Seroprevalence of Antibodies to SARS-CoV-2 in 10 Sites in the United States, March 23-May 12, 2020

Enhancing SARS-CoV-2 surveillance in Malawi using telephone syndromic surveillance from July 2020 to April 2022

INTRODUCTION

Monitoring the SARS-CoV-2 pandemic in low-resource countries such as Malawi requires cost-effective surveillance strategies. This study explored the potential utility of phone-based syndromic surveillance in terms of its reach, monitoring trends in reported SARS-CoV-2-like/influenza-like symptoms (CLS/ILS), SARS-CoV-2 testing and mortality.

METHODS

Mobile phone-based interviews were conducted between 1 July 2020 and 30 April 2022, using a structured questionnaire. Randomly digital dialled numbers were used to reach individuals aged ≥18 years who spoke Chichewa or English. Verbal consent was obtained, and trained research assistants with clinical and nursing backgrounds collected information on age, sex, region of residence, reported CLS/ILS in the preceding 2 weeks, SARS-CoV-2 testing and history of household illness and death. Data were captured on tablets using the Open Data Kit database. We performed a descriptive analysis and presented the frequencies and proportions with graphical representations over time.

FINDINGS

Among 356 525 active phone numbers, 138 751 (38.9%) answered calls, of which 104 360 (75.2%) were eligible, 101 617 (97.4%) consented to participate, and 100 160 (98.6%) completed the interview. Most survey respondents were aged 25-54 years (72.7%) and male (65.1%). The regional distribution of the respondents mirrored the regional population distribution, with 45% (44%) in the southern region, 41% (43%) in the central region and 14% (13%) in the northern region. The reported SARS-CoV2 positivity rate was 11.5% (107/934). Of the 7298 patients who reported CLS/ILS, 934 (12.8%) reported having undergone COVID-19 testing. Of the reported household deaths, 47.2% (982 individuals) experienced CLS/ILS 2 weeks before their death.

CONCLUSION

Telephonic surveillance indicated that the number of SARS-CoV-2 cases was at least twice as high as the number of confirmed cases in Malawi. Our findings also suggest a substantial under-reporting of SARS-CoV-2-related deaths. Telephonic surveillance has proven feasible in Malawi, achieving the ability to characterise SARS-CoV-2 morbidity and mortality trends in low-resource settings.

Adjusting Incidence Estimates with Laboratory Test Performances: A Pragmatic Maximum Likelihood Estimation-Based Approach

Understanding the incidence of disease is often crucial for public policy decision-making, as observed during the COVID-19 pandemic. Estimating incidence is challenging, however, when the definition of incidence relies on tests that imperfectly measure disease, as in the case when assays with variable performance are used to detect the SARS-CoV-2 virus. To our knowledge, there are no pragmatic methods to address the bias introduced by the performance of labs in testing for the virus. In the setting of a longitudinal study, we developed a maximum likelihood estimation-based approach to estimate laboratory performance-adjusted incidence using the expectation-maximization algorithm. We constructed confidence intervals (CIs) using both bootstrapped-based and large-sample interval estimator approaches. We evaluated our methods through extensive simulation and applied them to a real-world study (TrackCOVID), where the primary goal was to determine the incidence of and risk factors for SARS-CoV-2 infection in the San Francisco Bay Area from July 2020 to March 2021. Our simulations demonstrated that our method converged rapidly with accurate estimates under a variety of scenarios. Bootstrapped-based CIs were comparable to the large-sample estimator CIs with a reasonable number of incident cases, shown via a simulation scenario based on the real TrackCOVID study. In more extreme simulated scenarios, the coverage of large-sample interval estimation outperformed the bootstrapped-based approach. Results from the application to the TrackCOVID study suggested that assuming perfect laboratory test performance can lead to an inaccurate inference of the incidence. Our flexible, pragmatic method can be extended to a variety of disease and study settings.

SARS-CoV-2 infection is associated with self-reported post-acute neuropsychological symptoms within six months of follow-up

BACKGROUND

Chronic neuropsychological sequelae following SARS-CoV-2 infection, including depression, anxiety, fatigue, and general cognitive difficulties, are a major public health concern. Given the potential impact of long-term neuropsychological impairment, it is important to characterize the frequency and predictors of this post-infection phenotype.

METHODS

The Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) study is a longitudinal study assessing the impact of SARS-CoV-2 infection in U.S. Military Healthcare System (MHS) beneficiaries, i.e. those eligible for care in the MHS including active duty servicemembers, dependents, and retirees. Four broad areas of neuropsychological symptoms were assessed cross-sectionally among subjects 1-6 months post-infection/enrollment, including: depression (Patient Health Questionnaire-9), anxiety (General Anxiety Disorder-7), fatigue (PROMIS® Fatigue 7a), and cognitive function (PROMIS® Cognitive Function 8a and PROMIS® Cognitive Function abilities 8a). Multivariable Poisson regression models compared participants with and without SARS-CoV-2 infection history on these measures, adjusting for sex, ethnicity, active-duty status, age, and months post-first positive or enrollment of questionnaire completion (MPFP/E); models for fatigue and cognitive function were also adjusted for depression and anxiety scores.

RESULTS

The study population included 2383 participants who completed all five instruments within six MPFP/E, of whom 687 (28.8%) had at least one positive SARS-CoV-2 test. Compared to those who had never tested positive for SARS-CoV-2, the positive group was more likely to meet instrument-based criteria for depression (15.4% vs 10.3%, p<0.001), fatigue (20.1% vs 8.0%, p<0.001), impaired cognitive function (15.7% vs 8.6%, p<0.001), and impaired cognitive function abilities (24.3% vs 16.3%, p<0.001). In multivariable models, SARS-CoV-2 positive participants, assessed at an average of 2.7 months after infection, had increased risk of moderate to severe depression (RR: 1.44, 95% CI 1.12-1.84), fatigue (RR: 2.07, 95% CI 1.62-2.65), impaired cognitive function (RR: 1.64, 95% CI 1.27-2.11), and impaired cognitive function abilities (RR: 1.41, 95% CI 1.15-1.71); MPFP/E was not significant.

CONCLUSIONS

Participants with a history of SARS-CoV-2 infection were up to twice as likely to report cognitive impairment and fatigue as the group without prior SARS-CoV-2 infection. These findings underscore the continued importance of preventing SARS-CoV-2 infection and while time since infection/enrollment was not significant through 6 months of follow-up, this highlights the need for additional research into the long-term impacts of COVID-19 to mitigate and reverse these neuropsychological outcomes.

Representative Public Health Surveys Pose Several Challenges: Lessons Learned Across 9 Communities During the COVID-19 Pandemic

Community surveillance surveys offer an opportunity to obtain important and timely public health information that may help local municipalities guide their response to public health threats. The objective of this paper is to present approaches, challenges, and solutions from SARS-CoV-2 surveillance surveys conducted in different settings by 2 research teams. For rapid assessment of a representative sample, a 2-stage cluster sampling design was developed by an interdisciplinary team of researchers at Oregon State University between April 2020 and June 2021 across 6 Oregon communities. In 2022, these methods were adapted for New York communities by a team of veterinary, medical, and public health practitioners. Partnerships were established with local medical facilities, health departments, COVID-19 testing sites, and health and public safety staff. Field staff were trained using online modules, field manuals describing survey methods and safety protocols, and in-person meetings with hands-on practice. Private and secure data integration systems and public awareness campaigns were implemented. Pilot surveys and field previews revealed challenges in survey processes that could be addressed before surveys proceeded. Strong leadership, robust trainings, and university-community partnerships proved critical to successful outcomes. Cultivating mutual trust and cooperation among stakeholders is essential to prepare for the next pandemic.

Evolution of SARS-CoV-2 in the Rhine-Neckar/Heidelberg Region 01/2021 - 07/2023

In January 2021, the monitoring of circulating variants of SARS-CoV-2 was initiated in Germany under the Corona Surveillance Act, which was discontinued after July 2023. This initiative aimed to enhance pandemic containment, as specific amino acid changes, particularly in the spike protein, were associated with increased transmission and reduced vaccine efficacy. Our group conducted whole genome sequencing using the ARTIC protocol (currently V4) on Illumina's NextSeq 500 platform (and, starting in May 2023, on the MiSeq DX platform) for SARS-CoV-2 positive specimen from patients at Heidelberg University Hospital, associated hospitals, and the public health office in the Rhine-Neckar/Heidelberg region. In total, we sequenced 26,795 SARS-CoV-2-positive samples between January 2021 and July 2023. Valid sequences, meeting the requirements for upload to the German electronic sequencing data hub (DESH) operated by the Robert Koch Institute (RKI), were determined for 24,852 samples, and the lineage/clade could be identified for 25,912 samples. The year 2021 witnessed significant dynamics in the circulating variants in the Rhine-Neckar/Heidelberg region, including A.27.RN, followed by the emergence of B.1.1.7 (Alpha), subsequently displaced by B.1.617.2 (Delta), and the initial occurrences of B.1.1.529 (Omicron). By January 2022, B.1.1.529 had superseded B.1.617.2, dominating with over 90%. The years 2022 and 2023 were then characterized by the dominance of B.1.1.529 and its sublineages, particularly BA.5 and BA.2, and more recently, the emergence of recombinant variants like XBB.1.5. Since the global dominance of B.1.617.2, the identified variant distribution in our local study, apart from a time delay in the spread of new variants, can be considered largely representative of the global distribution. om a time delay in the spread of new variants, can be considered largely representative of the global distribution.

Advancements in Airborne Viral Nucleic Acid Detection with Wearable Devices

Wearable health sensors for an expanding range of physiological parameters have experienced rapid development in recent years and are poised to disrupt the way healthcare is tracked and administered. The monitoring of environmental contaminants with wearable technologies is an additional layer of personal and public healthcare and is also receiving increased focus. Wearable sensors that detect exposure to airborne viruses could alert wearers of viral exposure and prompt proactive testing and minimization of viral spread, benefitting their own health and decreasing community risk. With the high levels of asymptomatic spread of COVID-19 observed during the pandemic, such devices could dramatically enhance our pandemic response capabilities in the future. To facilitate advancements in this area, this review summarizes recent research on airborne viral detection using wearable sensing devices as well as technologies suitable for wearables. Since the low concentration of viral particles in the air poses significant challenges to detection, methods for airborne viral particle collection and viral sensing are discussed in detail. A special focus is placed on nucleic acid-based viral sensing mechanisms due to their enhanced ability to discriminate between viral subtypes. Important considerations for integrating airborne viral collection and sensing on a single wearable device are also discussed.

Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure

The COVID-19 pandemic has had a profound impact on various aspects of our lives, affecting personal, occupational, economic, and social spheres. Much has been learned since the early 2020s, which will be very useful when the next pandemic emerges. In general, mobility and virus spread are strongly related. However, most studies analyze the impact of COVID-19 on mobility, but not much research has focused on analyzing the impact of mobility on virus transmission, especially from the point of view of monitoring virus incidence, which is extremely important for making sound decisions to control any epidemiological threat to public health. As a result of a thorough analysis of COVID-19 and mobility data, this work introduces a novel measure, the Infection Ratio (IR), which is not sensitive to underestimation of positive cases and is very effective in monitoring the pandemic's upward or downward evolution when it appears to be more stable, thus anticipating possible risk situations. For a bounded spatial context, we can infer that there is a significant threshold in the restriction of mobility that determines a change of trend in the number of infections that, if maintained for a minimum period, would notably increase the chances of keeping the spread of disease under control. Results show that IR is a reliable indicator of the intensity of infection, and an effective measure for early monitoring and decision making in smart cities.

Impact of public health policy and mobility change on transmission potential of severe acute respiratory syndrome coronavirus 2 in Rhode Island, March 2020 - November 2021

To study the SARS-CoV-2 transmission potential in Rhode Island (RI) and its association with policy changes and mobility changes, the time-varying reproduction number, Rt, was estimated. The daily incident case counts (16 March 2020, through 30 November 2021) were bootstrapped within a 15-day sliding window and multiplied by Poisson-distributed multipliers (λ = 4, sensitivity analysis: 11) to generate 1000 estimated infection counts, to which EpiEstim was applied to generate Rt time series. The median Rt percentage change when policies changed was estimated. The time lag correlations were assessed between the 7-day moving average of the relative changes in Google mobility data in the first 90 days, and Rt and estimated infection count, respectively. There were three major pandemic waves in RI in 2020-2021: spring 2020, winter 2020-2021 and fall-winter 2021. The median Rt fluctuated within the range of 0.5-2 from April 2020 to November 2021. Mask mandate (18 April 2020) was associated with a decrease in Rt (-25.99%, 95% CrI: -37.42%, -14.30%). Termination of mask mandates on 6 July 2021 was associated with an increase in Rt (36.74%, 95% CrI: 27.20%, 49.13%). Positive correlations were found between changes in grocery and pharmacy, Rt retail and recreation, transit, and workplace visits, for both Rt and estimated infection count, respectively. Negative correlations were found between changes in residential area visits for both Rt and estimated infection count, respectively. Public health policies enacted in RI were associated with changes in the pandemic trajectory. This ecological study provides further evidence of how non-pharmaceutical interventions and vaccination slowed COVID-19 transmission in RI.

SARS-CoV-2 Pandemic: A Comparison Between the Epidemiological Situation in Greece and Romania

Introduction Since the onset of the SARS-CoV-2 pandemic, there seems to be scarce data targeting the comparison of epidemiological data among different countries. In an attempt to reveal and characterize the epidemiological profile in the Balkan peninsula, a cross-sectional study has been conducted, aiming to retrospectively collect all the existing information regarding the SARS-CoV-2 pandemic over a period of three years, starting from March 2020 until March 2023. The comparative analysis of the epidemiological features and the main indicators between Romania and Greece can generate a good overview of the factors that can influence public health and create an adequate system of measures to limit the COVID-19 pandemic in the area. A retrospective comparative study aiming to detect and associate the main indicators determining the evolution of the SARS-CoV-2 pandemic data with the control measures adopted in Romania and Greece was performed. Methods Publicly available data were obtained from official sources such as the World Health Organization, the European Centre for Disease Control, the Romanian and Greek Ministries of Health, and the Romanian National Centre for Surveillance and Control of Communicable Diseases. The reported number of cases, in total and in conjunction with the age distribution, total number of deaths, and vaccination coverage, from the onset of the pandemic in March 2020 until March 2023, were collected. All officially reported cases of COVID-19 were included in this analysis. Reports with missing or incomplete values regarding the timeframe, age distribution, and vaccination status were excluded. Results During the timeframe of the study, from March 2020 until March 2023, Greece reported a higher number of confirmed SARS-CoV-2 cases as compared to Romania (5,910,103 cases and 3,352,356 cases, respectively). Still, in terms of the overall death toll, Romania recorded a higher mortality rate than Greece during the pandemic (67.773 deaths vs. 36.372 deaths). Concerning both cumulative incidence rates and the 14-day case notification rate per 100.000 inhabitants, it is evident that Romania exhibited greater numbers throughout the course of the pandemic. Although it is not clearly stated, the compulsory vaccination of elderly people that was set as a high priority in Greece may have contributed to the above results. In terms of the 14-day death notification rate per 100.000 inhabitants in 2020 and 2021, Romania showed a higher rate than Greece, while Greece reported a greater rate in 2022 and up until March 2023. Between 2020 and 2023, Greece presented both a higher number of vaccinated individuals and a higher vaccination coverage with two doses (7,034,695 individuals, 70% of the general population), as compared to Romania (6,467,804 individuals, 33.68% of the general population, p<0.0001). Conclusions Despite the similar restrictions and preventive actions adopted by Romania and Greece, some of the epidemiological data between the two countries tends to differ. It must not be ignored that every nation should be considered a unique entity with distinct features, including individuals, customs, and policies, rather than being categorized with other countries based on geographic proximity or regionalization.

Prevalence and determinants of post-acute sequelae of COVID-19 in Liberia

BACKGROUND

Evidence from resource-rich settings indicates that many people continue to have persistent symptoms following acute SARS-CoV-2 infection, called post-acute sequelae of COVID-19 (PASC). Only a few studies have described PASC in sub-Saharan Africa (SSA). We aimed to describe PASC in Liberia.

METHODS

We randomly sampled all people who were reported from the most populous county to the Liberian Ministry of Health (MOH) as having a laboratory-confirmed SARS-CoV-2 infection from June to August 2021. We interviewed individuals by phone 3 to 6 months later. Those with persistence of at least one symptom were considered to have PASC.

RESULTS

From among 2848 people reported to the MOH from Montserrado County during the period of interest, we randomly selected 650; of these, 548 (84.3%) were reached and 505 (92.2%) of those who were contacted were interviewed. The median age was 38 years (interquartile range (IQR), 30-49), and 43.6% were female. During acute infection, 40.2% were asymptomatic, 53.9% had mild/moderate disease and 6.9% had severe/critical disease. Among the 59.8% (n = 302) who were initially symptomatic, 50.2% (n = 152) reported at least one persistent symptom; the most common persistent symptoms were fatigue (21.2%), headache (16.2%) and cough (12.6%); 40.1% reported that PASC significantly affected their daily activities. Being hospitalized with moderate disease [adjusted prevalence ratio (aPR), 2.00 (95% CI, 1.59 to 2.80] or severe/critical disease [aPR, 2.11 (95% CI, 1.59 to 2.80)] was associated with PASC, compared with those not hospitalized. Females were more likely than males to report persistent fatigue [aPR, 1.67 (95% CI, 1.08 to 2.57)].

CONCLUSIONS

Our findings suggest that persistent symptoms may have affected a large proportion of people with initially symptomatic COVID-19 in west Africa and highlight the need to create awareness among infected people and health care professionals.

Analysis of SARS-CoV-2 antibody seroprevalence in Northern Ireland during 2020-2021

Background

With the spread of SARS-CoV-2 impacting upon public health directly and socioeconomically, further information was required to inform policy decisions designed to limit virus spread during the pandemic. This study sought to contribute to serosurveillance work within Northern Ireland to track SARS-CoV-2 progression and guide health strategy.

Methods

Sera/plasma samples from clinical biochemistry laboratories were analysed for anti-SARS-CoV-2 antibodies. Samples were assessed using an Elecsys anti-SARS-CoV-2 or anti-SARS-CoV-2 S ECLIA (Roche) on an automated cobas e 801 analyser. Samples were also assessed via an anti-SARS-CoV-2 ELISA (Euroimmun). A subset of samples assessed via the Elecsys anti-SARS-CoV-2 ECLIA were subsequently analysed in an ACE2 pseudoneutralisation assay using a V-PLEX SARS-CoV-2 Panel 7 for IgG and ACE2 (Meso Scale Diagnostics).

Results

Across three testing rounds (June-July 2020, November-December 2020 and June-July 2021 (rounds 1-3 respectively)), 4844 residual sera/plasma specimens were assayed for anti-SARS-CoV-2 antibodies. Seropositivity rates increased across the study, peaking at 11.6 % (95 % CI 10.4 %-13.0 %) during round 3. Varying trends in SARS-CoV-2 seropositivity were noted based on demographic factors. For instance, highest rates of seropositivity shifted from older to younger demographics across the study period. In round 3, Alpha (B.1.1.7) variant neutralising antibodies were most frequently detected across age groups, with median concentration of anti-spike protein antibodies elevated in 50-69 year olds and anti-S1 RBD antibodies elevated in 70+ year olds, relative to other age groups.

Conclusions

With seropositivity rates of <15 % across the assessment period, it can be concluded that the significant proportion of the Northern Ireland population had not yet naturally contracted the virus by mid-2021.

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.

A community based seroprevalence of SARS-CoV-2 antibodies in Somali Region, Eastern Ethiopia

BACKGROUND

Coronavirus disease 19 (COVID-19) is life-threatening infectious disease caused by SARS-CoV-2 virus that caused a global pandemic. SARS-CoV-2 has been widely transmitted throughout Ethiopia, with over 501,060 cases confirmed and 7574 deaths until November 2023. This study assessed for the first time the seroprevalence SARS-CoV-2 in the general population of the Somali Region during the COVID-19 pandemic.

METHODS

A cross-sectional study design was conducted from May to June 2021 in 14 districts of Somali Region. Blood samples were collected in 820 participants in addition to administering a questionnaire that included sociodemographic characteristics and history of clinical symptoms of COVID-19. Blood samples were tested for the presence or absence of anti-SARS-CoV-2 using a commercial Enzyme-Linked Immunosorbent Assay (ELISA) kit (Euroimmun).

RESULTS

Overall, 477 (58.2%) were male and 343 (41.8%) were female. The majority of the participants (N = 581; 70.9%) were between 18 and 34 years old and not vaccinated against COVID-19 (N = 793; 96.7%). The overall seroprevalence of SARS-CoV-2 antibodies was 41.7% (95% CI: 33.3%-47.6%). The highest prevalence was found in Goljano district (70%) and the lowest in Gunagado district (22.5%). Only age was found to be associated with COVID-19 seropositivity.

CONCLUSION

Prevalence of SARS-CoV-2 antibodies was the highest ever reported in Ethiopia, indicating that a large proportion of the population had been infected 14 months after the start of the outbreak in the country. Such studies are important to swiftly reassess and improve specific COVID-19 preventive and control measures to reduce transmissions within the community in a given setting.

Estimating seroconversion rates accounting for repeated infections by approximate Bayesian computation

This study presents a novel approach for inferring the incidence of infections by employing a quantitative model of the serum antibody response. Current methodologies often overlook the cumulative effect of an individual's infection history, making it challenging to obtain a marginal distribution for antibody concentrations. Our proposed approach leverages approximate Bayesian computation to simulate cross-sectional antibody responses and compare these to observed data, factoring in the impact of repeated infections. We then assess the empirical distribution functions of the simulated and observed antibody data utilizing Kolmogorov deviance, thereby incorporating a goodness-of-fit check. This new method not only matches the computational efficiency of preceding likelihood-based analyses but also facilitates the joint estimation of antibody noise parameters. The results affirm that the predictions generated by our within-host model closely align with the observed distributions from cross-sectional samples of a well-characterized population. Our findings mirror those of likelihood-based methodologies in scenarios of low infection pressure, such as the transmission of pertussis in Europe. However, our simulations reveal that in settings of higher infection pressure, likelihood-based approaches tend to underestimate the force of infection. Thus, our novel methodology presents significant advancements in estimating infection incidence, thereby enhancing our understanding of disease dynamics in the field of epidemiology.

Seroprevalence as an Indicator of Undercounting of COVID-19 Cases in a Large Well-Described Cohort

Introduction

Reported confirmed cases represent a small portion of overall true cases for many infectious diseases. The undercounting of true cases can be considerable when a significant portion of infected individuals are asymptomatic or minimally symptomatic, as is the case with COVID-19. Seroprevalence studies are an efficient way to assess the extent to which true cases are undercounted during a large-scale outbreak and can inform efforts to improve case identification and reporting.

Methods

A longitudinal seroprevalence study of active duty U.S. military members was conducted from May 2020 through June 2021. A random selection of service member serum samples submitted to the Department of Defense Serum Repository was analyzed for the presence of antibodies reactive to SARS-CoV-2. The monthly seroprevalence rates were compared with those of cumulative confirmed cases reported during the study period.

Results

Seroprevalence was 2.3% in May 2020 and increased to 74.0% by June 2021. The estimated true case count based on seroprevalence was 9.3 times greater than monthly reported cases at the beginning of the study period and fell to 1.7 by the end of the study.

Conclusions

In our sample, confirmed case counts significantly underestimated true cases of COVID-19. The increased availability of testing over the study period and enhanced efforts to detect asymptomatic and minimally symptomatic cases likely contributed to the fall in the seroprevalence to reported case ratio.

Immunity of Canadians and risk of epidemics workshop - Conference report

On November 18-19, 2019, the Immunity of Canadians and Risk of Epidemics (iCARE) Network convened a workshop in Toronto, Ontario, Canada. The objectives of the workshop were to raise the profile of sero-epidemiology in Canada, discuss best practice and methodological innovations, and strategize on the future direction of sero-epidemiology work in Canada. In this conference report, we describe the presentations and discussions from the workshop, and comment on the impact of the COVID-19 pandemic on serosurveillance initiatives, both in Canada and abroad.

Seroprevalence of SARS-CoV-2 in Niger State: Pilot Cross-Sectional Study

Background

The COVID-19 pandemic caused by SARS-CoV-2 is causing ongoing human and socioeconomic losses.

Objective

To know how far the virus has spread in Niger State, Nigeria, a pilot study was carried out to determine the SARS-CoV-2 seroprevalence, patterns, dynamics, and risk factors in the state.

Methods

A cross-sectional study design and clustered, stratified random sampling strategy were used to select 185 test participants across the state. SARS-CoV-2 IgG and IgM rapid test kits (colloidal gold immunochromatography lateral flow system) were used to determine the presence or absence of antibodies to the virus in the blood of sampled participants across Niger State from June 26 to 30, 2020. The test kits were validated using the blood samples of some of the Nigeria Center for Disease Control-confirmed positive and negative COVID-19 cases in the state. SARS-CoV-2 IgG and IgM test results were entered into the Epi Info questionnaire administered simultaneously with each test. Epi Info was then used to calculate the arithmetic mean and percentage, odds ratio, χ2 statistic, and regression at a 95% CI of the data generated.

Results

The seroprevalence of SARS-CoV-2 in Niger State was found to be 25.4% (47/185) and 2.2% (4/185) for the positive IgG and IgM results, respectively. Seroprevalence among age groups, genders, and occupations varied widely. The COVID-19 asymptomatic rate in the state was found to be 46.8% (22/47). The risk analyses showed that the chances of infection are almost the same for both urban and rural dwellers in the state. However, health care workers, those who experienced flulike symptoms, and those who had contact with a person who traveled out of Nigeria in the last 6 months (February to June 2020) were at double the risk of being infected with the virus. More than half (101/185, 54.6%) of the participants in this study did not practice social distancing at any time since the pandemic started. Participants' knowledge, attitudes, and practices regarding COVID-19 are also discussed.

Conclusions

The observed Niger State SARS-CoV-2 seroprevalence and infection patterns meansuggest that the virus has widely spread, far more SARS-CoV-2 infections have occurred than the reported cases, and there is a high asymptomatic COVID-19 rate across the state.

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.

Estimating SARS-CoV-2 seroprevalence

Governments and public health authorities use seroprevalence studies to guide responses to the COVID-19 pandemic. Seroprevalence surveys estimate the proportion of individuals who have detectable SARS-CoV-2 antibodies. However, serologic assays are prone to misclassification error, and non-probability sampling may induce selection bias. In this paper, non-parametric and parametric seroprevalence estimators are considered that address both challenges by leveraging validation data and assuming equal probabilities of sample inclusion within covariate-defined strata. Both estimators are shown to be consistent and asymptotically normal, and consistent variance estimators are derived. Simulation studies are presented comparing the estimators over a range of scenarios. The methods are used to estimate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in New York City, Belgium, and North Carolina.

Seroprevalence of SARS-CoV-2 anti-nucleocapsid total Ig, anti-RBD IgG antibodies, and infection in Thailand: a cross-sectional survey from October 2022 to January 2023

Seroprevalence studies on SARS-CoV-2 are essential for estimating actual prevalence rates of infection and vaccination in communities. This study evaluated infection rates based on total anti-nucleocapsid immunoglobulin (N) and/or infection history. We determined the seroprevalence of anti-receptor binding domain (RBD) antibodies across age groups. A cross-sectional study was conducted in Chonburi province, Thailand, between October 2022 and January 2023. Participants included newborns to adults aged up to 80 years. All serum samples were tested for anti-N total Ig and anti-RBD IgG. The interviewer-administered questionnaires queried information on infection history and vaccination records. Of 1459 participants enrolled from the Chonburi population, ~ 72.4% were infected. The number of infections was higher in children aged < 5 years, with evidence of SARS-CoV-2 infection decreasing significantly with increasing age. There were no significant differences based on sex or occupation. Overall, ~ 97.4% of participants had an immune response against SARS-CoV-2. The anti-RBD IgG seroprevalence rate was lower in younger vaccinated individuals and was slightly increased to 100% seropositivity at ages > 60 years. Our findings will help predict the exact number of infections and the seroprevalence of SARS-CoV-2 in the Thai population. Furthermore, this information is essential for public health decision-making and the development of vaccination strategies.

Estimation of SARS-CoV-2 Seroprevalence in Central North Carolina: Accounting for Outcome Misclassification in Complex Sample Designs

BACKGROUND

Population-based seroprevalence studies are crucial to understand community transmission of COVID-19 and guide responses to the pandemic. Seroprevalence is typically measured from diagnostic tests with imperfect sensitivity and specificity. Failing to account for measurement error can lead to biased estimates of seroprevalence. Methods to adjust seroprevalence estimates for the sensitivity and specificity of the diagnostic test have largely focused on estimation in the context of convenience sampling. Many existing methods are inappropriate when data are collected using a complex sample design.

METHODS

We present methods for seroprevalence point estimation and confidence interval construction that account for imperfect test performance for use with complex sample data. We apply these methods to data from the Chatham County COVID-19 Cohort (C4), a longitudinal seroprevalence study conducted in central North Carolina. Using simulations, we evaluate bias and confidence interval coverage for the proposed estimator compared with a standard estimator under a stratified, three-stage cluster sample design.

RESULTS

We obtained estimates of seroprevalence and corresponding confidence intervals for the C4 study. SARS-CoV-2 seroprevalence increased rapidly from 10.4% in January to 95.6% in July 2021 in Chatham County, North Carolina. In simulation, the proposed estimator demonstrates desirable confidence interval coverage and minimal bias under a wide range of scenarios.

CONCLUSION

We propose a straightforward method for producing valid estimates and confidence intervals when data are based on a complex sample design. The method can be applied to estimate the prevalence of other infections when estimates of test sensitivity and specificity are available.

A Saliva-Based Serological and Behavioral Analysis of SARS-CoV-2 Antibody Prevalence in Howard County, Maryland

The objective of the study was to estimate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in the Howard County, Maryland, general population and demographic subpopulations attributable to natural infection or coronavirus disease 2019 (COVID-19) vaccination and to identify self-reported social behaviors that may affect the likelihood of recent or past SARS-CoV-2 infection. A cross-sectional, saliva-based serological study of 2,880 residents of Howard County, Maryland, was carried out from July through September 2021. Natural SARS-CoV-2 infection prevalence was estimated by inferring infections among individuals according to anti-nucleocapsid immunoglobin G levels and calculating averages weighted by sample proportions of various demographics. Antibody levels between BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) recipients were compared. Antibody decay rate was calculated by fitting exponential decay curves to cross-sectional indirect immunoassay data. Regression analysis was carried out to identify demographic factors, social behaviors, and attitudes that may be linked to an increased likelihood of natural infection. The estimated overall prevalence of natural infection in Howard County, Maryland, was 11.9% (95% confidence interval, 9.2% to 15.1%), compared with 7% reported COVID-19 cases. Antibody prevalence indicating natural infection was highest among Hispanic and non-Hispanic Black participants and lowest among non-Hispanic White and non-Hispanic Asian participants. Participants from census tracts with lower average household income also had higher natural infection rates. After accounting for multiple comparisons and correlations between participants, none of the behavior or attitude factors had significant effects on natural infection. At the same time, recipients of the mRNA-1273 vaccine had higher antibody levels than those of BNT162b2 vaccine recipients. Older study participants had overall lower antibody levels compared with younger study participants. The true prevalence of SARS-CoV-2 infection is higher than the number of reported COVID-19 cases in Howard County, Maryland. A disproportionate impact of infection-induced SARS-CoV-2 positivity was observed across different ethnic/racial subpopulations and incomes, and differences in antibody levels across different demographics were identified. Taken together, this information may inform public health policy to protect vulnerable populations. IMPORTANCE We employed a highly innovative noninvasive multiplex oral fluid SARS-CoV-2 IgG assay to ascertain our seroprevalence estimates. This laboratory-developed test has been applied in NCI's SeroNet consortium, possesses high sensitivity and specificity according to FDA Emergency Use Authorization guidelines, correlates strongly with SARS-CoV-2 neutralizing antibody responses, and is Clinical Laboratory Improvement Amendments-approved by the Johns Hopkins Hospital Department of Pathology. It represents a broadly scalable public health tool to improve understanding of recent and past SARS-CoV-2 exposure and infection without drawing any blood. To our knowledge, this is the first application of a high-performance salivary SARS-CoV-2 IgG assay to estimate population-level seroprevalence, including identifying COVID-19 disparities. We also are the first to report differences in SARS-CoV-2 IgG responses by COVID-19 vaccine manufacturers (BNT162b2 [Pfizer-BioNTech] and mRNA-1273 [Moderna]). Our findings demonstrate remarkable consistency with those of blood-based SARS-CoV-2 IgG assays in terms of differences in the magnitude of SARS-CoV-2 IgG responses between COVID-19 vaccines.

Indirect Effects of the COVID-19 Pandemic on In-Hospital Outcomes among Internal Medicine Departments: A Double-Center Retrospective Study

The coronavirus disease 19 (COVID-19) emergency led to rearrangements of healthcare systems with a significant impact on those internal medicine departments that had not been converted to COVID-19 wards. A reduced number of departments, indeed, had to cope with the same number of patients along with a lack of management of patients' chronic diseases. We conducted a retrospective study aimed at examiningthe consequences of the COVID-19 pandemic on internal medicine departments that were not directly managing COVID-19 patients. Data from 619 patients were collected: 247 subjects hospitalized in 2019 (pre-COVID-19 era), 178 in 2020 (COVID-19 outbreak era) and 194 in 2021 (COVID-19 ongoing era). We found that in 2020 in-hospital mortality was significantly higher than in 2019 (17.4% vs. 5.3%, p = 0.009) as well as length of in-hospital stay (LOS) (12.7 ± 6.8 vs. 11 ± 6.2, p = 0.04). Finally, we performed a logistic regression analysis of the major determinants of mortality in the entire study population, which highlighted an association between mortality, being bedridden (β = 1.4, p = 0.004), respiratory failure (β = 1.5, p = 0.001), glomerular filtration rate (β = -0.16, p = 0.03) and hospitalization in the COVID-19 outbreak era (β = 1.6, p = 0.005). Our study highlights how the COVID-19 epidemic may have caused an increase in mortality and LOS even in patients not directly suffering from this infection.

Seroprevalence of SARS-CoV-2 immunoglobulin G in HIV-positive and HIV-negative individuals in KwaZulu-Natal, South Africa

Background

KwaZulu-Natal ranked second highest among South African provinces for the number of laboratory-confirmed cases during the second wave of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. The seroprevalence of SARS-CoV-2 among certain vulnerable groups, such as people living with HIV in KwaZulu-Natal, is unknown.

Objective

The study aimed to determine the prevalence of SARS-CoV-2 immunoglobulin G (IgG) in HIV-positive versus HIV-negative patients.

Methods

This was a retrospective analysis of residual clinical blood specimens unrelated to coronavirus disease 2019 (COVID-19) submitted for diagnostic testing at Inkosi Albert Luthuli Central Hospital, Durban, from 10 November 2020 to 09 February 2021. Specimens were tested for SARS-CoV-2 immunoglobulin G on the Abbott Architect analyser.

Results

A total of 1977/8829 (22.4%) specimens were positive for SARS-CoV-2 antibodies. Seroprevalence varied between health districts from 16.4% to 37.3%, and was 19% in HIV-positive and 35.3% in HIV-negative specimens. Seroprevalence was higher among female patients (23.6% vs 19.8%; p < 0.0001) and increased with increasing age, with a statistically significant difference between the farthest age groups (< 10 years and > 79 years; p < 0.0001). The seroprevalence increased from 17% on 10 November 2020 to 43% on 09 February 2021 during the second wave.

Conclusion

Our results highlight that during the second COVID-19 wave in KwaZulu-Natal a large proportion of people living with HIV were still immunologically susceptible. The reduced seropositivity in people with virological failure further emphasises the importance of targeted vaccination and vaccine response monitoring in these individuals.

What the study adds

This study contributes to data on SARS-CoV-2 seroprevalence before and during the second wave in KwaZulu-Natal, South Africa, which has the highest HIV prevalence globally. Reduced seropositivity was found among people living with HIV with virological failure, highlighting the importance of targeted booster vaccination and vaccine response monitoring.

Demographic, social, and behavioral correlates of SARS-CoV-2 seropositivity in a representative, population-based study of Minnesota residents

BACKGROUND

Monitoring COVID-19 infection risk in the general population is a public health priority. Few studies have measured seropositivity using representative, probability samples. The present study measured seropositivity in a representative population of Minnesota residents prior to vaccines and assess the characteristics, behaviors, and beliefs of the population at the outset of the pandemic and their association with subsequent infection.

METHODS

Participants in the Minnesota COVID-19 Antibody Study (MCAS) were recruited from residents of Minnesota who participated in the COVID-19 Household Impact Survey (CIS), a population-based survey that collected data on physical health, mental health, and economic security information between April 20 and June 8 of 2020. This was followed by collection of antibody test results between December 29, 2020 and February 26, 2021. Demographic, behavioral, and attitudinal exposures were assessed for association with the outcome of interest, SARS-CoV-2 seroprevalence, using univariate and multivariate logistic regression.

RESULTS

Of the 907 potential participants from the CIS, 585 respondents then consented to participate in the antibody testing (64.4% consent rate). Of these, results from 537 test kits were included in the final analytic sample, and 51 participants (9.5%) were seropositive. The overall weighted seroprevalence was calculated to be 11.81% (95% CI, 7.30%-16.32%) at of the time of test collection. In adjusted multivariate logistic regression models, significant associations between seroprevalence and the following were observed; being from 23-64 and 65+ age groups were both associated with higher odds of COVID-19 seropositivity compared to the 18-22 age group (17.8 [1.2-260.1] and 24.7 [1.5-404.4] respectively). When compared to a less than $30k annual income reference group, all higher income groups had significantly lower odds of seropositivity. Reporting practicing a number of 10 (median reported value in sample) or more of 19 potential COVID-19 mitigation factors (e.g. handwashing and mask wearing) was associated with lower odds of seropositivity (0.4 [0.1-0.99]) Finally, the presence of at least one household member in the age range of 6 to 17 years old was associated with higher odds of seropositivity (8.3 [1.2-57.0]).

CONCLUSIONS

The adjusted odds ratio of SARS-CoV-2 seroprevalence was significantly positively associated with increasing age and having household member(s) in the 6-17 year age group, while increasing income levels and a mitigation score at or above the median were shown to be significantly protective factors.

The Longitudinal Analysis of Convergent Antibody VDJ Regions in SARS-CoV-2-Positive Patients Using RNA-Seq

Severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) is an ongoing pandemic that continues to evolve and reinfect individuals. To understand the convergent antibody responses that evolved over the course of the pandemic, we evaluated the immunoglobulin repertoire of individuals infected by different SARS-CoV-2 variants for similarity between patients. We utilized four public RNA-seq data sets collected between March 2020 and March 2022 from the Gene Expression Omnibus (GEO) in our longitudinal analysis. This covered individuals infected with Alpha and Omicron variants. In total, from 269 SARS-CoV-2-positive patients and 26 negative patients, 629,133 immunoglobulin heavy-chain variable region V(D)J sequences were reconstructed from sequencing data. We grouped samples based on the SARS-CoV-2 variant type and/or the time they were collected from patients. Our comparison of patients within each SARS-CoV-2-positive group found 1011 common V(D)Js (same V gene, J gene and CDR3 amino acid sequence) shared by more than one patient and no common V(D)Js in the noninfected group. Taking convergence into account, we clustered based on similar CDR3 sequence and identified 129 convergent clusters from the SARS-CoV-2-positive groups. Within the top 15 clusters, 4 contain known anti-SARS-CoV-2 immunoglobulin sequences with 1 cluster confirmed to cross-neutralize variants from Alpha to Omicron. In our analysis of longitudinal groups that include Alpha and Omicron variants, we find that 2.7% of the common CDR3s found within groups were also present in more than one group. Our analysis reveals common and convergent antibodies, which include anti-SARS-CoV-2 antibodies, in patient groups over various stages of the pandemic.

Risk factors for SARS-CoV-2 seropositivity in a health care worker population during the early pandemic

BACKGROUND

While others have reported severe acute respiratory syndrome-related coronavirus 2(SARS-CoV-2) seroprevalence studies in health care workers (HCWs), we leverage the use of a highly sensitive coronavirus antigen microarray to identify a group of seropositive health care workers who were missed by daily symptom screening that was instituted prior to any epidemiologically significant local outbreak. Given that most health care facilities rely on daily symptom screening as the primary method to identify SARS-CoV-2 among health care workers, here, we aim to determine how demographic, occupational, and clinical variables influence SARS-CoV-2 seropositivity among health care workers.

METHODS

We designed a cross-sectional survey of HCWs for SARS-CoV-2 seropositivity conducted from May 15th to June 30th 2020 at a 418-bed academic hospital in Orange County, California. From an eligible population of 5,349 HCWs, study participants were recruited in two ways: an open cohort, and a targeted cohort. The open cohort was open to anyone, whereas the targeted cohort that recruited HCWs previously screened for COVID-19 or work in high-risk units. A total of 1,557 HCWs completed the survey and provided specimens, including 1,044 in the open cohort and 513 in the targeted cohort. Demographic, occupational, and clinical variables were surveyed electronically. SARS-CoV-2 seropositivity was assessed using a coronavirus antigen microarray (CoVAM), which measures antibodies against eleven viral antigens to identify prior infection with 98% specificity and 93% sensitivity.

RESULTS

Among tested HCWs (n = 1,557), SARS-CoV-2 seropositivity was 10.8%, and risk factors included male gender (OR 1.48, 95% CI 1.05-2.06), exposure to COVID-19 outside of work (2.29, 1.14-4.29), working in food or environmental services (4.85, 1.51-14.85), and working in COVID-19 units (ICU: 2.28, 1.29-3.96; ward: 1.59, 1.01-2.48). Amongst 1,103 HCWs not previously screened, seropositivity was 8.0%, and additional risk factors included younger age (1.57, 1.00-2.45) and working in administration (2.69, 1.10-7.10).

CONCLUSION

SARS-CoV-2 seropositivity is significantly higher than reported case counts even among HCWs who are meticulously screened. Seropositive HCWs missed by screening were more likely to be younger, work outside direct patient care, or have exposure outside of work.

Modeling SARS-CoV-2 antibody seroprevalence and its determinants in Ghana: A nationally representative cross-sectional survey

Estimates of SARS-CoV-2 transmission rates have significant public health policy implications since they shed light on the severity of illness in various groups and aid in strategic deployment of diagnostics, treatment and vaccination. Population-based investigations have not been conducted in Ghana to identify the seroprevalence of SARS-CoV-2. We conducted an age stratified nationally representative household study to determine the seroprevalence of SARS-CoV-2 and identify risk factors between February and December 2021. Study participants, 5 years and older regardless of prior or current infection COVID-19 infection from across Ghana were included in the study. Data on sociodemographic characteristics, contact with an individual with COVID-19-related symptoms, history of COVID-19-related illness, and adherence to infection prevention measures were collected. Serum obtained was tested for total antibodies with the WANTAI ELISA kit. The presence of antibodies against SAR-COV-2 was detected in 3,476 of 5,348 participants, indicating a seroprevalence of 67.10% (95% CI: 63.71-66.26). Males had lower seroprevalence (65.8% [95% CI: 63.5-68.04]) than females (68.4% [95% CI: 66.10-69.92]). Seroprevalence was lowest in >20 years (64.8% [95% CI: 62.36-67.19]) and highest among young adults; 20-39 years (71.1% [95% CI 68.83,73.39]). Seropositivity was associated with education, employment status and geographic location. Vaccination status in the study population was 10%. Exposure is more likely in urban than rural areas thus infection prevention protocols must be encouraged and maintained. Also, promoting vaccination in target groups and in rural areas is necessary to curb transmission of the virus.

Dynamic parameterization of a modified SEIRD model to analyze and forecast the dynamics of COVID-19 outbreaks in the United States

The rapid spread of the numerous outbreaks of the coronavirus disease 2019 (COVID-19) pandemic has fueled interest in mathematical models designed to understand and predict infectious disease spread, with the ultimate goal of contributing to the decision making of public health authorities. Here, we propose a computational pipeline that dynamically parameterizes a modified SEIRD (susceptible-exposed-infected-recovered-deceased) model using standard daily series of COVID-19 cases and deaths, along with isolated estimates of population-level seroprevalence. We test our pipeline in five heavily impacted states of the US (New York, California, Florida, Illinois, and Texas) between March and August 2020, considering two scenarios with different calibration time horizons to assess the update in model performance as new epidemiologic data become available. Our results show a median normalized root mean squared error (NRMSE) of 2.38% and 4.28% in calibrating cumulative cases and deaths in the first scenario, and 2.41% and 2.30% when new data are assimilated in the second scenario, respectively. Then, 2-week (4-week) forecasts of the calibrated model resulted in median NRMSE of cumulative cases and deaths of 5.85% and 4.68% (8.60% and 17.94%) in the first scenario, and 1.86% and 1.93% (2.21% and 1.45%) in the second. Additionally, we show that our method provides significantly more accurate predictions of cases and deaths than a constant parameterization in the second scenario (p < 0.05). Thus, we posit that our methodology is a promising approach to analyze the dynamics of infectious disease outbreaks, and that our forecasts could contribute to designing effective pandemic-arresting public health policies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00366-023-01816-9.

Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S

Reconstructing the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.S. CDC. They employed three assays, with different sensitivities and specificities, potentially introducing biases in seroprevalence estimates. Using models, we show that accounting for assays explains some of the observed state-to-state variation in seroprevalence, and when integrating case and death surveillance data, we show that when using the Abbott assay, estimates of proportions infected can differ substantially from seroprevalence estimates. We also found that states with higher proportions infected (before or after vaccination) had lower vaccination coverages, a pattern corroborated using a separate dataset. Finally, to understand vaccination rates relative to the increase in cases, we estimated the proportions of the population that received a vaccine prior to infection.

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.

A Pilot Study on Blood Components in COVID-19 Affected Subjects: A Correlation to UPR Signalling and ER-Stress

Abstract

The endoplasmic reticulum (ER) is the site for protein synthesis, its folding and secretion. An intricate set of signalling pathways, called UPR pathways, have been evolved by ER in mammalian cells, to allow the cell to respond the presence of misfolded proteins within the ER. Breaching of these signalling systems by disease oriented accumulation of unfolded proteins may develop cellular stress. The aim of this study is to explore whether COVID-19 infection is responsible for developing this kind of endoplasmic reticulum related stress (ER-stress). ER-stress was evaluated by checking the expression of ER-stress markers e.g. PERK (adapting) and TRAF2 (alarming). ER-stress was correlated to several blood parameters viz. IgG, pro- and anti-inflammatory cytokines, leukocytes, lymphocytes, RBC, haemoglobin and PaO₂/FiO₂ ratio (ratio of arterial oxygen partial pressure to fractional inspired oxygen) in COVID-19 affected subjects. COVID-19 infection was found to be a state of protein homeostasis (proteostasis) collapse. Changes in IgG levels showed very poor immune response by the infected subjects. At the initial phase of the disease, pro-inflammatory cytokine levels were high and anti-inflammatory cytokines levels were low; though they were partly compromised at later phase of the disease. Total leukocyte concentration increased over the period of time; while percentage of lymphocytes were dropped. No significant changes were observed in cases of RBC counts and haemoglobin (Hb) levels. Both RBC and Hb were maintained at their normal range. In mildly stressed group, PaO₂/FiO₂ ratio (oxygenation status) was in the higher side of normal range; whereas in other two groups the ratio was in respiratory distress syndrome mode. Virus could induce mild to severe ER-stress, which could be the cause of cellular death and systemic dysfunction introducing fatal consequences.

Graphical Abstract

Schematic representation of SARS-CoV-2 infection and related consequences.

Geographically skewed recruitment and COVID-19 seroprevalence estimates: a cross-sectional serosurveillance study and mathematical modelling analysis

OBJECTIVES

Convenience sampling is an imperfect but important tool for seroprevalence studies. For COVID-19, local geographic variation in cases or vaccination can confound studies that rely on the geographically skewed recruitment inherent to convenience sampling. The objectives of this study were: (1) quantifying how geographically skewed recruitment influences SARS-CoV-2 seroprevalence estimates obtained via convenience sampling and (2) developing new methods that employ Global Positioning System (GPS)-derived foot traffic data to measure and minimise bias and uncertainty due to geographically skewed recruitment.

DESIGN

We used data from a local convenience-sampled seroprevalence study to map the geographic distribution of study participants' reported home locations and compared this to the geographic distribution of reported COVID-19 cases across the study catchment area. Using a numerical simulation, we quantified bias and uncertainty in SARS-CoV-2 seroprevalence estimates obtained using different geographically skewed recruitment scenarios. We employed GPS-derived foot traffic data to estimate the geographic distribution of participants for different recruitment locations and used this data to identify recruitment locations that minimise bias and uncertainty in resulting seroprevalence estimates.

RESULTS

The geographic distribution of participants in convenience-sampled seroprevalence surveys can be strongly skewed towards individuals living near the study recruitment location. Uncertainty in seroprevalence estimates increased when neighbourhoods with higher disease burden or larger populations were undersampled. Failure to account for undersampling or oversampling across neighbourhoods also resulted in biased seroprevalence estimates. GPS-derived foot traffic data correlated with the geographic distribution of serosurveillance study participants.

CONCLUSIONS

Local geographic variation in seropositivity is an important concern in SARS-CoV-2 serosurveillance studies that rely on geographically skewed recruitment strategies. Using GPS-derived foot traffic data to select recruitment sites and recording participants' home locations can improve study design and interpretation.

A Randomized Clinical Trial Testing Hydroxychloroquine for Reduction of SARS-CoV-2 Viral Shedding and Hospitalization in Early Outpatient COVID-19 Infection

Early in the COVID-19 pandemic, no effective treatment existed to prevent clinical worsening of COVID-19 among recently diagnosed outpatients. At the University of Utah, Salt Lake City, Utah, we conducted a phase 2 prospective parallel group randomized placebo-controlled trial (NCT04342169) to determine whether hydroxychloroquine given early in disease reduces the duration of SARS-CoV-2 shedding. We enrolled nonhospitalized adults (≥18 years of age) with a recent positive diagnostic test for SARS-CoV-2 (within 72 h of enrollment) and adult household contacts. Participants received either 400 mg hydroxychloroquine by mouth twice daily on day 1 followed by 200 mg by mouth twice daily on days 2 to 5 or oral placebo with the same schedule. We performed SARS-CoV-2 nucleic acid amplification testing (NAAT) on oropharyngeal swabs on days 1 to 14 and 28 and monitored clinical symptomatology, rates of hospitalization, and viral acquisition by adult household contacts. We identified no overall differences in the duration of oropharyngeal carriage of SARS-CoV-2 (hazard ratio of viral shedding time comparing hydroxychloroquine to placebo, 1.21; 95% confidence interval [CI], 0.91, 1.62). Overall, 28-day hospitalization incidence was similar between treatments (4.6% hydroxychloroquine versus 2.7% placebo). No differences were seen in symptom duration, severity, or viral acquisition in household contacts between treatment groups. The study did not reach the prespecified enrollment target, which was likely influenced by a steep decline in COVID-19 incidence corresponding to the initial vaccine rollout in the spring of 2021. Oropharyngeal swabs were self-collected, which may introduce variability in these results. Placebo treatments were not identical to hydroxychloroquine treatments (capsules versus tablets) which may have led to inadvertent participant unblinding. In this group of community adults early in the COVID-19 pandemic, hydroxychloroquine did not significantly alter the natural history of early COVID-19 disease. (This study has been registered at ClinicalTrials.gov under registration no. NCT04342169). IMPORTANCE Early in the COVID-19 pandemic, no effective treatment existed to prevent clinical worsening of COVID-19 among recently diagnosed outpatients. Hydroxychloroquine received attention as a possible early treatment; however, quality prospective studies were lacking. We conducted a clinical trial to test the ability of hydroxychloroquine to prevent clinical worsening of COVID-19.

A multimodal strategy to improve race/ethnic group equity in administration of neutralizing monoclonal antibody treatment for COVID-19 outpatients

Introduction

Racial and ethnic minority groups have higher rates of SARS-CoV-2 infection, severe illness, and death; however, they receive monoclonal antibody (mAb) treatment at lower rates than non-Hispanic White patients. We report data from a systematic approach to improve equitable provision of COVID-19 neutralizing monoclonal antibody treatment.

Methods

Treatment was administered at a community health urgent care clinic affiliated with a safety-net urban hospital. The approach included a stable treatment supply, a same-day test and treat model, a referral process, patient outreach, and financial support. We analyzed the race/ethnicity data descriptively and compared proportions using a chi-square test.

Results

Over 17 months, 2524 patients received treatment. Compared to the demographics of county COVID-19-positive cases, a greater proportion of patients who received mAb treatment were Hispanic (44.7% treatment vs. 36.5% positive cases, p < 0.001), a lower proportion were White Non-Hispanic (40.7% treatment vs. 46.3% positive cases, p < 0.001), equal proportion were Black (8.2% treatment vs. 7.4% positive cases, P = 0.13), and equal proportion occurred for other race patients.

Discussion

Implementation of multiple systematic strategies to administer COVID-19 monoclonal antibodies resulted in an equitable race/ethnic distribution of treatment.

Development of a Method for Detection of SARS-CoV-2 Nucleocapsid Antibodies on Dried Blood Spot by DELFIA Immunoassay

Antibodies against the SARS-CoV-2 nucleocapsid protein are produced by the immune system in response to SARS-CoV-2 infection, but most available vaccines developed to fight the pandemic spread target the SARS-CoV-2 spike protein. The aim of this study was to improve the detection of antibodies against the SARS-CoV-2 nucleocapsid by providing a simple and robust method applicable to a large population. For this purpose, we developed a DELFIA immunoassay on dried blood spots (DBSs) by converting a commercially available IVD ELISA assay. A total of forty-seven paired plasma and dried blood spots were collected from vaccinated and/or previously SARS-CoV-2-infected subjects. The DBS-DELFIA resulted in a wider dynamic range and higher sensitivity for detecting antibodies against the SARS-CoV-2 nucleocapsid. Moreover, the DBS-DELFIA showed a good total intra-assay coefficient of variability of 14.6%. Finally, a strong correlation was found between SARS-CoV-2 nucleocapsid antibodies detected by the DBS-DELFIA and ELISA immunoassays (r = 0.9). Therefore, the association of dried blood sampling with DELFIA technology may provide an easier, minimally invasive, and accurate measurement of SARS-CoV-2 nucleocapsid antibodies in previously SARS-CoV-2-infected subjects. In conclusion, these results justify further research to develop a certified IVD DBS-DELFIA assay for detecting SARS-CoV-2 nucleocapsid antibodies useful for diagnostics as well as for serosurveillance studies.

Real-world utilization of SARS-CoV-2 serological testing in RNA positive patients across the United States

BACKGROUND

As diagnostic tests for COVID-19 were broadly deployed under Emergency Use Authorization, there emerged a need to understand the real-world utilization and performance of serological testing across the United States.

METHODS

Six health systems contributed electronic health records and/or claims data, jointly developed a master protocol, and used it to execute the analysis in parallel. We used descriptive statistics to examine demographic, clinical, and geographic characteristics of serology testing among patients with RNA positive for SARS-CoV-2.

RESULTS

Across datasets, we observed 930,669 individuals with positive RNA for SARS-CoV-2. Of these, 35,806 (4%) were serotested within 90 days; 15% of which occurred <14 days from the RNA positive test. The proportion of people with a history of cardiovascular disease, obesity, chronic lung, or kidney disease; or presenting with shortness of breath or pneumonia appeared higher among those serotested compared to those who were not. Even in a population of people with active infection, race/ethnicity data were largely missing (>30%) in some datasets-limiting our ability to examine differences in serological testing by race. In datasets where race/ethnicity information was available, we observed a greater distribution of White individuals among those serotested; however, the time between RNA and serology tests appeared shorter in Black compared to White individuals. Test manufacturer data was available in half of the datasets contributing to the analysis.

CONCLUSION

Our results inform the underlying context of serotesting during the first year of the COVID-19 pandemic and differences observed between claims and EHR data sources-a critical first step to understanding the real-world accuracy of serological tests. Incomplete reporting of race/ethnicity data and a limited ability to link test manufacturer data, lab results, and clinical data challenge the ability to assess the real-world performance of SARS-CoV-2 tests in different contexts and the overall U.S. response to current and future disease pandemics.

Severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) serology in the vaccination era and post booster vaccination

Background

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic has caused over 6 million deaths world-wide. In the pre-vaccination era, we noted a 5·3% SARS-CoV-2 IgG antibody positivity rate in 81,624 subjects.

Methods

Utilizing assays for serum SARS-CoV-2 spike (S) protein antibody (Roche) and neutralizing antibody (Diazyme), both >90% IgG, we measured antibodies in 13,189 subjects in the post-vaccination era, and in 69 subjects before and 60 days after booster vaccination.

Results

In 2021, in 10,267 subjects, 25·0% had negative S protein levels (<0.80 U/L), 24·4% had low positive levels (0.80-250 U/L), and 50·7% had high positive levels (>250 U/L). Median neutralizing antibody levels were 1·16 and 2·06 AU/mL in the low and high positive groups, respectively. In 2022, we evaluated 2,016 subjects where samples were diluted 1:100 if S protein antibody levels were >250 U/L. Median S protein and neutralizing antibody levels were 2,065 U/L (86.3% positivity) and 2·68 AU/mL (68.0% positivity), respectively. Antibody levels were also measured in 69 subjects before and 60 days after receiving SARS-CoV-2 booster vaccinations. Treatment resulted in a 15-fold increase in S protein antibody levels from 1,010 to 17,236 U/L, and a 6-fold increase in neutralizing antibody from 1·51 to 12·51 AU/mL in neutralizing antibody levels, respectively (both P<0.00001), with a wide variability in response.

Conclusions

Our data indicate that by early 2022 86% of subjects had positive SARS-CoV-2 S protein antibody levels, and that these levels and neutralizing antibody levels were increased 15-fold and 6-fold, respectively, 60 days after SARS-Cov-2 booster vaccination.

SARS-CoV-2 antibodies persist up to 12 months after natural infection in healthy employees working in non-medical contact-intensive professions

OBJECTIVES

This study aimed to evaluate dynamics of antibody levels after exposure to SARS-CoV-2 for 12 months in Dutch non-vaccinated hairdressers and hospitality staff.

METHODS

In this prospective cohort study, blood samples were collected every 3 months for 1 year and analyzed using a qualitative total antibody enzyme-linked immunosorbent assay (ELISA) and a quantitative immunoglobulin (Ig)G antibody ELISA. Participants completed questionnaires, providing information on demographics, health, and work. Differences in antibody levels were evaluated using Mann-Whitney U and Wilcoxon signed-rank tests. Beta coefficients (β) and 95% confidence intervals (CIs) were calculated using linear regression.

RESULTS

Ninety-five of 497 participants (19.1%) had ≥1 seropositive measurement before their last visit using the qualitative ELISA. Only 2.1% (2/95) seroreverted during follow-up. Of 95 participants, 82 (86.3%) tested IgG seropositive in the quantitative ELISA too. IgG antibody levels significantly decreased in the first months (P <0.01) but remained detectable for up to 12 months in all participants. Older age (β, 10-years increment: 24.6, 95% CI: 5.7-43.5) and higher body mass index (β, 5kg/m² increment: 40.0, 95% CI: 2.9-77.2) were significantly associated with a higher peak of antibody levels.

CONCLUSION

In this cohort, SARS-CoV-2 antibodies persisted for up to 1 year after initial seropositivity, suggesting long-term natural immunity.

Social Distancing, Vaccination and Evolution of COVID-19 Transmission Rates in Europe

This paper provides estimates of COVID-19 transmission rates and explains their evolution for selected European countries since the start of the pandemic taking account of changes in voluntary and government mandated social distancing, incentives to comply, vaccination and the emergence of new variants. Evidence based on panel data modeling indicates that the diversity of outcomes that we document may have resulted from the nonlinear interaction of mandated and voluntary social distancing and the economic incentives that governments provided to support isolation. The importance of these factors declined over time, with vaccine uptake driving heterogeneity in country experiences in 2021. Our approach also allows us to identify the basic reproduction number, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{R}}_{0}$$\end{document}R0, which is precisely estimated around 5, which is much larger than the values in the range of 2.4–3.9 assumed in the extant literature.

Modeling local coronavirus outbreaks

This article presents an overview of methods developed for the modeling and control of local coronavirus outbreaks. The article reviews early transmission dynamics featuring exponential growth in infections, and links this to a renewal epidemic model where the current incidence of infection depends upon the expected value of incidence randomly lagged into the past. This leads directly to simple formulas for the fraction of the population infected in an unmitigated outbreak, and reveals herd immunity as the solution to an optimization problem. The model also leads to direct and easy-to-understand formulas for aligning observable epidemic indicators such as cases, hospitalizations and deaths with the unobservable incidence of infection, and as a byproduct leads to a simple first-order approach for estimating the effective reproduction number R t . The model also leads naturally to direct assessments of the effectiveness of isolation in preventing the spread of infection. This is illustrated with application to repeat asymptomatic screening programs of the sort utilized by universities, sports teams and businesses to prevent the spread of infection.

Seroprevalence of anti-SARS-CoV-2 antibodies among blood donors from December 2020 to June 2021 in Koutiala district, Mali

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus associated with coronavirus disease (COVID-19). At the time of the study, little data on the level of exposure of the population in Koutiala district in Mali to SARS-CoV-2 was available. Although blood donors are not representative of the general population, a COVID-19 seroprevalence estimate in this population was intended to assess the extent of community transmission, serve as a health alert system, and help guide the public health response. We measured seroprevalence of anti-SARS-CoV-2 antibodies using NG-Biotech SARS-Cov-2 RDT and ECLIA test between January and June 2020. This is a cross-sectional study of volunteer blood donors aged 18 to 60 years, independent of any previous COVID-19 disease. A stratified analysis of seroprevalence by month of sample collection and a comparison of the results of the NG-Biotech SARS-Cov-2 RDT with those of the ECLIA test was performed. The overall prevalence of antibodies to SARS-Cov-2 virus assessed by the NG-Biotech SARS-Cov-2 RDT was 24.6% (95% CI 21.8-27.4) and by the ECLIA test was 70.2 (95% CI 64.9-75.5). Both estimates remained relatively stable over the study period. We observed SARS-CoV-2 exposure much higher than indicated by case-based surveillance. The national surveillance system, as it was, was not able to detect variations in incidence, and as such, we do not recommend it as an alert system. However, the discrepancy between the results of the rapid test and the ECLIA test shows that further research is required to assess the validity of these test before a more solid conclusion can be drawn it their use in surveillance.

Application of neighborhood-scale wastewater-based epidemiology in low COVID-19 incidence situations

Wastewater-based epidemiology (WBE), an emerging approach for community-wide COVID-19 surveillance, was primarily characterized at large sewersheds such as wastewater treatment plants serving a large population. Although informed public health measures can be better implemented for a small population, WBE for neighborhood-scale sewersheds is less studied and not fully understood. This study applied WBE to seven neighborhood-scale sewersheds (average population of 1471) from January to November 2021. Community testing data showed an average of 0.004 % incidence rate in these sewersheds (97 % of monitoring periods reported two or fewer daily infections). In 92 % of sewage samples, SARS-CoV-2 N gene fragments were below the limit of quantification. We statistically determined 10^-2.6 as the threshold of the SARS-CoV-2 N gene concentration normalized to pepper mild mottle virus (N/PMMOV) to alert high COVID-19 incidence rate in the studied sewershed. This threshold of N/PMMOV identified neighborhood-scale outbreaks (COVID-19 incidence rate higher than 0.2 %) with 82 % sensitivity and 51 % specificity. Importantly, neighborhood-scale WBE can discern local outbreaks that would not otherwise be identified by city-scale WBE. Our findings suggest that neighborhood-scale WBE is an effective community-wide disease surveillance tool when COVID-19 incidence is maintained at a low level.

A Bayesian susceptible-infectious-hospitalized-ventilated-recovered model to predict demand for COVID-19 inpatient care in a large healthcare system

The COVID-19 pandemic has strained healthcare systems in many parts of the United States. During the early months of the pandemic, there was substantial uncertainty about whether the large number of COVID-19 patients requiring hospitalization would exceed healthcare system capacity. This uncertainty created an urgent need to accurately predict the number of COVID-19 patients that would require inpatient and ventilator care at the local level. As the pandemic progressed, many healthcare systems relied on such predictions to prepare for COVID-19 surges and to make decisions regarding staffing, the discontinuation of elective procedures, and the amount of personal protective equipment (PPE) to purchase. In this work, we develop a Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered (SIHVR) model to predict the burden of COVID-19 at the healthcare system level. The Bayesian SIHVR model provides daily estimates of the number of new COVID-19 patients admitted to inpatient care, the total number of non-ventilated COVID-19 inpatients, and the total number of ventilated COVID-19 patients at the healthcare system level. The model also incorporates county-level data on the number of reported COVID-19 cases, and county-level social distancing metrics, making it locally customizable. The uncertainty in model predictions is quantified with 95% credible intervals. The Bayesian SIHVR model is validated with an extensive simulation study, and then applied to data from two regional healthcare systems in South Carolina. This model can be adapted for other healthcare systems to estimate local resource needs.

Population-Level Relative Effectiveness of the COVID-19 Vaccines and the Contribution of Naturally Acquired Immunity

BACKGROUND

Immune protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be induced by natural infection or vaccination or both. Interaction between vaccine-induced immunity and naturally acquired immunity at the population level has been understudied.

METHODS

We used regression models to evaluate whether the impact of coronavirus disease 2019 (COVID-19) vaccines differed across states with different levels of naturally acquired immunity from March 2021 to April 2022 in the United States. Analysis was conducted for 3 evaluation periods separately (Alpha, Delta, and Omicron waves). As a proxy for the proportion of the population with naturally acquired immunity, we used either the reported seroprevalence or the estimated proportion of the population ever infected in each state.

RESULTS

COVID-19 mortality decreased as coverage of ≥1 dose increased among people ≥65 years of age, and this effect did not vary by seroprevalence or proportion of the total population ever infected. Seroprevalence and proportion ever infected were not associated with COVID-19 mortality, after controlling for vaccine coverage. These findings were consistent in all evaluation periods.

CONCLUSIONS

COVID-19 vaccination was associated with a sustained reduction in mortality at state level during the Alpha, Delta, and Omicron periods. The effect did not vary by naturally acquired immunity.

Estimated SARS-CoV-2 antibody seroprevalence trends and relationship to reported case prevalence from a repeated, cross-sectional study in the 50 states and the District of Columbia, United States-October 25, 2020-February 26, 2022

Background

Sero-surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can reveal trends and differences in subgroups and capture undetected or unreported infections that are not included in case-based surveillance systems.

Methods

Cross-sectional, convenience samples of remnant sera from clinical laboratories from 51 U.S. jurisdictions were assayed for infection-induced SARS-CoV-2 antibodies biweekly from October 25, 2020, to July 11, 2021, and monthly from September 6, 2021, to February 26, 2022. Test results were analyzed for trends in infection-induced, nucleocapsid-protein seroprevalence using mixed effects models that adjusted for demographic variables and assay type.

Findings

Analyses of 1,469,792 serum specimens revealed U.S. infection-induced SARS-CoV-2 seroprevalence increased from 8.0% (95% confidence interval (CI): 7.9%-8.1%) in November 2020 to 58.2% (CI: 57.4%-58.9%) in February 2022. The U.S. ratio of the change in estimated seroprevalence to the change in reported case prevalence was 2.8 (CI: 2.8-2.9) during winter 2020-2021, 2.3 (CI: 2.0-2.5) during summer 2021, and 3.1 (CI: 3.0-3.3) during winter 2021-2022. Change in seroprevalence to change in case prevalence ratios ranged from 2.6 (CI: 2.3-2.8) to 3.5 (CI: 3.3-3.7) by region in winter 2021-2022.

Interpretation

Ratios of the change in seroprevalence to the change in case prevalence suggest a high proportion of infections were not detected by case-based surveillance during periods of increased transmission. The largest increases in the seroprevalence to case prevalence ratios coincided with the spread of the B.1.1.529 (Omicron) variant and with increased accessibility of home testing. Ratios varied by region and season with the highest ratios in the midwestern and southern United States during winter 2021-2022. Our results demonstrate that reported case counts did not fully capture differing underlying infection rates and demonstrate the value of sero-surveillance in understanding the full burden of infection. Levels of infection-induced antibody seroprevalence, particularly spikes during periods of increased transmission, are important to contextualize vaccine effectiveness data as the susceptibility to infection of the U.S. population changes.

Funding

This work was supported by the Centers for Disease Control and Prevention, Atlanta, Georgia.

Neurological manifestations of post-COVID-19 syndrome S1-guideline of the German society of neurology

Infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leads to COVID-19 (COrona VIrus Disease-2019). SARS-CoV-2 acute infection may be associated with an increased incidence of neurological manifestations such as encephalopathy and encephalomyelitis, ischemic stroke and intracerebral hemorrhage, anosmia and neuromuscular diseases. Neurological manifestations are commonly reported during the post-acute phase and are also present in Long-COVID (LCS) and post-COVID-19 syndrome (PCS). In October 2020, the German Society of Neurology (DGN, Deutsche Gesellschaft für Neurologie) published the first guideline on the neurological manifestations of COVID-19. In December 2021 this S1 guideline was revised and guidance for the care of patients with post-COVID-19 syndrome regarding neurological manifestations was added. This is an abbreviated version of the post-COVID-19 syndrome chapter of the guideline issued by the German Neurological society and published in the Guideline repository of the AWMF (Working Group of Scientific Medical Societies; Arbeitsgemeinschaft wissenschaftlicher Medizinischer Fachgesellschaften).

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.

Emerging complications of COVID-19 in a subset of Indian population: a pathological review with clinico-radiological case scenarios

Background

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was declared a pandemic by the World Health Organization on 11 March 2020 has been reported in most countries around the world since its origins in Wuhan, China. As of September 2021, there have been over 229 million cases of COVID-19 reported worldwide, with over 4.7 million COVID-19–associated deaths.

Body

The devastating second wave of the COVID-19 pandemic in India has seen a rise in various extrapulmonary manifestations. One of key components in the pathogenesis of COVID-19 is downregulation of ACE-2, which is expressed on many organs and counterbalances the pro-inflammatory effects of ACE/angiotensin-II axis. This leads to influx of inflammatory cells into alveoli, increased vascular permeability and activation of prothrombotic mediators. Imaging findings such as ground glass opacities, interlobular septal thickening, vascular dilatation and pulmonary thrombosis correlate well with the pathogenesis.

Conclusion

We hypothesize that the systemic complications of COVID-19 are caused by either direct viral invasion or effect of cytokine storm leading to inflammation and thrombosis or a combination of both. Gaining insights into pathobiology of SARS-CoV-2 will help understanding the various multisystemic manifestations of COVID-19. To date, only a few articles have been published that comprehensively describe the pathophysiology of COVID-19 along with its various multisystemic imaging manifestations.

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.