Python weighted_avg_germ_popの例

コード例 #1

ファイル: chain_binomial_sim_pop_disease_model_v070815.py プロジェクト: ate510/metapopulation_model

## 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

#q_1 = #see additional file of Apollini 2013
# Table 1 Mossong POLYMOD
# weighted avg for children and adults avg # of contacts

C_ij, C_ji, child_pop, adult_pop = func.weighted_avg_germ_pop()

C_ca = func.simmetrize_contacts(C_ij, C_ji, child_pop, adult_pop)

age = [5, 10, 15, 20, 30, 40, 50, 60]
child = age[0:3]
adult = age[3:8]
contacts = [14.81, 18.22, 17.58, 13.57, 14.14, 13.83, 12.30, 9.21] 
participants = [661, 713, 685, 879, 815, 908, 906, 728]

d_mean_contacts, d_num_part = func.contacts_per_agegroup(age, contacts, participants)

avg_q_ch = func.weighted_avg_q(child, d_mean_contacts, d_num_part)
avg_q_ad = func.weighted_avg_q(adult, d_mean_contacts, d_num_part)

n = func.calc_eta(avg_q_ch, avg_q_ad) # ratio of avg # contacts (adult / child)

コード例 #2

ファイル: chain_binomial_sim_pop_disease_model_v070615.py プロジェクト: ate510/metapopulation_model

## 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

#q_1 = #see additional file of Apollini 2013
# Table 1 Mossong POLYMOD
# weighted avg for children and adults avg # of contacts

func.weighted_avg_germ_pop()

age = [5, 10, 15, 20, 30, 40, 50, 60]
child = age[0:3]
adult = age[3:8]
contacts = [14.81, 18.22, 17.58, 13.57, 14.14, 13.83, 12.30, 9.21] 
participants = [661, 713, 685, 879, 815, 908, 906, 728]

d_mean_contacts, d_num_part = func.contacts_per_agegroup(age, contacts, participants)

avg_q_ch = func.weighted_avg_q(child, d_mean_contacts, d_num_part)
avg_q_ad = func.weighted_avg_q(adult, d_mean_contacts, d_num_part)

n = func.calc_eta(avg_q_ch, avg_q_ad) # ratio of avg # contacts (adult / child)
#print n # n = 0.752