Sensitivity Analysis for Activity Risk Estimates

Computes how activity micromort rankings shift when the base estimate is varied by ±pct%. Useful for communicating uncertainty around point estimates derived from sparse epidemiological data.

Usage

risk_sensitivity(activity = NULL, pct = 20)

Arguments

activity

Character scalar — activity name matching a row in common_risks(). Pass NULL (default) to return sensitivity for all activities.

pct

Numeric scalar — percentage variation applied symmetrically around the base estimate. Default 20 (i.e., ±20%). Must be in (0, 100).

Value

A tibble with columns:

activity

Activity name

micromorts_base

Base micromort estimate from common_risks()

micromorts_low

Low estimate: base * (1 - pct/100)

micromorts_high

High estimate: base * (1 + pct/100)

rank_base

Rank of the activity at the base estimate (1 = highest risk)

rank_change

Absolute rank positions shifted between low and high estimates

Details

Activities are sourced from common_risks(). The rank_change column reports the absolute number of ranking positions an activity moves between its low and high estimate when all activities are re-ranked.

See also

common_risks(), daily_hazard_rate()

Other analysis: annual_risk_budget(), compare_interventions(), daily_hazard_rate(), lifestyle_tradeoff(), toxicological_risk()

Examples

# Sensitivity for a single activity
risk_sensitivity("Skydiving (US)")
#> # A tibble: 1 × 6
#>   activity  micromorts_base micromorts_low micromorts_high rank_base rank_change
#>   <chr>               <dbl>          <dbl>           <dbl>     <int>       <int>
#> 1 Skydivin…               8            6.4             9.6        32           0

# Sensitivity for all activities at ±10%
risk_sensitivity(pct = 10)
#> # A tibble: 107 × 6
#>    activity micromorts_base micromorts_low micromorts_high rank_base rank_change
#>    <chr>              <dbl>          <dbl>           <dbl>     <int>       <int>
#>  1 Mt. Eve…           37932         34139.          41725.         1           0
#>  2 Himalay…           12000         10800           13200          2           0
#>  3 COVID-1…           10000          9000           11000          3           0
#>  4 Spanish…            3000          2700            3300          4           0
#>  5 Matterh…            2840          2556            3124          5           0
#>  6 Living …             500           450             550          6           0
#>  7 Living …             463           417.            509.         7           0
#>  8 Base ju…             430           387             473          8           0
#>  9 First d…             430           387             473          8           0
#> 10 COVID-1…             234           211.            257.        10           0
#> # ℹ 97 more rows

# Activities with the largest rank uncertainty
risk_sensitivity() |> dplyr::arrange(dplyr::desc(rank_change))
#> # A tibble: 107 × 6
#>    activity micromorts_base micromorts_low micromorts_high rank_base rank_change
#>    <chr>              <dbl>          <dbl>           <dbl>     <int>       <int>
#>  1 Mt. Eve…           37932         30346.          45518.         1           0
#>  2 Himalay…           12000          9600           14400          2           0
#>  3 COVID-1…           10000          8000           12000          3           0
#>  4 Spanish…            3000          2400            3600          4           0
#>  5 Matterh…            2840          2272            3408          5           0
#>  6 Living …             500           400             600          6           0
#>  7 Living …             463           370.            556.         7           0
#>  8 Base ju…             430           344             516          8           0
#>  9 First d…             430           344             516          8           0
#> 10 COVID-1…             234           187.            281.        10           0
#> # ℹ 97 more rows