Corley-Kilgore-McCown Exercise 10 Submission#3
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…e and max infectivity rates
lyy005
reviewed
Nov 26, 2017
| store_rs.iloc[:,i]=sim[:,0] | ||
| a=ggplot(store_rs,aes(x="time",y="r2")) | ||
| a+geom_point()+coord_cartesian() | ||
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To plot multiple lines on the same plot:
a+coord_cartesian()+geom_line()+geom_line(aes(x="time",y="r2",color="10"))+geom_line(aes(x="time",y="r3",color="50"))+geom_line(aes(x="time",y="r4",color="100"))+theme_classic()+labs(x="time",y="N")
lyy005
reviewed
Nov 27, 2017
| for j in range(0, 500): | ||
| #calculate incidence at every time step | ||
| incidence[j]=sim_df.iloc[j,0]-sim_df.iloc[j-1,0] | ||
| DataOut.iloc[i, 1]=max(incidence) #get the max |
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Maximum incidence should be DataOut.iloc[i, 0] in the array
DataOut.iloc[i, 0]=max(incidence) #get the max
lyy005
reviewed
Nov 27, 2017
| #creates a list to store incidence values | ||
| for j in range(0, 500): | ||
| #calculate incidence at every time step | ||
| incidence[j]=sim_df.iloc[j,0]-sim_df.iloc[j-1,0] |
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The calculate of incidence is I(t) - I(t-1). And I is the second the returned value, which should be:
incidence[j]=sim_df.iloc[j,1]-sim_df.iloc[j-1,1]
lyy005
reviewed
Nov 27, 2017
| #extract params from dataframe | ||
| for i in range(0,6): | ||
| I0=[1] | ||
| S0=[999,1,0] |
Owner
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In your epiSim function, I = y[0], S = y[1]. Then in your input, S0 should be [1, 999]
lyy005
reviewed
Nov 27, 2017
| #Gleaned information: | ||
| #increases in the gamma value appear to have a inverse-correlation with the calculated values | ||
| #increases in the beta values appear to have a direct-correlation with the calculated values | ||
| #Thus...increase beta to increase the spread of the disease |
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