def chain_binomial_monte_carlo(beta, gamma, num_sims, num_metro_zeros, num_child_zeros, num_adult_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
d_metro_age_pop = pop_func.calc_metro_age_pop(filename_metropop, alpha)
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(d_metro_age_pop, metro_ids, beta, gamma, air_network, num_metro_zeros, num_child_zeros, num_adult_zeros, C)
# Note we are not using the incidence time series right now
new_cases_incidence_time_series_metro_child, new_cases_incidence_time_series_metro_adult, incidence_time_series_metro_child, incidence_time_series_metro_adult, tot_incidence_time_series_child, tot_incidence_time_series_adult, outbreak_size_child, outbreak_size_adult = chain_binomial_one_simulation(d_metro_age_pop, metro_ids, beta, gamma, air_network, num_metro_zeros, num_child_zeros, num_adult_zeros, C)
outbreak_size = (outbreak_size_child + outbreak_size_adult)
## SB said don't worry about this for 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
#large_epidemic_sizes.append(outbreak_size)
# 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
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 = 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_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
# CONTACT MATRIX
C = pop_func.calc_contact_matrix(filename_germ_contact_data, filename_germ_pop_data, alpha)
# 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 = 0.015
#beta = 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 = 300