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epiflows: an R package for risk assessment of travel-related spread of disease [version 3; peer review: 2 approved]

Authors :
Paula Moraga
Ilaria Dorigatti
Zhian N. Kamvar
Pawel Piatkowski
Salla E. Toikkanen
VP Nagraj
Christl A. Donnelly
Thibaut Jombart
Author Affiliations :
<relatesTo>1</relatesTo>Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, UK<br /><relatesTo>2</relatesTo>MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, London, W2 1PG, UK<br /><relatesTo>3</relatesTo>International Institute of Molecular and Cell Biology, Warsaw, Poland<br /><relatesTo>4</relatesTo>National Institute for Health and Welfare, Helsinki, Finland<br /><relatesTo>5</relatesTo>School of Medicine, Research Computing, University of Virginia, Virginia, USA<br /><relatesTo>6</relatesTo>Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK<br /><relatesTo>7</relatesTo>Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
Source :
F1000Research. 7:1374
Publication Year :
2019
Publisher :
London, UK: F1000 Research Limited, 2019.

Abstract

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease. epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

Details

ISSN :
20461402
Volume :
7
Database :
F1000Research
Journal :
F1000Research
Notes :
Revised Amendments from Version 2 The previous version of the manuscript had a small error. In Section "Arguments of the estimate_risk_spread() function" we wrote `num_sim` instead of `n_sim`. This has been corrected in the new version., , [version 3; peer review: 2 approved]
Publication Type :
Academic Journal
Accession number :
edsfor.10.12688.f1000research.16032.3
Document Type :
software-tool
Full Text :
https://doi.org/10.12688/f1000research.16032.3