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