Your browser doesn't support javascript.
loading
Using predicted imports of 2019-nCoV cases to determine locations that may not be identifying all imported cases.
De Salazar, P M; Niehus, R; Taylor, A; Buckee, C; Lipsitch, M.
Affiliation
  • De Salazar PM; Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
  • Niehus R; Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
  • Taylor A; Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
  • Buckee C; Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
  • Lipsitch M; Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
medRxiv ; 2020 Feb 11.
Article de En | MEDLINE | ID: mdl-32511458
ABSTRACT
Cases from the ongoing outbreak of atypical pneumonia caused by the 2019 novel coronavirus (2019-nCoV) exported from mainland China can lead to self-sustained outbreaks in other populations. Internationally imported cases are currently being reported in several different locations. Early detection of imported cases is critical for containment of the virus. Based on air travel volume estimates from Wuhan to international destinations and using a generalized linear regression model we identify locations which may potentially have undetected internationally imported cases.
Mots clés

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Prognostic_studies / Risk_factors_studies / Screening_studies Langue: En Journal: MedRxiv Année: 2020 Type de document: Article Pays d'affiliation: États-Unis d'Amérique

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Prognostic_studies / Risk_factors_studies / Screening_studies Langue: En Journal: MedRxiv Année: 2020 Type de document: Article Pays d'affiliation: États-Unis d'Amérique