import numpy as np
# initialize the population vector from the origin-destination flow matrix
N_k = np.abs(np.diagonal(OD) + OD.sum(axis=0) - OD.sum(axis=1))
locs_len = len(N_k) # number of locations
SIR = np.zeros(
shape=(locs_len, 3)
) # make a numpy array with 3 columns for keeping track of the S, I, R groups
SIR[:, 0] = N_k # initialize the S group with the respective populations
first_infections = np.where(
SIR[:, 0] <= thresh, SIR[:, 0] // 20, 0
) # for demo purposes, randomly introduce infections
SIR[:, 0] = SIR[:, 0] - first_infections
SIR[:, 1] = SIR[:, 1] + first_infections # move infections to the I group
# row normalize the SIR matrix for keeping track of group proportions
row_sums = SIR.sum(axis=1)
SIR_n = SIR / row_sums[:, np.newaxis]
# initialize parameters
beta = 1.6
gamma = 0.04
public_trans = 0.5 # alpha
R0 = beta / gamma
beta_vec = np.random.gamma(1.6, 2, locs_len)
gamma_vec = np.full(locs_len, gamma)
public_trans_vec = np.full(locs_len, public_trans)
# make copy of the SIR matrices
SIR_sim = SIR.copy()
SIR_nsim = SIR_n.copy()
# run model
print(SIR_sim.sum(axis=0).sum() == N_k.sum())
from tqdm import tqdm_notebook
infected_pop_norm = []
susceptible_pop_norm = []
recovered_pop_norm = []
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-7-aa271ab2f1eb> in <module>
2
3 # initialize the population vector from the origin-destination flow matrix
----> 4 N_k = np.abs(np.diagonal(OD) + OD.sum(axis=0) - OD.sum(axis=1))
5 locs_len = len(N_k) # number of locations
6 SIR = np.zeros(
NameError: name 'OD' is not defined
For this analysis, we will use the aggregated $OD$ flow matrix of a typical day obtained from GPS data provided by local ride sharing company gg as a proxy for the mobility patterns in Yerevan city.
这是 OD 的定义。 需要在 https://www.ggtaxi.com/signin 下载。
Next, we need the population counts in each 250×250 m grid cell, which we approximate by proportionally scaling the extracted flow counts so that the total inflows in different locations sum up to approximately half of Yerevan’s population of 1.1 million. This is actually a bold assumption, but since varying this portion yielded very similar results, we will stick to it.
然后下载后的数据做一个计数矩阵，进行标准化处理即可。 这部分你可以把数据下载下来后，我这边处理。