#!/usr/bin/env python

# coding: utf-8

# In[1]:

get_ipython().run_line_magic('pylab', 'inline')

# In[2]:

from sci378 import *

# In[3]:

from pyndamics import Simulation

from pyndamics.emcee import *

# In[6]:

data=pd.read_csv('data/FluNetInteractiveReport.csv',skiprows=3)

data.head()

# In[17]:

t=array(data['Year']+(data['Week']-1)/52)

y=array(data['ALL_INF'])

y=y[(t>=2017.5) & (t<=2018.5)]

t=t[(t>=2017.5) & (t<=2018.5)]

t=(t-t[0])*365.25

# In[18]:

plot(t,y,'-o')

# In[19]:

sim=Simulation()

sim.add("N=S+I+R")

sim.add(" S' = -β*S*I/N",3e8)

sim.add(" I' = +β*S*I/N - γ*I",1,plot=True)

sim.add(" R' = +γ*I",0)

sim.add_data(t=t,I=y,plot=True)

sim.params(β=0.3,γ=0.1)

sim.run(0,365)

# In[27]:

sim=Simulation()

sim.add("N=S+I+R")

sim.add(" S' = -β*S*I/N",3e8)

sim.add(" I' = +β*S*I/N - γ*I",1,plot=True)

sim.add(" R' = +γ*I",0)

sim.add_data(t=t,I=y,plot=True)

sim.params(β=0.08,γ=0.06)

sim.run(0,365)

# In[29]:

t1=t[t<200]

y1=log(y[t<200])

plot(t1,y1,'-o')

# In[30]:

t1=t[(t<200) & (t>50)]

y1=log(y[(t<200) & (t>50)])

plot(t1,y1,'-o')

# In[31]:

model=ols('y ~ x', data={'y':y1,'x':t1})

results=model.fit()

results.summary()

# In[32]:

m=results.params['x']

m

# In[33]:

t2=t[(t>200) & (t<365)]

y2=log(y[(t>200) & (t<365)])

plot(t2,y2,'-o')

# In[35]:

model=ols('y ~ x', data={'y':y2,'x':t2})

results=model.fit()

print(results.summary())

m=results.params['x']

m

# In[45]:

sim=Simulation()

sim.add("N=S+I+R")

sim.add(" S' = -β*S*I/N",3e8)

sim.add(" I' = +β*S*I/N - γ*I",300,plot=True)

sim.add(" R' = +γ*I",0)

sim.add_data(t=t,I=y,plot=True)

sim.params(β=0.037,γ=0.03)

sim.run(0,365)

# In[62]:

model=MCMCModel(sim,

β=Normal(0.05,2,all_positive=True),

γ=Normal(0.05,2,all_positive=True),

initial_I=Uniform(1,100),)

# In[63]:

model.run_mcmc(500,repeat=5)

model.plot_chains()

# In[64]:

sim.run(0,365)

# In[76]:

Ro=model.eval('β/γ')

# In[77]:

model.plot_distributions(Ro)

# In[78]:

model.plot_distributions()

# ## Measles in a school

# In[79]:

data=pd.read_excel('data/measles_hagelloch_1861.xlsx')

data.head()

# In[80]:

days=array(data['day'])

t=sorted(list(set(days)))

I=[]

for _ in t:

I.append(sum(days==_))

t=array(t)

I=array(I)

t=t[:-1]

I=I[:-1]

# In[81]:

plot(t,I,'-o')

# In[82]:

t,I

# In[86]:

sim=Simulation()

sim.add("N=S+I+R")

sim.add("S'=-β*S*I/N",188)

sim.add("I'=β*S*I/N-γ*I",.1,plot=True)

sim.add("R'=γ*I",0)

sim.params(β=4,γ=4)

sim.add_data(t=t,I=I,plot=True)

sim.run(0,t.max())

# In[87]:

model=MCMCModel(sim,

)

# In[88]:

model.run_mcmc(500,repeat=2)

model.plot_chains()

# In[89]:

model.plot_distributions()

# In[95]:

model.plot_distributions(Ro=model.eval('β/γ'))

# In[91]:

sim.run(0,t.max())

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