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 data
Estimating the number of new cases flowing to other countries from Espirito Santo, Brazil (Dorigatti et al., 2017).
library("epiflows")## epiflows is loaded with the following global variables in `global_vars()`:
## coordinates, pop_size, duration_stay, first_date, last_date, num_cases
library("ggplot2")
data("Brazil_epiflows")
print(Brazil_epiflows)##
## /// Epidemiological Flows //
##
## // class: epiflows, epicontacts
## // 15 locations; 100 flows; directed
## // optional variables: pop_size, duration_stay, num_cases, first_date, last_date
##
## // locations
##
## # A tibble: 15 x 6
## id location_popula… num_cases_time_… first_date_cases last_date_cases
## * <chr> <dbl> <dbl> <fct> <fct>
## 1 Espi… 3973697 2600 2017-01-04 2017-04-30
## 2 Mina… 20997560 4870 2016-12-19 2017-04-20
## 3 Rio … 16635996 170 2017-02-19 2017-05-10
## 4 Sao … 44749699 200 2016-12-17 2017-04-20
## 5 Sout… 86356952 7840 2016-12-17 2017-05-10
## 6 Arge… NA NA <NA> <NA>
## 7 Chile NA NA <NA> <NA>
## 8 Germ… NA NA <NA> <NA>
## 9 Italy NA NA <NA> <NA>
## 10 Para… NA NA <NA> <NA>
## 11 Port… NA NA <NA> <NA>
## 12 Spain NA NA <NA> <NA>
## 13 Unit… NA NA <NA> <NA>
## 14 Unit… NA NA <NA> <NA>
## 15 Urug… NA NA <NA> <NA>
## # ... with 1 more variable: length_of_stay <dbl>
##
## // flows
##
## # A tibble: 100 x 3
## from to n
## <chr> <chr> <dbl>
## 1 Espirito Santo Italy 2828.
## 2 Minas Gerais Italy 15714.
## 3 Rio de Janeiro Italy 8164.
## 4 Sao Paulo Italy 34039.
## 5 Southeast Brazil Italy 76282.
## 6 Espirito Santo Spain 3270.
## 7 Minas Gerais Spain 18176.
## 8 Rio de Janeiro Spain 9443.
## 9 Sao Paulo Spain 39371.
## 10 Southeast Brazil Spain 88231.
## # ... with 90 more rows
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
)## Exportations done
## Importations done
res## mean_cases lower_limit_95CI upper_limit_95CI
## Italy 0.2233656 0.1520966 0.3078136
## Spain 0.2255171 0.1537452 0.3126801
## Portugal 0.2317019 0.1565528 0.3383112
## Germany 0.1864162 0.1259548 0.2721890
## United Kingdom 0.1613418 0.1195261 0.2089475
## United States of America 0.9253419 0.6252207 1.3511047
## Argentina 1.1283506 0.7623865 1.6475205
## Chile 0.2648277 0.1789370 0.3866836
## Uruguay 0.2408942 0.1627681 0.3517426
## Paraguay 0.1619724 0.1213114 0.1926966
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 theepicontactsclass from the epicontacts pacakge.make_epiflows(): a constructor forepiflowsfrom either a pair of data frames or inflows and outflows and location data frame.add_coordinates(): add latitude/longitude to the location data in anepiflowsobject usingggmap::geocode()
x[j = myLocations]: subset anepiflowsobject to location(s) myLocationsplot(): plot flows from anepiflowsobject on a leaflet world mapprint(): print summary for anepiflowsobject
These 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 metadata
get_flows(): return flow dataget_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:
- A Brief Introduction to epiflows:
vignette("introduction", package = "epiflows") - Constructing epiflows objects:
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