YF_Brazil.RdThis data set contains flows to and from five states in Brazil formatted in a list with the following items:
data("Brazil_epiflows") data("YF_coordinates") data("YF_locations") data("YF_flows") data("YF_Brazil")
An object of class list of length 4.
$states: a data frame containing metadata for five Brazilian States:
Espirito Santo, Minas Gerais, Rio de Janeiro, Sao Paulo, and Southeast
Brazil
$location_code : names of the states
$location_population : population size for each state
$num_cases_time_window : number of cases recorded between 2016-12 and 2017-05
$first_date_cases : date of first disease case in the given location in ISO 8601 format
$last_date_cases : date of last disease case in the given location in ISO 8601 format
$T_D A matrix containing the number of travellers from the infectious
location visiting other locations
$T_O A matrix containing the number of travellers visiting the infectious location
$length_of_stay A named vector containing the average length of stay in
days of travellers from other locations visiting the infectious locations.
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
make_epiflows() for transformation to an epiflows object
estimate_risk_spread()
# This is an example of an epiflows object 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_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 #># The above data was constructed from a data frame containing flows and # one containing location metadata data("YF_flows") data("YF_locations") ef <- 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" ) # Both of the above data frames were constructed like so: data("YF_Brazil") # Create the flows data frame from <- as.data.frame.table(YF_Brazil$T_D, stringsAsFactors = FALSE) to <- as.data.frame.table(t(YF_Brazil$T_O), stringsAsFactors = FALSE) flows <- rbind(from, to) colnames(flows) <- c("from", "to", "n") ## Create the locations data frame los <- data.frame(location_code = names(YF_Brazil$length_of_stay), length_of_stay = YF_Brazil$length_of_stay, stringsAsFactors = FALSE ) locations <- merge(x = YF_Brazil$states, y = los, by = "location_code", all = TRUE) ## Use both to create the epiflows object. ef <- make_epiflows(flows, 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" )