| September 10, 2018
In this post I show how to use the R package epiflows for risk assessment of travel-related spread of disease.
Infectious diseases can spread beyond national borders.
Residents of an infectious location can travel abroad and infect people living there.
Also, travelers can acquire a disease while staying in a foreign country, and
then seed new outbreaks in their home country after their return.
The amount of disease spread will depend on factors such as the number of infected cases,
population flows between locations,
lengths of stay, and
disease incubation and infectious periods.
The epiflows package uses the mathematical model developed by Dorigatti et al. (2017) to integrate all these factors and calculate the mean number of infections that could be spread to other locations together with uncertainty measures.
Here, I explain how to use epiflows to estimate the spread of yellow fever from Espirito Santo, Brazil, to other countries in the period December 2016 to May 2017. Details of the underlying model and other options of the package can be seen in Moraga et al. (2018).
Data
We use the data of yellow fever in Brazil that is contained in the epiflows package as data("Brazil_epiflows").
This data contains an object of class epiflows which has, for each location, the population, the number of disease cases, dates, and lengths of stay. Brazil_epiflows also contains the population flows between the Brazil states and other countries.
library(epiflows)
data("Brazil_epiflows")
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_populat~ num_cases_time_~ first_date_cases last_date_cases
## <chr> <dbl> <dbl> <fct> <fct>
## 1 Espirito~ 3973697 2600 2017-01-04 2017-04-30
## 2 Minas Ge~ 20997560 4870 2016-12-19 2017-04-20
## 3 Rio de J~ 16635996 170 2017-02-19 2017-05-10
## 4 Sao Paulo 44749699 200 2016-12-17 2017-04-20
## 5 Southeas~ 86356952 7840 2016-12-17 2017-05-10
## 6 Argentina NA NA <NA> <NA>
## 7 Chile NA NA <NA> <NA>
## 8 Germany NA NA <NA> <NA>
## 9 Italy NA NA <NA> <NA>
## 10 Paraguay NA NA <NA> <NA>
## 11 Portugal NA NA <NA> <NA>
## 12 Spain NA NA <NA> <NA>
## 13 United K~ NA NA <NA> <NA>
## 14 United S~ NA NA <NA> <NA>
## 15 Uruguay 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 rowsCall estimate_risk_spread()
We can use the function estimate_risk_spread() to calculate the mean number of cases spread from the state Espirito Santo to other locations and the 95% confidence intervals.
To call this function we need to specify the epiflows object (Brazil_epiflows), the code of the location ("Espirito Santo"), the functions of the disease incubation and infectious distributions, and the number of simulations.
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 = 100000
)
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 0.1926966Results
The results object is a data frame containing the mean number of cases spread to each country and the lower and upper limits of the 95% confidence intervals. We can plot the results with the function ggplot() of the ggplot2 package as follows.
library(ggplot2)
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))
Visualization of population flows
epiflows also incorporates functions to plot population flows between locations. There are three types of plots that can be produced:
- Interactive map. This type of plot requires
Brazil_epiflowsto contain the coordinates of the locations. In this example the coordinates are in data frameYF_coordinatesand can be added toBrazil_epiflowswith theadd_coordinates()function ofepiflows.
data("YF_coordinates")
Brazil_epiflows <- add_coordinates(Brazil_epiflows, coordinates = YF_coordinates[, -1])
plot(Brazil_epiflows, type = "map")- Dynamic network with locations shown as nodes and connections between them representing population flows.
plot(Brazil_epiflows, type = "network")- Grid with population flows shown as points.
plot(Brazil_epiflows, type = "grid")
Conclusion
The epiflows package allows the
identification of locations where diseases are most likely to spread. This information can help public health officials to limit the global spread of local outbreaks.
epiflows has been developed by several members of the R Epidemics Consortium (RECON).
You can see the development version here.
Please get in touch via GitHub issues if you have any comment, question or would like to contribute!
References
Moraga P, Dorigatti I, Kamvar ZN, Piatkowski P, Toikkanen SE, Nagraj VP, Donnelly CA and Jombart T. (2018), epiflows: an R package for risk assessment of travel-related spread of disease. F1000Research, 7:1374.
Dorigatti I, Hamlet A, Aguas R, Cattarino L, Cori A, Donnelly CA, Garske T, Imai N and Ferguson NM (2017), International risk of yellow fever spread from the ongoing outbreak in Brazil, December 2016 to May 2017. Euro Surveill. 22(28):pii=30572.