Get epidemic costs from a DAEDALUS model run

Usage

get_costs(x, summarise_as = c("none", "total", "domain"))

Arguments

x: A <daedalus_output> object from a call to daedalus().
summarise_as: A string from among "none", "total", or "domain", for how the costs should be returned. Select "none", the default, for the raw costs along with overall and domain-specific totals; "total" for the overall cost, and "domain" for the total costs per domain; the domains are 'economic', 'education', and 'life years'.

Value

A list of different cost values, including the total cost. See Details for more information.

Details

The total cost in million dollars is returned as total_cost. This is comprised of the following costs.

Economic costs

A three element list of economic_cost_total, the total costs from pandemic impacts on economic sectors, including both costs of lost gross value added (GVA) due to pandemic-control restrictions or closures (economic_cost_closures), and pandemic-related absences due to illness and death (economic_cost_absences).

Educational costs

A three element list of education_cost_total, the total costs from pandemic impacts on education due to pandemic-control restrictions or closures (education_cost_closures), and pandemic-related absences due to illness and death (education_cost_absences).

Life-value lost

A four-element vector (for the number of age groups) giving the value of life-years lost per age group. This is calculated as the life-expectancy of each age group times the value of a statistical life, with all years assumed to have the same value.

Life-years lost

A four-element vector (for the number of age groups) giving the value of life-years lost per age group. This is calculated as the life-expectancy of each age group times the number of deaths in that age group. No quality adjustment is applied.

Examples

output <- daedalus("Canada", "influenza_1918")

get_costs(output)
#> $total_cost
#> [1] 1651142
#> 
#> $economic_costs
#> $economic_costs$economic_cost_total
#> [1] 35682.59
#> 
#> $economic_costs$economic_cost_closures
#> [1] 0
#> 
#> $economic_costs$economic_cost_absences
#> [1] 35682.59
#> 
#> $economic_costs$sector_cost_closures
#>  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [39] 0 0 0 0 0 0 0
#> 
#> $economic_costs$sector_cost_absences
#>  [1]  647.65539   56.67554 1147.03728  445.31635  248.25682  645.00256
#>  [7]   46.17035  199.54809  243.24310  208.25524  287.21111   99.44465
#> [13]  194.44262  145.76317  231.91534  280.29226  120.72997   77.01375
#> [19]  297.53826  352.48019  225.99676  317.43580  763.96732  196.18468
#> [25] 2645.52834 4296.04264  966.55736   32.24520  192.08673  424.41733
#> [31]  165.03155  914.73649  363.21530  681.80361  711.45540 2743.43593
#> [37] 4700.69253 1687.42845 1225.56992 2736.67017 2189.31966 2933.48646
#> [43]  311.86851  412.94223   59.80213
#> 
#> 
#> $education_costs
#> $education_costs$education_cost_total
#> [1] 2189.32
#> 
#> $education_costs$education_cost_closures
#> [1] 0
#> 
#> $education_costs$education_cost_absences
#> [1] 2189.32
#> 
#> 
#> $life_value_lost
#> $life_value_lost$life_value_lost_total
#> [1] 1613271
#> 
#> $life_value_lost$life_value_lost_age
#>      0-4     5-19    20-64      65+ 
#> 290122.8 648162.7 444888.8 230096.2 
#> 
#> 
#> $life_years_lost
#> $life_years_lost$life_years_lost_total
#> [1] 35033019
#> 
#> $life_years_lost$life_years_lost_age
#>      0-4     5-19    20-64      65+ 
#>  6300170 14075194  9660994  4996661 
#> 
#>