Letter to the editor: Plenty of coronaviruses but no SARS-CoV-2

Colson, Philippe; Scola, Bernard La; Esteves-Vieira, Véra; Ninove, Laëtitia; Zandotti, Christine; Jimeno, Marie Thérése; Gazin, Céline; Bedotto, Marielle; Filosa, Véronique; Giraud-Gatineau, Audrey; Chaudet, Hervé; Brouqui, Philippe; Lagier, Jean-Christophe; Raoult, Didier

doi:10.2807/1560-7917.es.2020.25.8.2000171

Cited by 10 publications

(8 citation statements)

References 5 publications

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“…This nasopharyngeal swab sample arrived at 8:30 pm in our clinical microbiology and virology laboratory at IHU Méditerranée Infection, which has been performing the routine diagnosis of SARS-CoV-2, 24 h a day, 7 days a week since the end of January 2020 for all suspected cases of Covid-19 (the SARS-CoV-2-associated disease). We have tested more than 4084 respiratory samples since January 2020 and until February 19, 2020 [9]. We carried out SARS-CoV-2 RNA detection by two first-line real-time reverse transcription (RT)-PCR assays [9], one of which (SpikeP_ps80) was developed in-house as soon as the first SARS-CoV-2 genome (GenBank Accession no.…”

Section: Resultsmentioning

confidence: 99%

“…We have tested more than 4084 respiratory samples since January 2020 and until February 19, 2020 [9]. We carried out SARS-CoV-2 RNA detection by two first-line real-time reverse transcription (RT)-PCR assays [9], one of which (SpikeP_ps80) was developed in-house as soon as the first SARS-CoV-2 genome (GenBank Accession no. MN908947) was released by Chinese teams (on January 10, 2020).…”

Section: Resultsmentioning

confidence: 99%

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Ultrarapid diagnosis, microscope imaging, genome sequencing, and culture isolation of SARS-CoV-2

Colson

Lagier

Baudoin

et al. 2020

Eur J Clin Microbiol Infect Dis

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Ultrarapid diagnosis, microscope imaging, genome sequencing, and culture isolation of SARS-CoV-2

Colson

Lagier

Baudoin

et al. 2020

Eur J Clin Microbiol Infect Dis

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“…To obtain a reference point and upper limit for the magnitude of q, we therefore look at percentages of symptomatic patients with confirmed non-SARS-CoV-2 respiratory pathogen infection who additionally were tested positive for SARS-CoV-2. Such co-infection rates were found to vary from 0/3380 in French patients tested since January 1 2020 (Colson et al 2020 ) to 2/239 (0.8%) in Chinese children (Jian et al 2020 ), 6/186 (3.2%) in Chinese adults (Lin et al 2020 ) and 24/318 (7.5%) in Californian patients (Kim et al 2020 ). Based on these data, we set an upper limit of q at 10%.…”

Section: The Two Questions Revisitedmentioning

confidence: 99%

“…In contrast, if ¬C is true, the symptoms are caused by a pathogen other than SARS-CoV-2, so q′ represents the sum of (i) the probability of a single influenza A infection plus (ii) the probability of co-infection of a non-SARS-CoV-2 pathogen with influenza A plus (iii) the probability of SARS-CoV-2 infection not causing the symptoms with influenza A causing the symptoms. The prevalence of influenza A infection among symptomatic patients testing negative for SARS-CoV-2 ranges from 10/845 (1.2%) (Nowak et al 2020 ) over 29/1101 (2.6%) (Kim et al 2020 ) to 794/3380 (23.5%) (Colson et al 2020 ). In these patients, because SARS-CoV-2 was not detected, we assume that ¬C was true.…”

Section: The Two Questions Revisitedmentioning

confidence: 99%

The Epistemology of a Positive SARS-CoV-2 Test

Klement

Bandyopadhyay

2020

Acta Biotheor

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We investigate the epistemological consequences of a positive polymerase chain reaction SARS-CoV test for two relevant hypotheses: (i) V is the hypothesis that an individual has been infected with SARS-CoV-2; (ii) C is the hypothesis that SARS-CoV-2 is the cause of flu-like symptoms in a given patient. We ask two fundamental epistemological questions regarding each hypothesis: First, how much confirmation does a positive test lend to each hypothesis? Second, how much evidence does a positive test provide for each hypothesis against its negation? We respond to each question within a formal Bayesian framework. We construe degree of confirmation as the difference between the posterior probability of the hypothesis and its prior, and the strength of evidence for a hypothesis against its alternative in terms of their likelihood ratio. We find that test specificity-and coinfection probabilities when making inferences about C-were key determinants of confirmation and evidence. Tests with < 87% specificity could not provide strong evidence (likelihood ratio > 8) for V against ¬V regardless of sensitivity. Accordingly, low specificity tests could not provide strong evidence in favor of C in all plausible scenarios modeled. We also show how a positive influenza A test disconfirms C and provides weak evidence against C in dependence on the probability that the patient is influenza A infected given that his/her symptoms are not caused by SARS-CoV-2. Our analysis points out some caveats that should be considered when attributing symptoms or death of a positively tested patient to SARS-CoV-2.

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“…These are sens'=95.6%, spec'=82.3%. Influenza A is a very common virus associated with flu-like symptoms and was present in 794 out of 3380 cases with respiratory viruses (23.5%) that were tested negatively for SARS-CoV-2 (Colson et al 2020), so that we assume that ¬C was true in these cases. Accordingly, we set p=0.235 and vary q from zero to 0.235 (because p>q).…”

Section: V3 Covid-19 and A Positive Test For Influenza Amentioning

confidence: 99%

The epistemology of the SARS-CoV-2 test

Klement

Bandyopadhyay

2020

Preprint

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We investigate the epistemological consequences of a positive SARS-CoV-2 test for two relevant hypotheses: (i) V is the hypothesis that an individual has been infected with SARS-CoV-2; (ii) C is the hypothesis that SARS-CoV-2 is the sole cause of flu-like symptoms in a given patient. We ask two fundamental epistemological questions regarding each hypothesis: First, given a positive SARS-CoV-2 test, what should we believe about the hypothesis and to what degree? Second, how much evidence does a positive test provide for a hypothesis against its negation? We respond to each question within a formal Bayesian framework. We construe degree of confirmation as the difference between the posterior probability of the hypothesis and its prior, and the strength of evidence for a hypothesis against its alternative in terms of their likelihood ratio. We find that for realistic assumptions about the base rate of infected individuals, P(V)≲20%, positive tests having low specificity (75%) would not raise the posterior probability for V to more than 50%. Furthermore, if the test specificity is less than 88.1%, even a positive test having 95% sensitivity would only yield weak to moderate evidence for V against ¬V. We also find that in plausible scenarios, positive tests would only provide weak to moderate evidence for C unless the tests have a high specificity. One has thus to be careful in ascribing the symptoms or death of a positively tested patient to SARS-CoV-2, if the possibility exists that the disease has been caused by another pathogen.

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Letter to the editor: Plenty of coronaviruses but no SARS-CoV-2

Cited by 10 publications

References 5 publications

Ultrarapid diagnosis, microscope imaging, genome sequencing, and culture isolation of SARS-CoV-2

Ultrarapid diagnosis, microscope imaging, genome sequencing, and culture isolation of SARS-CoV-2

The Epistemology of a Positive SARS-CoV-2 Test

The epistemology of the SARS-CoV-2 test

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