epiflows (version 0.2.2)

YF_Brazil: Yellow Fever Data from Brazil; 2016-12 to 2017-05

Description

This data set contains flows to and from five states in Brazil formatted in a list with the following items:

Usage

data("Brazil_epiflows")
  data("YF_coordinates")
  data("YF_locations")
  data("YF_flows")
  data("YF_Brazil")

Format

An object of class list of length 4.

Details

• $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.

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: tools:::Rd_expr_doi("10.2807/1560-7917.ES.2017.22.28.30572")

See Also

make_epiflows() for transformation to an epiflows object estimate_risk_spread()

Examples

# This is an example of an epiflows object
data("Brazil_epiflows")
Brazil_epiflows

# 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"
)