#' @title Title
#'
#' @description Description
#'
#' @param x A number.
#' @param y A number.
#' @return return value here.
#' @details
#' Additional details here
#' @examples
#' example function call here
#' @export
input_parameters_asmr <- function(data_name="usa_men_18_to_100",min_age,max_age)
{
#description:
#stores different ASMR data sets and converts annual values to
#daily rates and subsets out values for requested age range
#daily rates are used in "vital_deaths_non_aids" fxn to
asmr_data_list <- list()
########## Alternative AMSR tables that include elderly persons. #########################
# Data obtained from the CDC "WONDER" webpage (wonder.cdc.gov) for USA men from 1999-2003
# downloaded on 8/25/15. The CDC data only apply to people 85 and under. To fill in
# the rest, I (John) made various extrapolations and approximations, as follows...
# (1) In the absence of data, I assumed zero people over 85 years at the beginning of the
# simulation. The model does, however, allow people to age-in to ages 86-100 over time.
# (2) Death rate data for those over 86 were obtained from Society of Actuaries' "Social
# Security" data set (pretty closely matches the CDC estimate for those 85!). Obtained from
# https://www.soa.org/research/software-tools/research-simple-life-calculator.aspx
# downloaded on 8/25/15.
# (3) To keep this from getting totally out of hand, I assumed a 0% chance of living past 100.
#
# While these approximations and extrapolations are imperfect, I figure that the advanced
# elderly are rare enough (and so inactive sexually) that any imperfections will have
# little-to-no effect on our conclusions.
#
# Note: Eldery persons can easily be excluded from the simulations by setting max_age to
# something less than 100 (current default, as of 8/25/15, is max_age = 55 years)
#
asmr_data_list$"usa_men_18_to_100"<- list(
asmr =
c(0.0011, 0.0012, 0.0013, 0.0014, 0.0014, 0.0014, 0.0014, 0.0014,
0.0014, 0.0014, 0.0014, 0.0014, 0.0014, 0.0015, 0.0015, 0.0016,
0.0016, 0.0017, 0.0018, 0.0019, 0.0021, 0.0022, 0.0024, 0.0026,
0.0028, 0.0030, 0.0033, 0.0036, 0.0039, 0.0043, 0.0046, 0.0050,
0.0055, 0.0059, 0.0064, 0.0069, 0.0074, 0.0080, 0.0086, 0.0093,
0.0100, 0.0108, 0.0117, 0.0125, 0.0136, 0.0147, 0.0160, 0.0172,
0.0186, 0.0203, 0.0220, 0.0240, 0.0263, 0.0286, 0.0314, 0.0344,
0.0374, 0.0413, 0.0450, 0.0495, 0.0543, 0.0599, 0.0660, 0.0727,
0.0805, 0.0886, 0.0986, 0.1100, 0.1300, 0.1440, 0.1600, 0.1760,
0.1930, 0.2110, 0.2310, 0.2510, 0.2730, 0.2940, 0.3160, 0.3400,
1.0000),
age_range=c(18,100))
asmr_data_list$"south_africa_female"<- list(
asmr =c(0.0013, 0.0013, 0.0013, 0.0013,
0.0035, 0.0035, 0.0035, 0.0035, 0.0035,
0.0072, 0.0072, 0.0072, 0.0072, 0.0072,
0.0113, 0.0113, 0.0113, 0.0113, 0.0113,
0.0130, 0.0130, 0.0130, 0.0130, 0.0130,
0.0129, 0.0129, 0.0129, 0.0129, 0.0129,
0.0127, 0.0127, 0.0127, 0.0127, 0.0127,
0.0125, 0.0125, 0.0125, 0.0125, 0.0125,
0.0127, 0.0127, 0.0127, 0.0127, 0.0127,
0.0194, 0.0194, 0.0194, 0.0194, 0.0194,
0.0269, 0.0269, 0.0269, 0.0269, 0.0269,
0.0379, 0.0379, 0.0379, 0.0379, 0.0379,
0.0563, 0.0563, 0.0563, 0.0563, 0.0563,
0.1403, 0.1403, 0.1403, 0.1403, 0.1403,
0.1403, 0.1403, 0.1403, 0.1403, 0.1403,
0.1403, 0.1403, 0.1403, 0.1403, 0.1403,
0.1403, 0.1403, 0.1403, 0.1403, 0.1403,
0.1403), age_range=c(16,100))
asmr_data_list$"south_africa_male"<- list(
asmr =c(0.0018, 0.0018, 0.0018, 0.0018,
0.0039, 0.0039, 0.0039, 0.0039, 0.0039,
0.0071, 0.0071, 0.0071, 0.0071, 0.0071,
0.0117, 0.0117, 0.0117, 0.0117, 0.0117,
0.0157, 0.0157, 0.0157, 0.0157, 0.0157,
0.0185, 0.0185, 0.0185, 0.0185, 0.0185,
0.0197, 0.0197, 0.0197, 0.0197, 0.0197,
0.0197, 0.0197, 0.0197, 0.0197, 0.0197,
0.0219, 0.0219, 0.0219, 0.0219, 0.0219,
0.0325, 0.0325, 0.0325, 0.0325, 0.0325,
0.0441, 0.0441, 0.0441, 0.0441, 0.0441,
0.0582, 0.0582, 0.0582, 0.0582, 0.0582,
0.0815, 0.0815, 0.0815, 0.0815, 0.0815,
0.1629, 0.1629, 0.1629, 0.1629, 0.1629,
0.1629, 0.1629, 0.1629, 0.1629, 0.1629,
0.1629, 0.1629, 0.1629, 0.1629, 0.1629,
0.1629, 0.1629, 0.1629, 0.1629, 0.1629,
0.1629),age_range=c(16,100))
data_range <- asmr_data_list[[data_name]]$age_range[1]:asmr_data_list[[data_name]]$age_range[2]
user_range <- min_age:(max_age-1)
data_ix <- match(user_range,data_range)
final_asmr <- asmr_data_list[[data_name]]$asmr[data_ix]
mort_per_timestep<- utilities_annual_mortality_conversion(final_asmr,
user_range, 365)
return(mort_per_timestep)
}
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