COVID-19 Surveillance Data Research Articles

Geographical coverage of SARS-CoV-2 screening and care centers in Haiti: what do national surveillance data tell us?

SummaryBackgroundIn Haiti, reported incidence and mortality rates for COVID-19 were lower than expected. We aimed to analyze factors at communal and individual level that might lead to an underestimation of the true burden of the COVID-19 epidemic in Haiti during its first two years.MethodsWe analyzed national COVID-19 surveillance data from March 2020 to December 2021, to describe the epidemic using cluster detection, time series, and cartographic approach. We performed multivariate Quasi-Poisson regression models to determine socioeconomic factors associated with incidence and mortality. We performed a mixed-effect logistic regression model to determine individual factors associated with the infection.ResultsAmong the 140 communes of Haiti, 57 (40.7%) had a COVID-19 screening center, and the incidence was six times higher in these than in those without. Only 22 (15.7%) communes had a COVID-19 care center, and the mortality was five times higher in these than in those without. All the richest communes had a COVID-19 screening center while only 30.8% of the poorest had one. And 75% of the richest communes had a COVID-19 care center while only 15.4% of the poorest had one. Having more than three healthcare workers per 1000 population in the commune was positively associated with the incidence (SIR: 3.31; IC95%: 2.50, 3.93) and the mortality (SMR: 2.73; IC95%: 2.03, 3.66). At the individual level, male gender (adjusted OR: 1.11; IC95%: 1.01, 1.22), age with a progressive increase of the risk compared to youngers, and having Haitian nationality only (adjusted OR:2.07; IC95%: 1.53, 2.82) were associated with the infection.ConclusionsThis study highlights the weakness of SARS-CoV-2 screening and care system in Haiti, particularly in the poorest communes, suggesting that the number of COVID-19 cases and deaths were probably greatly underestimated.

Data Equity as a Building Block for Health Equity: Improving Surveillance Data for People With Disabilities, With Substance Use Disorder, or Experiencing Homelessness, United States.

People with disabilities, people experiencing homelessness, and people who have substance use disorders face unique health challenges. Gaps in public health surveillance data limit the identification of public health needs of these groups and data-driven action. This study aimed to identify current practices, challenges, and opportunities for collecting and reporting COVID-19 surveillance data for these populations. We used a rapid qualitative assessment to explore COVID-19 surveillance capacities. From July through October 2021, we virtually interviewed key informants from the Centers for Disease Control and Prevention, state and local health departments, and health care providers across the United States. We thematically analyzed and contextualized interview notes, peer-reviewed articles, and participant documents using a literature review. We identified themes centered on foundational structural and systems issues that hinder actionable surveillance data for these and other populations that are disproportionately affected by multiple health conditions. Qualitative data analysis of 61 interviews elucidated 4 primary challenges: definitions and policies, resources, data systems, and articulation of the purpose of data collection to these groups. Participants noted the use of multisector partnerships, automated data collection and integration, and data scorecards to circumvent challenges. This study highlights the need for multisector, systematic improvements in surveillance data collection and reporting to advance health equity. Improvements must be buttressed with adequate investment in data infrastructure and promoted through clear communication of how data are used to protect health.

Gender/Sex Disparities in the COVID-19 Cascade From Testing to Mortality: An Intersectional Analysis of Swiss Surveillance Data.

This study investigates gender and sex disparities in COVID-19 epidemiology in the Canton of Vaud, Switzerland, focusing on the interplay with socioeconomic position (SEP) and age. We analyzed COVID-19 surveillance data from March 2020 to June 2021, using an intersectional approach. Negative binomial regression models assessed disparities between women and men, across SEP quintiles and age groups, in testing, positivity, hospitalizations, ICU admissions, and mortality (Incidence Rate Ratios [IRR], with 95% Confidence Intervals [CI]). Women had higher testing and positivity rates than men, while men experienced more hospitalizations, ICU admissions, and deaths. The higher positivity in women under 50 was mitigated when accounting for their higher testing rates. Within SEP quintiles, gender/sex differences in testing and positivity were not significant. In the lowest quintile, women's mortality risk was 68% lower (Q1: IRR 0.32, CI 0.20-0.52), with decreasing disparities with increasing SEP quintiles (Q5: IRR 0.66, CI 0.41-1.06). Our findings underscore the complex epidemiological patterns of COVID-19, shaped by the interactions of gender/sex, SEP, and age, highlighting the need for intersectional perspectives in both epidemiological research and public health strategy development.

Estimating vaccine efficacy during open-label follow-up of COVID-19 vaccine trials based on population-level surveillance data

While rapid development and roll out of COVID-19 vaccines is necessary in a pandemic, the process limits the ability of clinical trials to assess longer-term vaccine efficacy. We leveraged COVID-19 surveillance data in the U.S. to evaluate vaccine efficacy in U.S. Government-funded COVID-19 vaccine efficacy trials with a three-step estimation process. First, we used a compartmental epidemiological model informed by county-level surveillance data, a “population model”, to estimate SARS-CoV-2 incidence among the unvaccinated. Second, a “cohort model” was used to adjust the population SARS-CoV-2 incidence to the vaccine trial cohort, taking into account individual participant characteristics and the difference between SARS-CoV-2 infection and COVID-19 disease. Third, we fit a regression model estimating the offset between the cohort-model-based COVID-19 incidence in the unvaccinated with the placebo-group COVID-19 incidence in the trial during blinded follow-up. Counterfactual placebo COVID-19 incidence was estimated during open-label follow-up by adjusting the cohort-model-based incidence rate by the estimated offset. Vaccine efficacy during open-label follow-up was estimated by contrasting the vaccine group COVID-19 incidence with the counterfactual placebo COVID-19 incidence. We documented good performance of the methodology in a simulation study. We also applied the methodology to estimate vaccine efficacy for the two-dose AZD1222 COVID-19 vaccine using data from the phase 3 U.S. trial (ClinicalTrials.gov # NCT04516746). We estimated AZD1222 vaccine efficacy of 59.1% (95% uncertainty interval (UI): 40.4%–74.3%) in April, 2021 (mean 106 days post-second dose), which reduced to 35.7% (95% UI: 15.0%–51.7%) in July, 2021 (mean 198 days post-second-dose). We developed and evaluated a methodology for estimating longer-term vaccine efficacy. This methodology could be applied to estimating counterfactual placebo incidence for future placebo-controlled vaccine efficacy trials of emerging pathogens with early termination of blinded follow-up, to active-controlled or uncontrolled COVID-19 vaccine efficacy trials, and to other clinical endpoints influenced by vaccination.

Monitoring SARS-CoV-2 RNA in wastewater from a shared septic system and sub-sewershed sites to expand COVID-19 disease surveillance

ABSTRACT Wastewater-based epidemiology has expanded as a tool for collecting COVID-19 surveillance data, but there is limited information on the feasibility of this form of surveillance within decentralized wastewater systems (e.g., septic systems). This study assessed SARS-CoV-2 RNA concentrations in wastewater samples from a septic system servicing a mobile home park (66 households) and from two pumping stations serving a similarly sized (71 households) and a larger (1,000 households) neighborhood within a nearby sewershed over 35 weeks in 2020. Also, raw wastewater from a hospital in the same sewershed was sampled. The mobile home park samples had the highest detection frequency (39/39 days) and mean concentration of SARS-CoV-2 RNA (2.7 × 107 gene copies/person/day for the N1) among the four sampling sites. N1 gene and N2 gene copies were highly correlated across mobile home park samples (Pearson's r = 0.93, p < 0.0001). In the larger neighborhood, new COVID-19 cases were reported every week during the sampling period; however, we detected SARS-CoV-2 RNA in 12% of the corresponding wastewater samples. The results of this study suggest that sampling from decentralized wastewater infrastructure can be used for continuous monitoring of SARS-CoV-2 infections.

Comparing frequency of booster vaccination to prevent severe COVID-19 by risk group in the United States

There is a public health need to understand how different frequencies of COVID-19 booster vaccines may mitigate the risk of severe COVID-19, while accounting for waning of protection and differential risk by age and immune status. By analyzing United States COVID-19 surveillance and seroprevalence data in a microsimulation model, here we show that more frequent COVID-19 booster vaccination (every 6–12 months) in older age groups and the immunocompromised population would effectively reduce the burden of severe COVID-19, while frequent boosters in the younger population may only provide modest benefit against severe disease. In persons 75+ years, the model estimated that annual boosters would reduce absolute annual risk of severe COVID-19 by 199 (uncertainty interval: 183–232) cases per 100,000 persons, compared to a one-time booster vaccination. In contrast, for persons 18–49 years, the model estimated that annual boosters would reduce this risk by 14 (10–19) cases per 100,000 persons. Those with prior infection had lower benefit of more frequent boosting, and immunocompromised persons had larger benefit. Scenarios with emerging variants with immune evasion increased the benefit of more frequent variant-targeted boosters. This study underscores the benefit of considering key risk factors to inform frequency of COVID-19 booster vaccines in public health guidance and ensuring at least annual boosters in high-risk populations.

Inference under superspreading: Determinants of SARS-CoV-2 transmission in Germany.

Superspreading, under-reporting, reporting delay, and confounding complicate statistical inference on determinants of disease transmission. A model that accounts for these factors within a Bayesian framework is estimated using German Covid-19 surveillance data. Compartments based on date of symptom onset, location, and age group allow to identify age-specific changes in transmission, adjusting for weather, reported prevalence, and testing and tracing. Several factors were associated with a reduction in transmission: public awareness rising, information on local prevalence, testing and tracing, high temperature, stay-at-home orders, and restaurant closures. However, substantial uncertainty remains for other interventions including school closures and mandatory face coverings. The challenge of disentangling the effects of different determinants is discussed and examined through a simulation study. On a broader perspective, the study illustrates the potential of surveillance data with demographic information and date of symptom onset to improve inference in the presence of under-reporting and reporting delay.

COVID-19-Related School Closures, United States, July 27, 2020-June 30, 2022.

As part of a multiyear project that monitored illness-related school closures, we conducted systematic daily online searches during July 27, 2020-June 30, 2022, to identify public announcements of COVID-19-related school closures (COVID-SCs) in the United States lasting >1 day. We explored the temporospatial patterns of COVID-SCs and analyzed associations between COVID-SCs and national COVID-19 surveillance data. COVID-SCs reflected national surveillance data: correlation was highest between COVID-SCs and both new PCR test positivity (correlation coefficient [r] = 0.73, 95% CI 0.56-0.84) and new cases (r= 0.72, 95% CI 0.54-0.83) during 2020-21 and with hospitalization rates among all ages (r=0.81, 95% CI 0.67-0.89) during 2021-22. The numbers of reactive COVID-SCs during 2020-21 and 2021-22 greatly exceeded previously observed numbers of illness-related reactive school closures in the United States, notably being nearly 5-fold greater than reactive closures observed during the 2009 influenza (H1N1) pandemic.

Comparative 60-day effectiveness of bivalent versus monovalent mRNA vaccines in Shelby County: a population-level analysis.

Two monovalent mRNA vaccines, available in December 2020, were demonstrated to have high efficacy against both the original SARS-CoV-2 strain and variants circulating through the summer and into the fall of 2021. In the context of the Omicron/BA.1 variant, which was predominant from late fall 2021 into winter of 2022 in the United States, and subsequent Omicron subvariants that have been predominant thereafter, vaccine effectiveness of the monovalent mRNA vaccine option is attenuated. We aim to investigate the relative effectiveness of the bivalent booster compared to the monovalent booster against SARS-CoV-2 infection in the 60 days following administration in Shelby County, TN. This observational population-based cohort study utilizes COVID-19 surveillance data to identify adults who were vaccinated with a monovalent booster dose between August 1, 2022 and August 30, 2022 or a bivalent booster dose between September 1, 2022 and September 30, 2022. Both groups were followed for COVID-19 status for 60 days from their administration date. We calculated incidence rates with 95% confidence intervals and propensity-adjusted hazard ratios with 95% confidence intervals of COVID-19 diagnosis in the 60 days following administration of the booster dose between the bivalent group and the monovalent group. Stratified analysis was conducted by age group (18-34, 35-64, and 65+ years old). The incidence of reported SARS-CoV-2 infection was substantially higher for those who received the monovalent booster, across age groups. Overall, we observed a 51% lower hazard of infection during the study period among those who received the bivalent booster, compared to the monovalent booster. These results support and extend prior findings that the bivalent booster dose may be more effective in preventing infection against the Omicron sub-variants of SARS-CoV-2.

Evaluation of a Targeted COVID-19 Community Outreach Intervention: Case Report for Precision Public Health.

Cameron County, a low-income south Texas-Mexico border county marked by severe health disparities, was consistently among the top counties with the highest COVID-19 mortality in Texas at the onset of the pandemic. The disparity in COVID-19 burden within Texas counties revealed the need for effective interventions to address the specific needs of local health departments and their communities. Publicly available COVID-19 surveillance data were not sufficiently timely or granular to deliver such targeted interventions. An agency-academic collaboration in Cameron used novel geographic information science methods to produce granular COVID-19 surveillance data. These data were used to strategically target an educational outreach intervention named "Boots on the Ground" (BOG) in the City of Brownsville (COB). This study aimed to evaluate the impact of a spatially targeted community intervention on daily COVID-19 test counts. The agency-academic collaboration between the COB and UTHealth Houston led to the creation of weekly COVID-19 epidemiological reports at the census tract level. These reports guided the selection of census tracts to deliver targeted BOG between April 21 and June 8, 2020. Recordkeeping of the targeted BOG tracts and the intervention dates, along with COVID-19 daily testing counts per census tract, provided data for intervention evaluation. An interrupted time series design was used to evaluate the impact on COVID-19 test counts 2 weeks before and after targeted BOG. A piecewise Poisson regression analysis was used to quantify the slope (sustained) and intercept (immediate) change between pre- and post-BOG COVID-19 daily test count trends. Additional analysis of COB tracts that did not receive targeted BOG was conducted for comparison purposes. During the intervention period, 18 of the 48 COB census tracts received targeted BOG. Among these, a significant change in the slope between pre- and post-BOG daily test counts was observed in 5 tracts, 80% (n=4) of which had a positive slope change. A positive slope change implied a significant increase in daily COVID-19 test counts 2 weeks after targeted BOG compared to the testing trend observed 2 weeks before intervention. In an additional analysis of the 30 census tracts that did not receive targeted BOG, significant slope changes were observed in 10 tracts, of which positive slope changes were only observed in 20% (n=2). In summary, we found that BOG-targeted tracts had mostly positive daily COVID-19 test count slope changes, whereas untargeted tracts had mostly negative daily COVID-19 test count slope changes. Evaluation of spatially targeted community interventions is necessary to strengthen the evidence base of this important approach for local emergency preparedness. This report highlights how an academic-agency collaboration established and evaluated the impact of a real-time, targeted intervention delivering precision public health to a small community.

1771. A Novel Approach to Distributing COVID-19 Oral Antivirals During a Public Health Emergency Using a Pharmacy Home Delivery Model in New York City, 2022

Abstract Background In December 2021, the oral antivirals (OAVs) nirmatrelvir/ritonavir and molnupiravir were authorized for the treatment of COVID-19 in high-risk patients. Initial supplies were limited and states designated pharmacies to receive OAVs from a weekly federal allocation. New York City (NYC) has nearly 3,000 pharmacies serving a diverse population across a large urban area. To ensure fair access to OAVs, the NYC Health Department partnered with an online pharmacy to provide rapid delivery to any NYC address. Here we describe characteristics of patients served and metrics used to evaluate the program as the pandemic and OAV access evolved in 2022. Methods The NYC partner pharmacy provided data on OAV prescriptions including patient age, sex, delivery zip code, and dates of prescription and delivery. These data were matched with the NYC Health Department’s COVID-19 surveillance data including positive lab reported cases, Citywide Immunization Registry, deaths, all-cause emergency department (ED) visits and hospital admissions. Descriptive statistics were calculated using R Statistical Software. Results A total of 48,947 OAV prescriptions were delivered in 2022, reaching 99.5% of residential zip codes, accounting for 17.5% of the OAVs dispensed in NYC. The median time from prescription receipt to delivery was 17 hours (IQR 6-24) with half (49%) of deliveries going to high social vulnerability zip codes. Of OAV recipients, most (58%) were female, with a median age of 56 years (IQR 39, 68) with 32% aged 65 years or older; 78% had received at least a primary vaccination series; 36% matched with a lab reported COVID-19 case; 5.5% had an ED visit and 0.8% were hospitalized in NYC for any cause within 30 days of prescription date. Conclusion The NYC antiviral delivery program provided broad and timely access to COVID OAVs, serving a significant proportion of patients in high social vulnerability neighborhoods and older adults at higher risk for severe outcomes. Of note, an increasing proportion of people receiving OAVs were not reported as cases to public health, which likely reflects the increasing use of home tests in 2022. Limitations include the lack of data on race/ethnicity, underlying medical conditions and for patients receiving OAVs through other pharmacies. Disclosures All Authors: No reported disclosures

Thrombocytopenia and venous thromboembolic events after BNT162b2, CoronaVac, ChAdOx1 vaccines and SARS-CoV-2 infection: a self-controlled case series study

This study assessed the association between COVID-19 vaccines, SARS-CoV-2 infection and the risk of thrombocytopenia and venous thromboembolism (VTE). This self-controlled case series study used hospital records between 1st February 2021 and 28th February 2022 linked to the national immunisation registry and COVID-19 surveillance data in Malaysia. Conditional Poisson regression was used to estimate incidence rate ratios (IRR) of events in the risk period (day 1–21 post-exposure) relative to control period with the corresponding 95% confidence interval (CI) adjusted for calendar period. We found no significant increased risk of thrombocytopenia in 1–21 days following BNT162b2, CoronaVac and ChAdOx1 vaccines while the risk was increased following SARS-CoV-2 infection (IRR 15.52, 95% CI 13.38–18.00). Similarly, vaccination with BNT162b2, CoronaVac, or ChAdOx1 was not associated with an increased risk of VTE during the 1–21 days risk period. SARS-CoV-2 infection was associated with increased risk of VTE (IRR 39.84, 95% CI 27.45–32.44). Our findings showed low event rates of thrombocytopenia and VTE following booster vaccination with comparable safety profiles between those who received homologous and heterologous booster combinations. Our findings showed the risk of thrombocytopenia and VTE was not increased after COVID-19 vaccination while the risks were substantially higher after SARS-CoV-2 infection.

Logic-Based Reverse Analysis: A Covid-19 Surveillance Data Set Classification Problem

This study focuses on the application of formal logic systems to real-world problem-solving, specifically in the classification of the COVID-19 Surveillance Data Set (CSDS). The research introduces the integration of a random three satisfiability problem of Boolean logic into a Hopfield Neural Network (HNN) to obtain an optimal representation of Random kSatisfiability for CSDS classification. The primary goal is to utilize the optimization capabilities of the Lyapunov energy function in the HNN to extract logical relationships and identify significant features contributing to COVID-19 detection. The CSDS used in this study is sourced from the reputable UCI dataset, and the HNN's energy minimization mechanism is employed for logical mining. Computational simulations are performed with varying numbers of clauses to validate the efficacy of the proposed model in training the CSDS for classification purposes. The results showcase the efficiency and robustness of employing reverse analysis using k-satisfiability in conjunction with a Hopfield Neural Network. This approach successfully extracts dominant features related to the logical framework underlying the CSDS. By combining formal logic systems with the power of neural networks, this research offers insights into the correlation between logical rules and COVID-19 detection. The findings contribute to our understanding of how the HNN can effectively learn and classify data, opening avenues for enhanced classification techniques in the healthcare sector and other domains.

Community transmission of SARS-CoV-2 during the Delta wave in New York City

BackgroundUnderstanding community transmission of SARS-CoV-2 variants of concern (VOCs) is critical for disease control in the post pandemic era. The Delta variant (B.1.617.2) emerged in late 2020 and became the dominant VOC globally in the summer of 2021. While the epidemiological features of the Delta variant have been extensively studied, how those characteristics shaped community transmission in urban settings remains poorly understood.MethodsUsing high-resolution contact tracing data and testing records, we analyze the transmission of SARS-CoV-2 during the Delta wave within New York City (NYC) from May 2021 to October 2021. We reconstruct transmission networks at the individual level and across 177 ZIP code areas, examine network structure and spatial spread patterns, and use statistical analysis to estimate the effects of factors associated with COVID-19 spread.ResultsWe find considerable individual variations in reported contacts and secondary infections, consistent with the pre-Delta period. Compared with earlier waves, Delta-period has more frequent long-range transmission events across ZIP codes. Using socioeconomic, mobility and COVID-19 surveillance data at the ZIP code level, we find that a larger number of cumulative cases in a ZIP code area is associated with reduced within- and cross-ZIP code transmission and the number of visitors to each ZIP code is positively associated with the number of non-household infections identified through contact tracing and testing.ConclusionsThe Delta variant produced greater long-range spatial transmission across NYC ZIP code areas, likely caused by its increased transmissibility and elevated human mobility during the study period. Our findings highlight the potential role of population immunity in reducing transmission of VOCs. Quantifying variability of immunity is critical for identifying subpopulations susceptible to future VOCs. In addition, non-pharmaceutical interventions limiting human mobility likely reduced SARS-CoV-2 spread over successive pandemic waves and should be encouraged for reducing transmission of future VOCs.

Association Between mRNA Vaccination and Infection From SARS-CoV-2 During the Delta and Omicron BA.1 Waves: A Population-Level Analysis

COVID-19 mRNA vaccine protection against the Omicron variant of SARS-CoV-2 has been shown to be attenuated. Previous research in Shelby County, Tennessee found that vaccine effectiveness might differ by age in the Omicron surge, a finding not reported for other variants. To assess whether patterns in vaccine effectiveness by age group differed on the basis of the predominant strain of SARS-CoV2, we evaluated vaccine effectiveness in Shelby County, Tennessee by age group in the Delta wave and Omicron BA.1 (Omicron) wave. Case and vaccination statuses of residents were assessed using COVID-19 surveillance data. Age was stratified as 18-34, 35-64, and ≥65 years. Vaccination groups included unvaccinated, fully vaccinated, and fully vaccinated+booster. Person time was counted in each wave by vaccination status until the time of a positive reported COVID-19 test or until the end of the study period. Incidence of COVID-19 was much higher during the Omicron wave than during the Delta wave across all vaccination groups. During the Delta wave, among adults, 79.2% fewer cases were identified in those fully vaccinated and 94.8% fewer in those fully vaccinated + booster, compared with 40.2% and 66.7%, respectively, in the Omicron wave, compared with those who were unvaccinated. This study found evidence that vaccine effectiveness differed by age group during the Omicron wave, where the same pattern was not prominent in the Delta wave. Further analysis investigating the influence of behavior patterns and other potential confounders on vaccine effectiveness would be useful in further understanding the relationship between age and vaccine effectiveness.

The importance of community health workers as frontline responders during the COVID-19 pandemic, Somalia, 2020-2021.

We examined the contribution of community health workers as frontline responders for the community-based surveillance in Somalia during the first year of the COVID-19 pandemic for detection of COVID-19 cases and identification of contacts. We retrieved COVID-19 surveillance data from 16 March 2020 to 31 March 2021 from the health ministry's central database. These data were collected through community health workers, health facilities or at the points of entry. We compared the number of suspected COVID-19 cases detected by the three surveillance systems and the proportion that tested positive using the chi-squared test. We used logistic regression analysis to assess association between COVID-19 infection and selected variables. During the study period, 154,004 suspected cases of COVID-19 were detected and tested, of which 10,182 (6.6%) were positive. Of the notified cases, 32.7% were identified through the community-based surveillance system, 54.0% through the facility-based surveillance system, and 13.2% at points of entry. The positivity rate of cases detected by the community health workers was higher than that among those detected at health facilities (8.6% versus 6.4%; p < 0.001). The community health workers also identified more contacts than those identified through the facility-based surveillance (13,279 versus 1,937; p < 0.001). The odds of COVID-19 detection generally increased by age. Community-based surveillance and health facility-based surveillance had similar odds of detecting COVID-19 cases compared with the points-of-entry surveillance (aOR: 7.0 (95% CI: 6.4, 7.8) and aOR: 7.5 (95% CI: 6.8, 8.3), respectively). The community health workers proved their value as first responders to COVID-19. They can be effective in countries with weak health systems for targeted community surveillance in rural and remote areas which are not covered by the facility-based surveillance system.

Public Health Interventions Guided by Houston's Wastewater Surveillance Program During the COVID-19 Pandemic.

Since the start of the COVID-19 pandemic, wastewater surveillance has emerged as a powerful tool used by public health authorities to track SARS-CoV-2 infections in communities. In May 2020, the Houston Health Department began working with a coalition of municipal and academic partners to develop a wastewater monitoring and reporting system for the city of Houston, Texas. Data collected from the system are integrated with other COVID-19 surveillance data and communicated through different channels to local authorities and the general public. This information is used to shape policies and inform actions to mitigate and prevent the spread of COVID-19 at municipal, institutional, and individual levels. Based on the success of this monitoring and reporting system to drive public health protection efforts, the wastewater surveillance program is likely to become a standard part of the public health toolkit for responding to infectious diseases and, potentially, other disease-causing outbreaks.

Contested Care: COVID-19 Surveillance and Health Data in the Workplace

Within the ongoing disruption of the COVID-19 pandemic, technologically mediated health surveillance programs have vastly intensified and expanded to new spaces. Popular understandings of medical and health data protections came into question as a variety of institutions introduced new tools for symptom tracking, contact tracing, and the management of related data. These systems have raised complex questions about who should have access to health information, under what circumstances, and how people and institutions negotiate relationships between privacy, public safety, and care during times of crisis. In this paper, we take up the case of a large public university working to keep campus productive during COVID-19 through practices of placemaking, symptom screeners, and vaccine mandate compliance databases. Drawing on a multi-methods study including thirty-eight interviews, organizational documents, and discursive analysis, we show where and for whom administrative care infrastructures either misrecognized or torqued (Bowker and Star 1999) the care relationships that made life possible for people in the university community. We argue that an analysis of care—including the social relations that enable it and those that attempt to hegemonically define it—opens important questions for how people relate to data they produce about their bodies as well as to the institutions that manage them. Furthermore, we argue that privacy frameworks that rely on individual rights, essential categories of “sensitive information,” or the normative legitimacy of institutional practices are not equipped to reveal how people negotiate privacy and care in times of crisis.

Epidemiology of COVID-19 among Children and Adolescents in Sudan 2020-2021.

Children and adolescents account for a small proportion of confirmed COVID-19 cases, with mild and self-limiting clinical manifestations. The distribution and determinants of COVID-19 among this group in Sudan are unclear. This study used national COVID-19 surveillance data to study the epidemiology of COVID-19 among children and adolescents in Sudan during 2020-2021. A cross-sectional study was performed to estimate the reported incidence of children and adolescents with COVID-19; the clinical features; and the mortality among those who tested positive for COVID-19. A total of 3150 suspected cases of COVID-19 infection fulfilled the study criteria. The majority of cases were above 10 years of age, 52% (1635) were males, and 56% (1765) were asymptomatic. The reported incidence rates of COVID-19 among children and adolescents in Sudan was 1.3 per 10,000 in 2021. Fever, cough, and headache were the most frequent symptoms reported among the suspected cases. The case fatality rate was 0.2%. Binary logistic regression revealed that loss of smell was the most significantly associated symptom with a positive test. We recommend further study to identify risk factors. Additionally, we recommend including these age groups in the vaccination strategy in Sudan.

Correlations and Timeliness of COVID-19 Surveillance Data Sources and Indicators - United States, October 1, 2020-March 22, 2023.

When the U.S. COVID-19 public health emergency declaration expires on May 11, 2023, national reporting of certain categories of COVID-19 public health surveillance data will be transitioned to other data sources or will be discontinued; COVID-19 hospitalization data will be the only data source available at the county level (1). In anticipation of the transition, national COVID-19 surveillance data sources and indicators were evaluated for purposes of ongoing monitoring. The timeliness and correlations among surveillance indicators were analyzed to assess the usefulness of COVID-19-associated hospital admission rates as a primary indicator for monitoring COVID-19 trends, as well as the suitability of other replacement data sources. During April 2022-March 2023, COVID-19 hospital admission rates from the National Healthcare Safety Network (NHSN)† lagged 1 day behind case rates and 4 days behind percentages of positive test results and COVID-19 emergency department (ED) visits from the National Syndromic Surveillance Program (NSSP). In the same analysis, National Vital Statistics System (NVSS) trends in the percentage of deaths that were COVID-19-associated, which is tracked by date of death rather than by report date, were observable 13 days earlier than those from aggregate death count data, which will be discontinued (1). During October 2020-March 2023, strong correlations were observed between NVSS and aggregate death data (0.78) and between the percentage of positive SARS-CoV-2 test results from the National Respiratory and Enteric Viruses Surveillance System (NREVSS) and COVID-19 electronic laboratory reporting (CELR) (0.79), which will also be discontinued (1). Weekly COVID-19 Community Levels (CCLs) will be replaced with levels of COVID-19 hospital admission rates (low, medium, or high) which demonstrated >99% concordance by county during February 2022-March 2023. COVID-19-associated hospital admission levels are a suitable primary metric for monitoring COVID-19 trends, the percentage of COVID-19 deaths is a timely disease severity indicator, and the percentages of positive SARS-CoV-2 test results from NREVSS and ED visits serve as early indicators for COVID-19 monitoring. Collectively, these surveillance data sources and indicators can support monitoring of the impact of COVID-19 and related prevention and control strategies as ongoing public health priorities.