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Epidemiological identification of a novel infectious disease in real time: Analysis of the atypical pneumonia outbreak in Wuhan, China, 2019-20
Sung-mok Jung; Ryo Kinoshita; Robin N. Thompson; Katsuma Hayashi; Natalie M. Linton; Yichi Yang; Andrei R. Akhmetzhanov; Hiroshi Nishiura.
Affiliation
  • Sung-mok Jung; Hokkaido University
  • Ryo Kinoshita; Hokkaido University
  • Robin N. Thompson; University of Oxford
  • Katsuma Hayashi; Hokkaido University
  • Natalie M. Linton; Hokkaido University
  • Yichi Yang; Hokkaido University
  • Andrei R. Akhmetzhanov; Hokkaido University
  • Hiroshi Nishiura; Hokkaido University
Preprint in En | PREPRINT-MEDRXIV | ID: ppmedrxiv-20018887
Journal article
A scientific journal published article is available and is probably based on this preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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ABSTRACT
ObjectiveVirological tests indicate that a novel coronavirus is the most likely explanation for the 2019-20 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. MethodsCharacteristics of the ongoing outbreak were collected in real time from two medical social media sites. These were compared against characteristics of ten existing pathogens that can induce 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. ResultsThe probability that Disease X is driving the outbreak was assessed as over 32% on 31 December 2019, one week before virus identification. After some specific pathogens were ruled out by laboratory tests on 5 Jan 2020, the inferred probability of Disease X was over 59%. ConclusionsWe showed quantitatively that the emerging outbreak of atypical pneumonia cases is consistent with causation by a novel pathogen. The proposed approach, that uses only routinely-observed non-virological data, can aid ongoing risk assessments even before virological test results become available.
License
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Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Etiology_studies / Experimental_studies / Observational_studies / Prognostic_studies Language: En Year: 2020 Document type: Preprint
Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Etiology_studies / Experimental_studies / Observational_studies / Prognostic_studies Language: En Year: 2020 Document type: Preprint