def synthetic_exp_with_seed(seed, beta, gamma, n, t, model):
random.seed(seed)
np.random.seed(seed)
# origin-destination flow matrix => should be replaced by estimation from flight network
population_0 = random_population(n)
sir_0 = random_SIR_0(population_0)
sir = COVID_MODELS[model](population_0, sir_0, t, beta, gamma)
no_sir, no_pop = sir.run(no_mobility_mobility_matrix(n))
ba_sir, ba_pop = sir.run(barabasi_albert_mobility_matrix(n, 3))
er_sir, er_pop = sir.run(erdos_renyi_graph_mobility_matrix(n, 0.25))
results = np.array([
compute_global_seir(no_sir, no_pop),
compute_global_seir(ba_sir, ba_pop),
compute_global_seir(er_sir, er_pop)
])
return results
def base_exp(e_0, i_0, r_0, beta, n, sigma, symptomatic_rate, gamma, t):
s_0 = n - e_0 - i_0 - r_0
seir_0 = np.array(
[s_0, e_0, i_0 * symptomatic_rate, i_0 * (1 - symptomatic_rate), r_0])
model2 = ClassicSEIR(np.array([n]),
seir_0,
beta=beta,
sigma=sigma,
symptomatic_rate=symptomatic_rate,
gamma=gamma,
num_days=t)
base_seir, base_pop = model2.run()
base = compute_global_seir(base_seir, base_pop)
return base
def estimate_base(beta_range, n, t, sigma, gamma, tr_sir, symptomatic_rate, e_0_range, i_0_range, r_0_range):
best_beta, best_e_0, best_i_0, best_r_0, min_error = 1, 1, 1, 1, sys.maxsize
for beta in beta_range:
for e_0 in e_0_range:
for i_0 in i_0_range:
for r_0 in r_0_range:
s_0 = n - e_0 - i_0 - r_0
seir_0 = np.array([s_0, e_0, i_0 * symptomatic_rate, i_0 * (1 - symptomatic_rate), r_0])
model2 = ClassicSEIR(np.array([n]), seir_0, beta=beta, sigma=sigma,
symptomatic_rate=symptomatic_rate, gamma=gamma, num_days=t)
base_seir, base_pop = model2.run()
results = compute_global_seir(base_seir, base_pop)
error = compute_error(results[:15, 2], tr_sir[:15, 2], t)
if error < min_error:
min_error = error
best_beta = beta
best_e_0 = e_0
best_i_0 = i_0
best_r_0 = r_0
return best_beta, best_e_0, best_i_0, best_r_0