epiflows: an R package for risk assessment of travel-related spread of disease

Exportations

Let C_S,W denote the cumulative number of infections in location S in time window W. Here, W denotes the temporal window between the first and the last disease case in location S. Note that Dorigatti et al.² calculated C_S,W by multiplying the number of confirmed and reported yellow fever cases by 10 to account for underreporting of asymptomatic and mild yellow fever cases.

Let pop_S be the resident population of the infectious location S, and $T_{S,D}^W$ the number of residents of location S travelling to location D in time window W. The per capita probability that a resident from the infectious location travelled to other location D during the time window W is given by

We assume that the incubation period (D_E) and the infectious period (D_I) are random variables, with associated probability distributions that are disease-specific. Using these, we can calculate the probability p_i that an infection incubated or is infectious in time window W as

Finally, the number of residents of the infectious location S that are infected and travel abroad during their incubation or infectious period during the time window W can be calculated as

                                                                                                                          E_S,D = C_S,W × p_D × p_i.

That is, E_S,D is a product of the cumulative number of infections in location S in time window W, the per capita probability that a resident of S travels to location D, and the probability that an infection incubated or is infectious in time window W.

Note here that if travel data are expressed annually $(T_{S,D}^A)$ instead of in the time window W, travel data in the time window can be obtained as $T_{S,D}^W$ = ($T_{S,D}^A$ × W)/365.

Importations

Let $T_{O,S}^W$ be the number of travelers visiting location S from location O in time window W, and let L_O denote the average length of stay. The per capita risk of infection of travelers visiting location S during their stay can be calculated as

The probability of returning to the home location while incubating or infectious is given by

Finally, the expected number of travelers infected during their stay in the infectious location and returning to their home location before the end of the infectious period can be calculated as the product of the number of travelers, the per capita risk of infection and the probability of returning home while incubating or infectious,

Note that, similarly to exportations, if travel data are expressed annually ($T_{O,S}^A$) instead of in the time window W, travel data in the time window can be obtained as $T_{O,S}^W$ = ($T_{O,S}^A$ × W)/365.

Total number of exportations and importations

Finally, the expected number of infections departing from the infectious location S to location O during the incubation or infectious periods can be computed as the sum of the number of infected residents of S travelling during their incubation or infectious periods, and the travelers from abroad that are infected during their stay in S and return to their origin location before the end of the infectious period. That is,

Average estimates and the relative uncertainty are calculated by taking into account the variability of the incubation and infectious periods. Specifically, the method samples a large number of times from the incubation and infectious distributions, which produces a full distribution for p_i (the probability that a disease case is incubated or infectious in the time window considered) and p_l (the probability of returning to the home location while incubating or infectious). This, in turn, creates variability in exportations E_S,O and importations I_S,O, and finally in the total number of infections introduced in location O, T_S,O.

Implementation

The R package epiflows [21] is hosted in the Comprehensive R Archive Network (CRAN) which is the main repository for R packages: http://CRAN.R-project.org/package=epiflows. Users can install epiflows in R by executing the following code:

install.packages("epiflows")

There is also a development version from GitHub which can be accessed at https://github.com/reconhub/epiflows. This version of the package may contain new features which are not incorporated in the version on CRAN yet but may be useful for some users. GitHub also includes issue tracking where users can note problems or suggestions for improvements. This development version from GitHub can be installed by using the install_github() function from the R package devtools³:

install.packages("devtools")       
library("devtools")                
install_github("reconhub/epiflows")

When installing epiflows, other R packages which epiflows depends on are also automatically installed. These packages include sp⁴ for manipulating spatial objects; geosphere⁵ for calculating distances between locations; and leaflet⁶ for visualization.

Operation

The main function of the package is estimate_risk_spread() which calculates the mean and 95% confidence intervals of the number of cases spread to different locations from an infectious location. It is also possible to use this function to produce a data frame with all simulations (not just the mean and 95% confidence intervals that is computed from the simulations). This permits the user to aggregate the estimates and calculate confidence intervals with different levels using single simulations. To execute this function the following information is needed:

Other useful functions are plot() which produces visualizations of population flows between locations, and add_coordinates() which finds the coordinates of the locations.

Data

We use the data YF_flows and YF_locations which are contained in the epiflows package as data(YF_flows) and data(YF_locations), respectively. These data contain the population size, the assumed number of yellow fever infections, dates of first and last case reporting, number of travelers and length of stay for the states of Espirito Santo, Minas Gerais, Rio de Janeiro, Sao Paulo, and for the whole region of Southeast Brazil (which comprises the four states of Espirito Santo, Minas Gerais, Rio de Janeiro and Sao Paulo) in the period December 2016 to May 2017 [19], [20].

Following Dorigatti et al.², the total number of yellow fever infections in each of the Brazilian states was calculated by multiplying the cumulative number of confirmed yellow fever cases reported in 7 by 10 to account for underreporting of asymptomatic and mild yellow fever cases. The dates of first and last case reported in each state were derived as described by Dorigatti et al.². Population data were obtained from the Brazilian Institute of Geography and Statistics website⁸. These data also contain the number of travelers in the specified time window between the states of Espirito Santo, Minas Gerais, Rio de Janeiro, Sao Paulo (and the whole Southeast Brazilian region) and other countries. These estimates were obtained from World Tourism Organization data on the volume of air, land and water border crossings for Brazil for the year 2015⁹, having assumed that travelers were distributed across the Brazilian states according to the relative population density and having accounted for information on the monthly distribution of tourism and on the average duration of stay of international visitors to Brazil¹⁰, as detailed in 2.

The epiflows object

To aid in data organization between flows and metadata, we have implemented the epiflows object. This inherits the epicontacts object from the epicontacts package¹¹, storing three elements:

Because a flow of cases from one location to another can be thought of as a contact with a wider scope, the epiflows object inherits from the epicontacts object, where locations are stored in the linelist element and flows are stored in the contacts element (though the user does not need to interact with these elements by name). By building on the epicontacts object, we ensure that all the methods for sub-setting an object of class epicontacts also applies to epiflows, reducing the maintenance effort.

An epiflows object can be created with the make_epiflows() function by providing a data frame flows with the number of travelers between locations, a data frame locations with information about the locations, and the names of the columns of data frame locations indicating the name of each variable.

In the data frame flows each row represents the number of travelers from one location to the next. flows has at least three columns: columns from and to indicating where the flow starts and ends, respectively, and column n indicating the number of travelers that are in the flow. Data frame YF_flows contains the population flows of the Brazil data.

library("epiflows")

## epiflows is loaded with the following global variables in `global_vars()`:
## coordinates, pop_size, duration_stay, first_date, last_date, num_cases

data("YF_flows") 
head(YF_flows) 

##               from    to         n
## 1   Espirito Santo Italy  2827.572
## 2     Minas Gerais Italy 15714.103
## 3   Rio de Janeiro Italy  8163.938
## 4        Sao Paulo Italy 34038.681
## 5 Southeast Brazil Italy 76281.763
## 6   Espirito Santo Spain  3270.500

In data frame locations each row represents a location, and columns specify useful information about the locations such as ID, population, number of cases, dates and length of stay. locations must contain at least one column specifying the location ID used in the flows data frame. YF_locations contains, for each Brazilian state considered in our example, the code (location_code), the population (location_population), the number of assumed infections in the time window (num_cases_time_window), and the dates of the first and last case reported (first_date_cases and last_date_cases, respectively). It also contains length_of_stay which are the lengths of stay (in days) of the travelers visiting Brazil from other countries.

data("YF_locations")
head(YF_locations)

##      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
##   first_date_cases last_date_cases length_of_stay
## 1       2017-01-04      2017-04-30             NA
## 2       2016-12-19      2017-04-20             NA
## 3       2017-02-19      2017-05-10             NA
## 4       2016-12-17      2017-04-20             NA
## 5       2016-12-17      2017-05-10             NA
## 6             <NA>            <NA>           10.9

Then, we can create an epiflows object called Brazil_epiflows as follows.

Brazil_epiflows <- make_epiflows(flows         = YF_flows,               
                                 locations     = YF_locations,           
                                 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"       
)                                                                        

Arguments of the estimate_risk_spread() function

The arguments that need to be specified in estimate_risk_spread() to calculate the cases or infections introduced in other countries are as follows. The first argument is an epiflows object containing the number of travelers between locations, the population size, the number of cases, and the first and last dates of reporting in the infectious location, and the average length of stay in days of travelers from other locations visiting the infectious location.

The second argument of estimate_risk_spread() is location_code which is a character string denoting the infectious location code. We also need to specify the incubation and infectious period distributions. Specifically, we need to provide functions with a single argument n that generate n random incubation and infectious periods. To do this, we can use random generation functions of distributions that are implemented in R including Normal rnorm(), LogNormal rlnorm(), Gamma rgamma(), Weibull rweibull(), and Exponential rexp(). Details about the meaning and arguments of these functions can be obtained by typing ? and the function name (e.g., ?rnorm). We should consider the literature carefully before deciding on appropriate distributions. Examples of systematic reviews of incubation period distributions are 12 and 13. In this example, we use the specified distributions and parameterisation following 14 and 15. Thus, we assume that the incubation period D_E is log-normally distributed with mean 4.6 days and variance 2.7 days, and that the infectious period D_I is normally distributed with mean 4.5 days and variance 0.6 days. We can define functions incubation() and infectious() as

incubation <- function(n) {
  rlnorm(n, 1.46, 0.35)    
}                          
                           
infectious <- function(n) {
  rnorm(n, 4.5, 1.5/1.96)  
}                          

Argument n_sim is the number of simulations to be drawn from the incubation and infectious period distributions. It is recommended to use at least 1,000 simulations. The last argument of estimate_risk_spread() is return_all_simulations. This is a logical value indicating whether the returned object should be a data frame with all simulations (return_all_simulations= TRUE), or a data frame with the mean and lower and upper limits of a 95% confidence interval of the number of infections spread to each location (return_all_simulations = FALSE).

Execution of the estimate_risk_spread() function

Once we have constructed the objects needed to call estimate_risk_spread() we can execute the function and obtain the estimated mean number of cases spread to each country and the 95% confidence intervals. The code to calculate the cases spread from Espirito Santo is the following:

set.seed(2018-07-25)                                         
res <- estimate_risk_spread(Brazil_epiflows,                 
                            location_code = "Espirito Santo",
                            r_incubation = incubation,       
                            r_infectious = infectious,       
                            n_sim = 1e5                      
)                                                            

The results returned by estimate_risk_spread() are stored in the res object. This is a data frame with the columns mean_cases indicating the mean number of cases spread to each location, and lower_limit_95CI and upper_limit_95CI indicating the lower and upper limits of 95% confidence intervals. The result object is shown below.

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

We can plot the results with ggplot() as follows (Figure 1).

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))                                                                       

Figure 1. Mean number of yellow fever cases and 95% CI spread from Espirito Santo to other locations.

Note that if we set return_all_simulations equal to TRUE, the result object res will be a data frame with all simulations.

res <- estimate_risk_spread(Brazil_epiflows,                 
                            location_code = "Espirito Santo",
                            r_incubation = incubation,       
                            r_infectious = infectious,       
                            n_sim = 1e5,                     
                            return_all_simulations = TRUE    
)                                                            

head(res)

##          Italy     Spain  Portugal   Germany United Kingdom
## [1,] 0.1946102 0.1967196 0.2003120 0.1611614      0.1483634
## [2,] 0.2861083 0.2875748 0.3035947 0.2442577      0.1937063
## [3,] 0.1883587 0.1904003 0.1938773 0.1559844      0.1455385
## [4,] 0.2128377 0.2151446 0.2190734 0.1762560      0.1566001
## [5,] 0.2087285 0.2109909 0.2148439 0.1728531      0.1547432
## [6,] 0.2747205 0.2744030 0.2853804 0.2296033      0.1857099
##      United States of America Argentina     Chile   Uruguay  Paraguay
## [1,]                0.7999806 0.9754866 0.2289530 0.2082646 0.1552200
## [2,]                1.2124582 1.4784567 0.3470033 0.3156478 0.1837588
## [3,]                0.7742827 0.9441508 0.2215983 0.2015745 0.1502338
## [4,]                0.8749078 1.0668519 0.2503970 0.2277709 0.1619986
## [5,]                0.8580163 1.0462546 0.2455627 0.2233735 0.1609097
## [6,]                1.1397160 1.3897558 0.3261846 0.2967103 0.1790695

Using res, we can calculate the mean and 95% confidence intervals as follows.

meancases <- colMeans(res, na.rm = TRUE)                                   
quant     <- t(apply(res, 2, stats::quantile, c(.025, .975), na.rm = TRUE))
data.frame(mean_cases = meancases,                                         
           lower_limit_95CI = quant[, 1],                                  
           upper_limit_95CI = quant[, 2]                                   
)                                                                          

##			   mean_cases lower_limit_95CI upper_limit_95CI
## Italy		    0.2233975	     0.1522848	      0.3081296
## Spain		    0.2255621	     0.1539354	      0.3130456
## Portugal		    0.2317602	     0.1567465	      0.3388166
## Germany		    0.1864633	     0.1261107	      0.2725956
## United Kingdom	    0.1613646	     0.1196739	      0.2091694
## United States of America 0.9255753	     0.6259942	      1.3531231
## Argentina		    1.1286353	     0.7633297	      1.6499817
## Chile		    0.2648933	     0.1791584	      0.3872613
## Uruguay		    0.2409532	     0.1629695	      0.3522681
## Paraguay		    0.1619776	     0.1214615	      0.1928268

Visualize population flows

We can visualize flows of people travelling between locations using plot() and passing as first parameter an epiflows object containing the population flows, and as second parameter the type of plot we wish to produce. Population flows can be displayed on an interactive map, as a network or as a grid between origins and destinations as described in the following sections.

Flows displayed on an interactive map

We can visualize population flows on an interactive map using plot() with the parameter type="map". For this option to work, the epiflows object needs to include the longitude and latitude of the locations in decimal degree format. If coordinates are known, they can be added to the epiflows object using the add_coordinates() function from the epiflows package. In our example, the longitude and latitude data are in the data frame YF_coordinates.

data("YF_coordinates")
head(YF_coordinates)  

##		   id	    lon	      lat
## 1   Espirito Santo -40.30886 -19.18342
## 2	 Minas Gerais -44.55503 -18.51218
## 3   Rio de Janeiro -43.17290 -22.90685
## 4	    Sao Paulo -46.63331 -23.55052
## 5 Southeast Brazil -46.20915 -20.33318
## 6	    Argentina -63.61667 -38.41610

They can be added as follows.

Brazil_epiflows <- add_coordinates(Brazil_epiflows,                   
                                   coordinates = YF_coordinates[, -1])

If coordinates are unknown, we may resort to one of the freely available tools for geocoding. For example, we can use the geocode() function from the ggmap package¹⁶. This function finds the latitude and longitude of a given location using either the Data Science Toolkit or Google Maps. We can also use add_coordinates() which uses geocode() to find the coordinates and directly add them to the epiflows object as follows.

Brazil_epiflows <- add_coordinates(Brazil_epiflows, overwrite = TRUE)

Once we have assigned coordinates to the epiflows object, we can use plot() with type="map" to visualize the population flows between locations in an interactive map (Figure 2).

plot(Brazil_epiflows, type = "map")

Figure 2. Population flows between Brazil states and other locations plotted using type = "map".

The produced map can be zoomed and permits an easy examination of flows. plot() uses the gcIntermediate() function from the geosphere package⁵ to obtain the great circle arcs between locations, and then uses the leaflet package⁶ to create an interactive map with the connection lines. The connection lines are coloured according to flow volume, and as the mouse passes over the lines, lines highlight and information about connections is shown. We can also include parameters to specify a title, the center of the map or a color palette. An interactive version of this visualization is shown here: https://www.repidemicsconsortium.org/epiflows/articles/introduction.html#introduction-epiflows-map.

Flows displayed as a network

Population flows can also be displayed as a dynamic network using plot() with type = "network" (Figure 3).

plot(Brazil_epiflows, type = "network")

Figure 3. Population flows between Brazil states and other locations plotted using type = "network".

Flows displayed as a grid between origins and destinations

Finally, population flows can also be shown as a grid between locations with the option type="grid" (Figure 4).

plot(Brazil_epiflows, type = "grid")

Figure 4. Population flows between Brazil states and other locations plotted using type = "grid".

This plot shows flows between locations as points by positioning origins and destination in y and x axes, respectively. When using this option, additional arguments can be passed to set the size, color or shape of the points as in function geom_point() of package ggplot2¹⁸. As the network plot, the grid plot can be used when coordinates of locations are missing.