AI Article Synopsis

  • A novel coronavirus was identified as the cause of the pneumonia outbreak in Wuhan through virological tests, but non-virological data could have indicated its presence earlier.
  • By analyzing characteristics from medical social media, researchers compared the outbreak to known pathogens and assessed the likelihood that it was caused by an unknown pathogen (referred to as "Disease X").
  • On December 31, 2019, the probability that Disease X was responsible was over 29%, increasing to over 49% after specific pathogens were ruled out, highlighting the potential of using non-virological data for early risk assessments.

Article Abstract

Virological tests have now shown conclusively that a novel coronavirus is causing the 2019-2020 atypical pneumonia outbreak in Wuhan, China. We demonstrate that non-virological descriptive characteristics could have determined that the outbreak is caused by a novel pathogen in advance of virological testing. Characteristics of the ongoing outbreak were collected in real time from two medical social media sites. These were compared against characteristics of eleven pathogens that have previously caused cases of atypical pneumonia. The probability that the current outbreak is due to "Disease X" (i.e., previously unknown etiology) as opposed to one of the known pathogens was inferred, and this estimate was updated as the outbreak continued. The probability (expressed as a percentage) that Disease X is driving the outbreak was assessed as over 29% on 31 December 2019, one week before virus identification. After some specific pathogens were ruled out by laboratory tests on 5 January 2020, the inferred probability of Disease X was over 49%. We showed quantitatively that the emerging outbreak of atypical pneumonia cases is consistent with causation by a novel pathogen. The proposed approach, which uses only routinely observed non-virological data, can aid ongoing risk assessments in advance of virological test results becoming available.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141128PMC
http://dx.doi.org/10.3390/jcm9030637DOI Listing

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