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] 1725196
#> 
#> $economic_costs
#> $economic_costs$economic_cost_total
#> [1] 35947.83
#> 
#> $economic_costs$economic_cost_closures
#> [1] 0
#> 
#> $economic_costs$economic_cost_absences
#> [1] 35947.83
#> 
#> $economic_costs$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]  650.70083   56.98792 1152.20196  447.87808  249.56622  649.21780
#>  [7]   46.48010  200.47841  244.71018  209.50231  289.01679  100.04724
#> [13]  195.58268  146.64801  233.38470  281.76040  121.50006   77.55513
#> [19]  299.58196  354.61053  227.24324  319.58650  769.23268  197.35266
#> [25] 2658.34878 4338.48496  973.98825   32.39956  193.38678  427.91734
#> [31]  166.56116  923.33625  365.76868  686.42090  715.95932 2761.64838
#> [37] 4740.05146 1700.09163 1235.92604 2755.51399 2215.25580 2961.06177
#> [43]  315.00263  415.02844   60.11038
#> 
#> 
#> $education_costs
#> $education_costs$education_cost_total
#> [1] 2215.256
#> 
#> $education_costs$education_cost_closures
#> [1] 0
#> 
#> $education_costs$education_cost_absences
#> [1] 2215.256
#> 
#> 
#> $life_value_lost
#> $life_value_lost$life_value_lost_total
#> [1] 1687032
#> 
#> $life_value_lost$life_value_lost_age
#>      0-4     5-19    20-65      65+ 
#> 308354.8 682241.0 459500.6 236936.1 
#> 
#> 
#> $life_years_lost
#> $life_years_lost$life_years_lost_total
#> [1] 36634799
#> 
#> $life_years_lost$life_years_lost_age
#>      0-4     5-19    20-65      65+ 
#>  6696087 14815222  9978297  5145193 
#> 
#>