{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["36(1)"],"submitter":["van Walraven C"],"pubmed_abstract":["<h4>Background</h4>The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease. There are concerns regarding limited testing capacity and the exclusion of cases from unproven screening criteria. Knowing COVID-19 risks can inform testing. This study derived and assessed a model to predict risk of SARS-CoV-2 in community-based people.<h4>Methods</h4>All people presenting to a community-based COVID-19 screening center answered questions regarding symptoms, possible exposure, travel, and occupation. These data were anonymously linked to SARS-CoV-2 testing results. Logistic regression was used to derive a model to predict SARS-CoV-2 infection. Bootstrap sampling evaluated the model.<h4>Results</h4>A total of 9172 consecutive people were studied. Overall infection ra"],"journal":["Journal of general internal medicine"],"pagination":["162-169"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7587546"],"repository":["biostudies-literature"],"pubmed_title":["Derivation and Internal Validation of a Model to Predict the Probability of Severe Acute Respiratory Syndrome Coronavirus-2 Infection in Community People."],"pmcid":["PMC7587546"],"pubmed_authors":["Desjardins M","van Walraven C","Forster AJ","Manuel DG"],"additional_accession":[]},"is_claimable":false,"name":"Derivation and Internal Validation of a Model to Predict the Probability of Severe Acute Respiratory Syndrome Coronavirus-2 Infection in Community People.","description":"<h4>Background</h4>The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease. There are concerns regarding limited testing capacity and the exclusion of cases from unproven screening criteria. Knowing COVID-19 risks can inform testing. This study derived and assessed a model to predict risk of SARS-CoV-2 in community-based people.<h4>Methods</h4>All people presenting to a community-based COVID-19 screening center answered questions regarding symptoms, possible exposure, travel, and occupation. These data were anonymously linked to SARS-CoV-2 testing results. Logistic regression was used to derive a model to predict SARS-CoV-2 infection. Bootstrap sampling evaluated the model.<h4>Results</h4>A total of 9172 consecutive people were studied. Overall infection ra","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Jan","modification":"2025-04-04T13:13:14.588Z","creation":"2020-10-30T08:36:34Z"},"accession":"S-EPMC7587546","cross_references":{"pubmed":["33107007"],"doi":["10.1007/s11606-020-06307-x"]}}