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model_v5.py
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model_v5.py
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#!/usr/bin/env python
# coding: utf-8
# ## Model 1: Dopady obmedzenia mobility na šírenie vírusu Covid-19
# In[18]:
## Nahratie balikov, pouzitie Anaconda Jupyter notebook + updatovane baliky.
import numpy as np
import pandas as pd
from tqdm import tqdm_notebook
import pickle
import numpy as np
import pandas as pd
import plotly
import plotly.graph_objects as go
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize':(11, 4)})
# In[19]:
## Funkcia pre výpočet priemeru zo simulácií
def sumlist(x):
tmp=x[0]
for i in x[1:]:
tmp=tmp+i
return tmp/len(x)
# In[20]:
## Údaje k počtu obyvateľov na obec
pop = pd.read_excel('./zdroje/munic_pop.xlsx')
pop_N = np.array(pop['popul'])
# In[21]:
## Priradenie GPS suradnic pre kazdu obec
def get_coors_long(x):
return float(df_coords.loc[df_coords.IDN4.apply(str)==x,'long'])
def get_coors_lat(x):
return float(df_coords.loc[df_coords.IDN4.apply(str)==x,'lat'])
df_coords=pd.read_excel('./zdroje/obce1.xlsx')
data_i=pop
data_i.loc[:,'long']=data_i.munic.apply(str).apply(get_coors_long)
data_i.loc[:,'lat']=data_i.munic.apply(str).apply(get_coors_lat)
# In[22]:
## Otvorenie OD (origin-destination) matice, ktora popisuje migracne pohyby
## obyvatelstva na zaklade realnych dat.
with open('./vystupy_model/OD_final.pickle','rb') as f:
OD=pickle.load(f)
# In[ ]:
## Stav nákazy 15.3.2020, priradenie poctov k jednotlivym obciam
#Bratislava (I.-V.) – 27
##Martin – 7
#Malacky (Kostolište, Vysoká pri Morave) – 4
#Nitra – 3
#Nové Mesto nad Váhom – 3
#Košice (I.-IV.) – 2
#Banská Bystrica – 2
#Trnava – 2
#Senec – 2
#Nové Zámky – 2
#Dunajská streda (Hviezdoslavov) – 1
#Svidník (Giraltovce) – 1
#Partizánske – 1
#Partizánske (Veľké Uherce) – 1
##Bytča – 1
#Trenčín – 1
#Spišská Nová Ves – 1
nakazy_sk=pd.DataFrame({'kod':[529346,529346,529320 , 512036,508063,500011,506338,598186,508438,506745,508217,503011,
501433,527106,505315,505315,517461,505820,526355],
'pocet':[4,3,25,7,4,3,3,2,2,2,2,2,1,1,1,1,1,1,1]})
first_infections=np.zeros(2926)
for i in np.arange(nakazy_sk.shape[0]):
first_infections[pop.munic==nakazy_sk.kod.iloc[i]]=nakazy_sk.pocet.iloc[i]
first_infections_original=first_infections
# In[ ]:
## Definicia simulacie (na zaklade verejne dostupneho kodu k SIR modelu)
## Hlavnym parametrom je public_trans (alfa), ktory kontroluje level mobility populacie.
def simul(public_trans):
N_k = pop.popul.to_numpy() # Populacia
locs_len = len(N_k) # Pocet obci
SIR = np.zeros(shape=(locs_len, 3)) # Dataframe pre S - susceptible, I - infected, R - recovered na kazdy den.
SIR[:,0] = N_k # Inicializacia susceptible ako celej populacie (nikto nie je imunny)
SIR[:, 0] = SIR[:, 0] - first_infections
SIR[:, 1] = SIR[:, 1] + first_infections # infikovani presunuti do I skupiny
## Standardizacia na pomer
row_sums = SIR.sum(axis=1)
SIR_n = SIR / row_sums[:, np.newaxis]
## Inicializacia parametrov
beta = 0.4 # "Transmission rate"
gamma = 0.10 # "Recovery rate"
R0 = beta/gamma # Reprodukcne cislo ("Basic reproduction number") - pocitame skor s pesimistickym scenarom.
gamma_vec = np.full(locs_len, gamma)
public_trans_vec = np.full(locs_len, public_trans)
## Vytvarame kopie matic
SIR_sim = SIR.copy()
SIR_nsim = SIR_n.copy()
## Prebiehame simulaciu
infected_pop_norm = []
susceptible_pop_norm = []
recovered_pop_norm = []
SIR_sim_arr=np.zeros((SIR_sim.shape[0],SIR_sim.shape[1],200))
j=0
for time_step in tqdm_notebook(range(200)):
## Transmission rate je na kazdu obec ina, prvotne data su z gamma distribucie
beta_vec = np.random.gamma(beta, 2, locs_len)
# Matice infekcii
infected_mat = np.array([SIR_nsim[:,1],]*locs_len).transpose()
OD_infected = np.round(OD*infected_mat)
# Pocet infikovanych cestujucich do kazdej obce (vratane zotrvania vo vlastnej obci)
inflow_infected = OD_infected.sum(axis=0)
inflow_infected = np.round(inflow_infected*public_trans_vec)
# Nove infekcie na zaklade rychlosti sirenia (beta), a novych nakaz,
# standardizovane na podiel
new_infect = beta_vec*SIR_sim[:, 0]*inflow_infected/(N_k + OD.sum(axis=0))
new_recovered = gamma_vec*SIR_sim[:, 1]
new_infect = np.where(new_infect>SIR_sim[:, 0], SIR_sim[:, 0], new_infect)
## Novoinfikovani odchadzaju z kategorie S
SIR_sim[:, 0] = SIR_sim[:, 0] - new_infect
## Novoinfikovani prichadzaju do kategorie I a z nej odchadzaju vylieceni
SIR_sim[:, 1] = SIR_sim[:, 1] + new_infect - new_recovered
## Vylieceni prichadzaju do kat. R
SIR_sim[:, 2] = SIR_sim[:, 2] + new_recovered
SIR_sim = np.where(SIR_sim<0,0,SIR_sim)
# Normalizacia
row_sums = SIR_sim.sum(axis=1)
SIR_nsim = SIR_sim / row_sums[:, np.newaxis]
SIR_sim_arr[:,:,j]=SIR_sim
j=j+1
S = SIR_sim[:,0].sum()/N_k.sum()
I = SIR_sim[:,1].sum()/N_k.sum()
R = SIR_sim[:,2].sum()/N_k.sum()
infected_pop_norm.append(I)
susceptible_pop_norm.append(S)
recovered_pop_norm.append(R)
## Vytvor konecnu maticu
res = pd.DataFrame(list(zip(infected_pop_norm, susceptible_pop_norm, recovered_pop_norm)), columns = ['inf','sus','rec'])
return res,SIR_sim_arr
# In[ ]:
## Data k poctu seniorov na obec pre scenar o ziadnej mobilite pre tuto populaciu
data_senior=pd.read_excel('./zdroje/OD_IFP/senior.xlsx')
data_senior.loc[:,'munic']=data_senior.munic.apply(lambda x: x[-6:]).apply(int)
data_senior=data_senior.sort_values(by=['munic'])
# In[ ]:
## Simulacia pre seniorov - vypnuta mobilita pre tuto populaciu.
def simul_senior(public_trans):
# Znizenie populacie o seniorov, ktori nebudu migrovat v ramci obce podla tejto hypotezy
N_k = pop.popul.to_numpy()-data_senior.senior.to_numpy()
locs_len = len(N_k)
SIR = np.zeros(shape=(locs_len, 3))
SIR[:,0] = N_k
SIR[:, 0] = SIR[:, 0] - first_infections
SIR[:, 1] = SIR[:, 1] + first_infections
row_sums = SIR.sum(axis=1)
SIR_n = SIR / row_sums[:, np.newaxis]
beta = 0.4
gamma = 0.10
R0 = beta/gamma
gamma_vec = np.full(locs_len, gamma)
public_trans_vec = np.full(locs_len, public_trans)
SIR_sim = SIR.copy()
SIR_nsim = SIR_n.copy()
infected_pop_norm = []
susceptible_pop_norm = []
recovered_pop_norm = []
SIR_sim_arr=np.zeros((SIR_sim.shape[0],SIR_sim.shape[1],200))
j=0
for time_step in tqdm_notebook(range(200)):
beta_vec = np.random.gamma(beta, 2, locs_len)
infected_mat = np.array([SIR_nsim[:,1],]*locs_len).transpose()
OD_infected = np.round(OD*infected_mat)
inflow_infected = OD_infected.sum(axis=0)
inflow_infected = np.round(inflow_infected*public_trans_vec)
new_infect = beta_vec*SIR_sim[:, 0]*inflow_infected/(N_k + OD.sum(axis=0))
new_recovered = gamma_vec*SIR_sim[:, 1]
new_infect = np.where(new_infect>SIR_sim[:, 0], SIR_sim[:, 0], new_infect)
SIR_sim[:, 0] = SIR_sim[:, 0] - new_infect
SIR_sim[:, 1] = SIR_sim[:, 1] + new_infect - new_recovered
SIR_sim[:, 2] = SIR_sim[:, 2] + new_recovered
SIR_sim = np.where(SIR_sim<0,0,SIR_sim)
row_sums = SIR_sim.sum(axis=1)
SIR_nsim = SIR_sim / row_sums[:, np.newaxis]
SIR_sim_arr[:,:,j]=SIR_sim
j=j+1
## Pridanie seniorov do celkovej populacie v tomto kroku pre spravny vypocet incidencie ochorenia
S = SIR_sim[:,0].sum()/(N_k+data_senior.senior.to_numpy()).sum()
I = SIR_sim[:,1].sum()/(N_k+data_senior.senior.to_numpy()).sum()
R = SIR_sim[:,2].sum()/(N_k+data_senior.senior.to_numpy()).sum()
infected_pop_norm.append(I)
susceptible_pop_norm.append(S)
recovered_pop_norm.append(R)
res = pd.DataFrame(list(zip(infected_pop_norm, susceptible_pop_norm, recovered_pop_norm)), columns = ['inf','sus','rec'])
return res,SIR_sim_arr
# In[ ]:
## Histogram R0 - distribucia Reproduction number
N_k = pop.popul.to_numpy()
locs_len = len(N_k)
beta = 0.4
gamma = 0.10
R0 = beta/gamma
beta_vec = np.random.gamma(beta, 2, locs_len)
R0_vec = beta_vec / gamma
plt.hist(R0_vec, normed=True, bins=25)
plt.ylabel('Probability')
plt.savefig('./plots/Histogram_R0')
# In[ ]:
## Inicializacia zoznamov, ktore budu zaplnene v simulacii
percSIR_high=[]
percSIR_med=[]
percSIR_low=[]
percSIR_low_senior=[]
SIR_high=[]
SIR_med=[]
SIR_low=[]
SIR_low_senior=[]
# In[ ]:
for sim in np.arange(50):
## Uprava prvych infekcii na zaklade odhadhovaneho realnu poctu nakaz,
## sirsia diskusia v paperi.
first_infections=first_infections_original*6
# Simulacia pre scenare vysoka mobilita (1), stredna mobilita (0.5) a nizka mobilita (0.3)
# Posledny scenar pre nulovu mobilitu pre seniorov.
a_high,b_high = simul(public_trans = 1)
a_med,b_med = simul(public_trans = 0.5)
a_low,b_low = simul(public_trans = 0.3)
a_low_senior,b_low_senior = simul_senior(public_trans = 0.3)
percSIR_high.append(a_high)
SIR_high.append(b_high)
percSIR_med.append(a_med)
SIR_med.append(b_med)
percSIR_low.append(a_low)
SIR_low.append(b_low)
percSIR_low_senior.append(a_low_senior)
SIR_low_senior.append(b_low_senior)
# In[ ]:
## Ulozenie suboru simulacii
#with open('./vystupy_model/simulations.pickle','wb') as f:
# pickle.dump([percSIR_high,percSIR_med,percSIR_low,SIR_high,SIR_med,SIR_low],f)
# In[24]:
## Otvorenie ulozeneho suboru simulacii
with open('./vystupy_model/simulations_17.3.2020.pickle','rb') as f:
percSIR_high,percSIR_med,percSIR_low,SIR_high,SIR_med,SIR_low=pickle.load(f)
f.close()
# In[26]:
## Porovnanie peakov podla mobility, relativne cisla graf
if True:
x = np.arange(1,201)
plt.rcParams['axes.facecolor']='white'
for data in (percSIR_high):
plt.plot(x,data.inf[0:200] ,c='red',alpha=0.7)
plt.xlim((0, 200))
plt.ylim((0, 0.5))
for data in (percSIR_med):
plt.plot(x,data.inf[0:200] ,c='orange',alpha=0.7)
plt.xlim((0, 200))
plt.ylim((0, 0.5))
for data in (percSIR_low):
plt.plot(x,data.inf[0:200] ,c='green',alpha=0.7)
plt.xlim((0, 200))
plt.ylim((0, 0.5))
for data in (percSIR_low_senior):
plt.plot(x,data.inf[0:200] ,c='blue',alpha=0.7)
plt.xlim((0, 200))
plt.ylim((0, 0.5))
plt.title('Porovnanie peaku infekcie podľa mobility')
plt.xlabel('Dni')
plt.ylabel('Pomer nakazených')
plt.savefig('./plots/plot_main_plus_senior2.png',dpi=300)
plt.close
# In[ ]:
## Denny narast poctu infikovanych v absolutnych cislach - graf
if True:
x = np.arange(1,200)
plt.rcParams['axes.facecolor']='white'
for data in [sumlist(SIR_high)[:,1,:].sum(0)]:
plt.plot(x,data[1:100]-data[0:99] ,c='red',alpha=1,linewidth=3)
plt.xlim((0, 100))
plt.ylim((0, 550000))
for data in [sumlist(SIR_med)[:,1,:].sum(0)]:
plt.plot(x,data[1:100]-data[0:99] ,c='orange',alpha=1,linewidth=3)
plt.xlim((0, 100))
plt.ylim((0, 550000))
for data in [sumlist(SIR_low)[:,1,:].sum(0)]:
plt.plot(x,data[1:100]-data[0:99] ,c='green',alpha=1,linewidth=3)
plt.xlim((0, 100))
plt.ylim((0, 550000))
plt.title('Denný nárast počtu infikovaných')
plt.xlabel('Dni')
plt.ylabel('Počet nových nákaz')
plt.subplots_adjust(left = 0.155)
plt.savefig('./plots/plot3_main.png',dpi=300)
plt.close
# In[ ]:
## Graf pre vsetky vs. zachytene pripady virusu
data_uk=pd.DataFrame({'Všetky prípady':np.concatenate([np.array([1,3,5,7,7,10,21,32,44]),sumlist(SIR_low)[:,1,:].sum(0)]),
'Zachytené prípady':np.concatenate([[0,0,0,0,0],np.array([1,3,5,7,7,10,21,32,44]),sumlist(SIR_low)[:,1,:].sum(0)[:-5]])})
x=np.arange(0,109)
plt.plot(x,data_uk['Zachytené prípady'] ,c='orange',alpha=1,linewidth=3)
plt.plot(x,data_uk['Všetky prípady'] ,c='red',alpha=1,linewidth=3)
plt.xlim(0,45)
plt.ylim(0,10000)
plt.xlabel('Dni')
plt.legend(['Zachytené prípady', 'Všetky prípady'])
plt.ylabel('Počet')
plt.savefig('./plots/zname_nezname.png',dpi=300)
# In[ ]:
## Ulozenie a export priemernej hodnoty zo 100 simulacii
pd.DataFrame(sumlist(SIR_low)[:,1,:]).to_csv('./results/I_low.csv')
pd.DataFrame(sumlist(SIR_low)[:,0,:]).to_csv('./results/S_low.csv')
pd.DataFrame(sumlist(SIR_low)[:,2,:]).to_csv('./results/R_low.csv')
pd.DataFrame(sumlist(SIR_med)[:,1,:]).to_csv('./results/I_med.csv')
pd.DataFrame(sumlist(SIR_med)[:,0,:]).to_csv('./results/S_med.csv')
pd.DataFrame(sumlist(SIR_med)[:,2,:]).to_csv('./results/R_med.csv')
pd.DataFrame(sumlist(SIR_high)[:,1,:]).to_csv('./results/I_high.csv')
pd.DataFrame(sumlist(SIR_high)[:,0,:]).to_csv('./results/S_high.csv')
pd.DataFrame(sumlist(SIR_high)[:,2,:]).to_csv('./results/R_high.csv')
percSIR_high_avg=sumlist(percSIR_high)
percSIR_med_avg=sumlist(percSIR_med)
percSIR_low_avg=sumlist(percSIR_low)
# In[ ]:
## Tabulka s absolutnymi cislami o pocte infikovanych a vyliecenych pre vybrane dni
I_high=pd.read_csv('./results/I_high.csv').iloc[:,1:].sum(0)
I_med=pd.read_csv('./results/I_med.csv').iloc[:,1:].sum(0)
I_low=pd.read_csv('./results/I_low.csv').iloc[:,1:].sum(0)
R_high=pd.read_csv('./results/R_high.csv').iloc[:,1:].sum(0)
R_med=pd.read_csv('./results/R_med.csv').iloc[:,1:].sum(0)
R_low=pd.read_csv('./results/R_low.csv').iloc[:,1:].sum(0)
pd.DataFrame({'dni':np.arange(200),
'I_high':I_high.to_numpy() ,
'R_high':R_high.to_numpy(),
'I_med':I_med.to_numpy() ,
'R_med':R_med.to_numpy(),
'I_low':I_low.to_numpy() ,
'R_low':R_low.to_numpy()
}).to_excel('excel2.xlsx',engine='xlsxwriter')
I_high.iloc[[4,9,19,29,39,49,59,79,99,149,199]].to_numpy()