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valueOfInterventionInPopulation(num_beneficiaries, population_age_distribution, benefitForPersonOfAge) = {
age_of_beneficiaries = sampleN(population_age_distribution, num_beneficiaries)
benefits_array = List.map(age_of_beneficiaries, {|a| benefitForPersonOfAge(a)})
total_benefits = List.reduce(benefits_array, 0, {|acc, value| acc + value})
total_benefits
}
valueWithUncertaintyAboutNumBeneficiaries(num_beneficiaries_dist, population_age_distribution, benefitForPersonOfAge) = {
referenceN = 1000
referenceValue = valueOfInterventionInPopulation(referenceN, population_age_distribution, benefitForPersonOfAge)
numSamples = 1001
num_beneficiaries_samples_list = sampleN(num_beneficiaries_dist, numSamples)
benefits_list = List.map(num_beneficiaries_samples_list, {|n| referenceValue*n/referenceN})
result = mixture(benefits_list)
result
}
valueWithUncertaintyAboutPopulationShape(num_beneficiaries_dist, population_age_distribution_list, benefitForPersonOfAge) = {
benefits_list = List.map(population_age_distribution_list, {|population_age_distribution| valueWithUncertaintyAboutNumBeneficiaries(num_beneficiaries_dist, population_age_distribution, benefitForPersonOfAge)})
result = mixture(benefits_list)
result
}
population_age_distribution_list = [to(1, 30),to(1, 40),to(1, 60), to(2, 30), to(2, 40), to(2, 60), to(3, 30), to(3, 40), to(3, 60), to(4, 50)]
num_beneficiaries_dist = 700 to 1500
life_expectancy = 40 to 60
benefitForPersonOfAge(age) = {
result = age > 5 ? mx(0) : {
counterfactual_child_mortality = SampleSet.fromDist(0.01 to 0.07)
// https://apps.who.int/gho/data/view.searo.61200?lang=en
child_mortality_after_intervention = counterfactual_child_mortality/2
chance_live_before = (1-(counterfactual_child_mortality))^(5-age)
chance_live_after = (1-(child_mortality_after_intervention))^(5-age)
value = (chance_live_after - chance_live_before) * (life_expectancy - age)
value
}
result
}
valueWithUncertaintyAboutPopulationShape(num_beneficiaries_dist, population_age_distribution_list, benefitForPersonOfAge)
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