def sir_likelihood(pop, event_db, recoverytimes, alpha, beta, gamma):
"""Determine the likelihood of the data from an SIR simulation given alpha, beta,
and gamma.
"""
daily_likelihood=[0]*(np.max(event_db.time)-1)
t=1
nonrecovered=find_nonrecovered(event_db, t)
susceptible=find_susceptible(pop, event_db, t)
for t in range(1, np.max(event_db.time)):
new_nonrecovered=find_nonrecovered(event_db, t+1)
new_susceptible=find_susceptible(pop, event_db, t+1)
infection_probs=infect_prob(pop, alpha, beta, nonrecovered, susceptible)
def new_nonrecovered_func(x):
return any(susceptible.ind_ID[x] == new_nonrecovered.ind_ID)
recovery_index = map(new_nonrecovered_func, susceptible.index)
daily_likelihood[t-1]=np.prod(np.subtract(1, infection_probs[np.where(np.invert(recovery_index))]))*np.prod(infection_probs[np.where(recovery_index)])
nonrecovered=new_nonrecovered
susceptible=new_susceptible
return np.prod(daily_likelihood) *geometric_likelihood(recoverytimes, (1./gamma))
def si_likelihood(pop, event_db, alpha, beta):
"""Determine the likelihood of the data from an SI simulation given alpha
and beta
"""
daily_likelihood=[0]*(np.max(event_db.time)-1)
t=1
infectious=find_infectious(event_db, t)
susceptible=find_susceptible(pop, event_db, t)
for t in range(1, np.max(event_db.time)):
new_infectious=find_infectious(event_db, t+1)
new_susceptible=find_susceptible(pop, event_db, t+1)
infection_probs=infect_prob(pop, alpha, beta, infectious, susceptible)
def new_infections_func(x):
return any(susceptible.ind_ID[x] == new_infectious.ind_ID)
new_infections = map(new_infections_func, susceptible.index)
daily_likelihood[t-1]=np.prod(np.subtract(1, infection_probs[np.where(np.invert(new_infections))]))*np.prod(infection_probs[np.where(new_infections)])
infectious=new_infectious
susceptible=new_susceptible
return np.prod(daily_likelihood)
