This paper is in the following e-collection/theme issue:

Outbreak and Pandemic Preparedness and Management (1679) JMIR Theme Issue: COVID-19 Special Issue (2394) Surveillance Systems (430) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1484) Infectious Diseases (non-STD/STI) (1490) Hygiene and Infection Prevention (300) Statistical Methods for Surveillance and Population Health (148) New Methods (911) Innovative Methods in Public Health and Surveillance (307)

Published on in Vol 7, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30648, first published .
Predicting COVID-19 Transmission to Inform the Management of Mass Events: Model-Based Approach

Predicting COVID-19 Transmission to Inform the Management of Mass Events: Model-Based Approach

Predicting COVID-19 Transmission to Inform the Management of Mass Events: Model-Based Approach

Authors of this article:

Claire Donnat1 Author Orcid Image ;   Freddy Bunbury2 Author Orcid Image ;   Jack Kreindler3 Author Orcid Image ;   David Liu1 Author Orcid Image ;   Filippos T Filippidis3 Author Orcid Image ;   Tonu Esko4 Author Orcid Image ;   Austen El-Osta3 Author Orcid Image ;   Matthew Harris3 Author Orcid Image
Article Authors Cited by (4) Tweetations (24) Metrics

Claire Donnat   1 , BSc, MSc, PhD ;   Freddy Bunbury   2 , BA, MRES, PhD ;   Jack Kreindler   3 , MD ;   David Liu   1 , BSc ;   Filippos T Filippidis   3 , MPH, MSc, MD, PhD ;   Tonu Esko   4 , PhD ;   Austen El-Osta   3 , BSc, MSc, MPA, PhD ;   Matthew Harris   3 , MBBS, MSc, FFPH, PGCE, DPhil

1 Department of Statistics, University of Chicago, Chicago, IL, United States

2 Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, United States

3 Faculty of Medicine, School of Public Health, Imperial College, London, United Kingdom

4 Institute of Genomics, University of Tartu, Tartu, Estonia

Corresponding Author:

  • Claire Donnat, BSc, MSc, PhD
  • Department of Statistics
  • University of Chicago
  • 5747 South Ellis Avenue
  • Chicago, IL, 60637
  • United States
  • Phone: 1 773-702-9890
  • Email: cdonnat@uchicago.edu