What does it do?
The main features of the package include:
Estimation of risk
estimate_risk_spread(): calculate estimates (point estimate and 95% CI) for disease spread from flow data
Example
Estimating 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)##
## /// 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 × 6
## id location_population num_cases_time_window first_date_cases
## <chr> <dbl> <dbl> <fct>
## 1 Espirito Santo 3973697 2600 2017-01-04
## 2 Minas Gerais 20997560 4870 2016-12-19
## 3 Rio de Janeiro 16635996 170 2017-02-19
## 4 Sao Paulo 44749699 200 2016-12-17
## 5 Southeast Brazil 86356952 7840 2016-12-17
## 6 Argentina NA NA <NA>
## 7 Chile NA NA <NA>
## 8 Germany NA NA <NA>
## 9 Italy NA NA <NA>
## 10 Paraguay NA NA <NA>
## 11 Portugal NA NA <NA>
## 12 Spain NA NA <NA>
## 13 United Kingdom NA NA <NA>
## 14 United States of … NA NA <NA>
## 15 Uruguay NA NA <NA>
## # ℹ 2 more variables: last_date_cases <fct>, length_of_stay <dbl>
##
## // flows
##
## # A tibble: 100 × 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.
## # ℹ 90 more rowsset.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 doneres## 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 0.1926966res$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))
Data structure to store flows and metadata
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()
The easiest way to create an epiflows object is from two data frames
(type vignette("epiflows-class") for more details:
data("YF_locations")
data("YF_flows")
data("YF_coordinates")
loc <- merge(x = YF_locations,
y = YF_coordinates,
by.x = "location_code",
by.y = "id",
sort = FALSE)
loc## location_code location_population num_cases_time_window
## 1 Espirito Santo 3973697 2600
## 2 Minas Gerais 20997560 4870
## 3 Rio de Janeiro 16635996 170
## 4 Sao Paulo 44749699 200
## 5 Southeast Brazil 86356952 7840
## 6 Argentina NA NA
## 7 Chile NA NA
## 8 Germany NA NA
## 9 Italy NA NA
## 10 Paraguay NA NA
## 11 Portugal NA NA
## 12 Spain NA NA
## 13 United Kingdom NA NA
## 14 United States of America NA NA
## 15 Uruguay NA NA
## first_date_cases last_date_cases length_of_stay lon lat
## 1 2017-01-04 2017-04-30 NA -40.308863 -19.18342
## 2 2016-12-19 2017-04-20 NA -44.555031 -18.51218
## 3 2017-02-19 2017-05-10 NA -43.172897 -22.90685
## 4 2016-12-17 2017-04-20 NA -46.633309 -23.55052
## 5 2016-12-17 2017-05-10 NA -46.209155 -20.33318
## 6 <NA> <NA> 10.9 -63.616672 -38.41610
## 7 <NA> <NA> 10.3 -71.542969 -35.67515
## 8 <NA> <NA> 22.3 10.451526 51.16569
## 9 <NA> <NA> 30.1 12.567380 41.87194
## 10 <NA> <NA> 7.3 -58.443832 -23.44250
## 11 <NA> <NA> 27.2 -8.224454 39.39987
## 12 <NA> <NA> 27.2 -3.749220 40.46367
## 13 <NA> <NA> 19.5 -3.435973 55.37805
## 14 <NA> <NA> 18.5 -95.712891 37.09024
## 15 <NA> <NA> 8.0 -55.765835 -32.52278ef <- make_epiflows(flows = YF_flows,
locations = loc,
coordinates = c("lon", "lat"),
pop_size = "location_population",
duration_stay = "length_of_stay",
num_cases = "num_cases_time_window",
first_date = "first_date_cases",
last_date = "last_date_cases"
)
ef##
## /// Epidemiological Flows //
##
## // class: epiflows, epicontacts
## // 15 locations; 100 flows; directed
## // optional variables: coordinates, pop_size, duration_stay, num_cases, first_date, last_date
##
## // locations
##
## # A tibble: 15 × 8
## id location_population num_cases_time_window first_date_cases
## <chr> <dbl> <dbl> <fct>
## 1 Espirito Santo 3973697 2600 2017-01-04
## 2 Minas Gerais 20997560 4870 2016-12-19
## 3 Rio de Janeiro 16635996 170 2017-02-19
## 4 Sao Paulo 44749699 200 2016-12-17
## 5 Southeast Brazil 86356952 7840 2016-12-17
## 6 Argentina NA NA <NA>
## 7 Chile NA NA <NA>
## 8 Germany NA NA <NA>
## 9 Italy NA NA <NA>
## 10 Paraguay NA NA <NA>
## 11 Portugal NA NA <NA>
## 12 Spain NA NA <NA>
## 13 United Kingdom NA NA <NA>
## 14 United States of … NA NA <NA>
## 15 Uruguay NA NA <NA>
## # ℹ 4 more variables: last_date_cases <fct>, length_of_stay <dbl>, lon <dbl>,
## # lat <dbl>
##
## // flows
##
## # A tibble: 100 × 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.
## # ℹ 90 more rowsBasic methods
x[j = myLocations]: subset anepiflowsobject to location(s) myLocations and all that it(they) interact(s) with.print(): print summary for anepiflowsobject
Plotting
You can use plot() to plot flows from an
epiflows object on one of:
- leaflet
world map (default if coordinates; standalone function:
map_epiflows()) - a visNetwork
interactive graph (default if no coordinates; standalone function:
vis_epiflows()) - a grid/bubble plot (standalone function:
grid_epiflows()).
vis_epiflows(ef)map_epiflows(ef)grid_epiflows(ef)## Warning: The `guide` argument in `scale_*()` cannot be `FALSE`. This was deprecated in
## ggplot2 3.3.4.
## ℹ Please use "none" instead.
## ℹ The deprecated feature was likely used in the epiflows package.
## Please report the issue at <https://github.com/reconhub/epiflows/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Accessors
get_flows(): return flow dataget_locations(): return metadata for all locationsget_vars(): access variables from metadataget_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)