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Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread.
Bhatia, Sangeeta; Lassmann, Britta; Cohn, Emily; Desai, Angel N; Carrion, Malwina; Kraemer, Moritz U G; Herringer, Mark; Brownstein, John; Madoff, Larry; Cori, Anne; Nouvellet, Pierre.
Afiliación
  • Bhatia S; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Faculty of Medicine, London, UK. s.bhatia@imperial.ac.uk.
  • Lassmann B; ProMED, International Society for Infectious Diseases, Brookline, MA, USA.
  • Cohn E; Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, MA, USA.
  • Desai AN; ProMED, International Society for Infectious Diseases, Brookline, MA, USA.
  • Carrion M; ProMED, International Society for Infectious Diseases, Brookline, MA, USA.
  • Kraemer MUG; Department of Health Science, Sargent College, Boston University, Boston, MA, USA.
  • Herringer M; Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, MA, USA.
  • Brownstein J; Department of Zoology, Tinbergen Building, Oxford University, Oxford, UK.
  • Madoff L; Department of Pediatrics, Harvard Medical School, Boston, USA.
  • Cori A; healthsites.io, London, UK.
  • Nouvellet P; Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, MA, USA.
NPJ Digit Med ; 4(1): 73, 2021 Apr 16.
Article en En | MEDLINE | ID: mdl-33864009
ABSTRACT
Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013-2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1-4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Etiology_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: NPJ Digit Med Año: 2021 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Etiology_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: NPJ Digit Med Año: 2021 Tipo del documento: Article País de afiliación: Reino Unido
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