By Paula Moraga | 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_populati~ num_cases_time_wi~ 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 o~ NA NA <NA>
## 15 Uruguay NA NA <NA>
## # ... with 2 more variables: last_date_cases <fct>, 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
```

# Call 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 0.1926966
```

# Results

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_epiflows`

to contain the coordinates of the locations. In this example the coordinates are in data frame`YF_coordinates`

and can be added to`Brazil_epiflows`

with the`add_coordinates()`

function of`epiflows`

.

```
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.