def calc_R_matrix(beta, gamma, alpha):
# calculate R matrix from beta value
# calc contact matrix C
C = func.calc_contact_matrix(alpha)
# assign components of C matrix
C_cc = C.item((0, 0))
C_ca = C.item((0, 1))
C_ac = C.item((1, 0))
C_aa = C.item((1, 1))
# multiply components of matrix C by alpha or (1-alpha)
mx_11 = C_cc * alpha
mx_12 = C_ca * alpha
mx_21 = C_ac * (1 - alpha)
mx_22 = C_aa * (1 - alpha)
# calc R matrix
# SB needs to check - might be (beta / (beta + gamma))
R = (beta / gamma) * (np.matrix([[mx_11, mx_12], [mx_21, mx_22]]))
return R
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
#Ec, Ea = func.calc_epsilon(avg_q_ch, avg_q_ad, C_ca, a, n)
#print Ec
#print Ea
####################################
#calc epsiolon for Germany (closest E to Europe average in Apollini)
#ad_contacts_w_5_9 = [0.03, 0.17, 0.26, 0.49, 0.23, 0.09, 0.20, 0.11, 0.14, 0.11]
#ad_contacts_w_10_14 = [0.12, 0.16, 0.14, 0.62, 0.67, 0.35, 0.16, 0.07, 0.07, 0.18]
#ad_contacts_w_15_19 = [0.90, 0.28, 0.19, 0.46, 1.11, 1.21, 0.27, 0.26, 0.11, 0.15]
C = func.calc_contact_matrix(a, n, E, avg_q_ch)
############################
## DISEASE ##
## Infc_list = [patient_Zero]
## Susc_list = [everyone_else]
## for t (time steps) in (1, 100):
## S --> I ?
## for s in susc:
## infect nodes with prob (1-e^-B(# of infec contacts)) # infected degree = #infected contacts
## I --> R ?
## for i in infc:
## recover with prob u
us_popdata = csv.reader(open("Dropbox/Anne_Bansal_lab/SDI_Data/totalpop_age.csv", "r"), delimiter=",")
dict_popdata, ages, years = pop_func.import_popdata(us_popdata, 0, 1, 2)
dict_childpop, dict_adultpop = pop_func.pop_child_adult(dict_popdata, years)
# READ Germany contact data
filename_germ_contact_data = "Dropbox/Anne_Bansal_lab/Contact_Data/polymod_germany_contact_matrix_Mossong_2008.csv"
filename_germ_pop_data = "Dropbox/Anne_Bansal_lab/UNdata_Export_2008_Germany_Population.csv"
# DEFINE POPULATION PARAMETERS
year = 2010
alpha = pop_func.calc_alpha(year, dict_childpop, dict_adultpop)
d_metro_age_pop = pop_func.calc_metro_age_pop(filename_metropop, alpha)
ch_travelers_r = 0.0 # fraction of children who travel
ad_travelers_s = 1
# CONTACT MATRIX
C = pop_func.calc_contact_matrix(filename_germ_contact_data, filename_germ_pop_data, alpha)
# print C
# DEFINE DISEASE PARAMETERS
R0 = 1.2
gamma = 0.5 # recovery rate based on (1/gamma) day infectious period
# beta = calculate_beta(R0, gamma, air_network)
# beta = 0
# beta = 0.037 #gamma 0.5
# jbeta = 0.005
num_metro_zeros = 1 # set how many metros to select patients from to start with
num_child_zeros = 1
num_adult_zeros = 0
time_end = 150
# DEFINE TRAVEL PARAMETERS
