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] 1755985
#> 
#> $economic_costs
#> $economic_costs$economic_cost_total
#> [1] 36488.35
#> 
#> $economic_costs$economic_cost_closures
#> [1] 0
#> 
#> $economic_costs$economic_cost_absences
#> [1] 36488.35
#> 
#> $economic_costs$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]  661.10805   57.90800 1171.20309  455.02758  253.48847  659.40898
#>  [7]   47.20704  203.77084  248.46282  212.84260  293.59225  101.63010
#> [13]  198.59874  148.98086  237.07437  286.32227  123.34150   78.75448
#> [19]  304.24507  360.22349  230.78741  324.56744  781.19763  200.48477
#> [25] 2700.68326 4399.18947  988.76414   32.93011  196.27834  434.27569
#> [31]  168.95260  936.35828  371.18711  697.12015  727.27985 2804.57500
#> [37] 4806.82935 1725.98922 1254.20054 2798.01011 2242.08565 3003.26311
#> [43]  319.39241  421.78042   61.05864
#> 
#> 
#> $education_costs
#> $education_costs$education_cost_total
#> [1] 2242.086
#> 
#> $education_costs$education_cost_closures
#> [1] 0
#> 
#> $education_costs$education_cost_absences
#> [1] 2242.086
#> 
#> 
#> $life_value_lost
#> $life_value_lost$life_value_lost_total
#> [1] 1717254
#> 
#> $life_value_lost$life_value_lost_age
#>      0-4     5-19    20-64      65+ 
#> 310761.9 692215.2 471382.4 242894.7 
#> 
#> 
#> $life_years_lost
#> $life_years_lost$life_years_lost_total
#> [1] 37291079
#> 
#> $life_years_lost$life_years_lost_age
#>      0-4     5-19    20-64      65+ 
#>  6748359 15031817 10236317  5274585 
#> 
#>