def chain_binomial_monte_carlo(beta, gamma, num_sims, num_patient_zeros): # run many (num_sims) instances of chain binomial simulation and return average epidemic size d_metropop, metro_ids = pop_func.import_metropop(filename_metropop, 2, 3) population_size = sum([d_metropop[x] for x in metro_ids]) threshold = 0.10 # 10% of population size is our threshold for a large epidemic large_epidemic_sizes = [] # will keep list of outbreak sizes that are large epidemics for sim in range(1, num_sims): incidence_time_series, outbreak_size = chain_binomial_one_simulation( beta, gamma, contact_network, num_patient_zeros ) # Note we are not using the incidence time series right now # figure out if this is small outbreak or large epidemic if outbreak_size > threshold * population_size: # if outbreak reached more than 10% of the population # call it a large epidemic and save its size large_epidemic_sizes.append(outbreak_size) # calculate average large epidemic size, and how frequent they were if large_epidemic_sizes: average_epidemic_size = np.mean(large_epidemic_sizes) / float(population_size) probability_epidemic = len(large_epidemic_sizes) / float(num_sims) else: average_epidemic_size = 0 probability_epidemic = 0 return average_epidemic_size
import operator ### local modules ### sys.path.append('/home/anne/Dropbox/Anne_Bansal_Lab') ### functions ### import population_parameters as pop_func import chain_binomial as bin_func ### program ### ################################################### # import data # # READ METRO NETWORK FROM FILE filename_metropop = 'Dropbox/Anne_Bansal_lab/Python_Scripts/Modeling_Project/air_traffic_data/metedges.txt' d_metropop, metro_ids = pop_func.import_metropop(filename_metropop, 2, 3) filename_air_network = 'Dropbox/Anne_Bansal_lab/Python_Scripts/Modeling_Project/air_traffic_data/air_traffic_edgelist.txt' air_network = bin_func.read_edgelist_anne(filename_air_network) # READ US population data 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_within_group_contact_data = 'Dropbox/Anne_Bansal_lab/Contact_Data/within_group_polymod_germany_contact_matrix_Mossong_2008.csv' filename_germ_all_contact_data = 'Dropbox/Anne_Bansal_lab/Contact_Data/all_ages_polymod_germany_contact_matrix_Mossong_2008.csv' # READ Germany population data filename_germ_pop_data = 'Dropbox/Anne_Bansal_lab/UNdata_Export_2008_Germany_Population.csv'