epiflows: an R package for risk assessment of travel-related spread of disease [version 3; peer review: 2 approved]
Paula Moraga,
Ilaria Dorigatti,
Zhian N. Kamvar,
Pawel Piatkowski,
Salla E. Toikkanen,
VP Nagraj,
Christl A. Donnelly,
Thibaut Jombart
Affiliations
Paula Moraga
Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, UK
Ilaria Dorigatti
MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, London, W2 1PG, UK
Zhian N. Kamvar
MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, London, W2 1PG, UK
Pawel Piatkowski
International Institute of Molecular and Cell Biology, Warsaw, Poland
Salla E. Toikkanen
National Institute for Health and Welfare, Helsinki, Finland
VP Nagraj
School of Medicine, Research Computing, University of Virginia, Virginia, USA
Christl A. Donnelly
Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
Thibaut Jombart
MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, London, W2 1PG, UK
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.
