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] 1049499
#> 
#> $economic_costs
#> $economic_costs$economic_cost_total
#> [1] 26819.05
#> 
#> $economic_costs$economic_cost_closures
#> [1] 0
#> 
#> $economic_costs$economic_cost_absences
#> [1] 26819.05
#> 
#> $economic_costs$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]  475.74420   42.19707  846.87209  331.40184  183.03886  483.72484
#>  [7]   34.77129  147.44294  180.10592  155.60012  215.63891   74.13942
#> [13]  143.84175  108.97155  174.18589  207.93008   89.55669   57.94210
#> [19]  224.33056  262.76237  166.86109  238.43748  576.09141  146.17245
#> [25] 1945.26186 3287.69264  732.78205   23.84745  142.77464  321.70453
#> [31]  126.37720  694.43846  270.33029  512.11716  534.33608 2052.03553
#> [37] 3522.08461 1273.32265  933.71782 2049.77008 1669.42218 2238.70342
#> [43]  239.97262  305.96081   44.05988
#> 
#> 
#> $education_costs
#> $education_costs$education_cost_total
#> [1] 1669.422
#> 
#> $education_costs$education_cost_closures
#> [1] 0
#> 
#> $education_costs$education_cost_absences
#> [1] 1669.422
#> 
#> 
#> $life_value_lost
#> $life_value_lost$life_value_lost_total
#> [1] 1021011
#> 
#> $life_value_lost$life_value_lost_age
#>      0-4     5-19    20-64      65+ 
#> 196274.4 417796.1 269441.7 137498.3 
#> 
#> 
#> $life_years_lost
#> $life_years_lost$life_years_lost_total
#> [1] 22171782
#> 
#> $life_years_lost$life_years_lost_age
#>     0-4    5-19   20-64     65+ 
#> 4262201 9072663 5851069 2985849 
#> 
#>