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] 1621184
#> 
#> $economic_costs
#> $economic_costs$economic_cost_total
#> [1] 33680.95
#> 
#> $economic_costs$economic_cost_closures
#> [1] 0
#> 
#> $economic_costs$economic_cost_absences
#> [1] 33680.95
#> 
#> $economic_costs$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]  537.03248   50.84374  946.93302  410.21804  215.27073  603.11998
#>  [7]   43.47715  173.94359  219.36720  190.95406  266.69491   92.57954
#> [13]  174.05570  134.09063  215.72759  252.67426  110.38123   73.04621
#> [19]  281.49833  327.98116  198.78545  298.98543  723.26222  182.00506
#> [25] 2222.73601 4281.61437  932.96535   28.34752  177.94897  413.21152
#> [31]  163.56849  908.28872  339.11816  642.11836  661.67353 2596.84018
#> [37] 4525.82697 1618.92311 1199.98586 2606.20381 2201.33256 2906.40499
#> [43]  312.04938  368.75021   51.44708
#> 
#> 
#> $education_costs
#> $education_costs$education_cost_total
#> [1] 2201.333
#> 
#> $education_costs$education_cost_closures
#> [1] 0
#> 
#> $education_costs$education_cost_absences
#> [1] 2201.333
#> 
#> 
#> $life_value_lost
#> $life_value_lost$life_value_lost_total
#> [1] 1585301
#> 
#> $life_value_lost$life_value_lost_age
#>      0-4     5-19    20-65      65+ 
#> 307992.6 646654.1 432455.2 198199.6 
#> 
#> 
#> $life_years_lost
#> $life_years_lost$life_years_lost_total
#> [1] 34425657
#> 
#> $life_years_lost$life_years_lost_age
#>      0-4     5-19    20-65      65+ 
#>  6688221 14042434  9390993  4304009 
#> 
#>