraction was achieved using an automated system based on magnetic nanoparticles. Analysis of SARS-CoV-2 RNA was performed using reverse transcription quantitative PCR based on the Centers for Disease Control and Prevention (CDC) N1 and N2 assays. From the 362 samples collected, 86% showed positive detection of SARS-CoV-2 RNA. Wastewater monitoring was able to detect SARS-CoV-2 RNA in 96% of the samples from buildings housing students with COVID-19. Over the period of study, we observed significant correlations between the SARS-CoV-2 concentration (copy number mL-1) in wastewater and the number of positive cases on campus based on individual saliva testing. Although several reports have been published on the wastewater monitoring of COVID-19 in university campuses, our study is one of the very few that provides results that were obtained during the last phase of the pandemic (roughly the year 2022), when the large majority of students were vaccinated and back on campus.

Seroincidence of SARS-CoV-2 infection prior to and during the rollout of vaccines in a community-based prospective cohort of U.S. adults

Background

Infectious disease surveillance systems, which largely rely on diagnosed cases, underestimate the true incidence of SARS-CoV-2 infection, due to under-ascertainment and underreporting. We used repeat serologic testing to measure N-protein seroconversion in a well-characterized cohort of U.S. adults with no serologic evidence of SARS-CoV-2 infection to estimate the incidence of SARS-CoV-2 infection and characterize risk factors, with comparisons before and after the start of the SARS-CoV-2 vaccine and variant eras.

Methods

We assessed the incidence rate of infection and risk factors in two sub-groups (cohorts) that were SARS-CoV-2 N-protein seronegative at the start of each follow-up period: 1) the pre-vaccine/wild-type era cohort (n=3,421), followed from April to November 2020; and 2) the vaccine/variant era cohort (n=2,735), followed from November 2020 to June 2022. Both cohorts underwent repeat serologic testing with an assay for antibodies to the SARS-CoV-2 N protein (Bio-Rad Platelia SARS-CoV-2 total Ab). We estimated crude incidence and sociodemographic/epidemiologic risk factors in both cohorts. We used multivariate Poisson models to compare the risk of SARS-CoV-2 infection in the pre-vaccine/wild-type era cohort (referent group) to that in the vaccine/variant era cohort, within strata of vaccination status and epidemiologic risk factors (essential worker status, child in the household, case in the household, social distancing).

Findings

In the pre-vaccine/wild-type era cohort, only 18 of the 3,421 participants (0.53%) had ≥1 vaccine dose by the end of follow-up, compared with 2,497/2,735 (91.3%) in the vaccine/variant era cohort. We observed 323 and 815 seroconversions in the pre-vaccine/wild-type era and the vaccine/variant era and cohorts, respectively, with corresponding incidence rates of 9.6 (95% CI: 8.3-11.5) and 25.7 (95% CI: 24.2-27.3) per 100 person-years. Associations of sociodemographic and epidemiologic risk factors with SARS-CoV-2 incidence were largely similar in the pre-vaccine/wild-type and vaccine/variant era cohorts. However, some new epidemiologic risk factors emerged in the vaccine/variant era cohort, including having a child in the household, and never wearing a mask while using public transit. Adjusted incidence rate ratios (aIRR), with the entire pre-vaccine/wild-type era cohort as the referent group, showed markedly higher incidence in the vaccine/variant era cohort, but with more vaccine doses associated with lower incidence: aIRRun/undervaccinated=5.3 (95% CI: 4.2-6.7); aIRRprimary series only=5.1 (95% CI: 4.2-7.3); aIRRboosted once=2.5 (95% CI: 2.1-3.0), and aIRRboosted twice=1.65 (95% CI: 1.3-2.1). These associations were essentially unchanged in risk factor-stratified models.

Interpretation

In SARS-CoV-2 N protein seronegative individuals, large increases in incidence and newly emerging epidemiologic risk factors in the vaccine/variant era likely resulted from multiple co-occurring factors, including policy changes, behavior changes, surges in transmission, and changes in SARS-CoV-2 variant properties. While SARS-CoV-2 incidence increased markedly in most groups in the vaccine/variant era, being up to date on vaccines and the use of non-pharmaceutical interventions (NPIs), such as masking and social distancing, remained reliable strategies to mitigate the risk of SARS-CoV-2 infection, even through major surges due to immune evasive variants. Repeat serologic testing in cohort studies is a useful and complementary strategy to characterize SARS-CoV-2 incidence and risk factors.

Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties

Although the coronavirus disease (COVID-19) emergency status is easing, the COVID-19 pandemic continues to affect healthcare systems globally. It is crucial to have a reliable and population-wide prediction tool for estimating COVID-19-induced hospital admissions. We evaluated the feasibility of using wastewater-based epidemiology (WBE) to predict COVID-19-induced weekly new hospitalizations in 159 counties across 45 states in the United States of America (USA), covering a population of nearly 100 million. Using county-level weekly wastewater surveillance data (over 20 months), WBE-based models were established through the random forest algorithm. WBE-based models accurately predicted the county-level weekly new admissions, allowing a preparation window of 1-4 weeks. In real applications, periodically updated WBE-based models showed good accuracy and transferability, with mean absolute error within 4-6 patients/100k population for upcoming weekly new hospitalization numbers. Our study demonstrated the potential of using WBE as an effective method to provide early warnings for healthcare systems.

Socioeconomic determinants of stay-at-home policies during the first COVID-19 wave

Introduction

The COVID-19 pandemic has had a significant impact on public health and social systems worldwide. This study aims to evaluate the efficacy of various policies and restrictions implemented by different countries to control the spread of the virus.

Methods

To achieve this objective, a compartmental model is used to quantify the "social permeability" of a population, which reflects the inability of individuals to remain in confinement and continue social mixing allowing the spread of the virus. The model is calibrated to fit and recreate the dynamics of the epidemic spreading of 42 countries, mainly taking into account reported deaths and mobility across the populations.

Results

The results indicate that low-income countries have a harder time slowing the advance of the pandemic, even if the virus did not initially propagate as fast as in wealthier countries, showing the disparities between countries in their ability to mitigate the spread of the disease and its impact on vulnerable populations.

Discussion

This research contributes to a better understanding of the socioeconomic and environmental factors that affect the spread of the virus and the need for equitable policy measures to address the disparities in the global response to the pandemic.

Parent's Perspective of Problems Faced during the COVID-19 Pandemic Lockdown on the Care of Children with Epilepsy: A Qualitative Study

Background

COVID-19 has significantly impacted the care of children with chronic illness. There is a paucity of data on issues faced by parents of children with epilepsy (CWE) in an Indian setup.

Objectives

The objective was to describe the parental perspective of the problems faced by them on the care of their CWE during the first wave of the COVID-19 pandemic.

Materials and Methods

Parents of CWE who physically visited the clinic for their follow-up visit were asked to narrate their experiences about the problems they faced during the first lockdown due to COVID-19. The narratives were audio recorded, and transcripts were analyzed using thematic analysis to arrive at broad themes.

Results

Four broad themes were identified: transport-related issues, medication-related issues, issues related to doctor consultation, and diagnostic delay. Limited transportation facilities, lack of appropriate social distancing norms in public transport and outpatient units, rigorous frisking by personnel during travel, fear of viral transmission during outpatient visits, nonavailability of antiseizure medications (ASMs) in local markets, lack of discounts by pharmacy, change of brands of ASM, and inability to undergo scheduled diagnostic investigations were some of the major issues raised by parents of CWE.

Conclusion

Parents of CWE had trouble in transport to the hospital, inadequate access to ASMs, difficulties in doctor consultation, and delays in diagnostic investigations during the first COVID-19 pandemic lockdown.

Biological mechanisms underpinning the development of long COVID

As COVID-19 evolves from a pandemic to an endemic disease, the already staggering number of people that have been or will be infected with SARS-CoV-2 is only destined to increase, and the majority of humanity will be infected. It is well understood that COVID-19, like many other viral infections, leaves a significant fraction of the infected with prolonged consequences. Continued high number of SARS-CoV-2 infections, viral evolution with escape from post-infection and vaccinal immunity, and reinfections heighten the potential impact of Long COVID. Hence, the impact of COVID-19 on human health will be seen for years to come until more effective vaccines and pharmaceutical treatments become available. To that effect, it is imperative that the mechanisms underlying the clinical manifestations of Long COVID be elucidated. In this article, we provide an in-depth analysis of the evidence on several potential mechanisms of Long COVID and discuss their relevance to its pathogenesis.

Analytical solution of l-i SEIR model-Comparison of l-i SEIR model with conventional SEIR model in simulation of epidemic curves

The Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model has been commonly used to analyze the spread of infectious diseases. This 4-compartment (S, E, I and R) model uses an approximation of temporal homogeneity of individuals in these compartments to calculate the transfer rates of the individuals from compartment E to I to R. Although this SEIR model has been generally adopted, the calculation errors caused by temporal homogeneity approximation have not been quantitatively examined. In this study, a 4-compartment l-i SEIR model considering temporal heterogeneity was developed from a previous epidemic model (Liu X., Results Phys. 2021; 20:103712), and a closed-form solution of the l-i SEIR model was derived. Here, l represents the latent period and i represents the infectious period. Comparing l-i SEIR model with the conventional SEIR model, we are able to examine how individuals move through each corresponding compartment in the two SEIR models to find what information may be missed by the conventional SEIR model and what calculation errors may be introduced by using the temporal homogeneity approximation. Simulations showed that l-i SEIR model could generate propagated curves of infectious cases under the condition of l>i. Similar propagated epidemic curves were reported in literature, but the conventional SEIR model could not generate propagated curves under the same conditions. The theoretical analysis showed that the conventional SEIR model overestimates or underestimates the rate at which individuals move from compartment E to I to R in the rising or falling phase of the number of infectious individuals, respectively. Increasing the rate of change in the number of infectious individuals leads to larger calculation errors in the conventional SEIR model. Simulations from the two SEIR models with assumed parameters or with reported daily COVID-19 cases in the United States and in New York further confirmed the conclusions of the theoretical analysis.

Spatiotemporal characteristics of the SARS-CoV-2 Delta wave in North Carolina

We constructed county-level models to examine properties of the SARS-CoV-2 B.1.617.2 (Delta) variant wave of infections in North Carolina and assessed immunity levels (via prior infection, via vaccination, and overall) prior to the Delta wave. To understand how prior immunity shaped Delta wave outcomes, we assessed relationships among these characteristics. Peak weekly infection rate and total percent of the population infected during the Delta wave were negatively correlated with the proportion of people with vaccine-derived immunity prior to the Delta Wave, signaling that places with higher vaccine uptake had better outcomes. We observed a positive correlation between immunity via infection prior to Delta and percent of the population infected during the Delta wave, meaning that counties with poor pre-Delta outcomes also had poor Delta wave outcomes. Our findings illustrate geographic variation in outcomes during the Delta wave in North Carolina, highlighting regional differences in population characteristics and infection dynamics.

Public interest trends for Covid-19 and alignment with the disease trajectory: A time-series analysis of national-level data

Data from web search engines have become a valuable adjunct in epidemiology and public health, specifically during epidemics. We aimed to explore the concordance of web search popularity for Covid-19 across 6 Western nations (United Kingdom, United States, France, Italy, Spain and Germany) and how timeline changes align with the pandemic waves, Covid-19 mortality, and incident case trajectories. We used the Google Trends tool for web-search popularity, and "Our World in Data" on Covid-19 reported cases, deaths, and administrative responses (measured by stringency index) to analyze country-level data. The Google Trends tool provides spatiotemporal data, scaled to a range of <1 (lowest relative popularity) to 100 (highest relative popularity), for the selected search terms, timeframe, and region. We used "coronavirus" and "covid" as search terms and set the timeframe up to November 12, 2022. We obtained multiple consecutive samples using the same terms to validate against sampling bias. We consolidated national-level incident cases and deaths weekly and transformed them to a range between 0 to 100 through the min-max normalization algorithm. We calculated the concordance of relative popularity rankings between regions, using the non-parametric Kendall's W, which maps concordance between 0 (lack of agreement) to 1 (perfect match). We used a dynamic time-warping algorithm to explore the similarity between Covid-19 relative popularity, mortality, and incident case trajectories. This methodology can recognize the similarity of shapes between time-series through a distance optimization process. The peak popularity was recorded on March 2020, to be followed by a decline below 20% in the subsequent three months and a long-standing period of variation around that level. At the end of 2021, public interest spiked shortly to fade away to a low level of around 10%. This pattern was highly concordant across the six regions (Kendal's W 0.88, p< .001). In dynamic time warping analysis, national-level public interest yielded a high similarity with the Covid-19 mortality trajectory (Similarity indices range 0.60-0.79). Instead, public interest was less similar with incident cases (0.50-0.76) and stringency index trajectories (0.33-0.64). We demonstrated that public interest is better intertwined with population mortality, rather than incident case trajectory and administrative responses. As the public interest in Covid-19 gradually subsides, these observations could help predict future public interest in pandemic events.

Charting the spatial dynamics of early SARS-CoV-2 transmission in Washington state

The spread of SARS-CoV-2 has been geographically uneven. To understand the drivers of this spatial variation in SARS-CoV-2 transmission, in particular the role of stochasticity, we used the early stages of the SARS-CoV-2 invasion in Washington state as a case study. We analysed spatially-resolved COVID-19 epidemiological data using two distinct statistical analyses. The first analysis involved using hierarchical clustering on the matrix of correlations between county-level case report time series to identify geographical patterns in the spread of SARS-CoV-2 across the state. In the second analysis, we used a stochastic transmission model to perform likelihood-based inference on hospitalised cases from five counties in the Puget Sound region. Our clustering analysis identifies five distinct clusters and clear spatial patterning. Four of the clusters correspond to different geographical regions, with the final cluster spanning the state. Our inferential analysis suggests that a high degree of connectivity across the region is necessary for the model to explain the rapid inter-county spread observed early in the pandemic. In addition, our approach allows us to quantify the impact of stochastic events in determining the subsequent epidemic. We find that atypically rapid transmission during January and February 2020 is necessary to explain the observed epidemic trajectories in King and Snohomish counties, demonstrating a persisting impact of stochastic events. Our results highlight the limited utility of epidemiological measures calculated over broad spatial scales. Furthermore, our results make clear the challenges with predicting epidemic spread within spatially extensive metropolitan areas, and indicate the need for high-resolution mobility and epidemiological data.

Design of effective outpatient sentinel surveillance for COVID-19 decision-making: a modeling study

BACKGROUND

Decision-makers impose COVID-19 mitigations based on public health indicators such as reported cases, which are sensitive to fluctuations in supply and demand for diagnostic testing, and hospital admissions, which lag infections by up to two weeks. Imposing mitigations too early has unnecessary economic costs while imposing too late leads to uncontrolled epidemics with unnecessary cases and deaths. Sentinel surveillance of recently-symptomatic individuals in outpatient testing sites may overcome biases and lags in conventional indicators, but the minimal outpatient sentinel surveillance system needed for reliable trend estimation remains unknown.

METHODS

We used a stochastic, compartmental transmission model to evaluate the performance of various surveillance indicators at reliably triggering an alarm in response to, but not before, a step increase in transmission of SARS-CoV-2. The surveillance indicators included hospital admissions, hospital occupancy, and sentinel cases with varying levels of sampling effort capturing 5, 10, 20, 50, or 100% of incident mild cases. We tested 3 levels of transmission increase, 3 population sizes, and conditions of either simultaneous transmission increase or lagged increase in the older population. We compared the indicators' performance at triggering alarm soon after, but not prior, to the transmission increase.

RESULTS

Compared to surveillance based on hospital admissions, outpatient sentinel surveillance that captured at least 20% of incident mild cases could trigger an alarm 2 to 5 days earlier for a mild increase in transmission and 6 days earlier for a moderate or strong increase. Sentinel surveillance triggered fewer false alarms and averted more deaths per day spent in mitigation. When transmission increase in older populations lagged the increase in younger populations by 14 days, sentinel surveillance extended its lead time over hospital admissions by an additional 2 days.

CONCLUSIONS

Sentinel surveillance of mild symptomatic cases can provide more timely and reliable information on changes in transmission to inform decision-makers in an epidemic like COVID-19.

SARS-CoV-2 molecular testing and whole genome sequencing following RNA recovery from used BinaxNOW COVID-19 antigen self tests

Widespread use of over-the-counter rapid diagnostic tests for SARS-CoV-2 has led to a decrease in availability of clinical samples for viral genomic surveillance. As an alternative sample source, we evaluated RNA isolated from BinaxNOW swabs stored at ambient temperature for SARS-CoV-2 rRT-PCR and full viral genome sequencing. 81 of 103 samples (78.6%) yielded detectable RNA, and 46 of 57 samples (80.7 %) yielded complete genome sequences. Our results illustrate that SARS-CoV-2 RNA extracted from used Binax test swabs provides an important opportunity for improving SARS-CoV-2 genomic surveillance, evaluating transmission clusters, and monitoring within-patient evolution.

Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020

Background

Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada.

Design and methods

We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period.

Results

For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test.

Conclusions

Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.

Using an influenza surveillance system to estimate the number of SARS-CoV-2 infections in Beijing, China, weeks 2 to 6 2023

With COVID-19 public health control measures downgraded in China in January 2023, reported COVID-19 case numbers may underestimate the true numbers after the SARS-CoV-2 Omicron wave. Using a multiplier model based on our influenza surveillance system, we estimated that the overall incidence of SARS-CoV-2 infections was 392/100,000 population in Beijing during the 5 weeks following policy adjustment. No notable change occurred after the Spring Festival in early February. The multiplier model provides an opportunity for assessing the actual COVID-19 situation.

Cross-Canada Variability in Blood Donor SARS-CoV-2 Seroprevalence by Social Determinants of Health

We compared the seroprevalence of SARS-CoV-2 anti-nucleocapsid antibodies in blood donors across Canadian regions in 2021. The seroprevalence was the highest in Alberta and the Prairies, and it was so low in Atlantic Canada that few correlates were observed. Being male and of young age were predictive of seropositivity. Racialization was associated with higher seroprevalence in British Columbia and Ontario but not in Alberta and the Prairies. Living in a materially deprived neighborhood predicted higher seroprevalence, but it was more linear across quintiles in Alberta and the Prairies, whereas in British Columbia and Ontario, the most affluent 60% were similarly low and the most deprived 40% similarly elevated. Living in a more socially deprived neighborhood (more single individuals and one parent families) was associated with lower seroprevalence in British Columbia and Ontario but not in Alberta and the Prairies. These data show striking variability in SARS-CoV-2 seroprevalence across regions by social determinants of health. IMPORTANCE Canadian blood donors are a healthy adult population that shows clear disparities associated with racialization and material deprivation. This underscores the pervasiveness of the socioeconomic gradient on SARS-CoV-2 infections in Canada. We identify regional differences in the relationship between SARS-CoV-2 seroprevalence and social determinants of health. Cross-Canada studies, such as ours, are rare because health information is under provincial jurisdiction and is not available in sufficient detail in national data sets, whereas other national seroprevalence studies have insufficient sample sizes for regional comparisons. Ours is the largest seroprevalence study in Canada. An important strength of our study is the interpretation input from a public health team that represented multiple Canadian provinces. Our blood donor seroprevalence study has informed Canadian public health policy at national and provincial levels since the start of the SARS-CoV-2 pandemic.

Ascertainment rate of SARS-CoV-2 infections from healthcare and community testing in the UK

The proportion of SARS-CoV-2 infections ascertained through healthcare and community testing is generally unknown and expected to vary depending on natural factors and changes in test-seeking behaviour. Here we use population surveillance data and reported daily case numbers in the United Kingdom to estimate the rate of case ascertainment. We mathematically describe the relationship between the ascertainment rate, the daily number of reported cases, population prevalence, and the sensitivity of PCR and Lateral Flow tests as a function time since exposure. Applying this model to the data, we estimate that 20%-40% of SARS-CoV-2 infections in the UK were ascertained with a positive test with results varying by time and region. Cases of the Alpha variant were ascertained at a higher rate than the wild type variants circulating in the early pandemic, and higher again for the Delta variant and Omicron BA.1 sub-lineage, but lower for the BA.2 sub-lineage. Case ascertainment was higher in adults than in children. We further estimate the daily number of infections and compare this to mortality data to estimate that the infection fatality rate increased by a factor of 3 during the period dominated by the Alpha variant, and declined in line with the distribution of vaccines. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

The Impact of Funding Inpatient Treatments for COVID-19 on Health Equity in the United States: A Distributional Cost-Effectiveness Analysis

OBJECTIVES

We conducted a distributional cost-effectiveness analysis (DCEA) to evaluate how Medicare funding of inpatient COVID-19 treatments affected health equity in the United States.

METHODS

A DCEA, based on an existing cost-effectiveness analysis model, was conducted from the perspective of a single US payer, Medicare. The US population was divided based on race and ethnicity (Hispanic, non-Hispanic black, and non-Hispanic white) and county-level social vulnerability index (5 quintile groups) into 15 equity-relevant subgroups. The baseline distribution of quality-adjusted life expectancy was estimated across the equity subgroups. Opportunity costs were estimated by converting total spend on COVID-19 inpatient treatments into health losses, expressed as quality-adjusted life-years (QALYs), using base-case assumptions of an opportunity cost threshold of $150 000 per QALY gained and an equal distribution of opportunity costs across equity-relevant subgroups.

RESULTS

More socially vulnerable populations received larger per capita health benefits due to higher COVID-19 incidence and baseline in-hospital mortality. The total direct medical cost of inpatient COVID-19 interventions in the United States in 2020 was estimated at $25.83 billion with an estimated net benefit of 735 569 QALYs after adjusting for opportunity costs. Funding inpatient COVID-19 treatment reduced the population-level burden of health inequality by 0.234%. Conclusions remained robust across scenario and sensitivity analyses.

CONCLUSIONS

To the best of our knowledge, this is the first DCEA to quantify the equity implications of funding COVID-19 treatments in the United States. Medicare funding of COVID-19 treatments in the United States could improve overall health while reducing existing health inequalities.

SARS-CoV-2 molecular testing and whole genome sequencing following RNA recovery from used BinaxNOW COVID-19 Antigen Self Tests

Widespread use of over-the-counter rapid diagnostic tests for SARS-CoV-2 has led to a decrease in availability of clinical samples for viral genomic surveillance. As an alternative sample source, we evaluated RNA isolated from BinaxNOW swabs stored at ambient temperature for SARS-CoV-2 rRT-PCR and full viral genome sequencing. 81 of 103 samples (78.6%) yielded detectable RNA, and 46 of 57 samples (80.7 %) yielded complete genome sequences. Our results illustrate that SARS-CoV-2 RNA extracted from used Binax test swabs provides an important opportunity for improving SARS-CoV-2 genomic surveillance, evaluating transmission clusters, and monitoring within-patient evolution.

Correlation between SARS-CoV-2 RNA concentration in wastewater and COVID-19 cases in community: A systematic review and meta-analysis

Wastewater-based epidemiology (WBE) has been considered as a promising approach for population-wide surveillance of coronavirus disease 2019 (COVID-19). Many studies have successfully quantified severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater (CRNA). However, the correlation between the CRNA and the COVID-19 clinically confirmed cases in the corresponding wastewater catchments varies and the impacts of environmental and other factors remain unclear. A systematic review and meta-analysis were conducted to identify the correlation between CRNA and various types of clinically confirmed case numbers, including prevalence and incidence rates. The impacts of environmental factors, WBE sampling design, and epidemiological conditions on the correlation were assessed for the same datasets. The systematic review identified 133 correlation coefficients, ranging from -0.38 to 0.99. The correlation between CRNA and new cases (either daily new, weekly new, or future cases) was stronger than that of active cases and cumulative cases. These correlation coefficients were potentially affected by environmental and epidemiological conditions and WBE sampling design. Larger variations of air temperature and clinical testing coverage, and the increase of catchment size showed strong negative impacts on the correlation between CRNA and COVID-19 case numbers. Interestingly, the sampling technique had negligible impact although increasing the sampling frequency improved the correlation. These findings highlight the importance of viral shedding dynamics, in-sewer decay, WBE sampling design and clinical testing on the accurate back-estimation of COVID-19 case numbers through the WBE approach.

The potential clinical impact and cost-effectiveness of the updated COVID-19 mRNA fall 2023 vaccines in the United States

AIMS

To assess the potential clinical impact and cost-effectiveness of COVID-19 mRNA vaccines updated for fall 2023 in adults aged ≥18 years over a 1-year analytic time horizon (September 2023-August 2024).

MATERIALS AND METHODS

A compartmental Susceptible-Exposed-Infected-Recovered model was updated to reflect COVID-19 cases in summer 2023. The numbers of symptomatic infections, COVID-19-related hospitalizations and deaths, and costs and quality-adjusted life-years (QALYs) gained were calculated using a decision tree model. The incremental cost-effectiveness ratio (ICER) of a Moderna updated mRNA fall 2023 vaccine (Moderna Fall Campaign) was compared to no additional vaccination. Potential differences between the Moderna and the Pfizer-BioNTech fall 2023 vaccines were also examined.

RESULTS

Base case results suggest that the Moderna Fall Campaign would decrease the expected 64.2 million symptomatic infections by 7.2 million (11%) to 57.0 million. COVID-19-related hospitalizations and deaths are expected to decline by 343,000 (-29%) and 50,500 (-33%), respectively. The Moderna Fall Campaign would increase QALYs by 740,880 and healthcare costs by $5.7 billion relative to no vaccine, yielding an ICER of $7700 per QALY gained. Using a societal cost perspective, the ICER is $2100. Sensitivity analyses suggest that vaccine effectiveness, COVID-19 incidence, hospitalization rates, and costs drive cost-effectiveness. With a relative vaccine effectiveness of 5.1% for infection and 9.8% for hospitalization for the Moderna vaccine versus the Pfizer-BioNTech vaccine, use of the Moderna vaccine is expected to prevent 24,000 more hospitalizations and 3300 more deaths than the Pfizer-BioNTech vaccine.

LIMITATIONS AND CONCLUSIONS

As COVID-19 becomes endemic, future incidence, including patterns of infection, are highly uncertain. The effectiveness of fall 2023 vaccines is unknown, and it is unclear when a new variant that evades natural or vaccine immunity will emerge. Despite these limitations, our model predicts the Moderna Fall Campaign vaccine is highly cost-effective across all sensitivity analyses.

Severe COVID-19 outcomes in pediatrics: An observational cohort analysis comparing Alpha, Delta, and Omicron variants

Objective

COVID-19 can rarely lead to severe illness in pediatric patients. The aim of this study was to determine if severe outcomes in pediatric COVID-19 have changed over the course of the pandemic.

Methods

This was a multicenter, observational cohort analysis from a large regional healthcare system in metro Detroit using electronic health record data to evaluate emergency visits, hospitalization, and severe COVID-19 disease in pediatric patients. Consecutive pediatric patients presenting to the emergency department with a primary diagnosis of COVID-19 were included. Outcomes data was gathered from three distinct time intervals that coincided with Alpha, Delta, and Omicron variant predominance (Time interval 1 (T1) 1/1/2021-6/30/2021: Alpha, T2 7/1/2021-12/31/2021: Delta, T3 1/1/2022-6/16/2022): Omicron. The primary outcome was severe disease inclusive of composite intensive care unit admission, mechanical ventilation, multisystem inflammatory syndrome in children (MIS-C), myocarditis, or death. Secondary outcomes included severe outcomes considering viral coinfection and vaccination status.

Results

Between 1/1/2021 and 6/16/2022, there were 4517 emergency COVID-19 visits, of which 12.5% (566) of children were hospitalized. 24.4% (138), 31.6% (179), and 44.0% (249) of admissions occurred during T1, T2 and T3 respectively. Most patients were male (55.1%) and 59.9% identified as Caucasian. The median age was 5.0 (interquartile range 1.0, 13.0) with infants comprising 22.8% (129), toddlers 25.1% (142), children 23.0% (130), and teenagers 29.2% (165). Over the course of the pandemic, the proportion of infants in hospitalization increased from 16.7% in T1 to 19.6% in T2 to 28.5% in T3 (p < 0.01) while the proportion of teenagers in hospitalization decreased from 39.1% in T1 to 31.3% in T2 to 22.1% in T3 (p < 0.001). Oxygen therapy was required in a minority (29.9%) of cases with supplemental oxygen utilized the least in T3 (16.5%) and most in T2 (30.2%). Composite severe disease decreased throughout the pandemic occurring in 36.2% in T1, 27.4% in T2, and 18.9% in T3. A multivariable logistic regression analysis revealed the odds of composite severe disease was significantly lower in T3 compared to T1 (adjusted odds ratio [aOR] 0.35, 95% Confidence Interval 0.21-0.60, p < 0.001). Fully vaccinated or fully vaccinated and boosted admission rates remained low throughout all periods with 4.4% in T1, 4.5% in T2 and 8.4% in T3. Viral coinfection was most common during T2 (16.8%) followed by T3 (12.5%) and least common in T1 (5.1%) (p = 0.006). Coinfection occurred more commonly in younger children with a median age of 1.2 (0.0, 4.5) compared to those with mono-infection with a median age of 6 (1.0, 14.0) (p < 0.001). Severe outcomes occurred in 45.6% of coinfection cases compared to 22.1% without coinfection (p < 0.001).

Conclusions

While Omicron cases had the highest admission frequency, severe illness was lower than Delta and Alpha variants. Coinfection with respiratory viruses increased the risk of severe outcomes and impacted infants more than older children.

Funding

None.

Socioeconomic status correlations with confirmed COVID-19 cases and SARS-CoV-2 wastewater concentrations in small-medium sized communities

Over two years into the COVID-19 pandemic, it is apparent that some populations across the world are more susceptible than others to SARS-CoV-2 infection and spread. Understanding how populations with varying demographic patterns are impacted by COVID-19 may highlight which factors are most important in targeting to combat global suffering. The first objective of this study was to investigate the association of various socioeconomic status (SES) parameters and confirmed COVID-19 cases in the state of Ohio, USA. This study examines the largest and capital city of Ohio (Columbus) and various small-medium-sized communities. The second objective was to determine the relationship between SES parameters and community-level SARS-CoV-2 concentrations using municipal wastewater samples from each city's respective wastewater treatment plants from August 2020 to January 2021. SES parameters include population size, median income, poverty, race/ethnicity, education, health care access, types of COVID-19 testing sites, and social vulnerability index. Statistical analysis results show that confirmed (normalized and/or non-normalized) COVID-19 cases were negatively associated with White percentage and registered hospitals, and positively associated with registered physicians and various COVID-19 testing sites. Wastewater viral concentrations were negatively associated with poverty, and positively associated with median income, community health centers, and onsite rapid testing locations. Additional analyses conclude that population is a significant factor in determining COVID-19 cases and SARS-CoV-2 wastewater concentrations. Results indicate that community healthcare parameters relate to a negative health outcome (COVID-19) and that demographic parameters can be associated with community-level SARS-CoV-2 wastewater concentrations. As the first study that examines the association between socioeconomic parameters and SARS-CoV-2 wastewater concentrations as well as confirmed COVID-19 cases, it is apparent that social determinants have an impact in determining the health burden of small-medium sized Ohioan cities. This study design and innovative approach are scalable and applicable for endemic and pandemic surveillance across the world.

Use of COVID-19 testing in the first year of the COVID-19 pandemic among cohorts of people at the intersection of drug use and HIV

People living with (PLWH) and at risk for HIV and people who use drugs (PWUD) are at heightened risk for health consequences of COVID-19 because of compromised immunity and high comorbidities. We studied their use of COVID-19 testing during the first year of the COVID-19 pandemic. Eight NIDA funded cohorts across North America in the Collaborating Consortium of Cohorts Producing NIDA Opportunities (C3PNO) administered multiple waves of a COVID-19 survey. Respondents were at least 18 years of age, half PLWH, and many active substance users. Wave one of the COVID-19 survey was May-November, 2020 and wave two October 2020-April 2021. Associations of COVID-19 testing with demographics, socio-demographics, substance use, and HIV-status were assessed. Of the 3762 responses from 2331 individuals, half reported ever COVID-19 testing (49.1 %), with 4.3 % reporting a positive test (163/3762 surveys=4.3 %) and 41.5 % of people reporting current symptoms reported having been tested. In multivariable analysis adjusting for age, sex, and cohort type associations with COVID-19 testing included African American/Black identification compared to Caucasian/white (adjusted odds ratio (AOR)= 0.68; 95 % confidence interval (CI) 0.53, 0.88); being unemployed (AOR=0.61; 95 % CI 0.51, 0.73), and living with HIV (AOR=0.76; 95 % CI0.65, 0.90). Findings from these C3PNO COVID-19 modules suggests that in the first year of the pandemic COVID-19 testing was not broadly accessed by these marginalized populations including PLWH and those unemployed. Factors associated with not testing may also parallel those for vaccination and identify populations needing better access to COVID-19 prevention.

COVID-19 SeroHub, an online repository of SARS-CoV-2 seroprevalence studies in the United States

Seroprevalence studies provide useful information about the proportion of the population either vaccinated against SARS-CoV-2, previously infected with the virus, or both. Numerous studies have been conducted in the United States, but differ substantially by dates of enrollment, target population, geographic location, age distribution, and assays used. This can make it challenging to identify and synthesize available seroprevalence data by geographic region or to compare infection-induced versus combined infection- and vaccination-induced seroprevalence. To facilitate public access and understanding, the National Institutes of Health and the Centers for Disease Control and Prevention developed the COVID-19 Seroprevalence Studies Hub (COVID-19 SeroHub, https://covid19serohub.nih.gov/), a data repository in which seroprevalence studies are systematically identified, extracted using a standard format, and summarized through an interactive interface. Within COVID-19 SeroHub, users can explore and download data from 178 studies as of September 1, 2022. Tools allow users to filter results and visualize trends over time, geography, population, age, and antigen target. Because COVID-19 remains an ongoing pandemic, we will continue to identify and include future studies.

Persistence of Endogenous SARS-CoV-2 and Pepper Mild Mottle Virus RNA in Wastewater-Settled Solids

Limited information is available on the decay rate of endogenous SARS-CoV-2 and pepper mild mottle virus (PMMoV) RNA in wastewater and primary settled solids, potentially limiting an understanding of how transit or holding times within wastewater infrastructure might impact RNA measurements and their relationship to community COVID-19 infections. In this study, primary settled solids samples were collected from two wastewater treatment plants in the San Francisco Bay Area. Samples were thoroughly mixed, aliquoted into subsamples, and stored at 4, 22, and 37 °C for 10 days. The concentrations of SARS-CoV-2 (N1 and N2 targets) and PMMoV RNA were measured using an RT-ddPCR. Limited decay (<1 log10 reduction) was observed in the detection of viral RNA targets at all temperature conditions, suggesting that SARS-CoV-2 and PMMoV RNA can be highly persistent in solids. First-order decay rate constants ranged from 0.011 to 0.098 day-1 for SARS-CoV-2 RNA and from 0.010 to 0.091 day-1 for PMMoV RNA depending on the temperature conditions. A slower decay was observed for SARS-CoV-2 RNA in primary settled solids compared to previously reported decay in wastewater influent. Further research is needed to understand if solid content and wastewater characteristics might influence the persistence of viral RNA targets.

Assessment of a mass balance equation for estimating community-level prevalence of COVID-19 using wastewater-based epidemiology in a mid-sized city

Wastewater-based epidemiology (WBE) has emerged as a valuable epidemiologic tool to detect the presence of pathogens and track disease trends within a community. WBE overcomes some limitations of traditional clinical disease surveillance as it uses pooled samples from the entire community, irrespective of health-seeking behaviors and symptomatic status of infected individuals. WBE has the potential to estimate the number of infections within a community by using a mass balance equation, however, it has yet to be assessed for accuracy. We hypothesized that the mass balance equation-based approach using measured SARS-CoV-2 wastewater concentrations can generate accurate prevalence estimates of COVID-19 within a community. This study encompassed wastewater sampling over a 53-week period during the COVID-19 pandemic in Gainesville, Florida, to assess the ability of the mass balance equation to generate accurate COVID-19 prevalence estimates. The SARS-CoV-2 wastewater concentration showed a significant linear association (Parameter estimate = 39.43, P value < 0.0001) with clinically reported COVID-19 cases. Overall, the mass balance equation produced accurate COVID-19 prevalence estimates with a median absolute error of 1.28%, as compared to the clinical reference group. Therefore, the mass balance equation applied to WBE is an effective tool for generating accurate community-level prevalence estimates of COVID-19 to improve community surveillance.

The variant-specific burden of SARS-CoV-2 in Michigan: March 2020 through November 2021

Accurate estimates of the total burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed to inform policy, planning, and response. We sought to quantify SARS-CoV-2 cases, hospitalizations, and deaths by age in Michigan. Coronavirus disease 2019 cases reported to the Michigan Disease Surveillance System were multiplied by age and time-specific adjustment factors to correct for under-detection. Adjustment factors were estimated in a model fit to incidence data and seroprevalence estimates. Age-specific incidence of SARS-CoV-2 hospitalization, death, vaccination, and variant proportions were estimated from publicly available data. We estimated substantial under-detection of infection that varied by age and time. Accounting for under-detection, we estimate the cumulative incidence of infection in Michigan reached 75% by mid-November 2021, and over 87% of Michigan residents were estimated to have had ≥1 vaccination dose and/or previous infection. Comparing pandemic waves, the relative burden among children increased over time. In general, the proportion of cases who were hospitalized or who died decreased over time. Our results highlight the ongoing risk of periods of high SARS-CoV-2 incidence despite widespread prior infection and vaccination. This underscores the need for long-term planning for surveillance, vaccination, and other mitigation measures amidst continued response to the acute pandemic.

Estimating the infection burden of COVID-19 in Malaysia

Malaysia has reported 2.75 million cases and 31,485 deaths as of 30 December 2021. Underestimation remains an issue due to the underdiagnosis of mild and asymptomatic cases. We aimed to estimate the burden of COVID-19 cases in Malaysia based on an adjusted case fatality rate (aCFR). Data on reported cases and mortalities were collated from the Ministry of Health official GitHub between 1 March 2020 and 30 December 2021. We estimated the total and age-stratified monthly incidence rates, mortality rates, and aCFR. Estimated new infections were inferred from the age-stratified aCFR. The total estimated infections between 1 March 2020 and 30 December 2021 was 9,955,000-cases (95% CI: 6,626,000-18,985,000). The proportion of COVID-19 infections in ages 0-11, 12-17, 18-50, 51-65, and above 65 years were 19.9% (n = 1,982,000), 2.4% (n = 236,000), 66.1% (n = 6,577,000), 9.1% (n = 901,000), 2.6% (n = 256,000), respectively. Approximately 32.8% of the total population in Malaysia was estimated to have been infected with COVID-19 by the end of December 2021. These estimations highlight a more accurate infection burden in Malaysia. It provides the first national-level prevalence estimates in Malaysia that adjusted for underdiagnosis. Naturally acquired community immunity has increased, but approximately 68.1% of the population remains susceptible. Population estimates of the infection burden are critical to determine the need for booster doses and calibration of public health measures.

SARS-CoV-2 Seroprevalence: Demographic and Behavioral Factors Associated With Seropositivity Among College Students in a University Setting

PURPOSE

Examine SARS-CoV-2 seroprevalence and the association of seropositivity with demographic, geographic, and behavioral variables among University of North Carolina Chapel Hill (UNC-CH) undergraduate students enrolled in the fall 2020 semester.

METHODS

All UNC-CH undergraduate students were invited to participate in the Heelcheck study; participants were weighted to the UNC-CH undergraduate population using raking methods. We estimate SARS-CoV-2 seroprevalence at study entrance (11/12/2020-12/10/2020) and bivariable associations using log-binomial regression.

RESULTS

SARS-CoV-2 seroprevalence was 7.3% (95% confidence interval (CI): 5.4%-9.2%) at baseline. Compared to students who were living off-campus in the Chapel Hill/Carrboro area (CH) for the Fall 2020 semester (8.6% seroprevalence), students who never returned to CH had lower seroprevalence (1.9%, prevalence ratio (PR), 95% CI: 0.22, 0.06-0.81), whereas, students who started the semester on-campus and moved to off-campus CH housing had 18.9% seroprevalence (PR, 95% CI: 2.21, 1.04-4.72) and students who spent the semester living in a Sorority/Fraternity house had 46.8% seroprevalence (PR, 95% CI: 5.47, 2.62-11.46). Those who predicted they would join an indoor party unmasked had 3.8 times the seroprevalence of those who indicated they would not attend (PR, 95% CI: 3.80, 1.58-9.16). Compared to students who disagreed with the statement "…I am not going to let COVID-19 stop me from having fun…", those who agreed had higher seroprevalence (14.0% vs. 5.7%; (PR, 95% CI: 2.45, 1.13-5.32)).

DISCUSSION

Increased seroprevalence was associated with congregate living and participation (actual or endorsed) in social activities. During pandemics, universities must create safe socializing opportunities while minimizing transmission.

NC-COVID: A Time-Varying Compartmental Model for Estimating SARS-CoV-2 Infection Dynamics in North Carolina, US

Efforts to track and model SARS-CoV-2 infection dynamics in the population have been complicated by certain aspects of the transmission characteristics, which include a pre-symptomatic infectious phase as well as asymptomatic infectious individuals. Another problem is that many models focus on case count, as there has been (and is) limited data regarding infection status of members of the population, which is the most important aspect for constructing transmission models. This paper describes and explains the parameterization, calibration, and revision of the NC-COVID model, a compartmental model to estimate SARS-CoV-2 infection dynamics for the state of North Carolina, US. The model was developed early in the pandemic to provide rapid, up-to-date state-level estimates of the number of people who were currently infected, were immune from a prior infection, and remained susceptible to infection. As a post modeling exercise, we assessed the veracity of the model by comparing its output to SARS-CoV-2 viral particle concentrations detected in wastewater data and to estimates of people infected using COVID-19 deaths. The NC-COVID model was highly correlated with these independently derived estimates, suggesting that it produced accurate estimates of SARS-CoV-2 infection dynamics in North Carolina.

Blockchain solution benefits for controlling pandemics: Bottom-up decentralization, automation with real-time update, and immutability with privacy preservation

The current COVID-19 pandemic has created turmoil around the world. To fight this ongoing global crisis and future ones, all stakeholders must collaborate and share timely and truthful information. This paper proposes a blockchain solution based on its inherent technological advantages. We posit that benefits can be derived from three unique blockchain features: bottom-up decentralization, automation with real-time update, and immutability with privacy preservation. A decentralized common platform provides easy access and increases participation in disease surveillance, which reduces the estimation errors of the compartmental model parameters. Automation with real-time update facilitates prompt detection and diagnosis, accurate contact tracing, and targeted mitigation and containment, achieving faster recovery and slower transmission. Being immutable while preserving privacy, the blockchain solution enhances respondents' willingness to truthfully report their contact history, avoiding false and erroneous data that will cause wrong estimates on pandemic transmission and recovery. Thus, the blockchain solution mitigates three types of risks: sample variance, delay, and bias. Through simulation, we quantify the value of the blockchain solution in these three aspects. Accordingly, we provide specific action plans based on our research findings: before building blockchain solutions for controlling COVID-19, governments and organizations can calculate the blockchain benefits and decide whether or not they should invest in such blockchain solutions by conducting a cost-benefit analysis.

Trends in psychotropic drug consumption among French military personnel during the COVID-19 epidemic

BACKGROUND

The coronavirus disease (COVID-19) pandemic may have had significant mental health consequences for military personnel, which is a population already exposed to psychological stress. To assess the potential impact of the COVID-19 pandemic, we analyzed the dispensing of three classes of psychotropic drugs (anxiolytics, hypnotics, and antidepressants) among French military personnel.

METHODS

A retrospective analysis was conducted using the individualized medico-administrative data of persons insured by the National Military Social Security Fund from the National Health Data System. All active French military personnel aged 18-64 who received outpatient care and to whom drugs were dispensed between January 1, 2019, and April 30, 2021, were included from the French national health database. Rate ratios of dispensed anxiolytics, hypnotics and antidepressants (based on drug reimbursement) were estimated from negative binomial regressions before and after the start of the COVID-19 pandemic.

RESULTS

Three hundred eighty-one thousand seven hundred eleven individuals were included. Overall, 45,148 military personnel were reimbursed for anxiolytics, 10,637 for hypnotics, and 4328 for antidepressants. Drugs were dispensed at a higher rate in 2020 and 2021 than in 2019. There was a notable peak at the beginning of the first lockdown followed by a decrease limited to the duration of the first lockdown. During the first lockdown only, there were temporary phenomena including a brief increase in drug dispensing during the first week followed by a decrease during the rest of lockdown, possibly corresponding to a stocking-up effect. For the study period overall, while there was a significant downward trend in psychotropic drug dispensing before the occurrence of COVID-19 (p < 0.001), the pandemic period was associated with an increase in dispensed anxiolytics (rate ratio, 1.03; 95% CI, 1.02-1.04, p < 0.05), hypnotics (rate ratio, 1.13; 95% CI, 1.11-1.16, p < 0.001) and antidepressants (rate ratio, 1.12; 95% CI, 1.10-1.13, p < 0.001) in the military population.

CONCLUSIONS

The COVID-19 pandemic has probably had a significant impact on the mental health of French military personnel, as suggested by the trends in dispensed psychotropic drugs. The implementation of mental health prevention measures should be investigated for this population.

Identifying behavior of long-distance virus transmission and mitigation performance from a COVID-19 outbreak of a daycare center

During the last two years, hundreds of millions of people in the world have been infected with SARS-CoV-2 due to recurrent waves and closed spaces. Daycare centers are critical infrastructures that cannot be replaced, even during the COVID-19 period. However, the existing settings in daycare centers may pose risks of inevitable close contact between teachers and children, as well as fomite and airborne transmission during care hours. Therefore, reinforced mitigation strategies have been applied in daycare centers to reduce potential indoor virus transfer in many countries. However, numerous outbreaks of COVID-19 have been reported in daycare centers. Therefore, in this study, researchers focused on the risk and behavior of long-distance virus transmission based on the detected viruses on air purifier filter sampling in a daycare center outbreak in Korea. Various experiments of possible situations were conducted in nursing rooms based on field interviews. The experiments monitored the long-distance transmission behavior of aerosol-sized particles and visualized particle behavior at the daycare center. The results of this study revealed that long-distance virus transmission is possible under the current settings in the daycare center, and flush-out can be an important countermeasure with reinforced ventilation methods to prevent potential airborne spread in the daycare center. The results of air purifiers represented that air purifiers should be properly installed and operated in the daycare center to prevent airborne virus spread by airflow during occupied hours. The findings of this study will contribute to the understanding of airborne virus risk and the development of customized virus measures for daycare centers.

COVID-19 Self-Test Data: Challenges and Opportunities - United States, October 31, 2021-June 11, 2022

Self-tests* to detect current infection with SARS-CoV-2, the virus that causes COVID-19, are valuable tools that guide individual decision-making and risk reduction† (1-3). Increased self-test use (4) has likely contributed to underascertainment of COVID-19 cases (5-7), because unlike the requirements to report results of laboratory-based and health care provider-administered point-of-care COVID-19 tests,§ public health authorities do not require reporting of self-test results. However, self-test instructions include a recommendation that users report results to their health care provider so that they can receive additional testing and treatment if clinically indicated.¶ In addition, multiple manufacturers of COVID-19 self-tests have developed websites or companion mobile applications for users to voluntarily report self-test result data. Federal agencies use the data reported to manufacturers, in combination with manufacturing supply chain information, to better understand self-test availability and use. This report summarizes data voluntarily reported by users of 10.7 million self-tests from four manufacturers during October 31, 2021-June 11, 2022, and compares these self-test data with data received by CDC for 361.9 million laboratory-based and point-of-care tests performed during the same period. Overall trends in reporting volume and percentage of positive results, as well as completeness of reporting demographic variables, were similar across test types. However, the limited amount and quality of data reported from self-tests currently reduces their capacity to augment existing surveillance. Self-tests provide important risk-reduction information to users, and continued development of infrastructure and methods to collect and analyze data from self-tests could improve their use for surveillance during public health emergencies.

A Rapid and Sensitive Microfluidics-Based Tool for Seroprevalence Immunity Assessment of COVID-19 and Vaccination-Induced Humoral Antibody Response at the Point of Care

As of 8 August 2022, SARS-CoV-2, the causative agent of COVID-19, has infected over 585 million people and resulted in more than 6.42 million deaths worldwide. While approved SARS-CoV-2 spike (S) protein-based vaccines induce robust seroconversion in most individuals, dramatically reducing disease severity and the risk of hospitalization, poorer responses are observed in aged, immunocompromised individuals and patients with certain pre-existing health conditions. Further, it is difficult to predict the protection conferred through vaccination or previous infection against new viral variants of concern (VoC) as they emerge. In this context, a rapid quantitative point-of-care (POC) serological assay able to quantify circulating anti-SARS-CoV-2 antibodies would allow clinicians to make informed decisions on the timing of booster shots, permit researchers to measure the level of cross-reactive antibody against new VoC in a previously immunized and/or infected individual, and help assess appropriate convalescent plasma donors, among other applications. Utilizing a lab-on-a-chip ecosystem, we present proof of concept, optimization, and validation of a POC strategy to quantitate COVID-19 humoral protection. This platform covers the entire diagnostic timeline of the disease, seroconversion, and vaccination response spanning multiple doses of immunization in a single POC test. Our results demonstrate that this platform is rapid (~15 min) and quantitative for SARS-CoV-2-specific IgG detection.

Forecasting Covid-19 in the United Kingdom: A dynamic SIRD model

Making use of a state space framework, we present a stochastic generalization of the SIRD model, where the mortality, infection, and underreporting rates change over time. A new format to the errors in the Susceptible-Infected-Recovered-Dead compartments is also presented, that permits reinfection. The estimated trajectories and (out-of-sample) forecasts of all these variables are presented with their confidence intervals. The model only uses as inputs the number of reported cases and deaths, and was applied for the UK from April, 2020 to Sep, 2021 (daily data). The estimated infection rate has shown a trajectory in waves very compatible with the emergence of new variants and adopted social measures. The estimated mortality rate has shown a significant descendant behaviour in 2021, which we attribute to the vaccination program, and the estimated underreporting rate has been considerably volatile, with a downward tendency, implying that, on average, more people are testing than in the beginning of the pandemic. The evolution of the proportions of the population divided into susceptible, infected, recovered and dead groups are also shown with their confidence intervals and forecast, along with an estimation of the amount of reinfection that, according to our model, has become quite significant in 2021. Finally, the estimated trajectory of the effective reproduction rate has proven to be very compatible with the real number of cases and deaths. Its forecasts with confident intervals are also presented.

Risk and Phenotype of Multisystem Inflammatory Syndrome in Vaccinated and Unvaccinated Danish Children Before and During the Omicron Wave

This cohort study investigates the risk of multisystem inflammatory syndrome after SARS-CoV-2 infection in vaccinated and unvaccinated children before and during the Omicron wave in Denmark.

Evaluating the impact of stay-at-home and quarantine measures on COVID-19 spread

BACKGROUND

During the early stage of the COVID-19 pandemic, many countries implemented non-pharmaceutical interventions (NPIs) to control the transmission of SARS-CoV-2, the causative pathogen of COVID-19. Among those NPIs, stay-at-home and quarantine measures were widely adopted and enforced. Understanding the effectiveness of stay-at-home and quarantine measures can inform decision-making and control planning during the ongoing COVID-19 pandemic and for future disease outbreaks.

METHODS

In this study, we use mathematical models to evaluate the impact of stay-at-home and quarantine measures on COVID-19 spread in four cities that experienced large-scale outbreaks in the spring of 2020: Wuhan, New York, Milan, and London. We develop a susceptible-exposed-infected-removed (SEIR)-type model with components of self-isolation and quarantine and couple this disease transmission model with a data assimilation method. By calibrating the model to case data, we estimate key epidemiological parameters before lockdown in each city. We further examine the impact of stay-at-home and quarantine rates on COVID-19 spread after lockdown using counterfactual model simulations.

RESULTS

Results indicate that self-isolation of susceptible population is necessary to contain the outbreak. At a given rate, self-isolation of susceptible population induced by stay-at-home orders is more effective than quarantine of SARS-CoV-2 contacts in reducing effective reproductive numbers [Formula: see text]. Variation in self-isolation and quarantine rates can also considerably affect the duration of outbreaks, attack rates and peak timing. We generate counterfactual simulations to estimate effectiveness of stay-at-home and quarantine measures. Without these two measures, the cumulative confirmed cases could be much higher than reported numbers within 40 days after lockdown in Wuhan, New York, Milan, and London.

CONCLUSIONS

Our findings underscore the essential role of stay-at-home orders and quarantine of SARS-CoV-2 contacts during the early phase of the pandemic.

Efficacy and safety of upadacitinib for active ankylosing spondylitis refractory to biological therapy: a double-blind, randomised, placebo-controlled phase 3 trial

Objectives

To evaluate the efficacy and safety of upadacitinib, a Janus kinase inhibitor, in patients with active ankylosing spondylitis (AS) with an inadequate response (IR) to biological disease-modifying antirheumatic drugs (bDMARDs).

Methods

Adults with active AS who met modified New York criteria and had an IR to one or two bDMARDs (tumour necrosis factor or interleukin-17 inhibitors) were randomised 1:1 to oral upadacitinib 15 mg once daily or placebo. The primary endpoint was Assessment of SpondyloArthritis international Society 40 (ASAS40) response at week 14. Sequentially tested secondary endpoints included Ankylosing Spondylitis Disease Activity score, Spondyloarthritis Research Consortium of Canada MRI spine inflammation score, total back pain, nocturnal back pain, Bath Ankylosing Spondylitis Functional Index, Bath Ankylosing Spondylitis Metrology Index and Maastricht Ankylosing Spondylitis Enthesitis Score. Results are reported from the 14-week double-blind treatment period.

Results

A total of 420 patients with active AS were randomised (upadacitinib 15 mg, n=211; placebo, n=209). Significantly more patients achieved the primary endpoint of ASAS40 at week 14 with upadacitinib vs placebo (45% vs 18%; p<0.0001). Statistically significant improvements were observed with upadacitinib vs placebo for all multiplicity-controlled secondary endpoints (p<0.0001). Adverse events were reported for 41% of upadacitinib-treated and 37% of placebo-treated patients through week 14. No events of malignancy, major adverse cardiovascular events, venous thromboembolism or deaths were reported with upadacitinib.

Conclusion

Upadacitinib 15 mg was significantly more effective than placebo over 14 weeks of treatment in bDMARD-IR patients with active AS. No new safety risks were identified with upadacitinib.

Trial registration number

NCT04169373.

Estimated Number of COVID-19 Infections, Hospitalizations, and Deaths Prevented Among Vaccinated Persons in the US, December 2020 to September 2021

IMPORTANCE

The number of SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths prevented among vaccinated persons, independent of the effect of reduced transmission, is a key measure of vaccine impact.

OBJECTIVE

To estimate the number of SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths prevented among vaccinated adults in the US.

DESIGN, SETTING, AND PARTICIPANTS

In this modeling study, a multiplier model was used to extrapolate the number of SARS-CoV-2 infections and COVID-19-associated deaths from data on the number of COVID-19-associated hospitalizations stratified by state, month, and age group (18-49, 50-64, and ≥65 years) in the US from December 1, 2020, to September 30, 2021. These estimates were combined with data on vaccine coverage and effectiveness to estimate the risks of infections, hospitalizations, and deaths. Risks were applied to the US population 18 years or older to estimate the expected burden in that population without vaccination. The estimated burden in the US population 18 years or older given observed levels of vaccination was subtracted from the expected burden in the US population 18 years or older without vaccination (ie, counterfactual) to estimate the impact of vaccination among vaccinated persons.

EXPOSURES

Completion of the COVID-19 vaccination course, defined as 2 doses of messenger RNA (BNT162b2 or mRNA-1273) vaccines or 1 dose of JNJ-78436735 vaccine.

MAIN OUTCOMES AND MEASURES

Monthly numbers and percentages of SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths prevented were estimated among those who have been vaccinated in the US.

RESULTS

COVID-19 vaccination was estimated to prevent approximately 27 million (95% uncertainty interval [UI], 22 million to 34 million) infections, 1.6 million (95% UI, 1.4 million to 1.8 million) hospitalizations, and 235 000 (95% UI, 175 000-305 000) deaths in the US from December 1, 2020, to September 30, 2021, among vaccinated adults 18 years or older. From September 1 to September 30, 2021, vaccination was estimated to prevent 52% (95% UI, 45%-62%) of expected infections, 56% (95% UI, 52%-62%) of expected hospitalizations, and 58% (95% UI, 53%-63%) of expected deaths in adults 18 years or older.

CONCLUSIONS AND RELEVANCE

These findings indicate that the US COVID-19 vaccination program prevented a substantial burden of morbidity and mortality through direct protection of vaccinated individuals.

Point Prevalence Estimates of Activity-Limiting Long-Term Symptoms among U.S. Adults ≥1 Month After Reported SARS-CoV-2 Infection, November 1, 2021

Background

Although most adults infected with SARS-CoV-2 fully recover, a proportion have ongoing symptoms, or post-COVID conditions (PCC), after infection. The objective of this analysis was to estimate the number of US adults with activity-limiting PCC on November 1, 2021.

Methods

We modeled the prevalence of PCC using reported infections occurring from February 1, 2020 – September 30, 2021, and population-based, household survey data on new activity-limiting symptoms ≥1 month following SARS-CoV-2 infection. From these data sources, we estimated the number and proportion of US adults with activity-limiting PCC on November 1, 2021, as 95% uncertainty intervals, stratified by sex and age. Sensitivity analyses adjusted for under-ascertainment of infections and uncertainty about symptom duration.

Results

On November 1, 2021, at least 3.0–5.0 million US adults were estimated to have activity-limiting PCC of ≥1 month duration, or 1.2%–1.9% of US adults. Population prevalence was higher in females (1.4%–2.2%) than males. The estimated prevalence after adjusting for under-ascertainment of infections was 1.7%–3.8%.

Conclusion

Millions of US adults were estimated to have activity-limiting PCC. These estimates can support future efforts to address the impact of PCC on the U.S. population.