epiflows
is a package for predicting and visualising spread of infectious diseases based on flows between geographical locations, e.g., countries.
epiflows
provides functions for calculating spread estimates, handling flow data, and visualization.
Currently, epiflows is a work in progress and can be installed from github using the remotes, ghit, or devtools package:
if (!require("remotes")) install.packages("remotes", repos = "https://cloud.rstudio.org") remotes::install_github("reconhub/epiflows")
A publication describing this package has been submitted to F1000 research and can be cited as:
Moraga P, Dorigatti I, Kamvar ZN, Piatkowski P, Toikkanen SE, Nagraj V, Donnelly CA, and Jombart T epiflows: an R package for risk assessment of travel-related spread of disease [version 1; referees: awaiting peer review]. F1000Research 2018, 7:1374 (doi: 10.12688/f1000research.16032.1)
The main features of the package include:
estimate_risk_spread()
: calculate estimates (point estimate and 95% CI) for disease spread from flow dataEstimating the number of new cases flowing to other countries from Espirito Santo, Brazil (Dorigatti et al., 2017).
library("epiflows") library("ggplot2") data("Brazil_epiflows") print(Brazil_epiflows) set.seed(2018-07-25) res <- estimate_risk_spread(Brazil_epiflows, location_code = "Espirito Santo", r_incubation = function(n) rlnorm(n, 1.46, 0.35), r_infectious = function(n) rnorm(n, 4.5, 1.5/1.96), n_sim = 1e5 ) res res$location <- rownames(res) ggplot(res, aes(x = mean_cases, y = location)) + geom_point(size = 2) + geom_errorbarh(aes(xmin = lower_limit_95CI, xmax = upper_limit_95CI), height = .25) + theme_bw(base_size = 12, base_family = "Helvetica") + ggtitle("Yellow Fever Spread from Espirito Santo, Brazil") + xlab("Number of cases") + xlim(c(0, NA))
epiflows
: an S3 class for storing flow data, as well as country metadata.
This class contains two data frames containing flows and location metadata based on the epicontacts
class from the epicontacts pacakge.make_epiflows()
: a constructor for epiflows
from either a pair of data frames or inflows and outflows and location data frame.add_coordinates()
: add latitude/longitude to the location data in an epiflows
object using ggmap::geocode()
x[j = myLocations]
: subset an epiflows
object to location(s) myLocationsplot()
: plot flows from an epiflows
object on a leaflet world mapprint()
: print summary for an epiflows
objectThese are variables that estimate_risk_spread()
understands from the epiflows
object. These represent keys that have values mapping to column names in your
locations metadata.
global_vars()
: view, set, and reset global variables for epiflowsget_vars()
: access variables from the locations metadataset_vars()
: map variables to columns in the locations metadataget_flows()
: return flow data get_locations()
: return metadata for all locationsget_coordinates()
: return coordinates for each location (if provided)get_id()
: return a vector of location identifiersget_n()
: return the number of cases per flowget_pop_size()
: return the population size for each location (if provided)An overview and examples of epiflows are provided in the vignettes:
vignette("introduction", package = "epiflows")
vignette("epiflows-class", package = "epiflows")
Bug reports and feature requests should be posted on github using the issue system. All other questions should be posted on the RECON forum:
http://www.repidemicsconsortium.org/forum/
Contributions are welcome via pull requests.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
Dorigatti I, Hamlet A, Aguas R, Cattarino L, Cori A, Donnelly CA, Garske T, Imai N, Ferguson NM. International risk of yellow fever spread from the ongoing outbreak in Brazil, December 2016 to May 2017. Euro Surveill. 2017;22(28):pii=30572. DOI: 10.2807/1560-7917.ES.2017.22.28.30572
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