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Background: Infections with the newly emerged severe acute respiratory syndrome virus 2 (SARS-CoV-2) have quickly reached pandemic proportions and are causing a global health crisis. First recognized for the induction of severe disease, the virus also causes asymptomatic infections or infections with mild symptoms that can resemble common colds. Since infections with mild course are probably a major contributor to the spread of SARS-CoV-2, better detection of such cases is important. To provide better understanding of these mild SARS-CoV-2 infections and to improve information for potentially infected individuals, we performed a detailed analysis of self-reported symptoms of SARS-CoV-2 positive and SARS-CoV-2 negative individuals. Methods: In an online-based survey, 963 individuals provided information on symptoms associated with an acute respiratory infection, 336 of the participants had tested positive for SARS-CoV-2 infection, 107 had tested negative, and 520 had not been tested for SARS-CoV-2 infection. Results: The symptoms reported most frequently by SARS-CoV-2 infected individuals were tiredness, loss of appetite, impairment of smell or taste and dry cough. The symptoms with the highest odds ratios between SARS-CoV-2 positive and negative individuals were loss of appetite and impairment of smell or taste. Based on the most distinguishing symptoms, we developed a Bayesian prediction model, which had a positive predictive value of 0.80 and a negative predictive value of 0.72 on the SARS-CoV-2 tested individuals. The model predicted 56 of 520 non-tested individuals to be SARS-CoV-2 positive with more than 75% probability, and another 84 to be SARS-CoV-2 positive with probability between 50% and 75%. Conclusions: A combination of symptoms can provide a good estimate of the probability of SARS-CoV-2 infection.
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?:doi
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10.1101/2020.11.25.20236752
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document_parses/pdf_json/517f029e4be121de24d2eb895e37dcf5b96e786e.json
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Symptom-based prediction model of SARS-1 CoV-2 infection developed from self-reported symptoms of SARS-CoV-2-infected individuals in an online survey
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