def regular_exp(t,
n,
k,
beta,
sigma,
gamma,
symptomatic_rate,
sir_0,
seed,
npi=None,
t_apply_npi=None,
t_open_up=None,
**kwargs):
random.seed(seed)
np.random.seed(seed)
G = nx.random_regular_graph(k, n)
graph = construct_network(G, n, sir_0)
transmissibility = beta / k
seir = np.zeros((t, len(SEIR_COMPARTMENTS)))
if npi is None:
seir = network_exp_with_seed(transmissibility, sigma, symptomatic_rate,
gamma, n, t, graph)
elif t_open_up is None:
seir = network_exp_with_npi(transmissibility, sigma, symptomatic_rate,
gamma, n, t, graph, npi, t_apply_npi,
**kwargs)
else:
seir = network_exp_with_open_up(transmissibility, sigma,
symptomatic_rate, gamma, n, t, graph,
npi, t_apply_npi, t_open_up, **kwargs)
# print(seir.shape)
return seir
def ba_exp(t,
n,
m,
p,
transmissibility,
sigma,
gamma,
symptomatic_rate,
sir_0,
seed,
npi=None,
t_apply_npi=None,
t_open_up=None,
**kwargs):
random.seed(seed)
np.random.seed(seed)
G = nx.dual_barabasi_albert_graph(n, m1=m, m2=1, p=p)
# print("ba number of edges", G.number_of_edges())
graph = construct_network(G, n, sir_0)
seir = np.zeros((t, len(SEIR_COMPARTMENTS)))
if npi is None:
seir = network_exp_with_seed(transmissibility, sigma, symptomatic_rate,
gamma, n, t, graph)
elif t_open_up is None:
seir = network_exp_with_npi(transmissibility, sigma, symptomatic_rate,
gamma, n, t, graph, npi, t_apply_npi,
**kwargs)
else:
seir = network_exp_with_open_up(transmissibility, sigma,
symptomatic_rate, gamma, n, t, graph,
npi, t_apply_npi, t_open_up, **kwargs)
return seir
def real_exp(t,
n,
G,
transmissibility,
sigma,
gamma,
symptomatic_rate,
sir_0,
seed,
scale=1,
npi=None,
t_apply_npi=None,
t_open_up=None,
**kwargs):
random.seed(seed)
np.random.seed(seed)
graph = G.copy()
graph = construct_network(graph, n, sir_0)
seir = np.zeros((t, len(SEIR_COMPARTMENTS)))
if npi is None:
seir = network_exp_with_seed(transmissibility, sigma, symptomatic_rate,
gamma, n, t, graph, scale)
elif t_open_up is None:
seir = network_exp_with_npi(transmissibility, sigma, symptomatic_rate,
gamma, n, t, graph, npi, t_apply_npi,
scale, **kwargs)
else:
seir = network_exp_with_open_up(transmissibility, sigma,
symptomatic_rate, gamma, n, t, graph,
npi, t_apply_npi, t_open_up, scale,
**kwargs)
return seir
def estimate_ER(n, p_range, seeds, transmissibility, sigma, symptomatic_rate, gamma, t, base_seir, sir_0):
best_p, min_error = 0, sys.maxsize
for p_idx, p in enumerate(p_range):
error_lst = np.zeros(len(seeds))
for seed_idx in range(len(seeds)):
random.seed(seeds[seed_idx])
np.random.seed(seeds[seed_idx])
G = nx.fast_gnp_random_graph(n, p)
graph = construct_network(G, n, sir_0)
seir = network_exp_with_seed(transmissibility, sigma, symptomatic_rate, gamma, n, t, graph)
error = compute_error(base_seir[:, 2], seir[:, 2], t)
error_lst[seed_idx] = error
avg_error_p = np.average(error_lst)
print("p", p,
" error avg:", avg_error_p,
" std:", np.std(error_lst),
" max:", np.max(error_lst),
" min:", np.min(error_lst),
" best_seed:", seeds[np.argmin(error_lst)])
if avg_error_p < min_error:
min_error = avg_error_p
best_p = p
return best_p
def estimate_regular(n, k_range, seeds, beta, sigma, symptomatic_rate, gamma, t, base_seir, sir_0):
best_k, min_error = 0, sys.maxsize
for k_idx, k in enumerate(k_range):
transmissibility = beta / k
error_lst = np.zeros(len(seeds))
for seed_idx in range(len(seeds)):
random.seed(seeds[seed_idx])
np.random.seed(seeds[seed_idx])
G = nx.random_regular_graph(k, n)
graph = construct_network(G, n, sir_0)
seir = network_exp_with_seed(transmissibility, sigma, symptomatic_rate, gamma, n, t, graph)
error = compute_error(base_seir[:, 2], seir[:, 2], t)
error_lst[seed_idx] = error
avg_error_k = np.average(error_lst)
print("k:", k,
" error avg:", avg_error_k,
" std:", np.std(error_lst),
" max:", np.max(error_lst),
" min:", np.min(error_lst),
" best_seed:", seeds[np.argmin(error_lst)])
if avg_error_k < min_error:
min_error = avg_error_k
best_k = k
return best_k
def estimate_BA(n, m_range, p_range, seeds, transmissibility, sigma, symptomatic_rate, gamma, t, base_SEIR, sir_0):
best_m, best_p, min_error = 0, 0, sys.maxsize
for idx, m in enumerate(m_range):
for idx, p in enumerate(p_range):
error_lst = np.zeros(len(seeds))
for seed_idx in range(len(seeds)):
random.seed(seeds[seed_idx])
np.random.seed(seeds[seed_idx])
G = nx.dual_barabasi_albert_graph(n, m1=m, m2=2, p=p, seed=seeds[seed_idx])
graph = construct_network(G, n, sir_0)
seir = network_exp_with_seed(transmissibility, sigma, symptomatic_rate, gamma, n, t, graph)
error = compute_error(base_SEIR[:, 2], seir[:, 2], t)
error_lst[seed_idx] = error
avg_error_p = np.average(error_lst)
print("m", m, " p", p,
" error avg:", avg_error_p,
" std:", np.std(error_lst),
" max:", np.max(error_lst),
" min:", np.min(error_lst),
" best_seed:", seeds[np.argmin(error_lst)])
if avg_error_p < min_error:
min_error = avg_error_p
best_p = p
best_m = m
return best_m, best_p
def er_exp(t,
n,
p,
transmissibility,
sigma,
gamma,
symptomatic_rate,
sir_0,
seed,
npi=None,
t_apply_npi=None,
**kwargs):
random.seed(seed)
np.random.seed(seed)
G = nx.fast_gnp_random_graph(n, p)
# print("er number of edges", G.number_of_edges())
graph = construct_network(G, n, sir_0)
seir = np.zeros((t, len(SEIR_COMPARTMENTS)))
if npi is None:
seir = network_exp_with_seed(transmissibility, sigma, symptomatic_rate,
gamma, n, t, graph)
else:
seir = network_exp_with_npi(transmissibility, sigma, symptomatic_rate,
gamma, n, t, graph, npi, t_apply_npi,
**kwargs)
return seir