County-Level Sociodemographic Factors Associated with COVID-19 Incidence and Mortality in North and South Carolina.

BACKGROUND There is limited research regarding associations between county-level factors and COVID-19 incidence and mortality. While the Carolinas are geographically connected, they are not homogeneous, with statewide political and intra-state socioeconomic differences leading to heterogeneous spread between and within states.METHODS Infection and mortality data from Johns Hopkins University during the 7 months since the first reported case in the Carolinas was combined with county-level socioeconomic/demographic factors. Time series imputations were performed whenever county-level reported infections were implausible. Multivariate Poisson regression models were fitted to extract incidence (infection and mortality) rate ratios by county-level factor. State-level differences in filtered trends were also calculated. Geospatial maps and Kaplan-Meier curves were constructed stratifying by median county-level factor. Differences between North and South Carolina were identified.RESULTS Incidence and mortality rates were lower in North Carolina than South Carolina. Statistically significant higher incidence and mortality rates were associated with counties in both states with higher proportions of Black/African American populations and those without health insurance aged < 65 years. Counties with larger populations aged ≥ 75 years were associated with increased mortality (but decreased incidence) rates.LIMITATIONS COVID-19 data contained multiple inconsistencies, so imputation was needed, and covariate-based data was not synchronous and potentially insufficient in granularity given the epidemiology of the disease. County-level analyses imply within-county homogeneity, an assumption increasingly breached by larger counties.CONCLUSION While statewide interventions were initially implemented, inter-county racial/ethnic and socioeconomic variability points to the need for more heterogeneous interventions, including policies, as populations within particular counties may be at higher risk.

Incidence of Chronic Kidney Disease Following Acute Coronavirus Disease 2019 Based on South Carolina Statewide Data.

BACKGROUND: Coronavirus disease 2019 (COVID-19) was associated with severe acute illness including multiple organ failure. Acute kidney injury (AKI) was a common finding, often requiring dialysis support. OBJECTIVE: Define the incidence of new clinically identified chronic kidney disease (CKD) among patients with COVID-19 and no pre-existing kidney disease. DESIGN PARTICIPANTS: The South Carolina (SC) Department of Health and Environmental Control (DHEC) COVID-19 mandatory reporting registry of SC residents testing for COVID-19 between March 2020 and October 2021 was included. DESIGN MAIN MEASURES: The primary outcome was a new incidence of a CKD diagnosis (N18.x) in those without a pre-existing diagnosis of CKD during the follow-up period of March 2020 to January 14, 2022. Patients were stratified by severity of illness (hospitalized or not, intensive care unit needed or not). The new incidence of CKD diagnosis was examined using logistic regression and cox proportional hazards analyses. KEY RESULTS: Among patients with COVID-19 (N = 683,958) without a pre-existing CKD diagnosis, 8322 (1.2 %) were found to have a new diagnosis of CKD. The strongest predictors for subsequent CKD diagnosis were age ≥ 60 years hazard ratio (HR) 31.5 (95% confidence interval [95%CI] 25.5-38.8), and intervening (between COVID-19 and CKD diagnoses) AKI diagnosis HR 20.7 (95%CI 19.7-21.7). The presence of AKI was associated with an HR of 23.6, 95% CI 22.3-25.0, among those not hospitalized, and HR of 6.2, 95% CI 5.7-6.8 among those hospitalized, for subsequent CKD. COVID-19 was not significantly associated with subsequent CKD after accounting for the severity of illness and comorbidities. CONCLUSION: Among SC residents, COVID-19 was not associated with CKD independent from indicators of the severity of illness, especially AKI diagnosis. Kidney-specific follow-up testing may be reserved for those high-risk for CKD development. Further prospective registries should examine the long-term kidney consequences to confirm these findings.

Food Insecurity, the Food Environment, and COVID-19 in Rural South Carolina.

In this study, we explored the relationship between the food environment and food security among rural adults during the COVID-19 pandemic. Researchers, with assistance from community partners, conducted a cross-sectional survey assessing the impact of COVID-19 on food access, food security, and physical activity in 9 rural South Carolina (SC) counties. This survey was administered to a purposive sample (N = 587) from August 2020 to March 2021. The dependent variable was a binary indicator of food insecurity (past 3 months), in accordance with the USDA Household Food Security Survey Module. Independent variables were sociodemographic characteristics, food environment factors (eg, shopping at grocery stores, partial markets, and farmers' markets), and shopping behaviors during the pandemic. Overall, 31% of respondents were food insecure. Food security status differed by income and household composition. Results indicate that the odds of food insecurity were higher for respondents who shopped frequently at partial markets (adjusted odds ratio [AOR] = 1.61, 95% confidence interval [CI]: 1.01-2.56) and shopped more for food before the pandemic than during the pandemic (AOR = 1.68, 95% CI: 1.07-2.64). Findings underscore the importance of examining the relationship between the food environment and food insecurity during COVID-19 in rural settings.

COVID-19 vaccine effectiveness among people living with and without HIV in South Carolina, USA: protocol of a population-based cohort study.

INTRODUCTION: Despite the effectiveness of COVID-19 vaccines in preventing severe COVID-19 outcomes, a small percentage of fully vaccinated persons will develop symptomatic or asymptomatic infections with SARS-CoV-2, which is referred to as 'breakthrough COVID-19'. People living with HIV (PLWH) appear to have an elevated risk of severe COVID-19 outcomes, yet the effectiveness of the COVID-19 vaccine in this population remains unclear due to the limited research efforts in this population in the real world. This study aims to characterise and compare the breakthrough COVID-19 (eg, prevalence and disease severity) between PLWH and non-PLWH and then examine whether HIV markers play a role in COVID-19 vaccine effectiveness within the PLWH population. METHODS AND ANALYSIS: This cohort study will merge electronic health records data from multiple data sources in South Carolina (SC), including the 'HIV Cohort' (n=12 203) identified from the statewide Enhanced HIV/AIDS Reporting System, 'Vaccine Cohort' from the Statewide Immunisation Online Network which provides patient-level immunisation records (n=~1.71 million), and 'COVID-19 Cohort' which includes healthcare encounters and COVID-19 diagnosis information for all individuals who were tested for COVID-19 (n=~3.41 million). The PLWH will be matched with a comparison group of non-PLWH by the propensity score matching method. To distinguish the role of immunity level in affecting the vaccine effectiveness, we will conduct subgroup analyses to compare the outcome of virally controlled and immunosuppressed PLWH with non-PLWH. Conditional logistic regression and generalised linear models will be employed to analyse the relationship between HIV status and protection durability by adjusting for potential confounders. ETHICS AND DISSEMINATION: The study was approved by the Institutional Review Board at the University of South Carolina (Pro00117583) as a Non-Human Subject study. The study's findings will be published in peer-reviewed journals and disseminated at national and international conferences and through social media.

Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina.

During the COVID-19 pandemic authorities have been striving to obtain reliable predictions for the spreading dynamics of the disease. We recently developed a multi-"sub-populations" (multi-compartments: susceptible, exposed, pre-symptomatic, infectious, recovered) model, that accounts for the spatial in-homogeneous spreading of the infection and shown, for a variety of examples, how the epidemic curves are highly sensitive to location of epicenters, non-uniform population density, and local restrictions. In the present work we test our model against real-life data from South Carolina during the period May 22 to July 22 (2020). During this period, minimal restrictions have been employed, which allowed us to assume that the local basic reproduction number is constant in time. We account for the non-uniform population density in South Carolina using data from NASA's Socioeconomic Data and Applications Center (SEDAC), and predict the evolution of infection heat-maps during the studied period. Comparing the predicted heat-maps with those observed, we find high qualitative resemblance. Moreover, the Pearson's correlation coefficient is relatively high thus validating our model against real-world data. We conclude that the model accounts for the major effects controlling spatial in-homogeneous spreading of the disease. Inclusion of additional sub-populations (compartments), in the spirit of several recently developed models for COVID-19, can be easily performed within our mathematical framework.

Differences in Motivating Factors for SARS-CoV-2 Vaccination and Perceptions of Infection Risk among Healthcare and EMS Personnel in South Carolina.

OBJECTIVES: Although medical workers were prioritized to receive the coronavirus disease 2019 (COVID-19) vaccination, many have declined. Even though studies have investigated differences in COVID-19-related attitudes and vaccination for workers in hospitals and long-term care facilities, none have included emergency medical services (EMS) personnel. We investigated the association between type of medical worker (EMS vs healthcare worker [HCW]) and COVID-19 vaccination, vaccine beliefs, vaccine motivators, personal protection behaviors, and risk perceptions. METHODS: The data for self-identified HCWs came from surveys distributed to randomly selected residents of South Carolina and EMS personnel recruited at a targeted surveillance testing event during the South Carolina EMS Symposium. Pearson χ2 and Fisher exact tests analyzed differences in the distribution of demographic characteristics and self-reported COVID-19 vaccination attitudes by medical workers. Multivariable logistic regression assessed the association between COVID-19 vaccination and type of medical worker, adjusting for age, sex, race, and frontline status, and assessed the associations among vaccine beliefs, vaccine motivators, personal protection behaviors, and risk perceptions by type of medical worker, adjusting for age, sex, race, frontline status, and vaccination status. RESULTS: Of the 126 respondents 57.9% were EMS, 42.1% were HCWs, and 73.6% of the cohort were self-reported frontline medical workers. Approximately two-thirds of respondents received a vaccine for COVID-19, with no significant differences between EMS and HCWs; however, EMS workers were significantly less likely to receive the vaccination out of concern about exposures at work/school (adjusted odds ratio [aOR] 0.22, 95% confidence interval [CI] 0.08-0.57), concern about exposures within the community (aOR 0.18, 95% CI 0.07-0.48), or to do their part to control the pandemic (aOR 0.20, 95% CI 0.06-0.69). EMS workers also were significantly less likely to wear a mask all/most of the time when outside the home (aOR 0.04, 95% CI 0.0-0.21) and less concerned about the spread of COVID-19 in their community as compared with HCWs (aOR 0.19, 95% CI 0.06-0.56). CONCLUSIONS: EMS personnel were significantly less concerned about the spread of COVID-19 in their community and significantly less likely to wear a mask all/most of the time while outside the home as compared with HCWs. Differences in the COVID-19-related attitudes and personal protection behaviors of EMS personnel should be used to develop targeted interventions to increase vaccine motivation and adherence to personal protection protocols.

SARS-CoV-2 Viral Incidence, Antibody Point Prevalence, Associated Population Characteristics, and Vaccine Attitudes, South Carolina, February 2021.

The SARS-CoV-2 outbreak from October 2020 through February 2021 was the largest outbreak as of February 2021, and timely information on current representative prevalence, vaccination, and loss of prior antibody protection was unknown. In February 2021, the South Carolina Department of Health and Environmental Control conducted a random sampling point prevalence investigation consisting of viral and antibody testing and an associated health survey, after selecting participants aged ≥5 years using a population proportionate to size of South Carolina residents. A total of 1917 residents completed a viral test, 1803 completed an antibody test, and 1463 completed ≥1 test and a matched health survey. We found an incidence of 2.16 per 100 residents and seroprevalence of 16.4% among South Carolina residents aged ≥5 years. Undetectable immunoglobulin G and immunoglobulin M antibodies were noted in 28% of people with a previous positive test result, highlighting the need for targeted education among people who may be susceptible to reinfection. We also found a low rate of vaccine hesitancy in the state (13%). The results of this randomly selected surveillance and associated health survey have important implications for prospective COVID-19 public health response efforts. Most notably, this article provides a feasible framework for prompt rollout of a statewide evidence-based surveillance initiative.

Space-time covid-19 Bayesian SIR modeling in South Carolina.

The Covid-19 pandemic has spread across the world since the beginning of 2020. Many regions have experienced its effects. The state of South Carolina in the USA has seen cases since early March 2020 and a primary peak in early April 2020. A lockdown was imposed on April 6th but lifting of restrictions started on April 24th. The daily case and death data as reported by NCHS (deaths) via the New York Times GitHUB repository have been analyzed and approaches to modeling of the data are presented. Prediction is also considered and the role of asymptomatic transmission is assessed as a latent unobserved effect. Two different time periods are examined and one step prediction is provided. The results suggest that both socio-economic disadvantage, asymptomatic transmission and spatial confounding are important ingredients in any model pertaining to county level case dynamics.

Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina.

Disparities and their geospatial patterns exist in morbidity and mortality of COVID-19 patients. When it comes to the infection rate, there is a dearth of research with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs). This work aimed to assess the temporal-geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina. We used the spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR) as global models and the geographically weighted regression model (GWR) as a local model. The data were retrieved from multiple sources including USAFacts, U.S. Census Bureau, and the Population Estimates Program. The percentage of males and the unemployed population were positively associated with geodistributions of COVID-19 infection (p values < 0.05) in global models throughout the time. The percentage of the white population and the obesity rate showed divergent spatial correlations at different times of the pandemic. GWR models fit better than global models, suggesting nonstationary correlations between a region and its neighbors. Characterized by temporal-geospatial patterns, disparities in COVID-19 infection rate and their PIDRs are different from the mortality and morbidity of COVID-19 patients. Our findings suggest the importance of prioritizing different populations and developing tailored interventions at different times of the pandemic.

COVID-19 Incidence and Mortality Among Long-Term Care Facility Residents and Staff in South Carolina.

OBJECTIVES: This study explored differences in COVID-19 incidence, mortality, and timing among long-term care facility (LTCF) residents and staff with those living in the community in South Carolina (SC). DESIGN: Longitudinal secondary data analysis. SETTING AND PARTICIPANTS: Adults age ≥18 in SC with confirmed COVID-19 diagnosis from 3/15/2020 and 1/2/2021 (n = 307,891). METHODS: COVID-19 data came from the SC Department of Health and Environmental Control (SCDHEC). We included all COVID-19 cases, hospitalizations, and deaths among adult residents. Residence and employment in LTCF were confirmed by SCDHEC. Descriptive statistics and trends for cases, hospitalizations, and deaths were calculated. We used Cox proportional hazards to compare COVID-19 mortality in LTCF residents and staff to community dwelling older adults and adults not employed in LTCF, respectively, controlling for age, gender, race, and pre-existing chronic health conditions. RESULTS: LTC residents experienced greater incidence of cases throughout the study period until the week ending on 1/2/21. LTCF residents with COVID-19 were more likely to be hospitalized compared to older adults in the community and 74% more likely to die (HR: 1.74, 95% CI: 1.59-1.90), after adjusting. LTC staff experienced greater incidence of cases compared to adults not employed in LTCF until the week ending on 12/26/2020, while experiencing similar incidence of death compared to the similar community members. After adjusting, LTC staff had 0.58 (HR = 0.58; CI: 0.39-0.88) times lower hazard of death compared to community members that did not work in a LTCF. CONCLUSIONS AND IMPLICATIONS: Narrowing of the gap between LTCF and community-wide infection and mortality rates over the study period suggests that early detection of COVID-19 in LTCFs could serve as a first indicator of disease spread in the greater community. Results also indicate that policies and regulations addressing staff testing and protection may help to slow or prevent spread within facilities.

Telehealth for HIV Care Services in South Carolina: Utilization, Barriers, and Promotion Strategies During the COVID-19 Pandemic.

To ensure continuing HIV care services during the COVID-19 pandemic, telehealth has been recommended and implemented in numerous HIV-related facilities. This study aims to understand telehealth utilization for HIV care services in South Carolina (SC), identify barriers to telehealth during COVID-19, and investigate strategies to facilitate remote HIV care delivery. In-depth interviews with 11 management personnel from 8 HIV-related facilities in SC were analyzed using thematic analysis. Utilizations of telehealth were diverse in delivering medical and non-medical HIV care services. Barriers included technological challenges, digital literacy, client/provider experiences, low socio-economic status of client population, and reimbursement issues. Various strategies were mentioned for promoting telehealth utilization, from client empowerment, provider training to improved organizational readiness. For successful telehealth use during and after COVID-19, it is necessary to continue efforts to promote telehealth and remove barriers to telehealth by implementing inclusive multi-level strategies for non-technologically savvy or disadvantaged populations living with HIV.

Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis.

BACKGROUND: Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. OBJECTIVE: The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. METHODS: This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. RESULTS: Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%. CONCLUSIONS: Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

Surveillance-based informative testing for detection and containment of SARS-CoV-2 outbreaks on a public university campus: an observational and modelling study.

BACKGROUND: Despite severe outbreaks of COVID-19 among colleges and universities across the USA during the Fall 2020 semester, the majority of institutions did not routinely test students. While high-frequency repeated testing is considered the most effective strategy for disease mitigation, most institutions do not have the necessary infrastructure or funding for implementation. Therefore, alternative strategies for testing the student population are needed. Our study detailed the implementation and results of testing strategies to mitigate SARS-CoV-2 spread on a university campus, and we aimed to assess the relative effectiveness of the different testing strategies. METHODS: For this retrospective cohort study, we included 6273 on-campus students arriving to a large public university in the rural USA (Clemson, SC, USA) for in-person instruction in the Fall 2020 semester (Sept 21 to Nov 25). Individuals arriving after Sept 23, those who tested positive for SARS-CoV-2 before Aug 19, and student athletes and band members were not included in this study. We implemented two testing strategies to mitigate SARS-CoV-2 spread during this period: a novel surveillance-based informative testing (SBIT) strategy, consisting of random surveillance testing to identify outbreaks in residence hall buildings or floors and target them for follow-up testing (Sept 23 to Oct 5); followed by a repeated weekly surveillance testing (Oct 6 to Nov 22). Relative changes in estimated weekly prevalence were examined. We developed SARS-CoV-2 transmission models to compare the relative effectiveness of weekly testing (900 daily surveillance tests), SBIT (450 daily surveillance tests), random surveillance testing (450 daily surveillance tests), and voluntary testing (0 daily surveillance tests) on disease mitigation. Model parameters were based on our empirical surveillance data in conjunction with published sources. FINDINGS: SBIT was implemented from Sept 23 to Oct 5, and identified outbreaks in eight residence hall buildings and 45 residence hall floors. Targeted testing of residence halls was 2·03 times more likely to detect a positive case than random testing (95% CI 1·67-2·46). Weekly prevalence was reduced from a peak of 8·7% to 5·6% during this 2-week period, a relative reduction of 36% (95% CI 27-44). Prevalence continued to decrease after implementation of weekly testing, reaching 0·8% at the end of in-person instruction (week 9). SARS-CoV-2 transmission models concluded that, in the absence of SBIT (ie, voluntary testing only), the total number of COVID-19 cases would have increased by 154% throughout the semester. Compared with SBIT, random surveillance testing alone would have resulted in a 24% increase in COVID-19 cases. Implementation of weekly testing at the start of the semester would have resulted in 36% fewer COVID-19 cases throughout the semester compared with SBIT, but it would require twice the number of daily tests. INTERPRETATION: It is imperative that institutions rigorously test students during the 2021 academic year. When high-frequency testing (eg, weekly) is not possible, SBIT is an effective strategy to mitigate disease spread among the student population that can be feasibly implemented across colleges and universities. FUNDING: Clemson University, USA.

Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases.

We suggest a novel mathematical framework for the in-homogeneous spatial spreading of an infectious disease in human population, with particular attention to COVID-19. Common epidemiological models, e.g., the well-known susceptible-exposed-infectious-recovered (SEIR) model, implicitly assume uniform (random) encounters between the infectious and susceptible sub-populations, resulting in homogeneous spatial distributions. However, in human population, especially under different levels of mobility restrictions, this assumption is likely to fail. Splitting the geographic region under study into areal nodes, and assuming infection kinetics within nodes and between nearest-neighbor nodes, we arrive into a continuous, "reaction-diffusion", spatial model. To account for COVID-19, the model includes five different sub-populations, in which the infectious sub-population is split into pre-symptomatic and symptomatic. Our model accounts for the spreading evolution of infectious population domains from initial epicenters, leading to different regimes of sub-exponential (e.g., power-law) growth. Importantly, we also account for the variable geographic density of the population, that can strongly enhance or suppress infection spreading. For instance, we show how weakly infected regions surrounding a densely populated area can cause rapid migration of the infection towards the populated area. Predicted infection "heat-maps" show remarkable similarity to publicly available heat-maps, e.g., from South Carolina. We further demonstrate how localized lockdown/quarantine conditions can slow down the spreading of disease from epicenters. Application of our model in different countries can provide a useful predictive tool for the authorities, in particular, for planning strong lockdown measures in localized areas-such as those underway in a few countries.

Blacks Are Less Likely to Present With Strokes During the COVID-19 Pandemic: Observations From the Buckle of the Stroke Belt.

BACKGROUND AND PURPOSE: The impact of the coronavirus disease 2019 (COVID-19) pandemic on stroke systems has not been systematically evaluated. Our study aims to investigate trends in telestroke consults during the pandemic. METHODS: We did retrospective chart review of consecutive patients seen through a telestroke network in South Carolina from March 2019 to April 2020. We dichotomized patients to preCOVID-19 pandemic (March 2019 to February 2020) and during COVID-19 pandemic (March to April 2020). RESULTS: A total of 5852 patients were evaluated during the study period, 613 (10.5%) were seen during the pandemic. The median number of weekly consults dropped from 112 to 77 during the pandemic, P=0.002. There was no difference in baseline features; however, Black patients were less likely to present with strokes during the pandemic (13.9% versus 29%, P≤0.002). CONCLUSIONS: The COVID-19 pandemic has led to a significant drop in telestroke volume. The impact seems to disproportionately affect Black patients.

SARS-CoV-2 Transmission among Marine Recruits during Quarantine.

BACKGROUND: The efficacy of public health measures to control the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has not been well studied in young adults. METHODS: We investigated SARS-CoV-2 infections among U.S. Marine Corps recruits who underwent a 2-week quarantine at home followed by a second supervised 2-week quarantine at a closed college campus that involved mask wearing, social distancing, and daily temperature and symptom monitoring. Study volunteers were tested for SARS-CoV-2 by means of quantitative polymerase-chain-reaction (qPCR) assay of nares swab specimens obtained between the time of arrival and the second day of supervised quarantine and on days 7 and 14. Recruits who did not volunteer for the study underwent qPCR testing only on day 14, at the end of the quarantine period. We performed phylogenetic analysis of viral genomes obtained from infected study volunteers to identify clusters and to assess the epidemiologic features of infections. RESULTS: A total of 1848 recruits volunteered to participate in the study; within 2 days after arrival on campus, 16 (0.9%) tested positive for SARS-CoV-2, 15 of whom were asymptomatic. An additional 35 participants (1.9%) tested positive on day 7 or on day 14. Five of the 51 participants (9.8%) who tested positive at any time had symptoms in the week before a positive qPCR test. Of the recruits who declined to participate in the study, 26 (1.7%) of the 1554 recruits with available qPCR results tested positive on day 14. No SARS-CoV-2 infections were identified through clinical qPCR testing performed as a result of daily symptom monitoring. Analysis of 36 SARS-CoV-2 genomes obtained from 32 participants revealed six transmission clusters among 18 participants. Epidemiologic analysis supported multiple local transmission events, including transmission between roommates and among recruits within the same platoon. CONCLUSIONS: Among Marine Corps recruits, approximately 2% who had previously had negative results for SARS-CoV-2 at the beginning of supervised quarantine, and less than 2% of recruits with unknown previous status, tested positive by day 14. Most recruits who tested positive were asymptomatic, and no infections were detected through daily symptom monitoring. Transmission clusters occurred within platoons. (Funded by the Defense Health Agency and others.).

Disparity in HIV Service Interruption in the Outbreak of COVID-19 in South Carolina.

To examine HIV service interruptions during the COIVD-19 outbreak in South Carolina (SC) and identify geospatial and socioeconomic correlates of such interruptions, we collected qualitative, geospatial, and quantitative data from 27 Ryan White HIV clinics in SC in March, 2020. HIV service interruptions were categorized (none, minimal, partial, and complete interruption) and analyzed for geospatial heterogeneity. Nearly 56% of the HIV clinics were partially interrupted and 26% were completely closed. Geospatial heterogeneity of service interruption existed but did not exactly overlap with the geospatial pattern of COVID-19 outbreak. The percentage of uninsured in the service catchment areas was significantly correlated with HIV service interruption (F = 3.987, P = .02). This mixed-method study demonstrated the disparity of HIV service interruptions in the COVID-19 in SC and suggested a contribution of existing socioeconomic gaps to this disparity. These findings may inform the resources allocation and future strategies to respond to public health emergencies.