README.md

In reconhub/epiflows: Predicting Disease Spread from Flow Data

Welcome to the epiflows package!

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.

Installing the package

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")

Citation

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)

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")

## 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))

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 the epicontacts class from the epicontacts pacakge.
• make_epiflows(): a constructor for epiflows from either a pair of data frames or inflows and outflows and location data frame.
• add_coordinates(): add latitude/longitude to the location data in an epiflows object using ggmap::geocode()

Basic methods

• x[j = myLocations]: subset an epiflows object to location(s) myLocations
• plot(): plot flows from an epiflows object on a leaflet world map
• print(): print summary for an epiflows object

Global variables

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 epiflows
• get_vars(): access variables from the locations metadata
• set_vars(): map variables to columns in the locations metadata

Accessors

• get_flows(): return flow data
• get_locations(): return metadata for all locations
• get_coordinates(): return coordinates for each location (if provided)
• get_id(): return a vector of location identifiers
• get_n(): return the number of cases per flow
• get_pop_size(): return the population size for each location (if provided)

Resources

Vignettes

An overview and examples of epiflows are provided in the vignettes:

1. A Brief Introduction to epiflows: vignette("introduction", package = "epiflows")
2. Constructing epiflows objects: vignette("epiflows-class", package = "epiflows")

Getting help online

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.

References

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

reconhub/epiflows documentation built on Sept. 21, 2023, 1:17 p.m.