# import csv file of pop data popdata = csv.reader(open('Dropbox/Anne_Bansal_lab/SDI_Data/totalpop_age.csv', 'r'),delimiter = ',') # import data into dicts d_pop_for_yr_age, ages, years = func.import_popdata(popdata, 0, 1, 2) #group ages into children and adults d_childpop, d_adultpop = func.pop_child_adult(d_pop_for_yr_age, years) # set value of alpha from U.S. pop data year = 2010 # decided to use pop from 2010 bc most recent a = func.pop_child_frac(year, d_childpop, d_adultpop) #print a # = 0.239 for 2010 with 60-69; was 0.27 without 60-69 metro, ages = func.assign_metro(a) print metro #totpop of metros = 10817 # ids of ppl = [0 - 10816] # 3b ## contact btwn children and adults ## 'C' = equation 3 in Apollini 2014 ### alpha (a) = fraction of ch --> calc in 3a ### (n) = ratio of ad/ch avg # contacts (q_a/q_c) ### (E) = avg fraction of contacts across age groups #### --> from Euro data in Table 2 in Apollini 2013 + Ref 22
# import csv file of pop data popdata = csv.reader(open('Dropbox/Anne_Bansal_lab/SDI_Data/totalpop_age.csv', 'r'),delimiter = ',') # import data into dicts d_pop_for_yr_age, ages, years = func.import_popdata(popdata, 0, 1, 2) #group ages into children and adults d_childpop, d_adultpop = func.pop_child_adult(d_pop_for_yr_age, years) # set value of alpha from U.S. pop data year = 2010 # decided to use pop from 2010 bc most recent a = func.pop_child_frac(year, d_childpop, d_adultpop) #print a # = 0.239 for 2010 with 60-69; was 0.27 without 60-69 metro = func.assign_metro(a) print metro # 3b ## contact btwn children and adults ## 'C' = equation 3 in Apollini 2014 ### alpha (a) = fraction of ch --> calc in 3a ### (n) = ratio of ad/ch avg # contacts (q_a/q_c) ### (E) = avg fraction of contacts across age groups #### --> from Euro data in Table 2 in Apollini 2013 + Ref 22 #Apollini 2013, Table 2, "Europe (average values)" #n = 0.79 E = 0.097 #avg fraction of contacts across age groups