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tive behaviors, vaccine hesitancy, and compliance with contact-tracing using a hypothetical viral pandemic. Gender-specific health education raising awareness of health protective behaviors may be beneficial to prevent future pandemics.

This study unveils gender differences in risk perception, health protective behaviors, vaccine hesitancy, and compliance with contact-tracing using a hypothetical viral pandemic. Gender-specific health education raising awareness of health protective behaviors may be beneficial to prevent future pandemics.Recent common coronavirus (CCV) infections are associated with reduced COVID-19 severity upon SARS-CoV-2 infection, however the immunological mechanisms involved are unknown. We completed serological assays using samples collected from health care workers to identify antibody types associated with SARS-CoV-2 protection and COVID-19 severity. Rare SARS-CoV-2 cross-reactive antibodies elicited by past CCV infections were not associated with protection; however, the duration of symptoms following SARS-CoV-2 infections was significantly reduced in individuals with higher common betacoronavirus (βCoV) antibody titers. Since antibody titers decline over time after CCV infections, individuals in our cohort with higher βCoV antibody titers were more likely recently infected with common βCoVs compared to individuals with lower antibody titers. Therefore, our data suggest that recent βCoV infections potentially limit the severity of SARS-CoV-2 infections through mechanisms that do not involve cross-reactive antibodies. Our data are consistent with the emerging hypothesis that cellular immune responses elicited by recent common βCoV infections transiently reduce disease severity following SARS-CoV-2 infections.Recent studies indicate that wearable sensors have the potential to capture subtle within-person changes that signal SARS-CoV-2 infection. However, it remains unclear the extent to which observed discriminative performance is attributable to behavioral change after receiving test results. We conducted a retrospective study in a sample of medical interns who received COVID-19 test results from March to December 2020. Our data confirmed that sensor data were able to differentiate between symptomatic COVID-19 positive and negative individuals with good accuracy (area under the curve (AUC) = 0.75). However, removing post-result data substantially reduced discriminative capacity (0.75 to 0.63; delta= -0.12, p=0.013). Removing data in the symptomatic period prior to receipt of test results did not produce similar reductions in discriminative capacity. These findings suggest a meaningful proportion of the discriminative capacity of wearable sensor data for SARS-CoV-2 infection may be due to behavior change after receiving test results.

Identification of SARS-CoV-2 infection via antibody assays is important for monitoring natural infection rates. Most antibody assays cannot distinguish natural infection from vaccination.

To assess the accuracy of a nucleocapsid-containing assay in identifying natural infection among vaccinated individuals.

A longitudinal cohort comprised of healthcare workers (HCW) in the Minneapolis/St. Paul metropolitan area was enrolled. Two rounds of seroprevalence studies separated by one month were conducted from 11/2020-1/2021. Capillary blood from round 1 and 2 was tested for IgG antibodies against SARS-CoV-2 spike proteins with a qualitative chemiluminescent ELISA (spike-only assay). In a subsample of participants (n=82) at round 2, a second assay was performed that measured IgGs reactive to SARS-CoV-2 nucleocapsid protein (nucleocapsid-containing assay). Round 1 biospecimen collections occurred prior to vaccination in all participants. Vaccination status at round 2 was determined via self-report.

The Minnea the specificity of the nucleocapsid-containing assay was 92% and while the specificity of the spike-only assay was 0%.

An IgG assay identifying reactivity to nucleocapsid protein is an accurate predictor of natural infection among vaccinated individuals while a spike-only assay performed poorly. In the era of SARS-CoV-2 vaccination, seroprevalence studies monitoring natural infection will require assays that do not rely on spike-protein response alone.

An IgG assay identifying reactivity to nucleocapsid protein is an accurate predictor of natural infection among vaccinated individuals while a spike-only assay performed poorly. In the era of SARS-CoV-2 vaccination, seroprevalence studies monitoring natural infection will require assays that do not rely on spike-protein response alone.

Availability of SARS-CoV-2 testing in the United States (U.S.) has fluctuated through the course of the COVID-19 pandemic, including in the U.S. state of Illinois. Despite substantial ramp-up in test volume, access to SARS-CoV-2 testing remains limited, heterogeneous, and insufficient to control spread.

We compared SARS-CoV-2 testing rates across geographic regions, over time, and by demographic characteristics (i.e., age and racial/ethnic groups) in Illinois during March through December 2020. We compared age-matched case fatality ratios and infection fatality ratios through time to estimate the fraction of SARS-CoV-2 infections that have been detected through diagnostic testing.

By the end of 2020, initial geographic differences in testing rates had closed substantially. Case fatality ratios were higher in non-Hispanic Black and Hispanic/Latino populations in Illinois relative to non-Hispanic White populations, suggesting that tests were insufficient to accurately capture the true burden of COVID-19 d demographic groups may enable policymakers to regularly monitor and evaluate the shifting landscape of diagnostic testing, allowing officials to prioritize allocation of testing resources to reduce disparities in COVID-19 burden and eventually reduce SARS-CoV-2 transmission.The global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). 5-Chlorodeoxyuridine;CldU We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number, R . For most counties, it makes accurate predictions of new incident cases, deaths and R values, up to 30 days in the future. Our framework can also be used to predict other targets that are useful indices for policymaking, for example hospitalization or the occupancy of intensive care units.