ScreenNC, a Study to Determine the Number of Asymptomatic Individuals Who Have Antibodies to the Virus That Causes COVID-19

ScreenNC is design ed for comprehensive virus screening and serology to assess exposure to SAR-CoV2 in persons not-indicated for COVID-19 testing as per the CDC screening algorithm and implemented at UNC Health Care (at the time of activation). This trial is designed to generate point prevalence of infected (I) persons, independent of clinical symptoms. The susceptible (S) class is the number of COVID-19 tested by any FDA-approved NP swab test or a SARS-CoV2 IgG antibody test. At present FDA does not have approved SARS-CoV serology and such serology is not used for diagnosis of acute infection. The recovered (R) class is the number of positives. This information allows for modeling of the COVID-19 epidemic in NC. The investigation is not testing the effectiveness or safety of either of these testing modalities, though serology may be offered in the future by UNC Healthcare laboratories. The blood samples to be obtained assess development of host response to SARS-CoV2 infection. ScreenNC will be implemented within the logistic and clinical limitations of the current outbreak. This is a single visit study. Since there is a shortage of PPE the investigators will rely on screening at locations different then the screening of presumed infected persons. Additionally, due to logistical considerations, volunteers may have a viral swab (nasopharyngeal or oropharyngeal) and/or a blood sample obtained. Thus, at recruitment, volunteers may be asked to give both samples or only one. Viral load testing will be done at LabCorp and/or UNC labs using compatible assays (Roche, ThermoFischer , WHO). Confirmatory testing (other PCR, sequencing, culture) will be done at UNC. Blood samples will be tested at the McClendon Clinical Immunology laboratory for IgG antibodies against SARS-CoV2. Because these patients are in the UNC Health Care system, The investigators will have access to their medical records, which will provide very granular information as to co-morbidities and other demographic factors which can be assessed in the context of SAR-CoV-2 (COVID-19) infection status. Future analyses of this cohort will employ statistical methods which allow us to account for sampling bias of this convenience sample to construct a representative sample of the state of NC .