covidReff
The goal of covidReff is to simulate Covid outbreaks in a partially
vaccinated population.
Installation
The development version from GitHub with:
# install.packages("remotes")
remotes::install_github("grattan/covidReff")Usage
The simulate_covid() runs simulations of Covid spreading and returns a
tibble with the results for each simulation (runid) on each day.
Using the default settings:
sim_results <- simulate_covid()
#> Scenario 1 | run 1
#>
#> Iteration: 1 ( day 5 )
#> Vaccination rate, dose 1: 51.2%
#> Vaccination rate, dose 2: 50.2%
#> Maybe infected: 29
#> New local cases: 22 ( 75.9% of maybe infected)
#> New overseas cases: 0
#> New cases fully vaccinated: 8 / 36%
#> New Hospital / ICU: 3 / 0
#> New dead: 0 NA were fully vaccinated
#> New vaccinated: 2,212
#> Total first dose: 3,576
#> Total second dose: 100,917
#> Total Pfizer: 66,875
#> Total AZ: 37,618
#> Total infected: 32 ( 0.0% of the 199,997 population)
#>
#> Iteration: 2 ( day 10 )
#> Vaccination rate, dose 1: 52.2%
#> Vaccination rate, dose 2: 50.5%
#> Maybe infected: 75
#> New local cases: 42 ( 56.0% of maybe infected)
#> New overseas cases: 5
#> New cases fully vaccinated: 12 / 26%
#> New Hospital / ICU: 3 / 0
#> New dead: 1 0% were fully vaccinated
#> New vaccinated: 2,196
#> Total first dose: 5,269
#> Total second dose: 101,350
#> Total Pfizer: 69,001
#> Total AZ: 37,618
#> Total infected: 79 ( 0.0% of the 199,997 population)
#>
#> Iteration: 3 ( day 15 )
#> Vaccination rate, dose 1: 53.3%
#> Vaccination rate, dose 2: 50.7%
#> Maybe infected: 159
#> New local cases: 96 ( 60.4% of maybe infected)
#> New overseas cases: 5
#> New cases fully vaccinated: 29 / 29%
#> New Hospital / ICU: 4 / 0
#> New dead: 0 NA were fully vaccinated
#> New vaccinated: 2,178
#> Total first dose: 6,939
#> Total second dose: 101,738
#> Total Pfizer: 71,059
#> Total AZ: 37,618
#> Total infected: 180 ( 0.1% of the 199,997 population)The resulting tibble is:
glimpse(sim_results)
#> Rows: 4
#> Columns: 27
#> Groups: runid [1]
#> $ scenario <chr> "1", "1", "1", "1"
#> $ runid <int> 1, 1, 1, 1
#> $ iteration <dbl> 0, 1, 2, 3
#> $ day <dbl> 0, 5, 10, 15
#> $ new_cases_i <dbl> 10, 22, 47, 101
#> $ new_local_cases_i <dbl> 10, 22, 42, 96
#> $ new_os_cases_i <dbl> 0, 0, 5, 5
#> $ new_cases_vaccinated2_i <dbl> 5, 8, 12, 29
#> $ new_dead_i <dbl> 0, 0, 1, 0
#> $ new_dead_vaccinated2_i <dbl> 0, 0, 0, 0
#> $ new_vaccinated_i <dbl> 100451, 2212, 2196, 2178
#> $ new_maybe_infected_i <int> NA, 29, 75, 159
#> $ new_hosp_i <int> NA, 3, 3, 4
#> $ new_icu_i <int> NA, 0, 0, 0
#> $ total_vaccinated1_i <int> NA, 3576, 5269, 6939
#> $ total_vaccinated2_i <int> NA, 100917, 101350, 101738
#> $ total_pf_i <int> NA, 66875, 69001, 71059
#> $ total_az_i <int> NA, 37618, 37618, 37618
#> $ total_cases_i <dbl> 10, 32, 79, 180
#> $ total_dead_i <dbl> 0, 0, 1, 1
#> $ total_vaccinated_i <dbl> 100451, 102663, 104859, 107037
#> $ in_population <int> 199997, 199997, 199997, 199997
#> $ current_hosp_i <dbl> 0, 3, 6, 9
#> $ current_icu_i <dbl> 0, 0, 0, 0
#> $ rt_i <dbl> NA, 2.200000, 1.909091, 2.042553
#> $ in_R <dbl> 4.5, 4.5, 4.5, 4.5
#> $ in_vaccination_levels <list> <0.00, 0.20, 0.40, 0.50, 0.60, 0.70, 0.90, 0.9…The key inputs of the simulate_covid() function – among many – are the
reproduction value of the virus in a ‘relaxed’ Australian society, R,
and the proportion of the whole population that are vaccinated,
vaccination_levels.
sim_r8_50 <- simulate_covid(R = 8,
vaccination_levels = .5)
#> Scenario 1 | run 1
#>
#> Iteration: 1 ( day 5 )
#> Vaccination rate, dose 1: 50.9%
#> Vaccination rate, dose 2: 50.0%
#> Maybe infected: 52
#> New local cases: 34 ( 65.4% of maybe infected)
#> New overseas cases: 0
#> New cases fully vaccinated: 6 / 18%
#> New Hospital / ICU: 5 / 0
#> New dead: 0 NA were fully vaccinated
#> New vaccinated: 2,216
#> Total first dose: 3,634
#> Total second dose: 100,408
#> Total Pfizer: 78,520
#> Total AZ: 25,522
#> Total infected: 44 ( 0.0% of the 199,997 population)
#>
#> Iteration: 2 ( day 10 )
#> Vaccination rate, dose 1: 52.0%
#> Vaccination rate, dose 2: 50.2%
#> Maybe infected: 222
#> New local cases: 122 ( 55.0% of maybe infected)
#> New overseas cases: 5
#> New cases fully vaccinated: 20 / 16%
#> New Hospital / ICU: 20 / 3
#> New dead: 1 0% were fully vaccinated
#> New vaccinated: 2,201
#> Total first dose: 5,343
#> Total second dose: 100,806
#> Total Pfizer: 80,627
#> Total AZ: 25,522
#> Total infected: 171 ( 0.1% of the 199,997 population)
#>
#> Iteration: 3 ( day 15 )
#> Vaccination rate, dose 1: 53.1%
#> Vaccination rate, dose 2: 50.4%
#> Maybe infected: 856
#> New local cases: 450 ( 52.6% of maybe infected)
#> New overseas cases: 5
#> New cases fully vaccinated: 85 / 19%
#> New Hospital / ICU: 56 / 9
#> New dead: 6 0% were fully vaccinated
#> New vaccinated: 2,183
#> Total first dose: 6,971
#> Total second dose: 101,212
#> Total Pfizer: 82,661
#> Total AZ: 25,522
#> Total infected: 626 ( 0.3% of the 199,997 population)The vaccination_levels is provided as either a single numeric for
vaccination levels uniformly-distributed across age groups (as above),
or you can provide a numeric vector of length 10 describing the
vaccination rates for age groups 1-10, 11-20, 21-30, …, 91-100.
sim_r4_50 <- simulate_covid(
R = 4,
vaccination_levels = c(
"0-10" = 0.00,
"11-20" = 0.40,
"21-30" = 0.60,
"31-40" = 0.60,
"41-50" = 0.60,
"51-60" = 0.70,
"61-70" = 0.90,
"71-80" = 0.90,
"81-90" = 0.95,
"91+" = 0.95)
)
#> Scenario 1 | run 1
#>
#> Iteration: 1 ( day 5 )
#> Vaccination rate, dose 1: 58.0%
#> Vaccination rate, dose 2: 57.1%
#> Maybe infected: 25
#> New local cases: 17 ( 68.0% of maybe infected)
#> New overseas cases: 0
#> New cases fully vaccinated: 8 / 47%
#> New Hospital / ICU: 0 / 0
#> New dead: 0 NA were fully vaccinated
#> New vaccinated: 2,075
#> Total first dose: 3,311
#> Total second dose: 114,672
#> Total Pfizer: 78,739
#> Total AZ: 39,244
#> Total infected: 27 ( 0.0% of the 199,997 population)
#>
#> Iteration: 2 ( day 10 )
#> Vaccination rate, dose 1: 59.0%
#> Vaccination rate, dose 2: 57.3%
#> Maybe infected: 46
#> New local cases: 19 ( 41.3% of maybe infected)
#> New overseas cases: 5
#> New cases fully vaccinated: 10 / 42%
#> New Hospital / ICU: 0 / 0
#> New dead: 0 NA were fully vaccinated
#> New vaccinated: 2,046
#> Total first dose: 4,907
#> Total second dose: 115,019
#> Total Pfizer: 80,682
#> Total AZ: 39,244
#> Total infected: 51 ( 0.0% of the 199,997 population)
#>
#> Iteration: 3 ( day 15 )
#> Vaccination rate, dose 1: 60.0%
#> Vaccination rate, dose 2: 57.5%
#> Maybe infected: 62
#> New local cases: 35 ( 56.5% of maybe infected)
#> New overseas cases: 5
#> New cases fully vaccinated: 17 / 42%
#> New Hospital / ICU: 2 / 0
#> New dead: 0 NA were fully vaccinated
#> New vaccinated: 2,014
#> Total first dose: 6,411
#> Total second dose: 115,379
#> Total Pfizer: 82,546
#> Total AZ: 39,244
#> Total infected: 91 ( 0.0% of the 199,997 population)
glimpse(sim_r4_50)
#> Rows: 4
#> Columns: 27
#> Groups: runid [1]
#> $ scenario <chr> "1", "1", "1", "1"
#> $ runid <int> 1, 1, 1, 1
#> $ iteration <dbl> 0, 1, 2, 3
#> $ day <dbl> 0, 5, 10, 15
#> $ new_cases_i <dbl> 10, 17, 24, 40
#> $ new_local_cases_i <dbl> 10, 17, 19, 35
#> $ new_os_cases_i <dbl> 0, 0, 5, 5
#> $ new_cases_vaccinated2_i <dbl> 6, 8, 10, 17
#> $ new_dead_i <dbl> 0, 0, 0, 0
#> $ new_dead_vaccinated2_i <dbl> 0, 0, 0, 0
#> $ new_vaccinated_i <dbl> 114198, 2075, 2046, 2014
#> $ new_maybe_infected_i <int> NA, 25, 46, 62
#> $ new_hosp_i <int> NA, 0, 0, 2
#> $ new_icu_i <int> NA, 0, 0, 0
#> $ total_vaccinated1_i <int> NA, 3311, 4907, 6411
#> $ total_vaccinated2_i <int> NA, 114672, 115019, 115379
#> $ total_pf_i <int> NA, 78739, 80682, 82546
#> $ total_az_i <int> NA, 39244, 39244, 39244
#> $ total_cases_i <dbl> 10, 27, 51, 91
#> $ total_dead_i <dbl> 0, 0, 0, 0
#> $ total_vaccinated_i <dbl> 114198, 116273, 118319, 120333
#> $ in_population <int> 199997, 199997, 199997, 199997
#> $ current_hosp_i <dbl> 0, 0, 0, 2
#> $ current_icu_i <dbl> 0, 0, 0, 0
#> $ rt_i <dbl> NA, 1.700000, 1.117647, 1.458333
#> $ in_R <dbl> 4, 4, 4, 4
#> $ in_vaccination_levels <list> <0.00, 0.40, 0.60, 0.60, 0.60, 0.70, 0.90, 0.9…