Burton-Doherty Submission#9
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lyy005
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Nov 28, 2017
| ms1=spint.odeint(func=mgrate,y0=N0,t=times,args=param1) | ||
| modelOutput=pandas.DataFrame({"t":times,"N":ms1[:,0]}) | ||
| mgraph=ggplot(modelOutput,aes(x="t",y="N"))+geom_line()+theme_classic() | ||
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Owner
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You want to put the following two lines in the loop to store the results
ms1=spint.odeint(func=mgrate,y0=N0,t=times,args=param1)
modelOutput=pandas.DataFrame({"t":times,"N":ms1[:,0]})
-0.1 pts
lyy005
reviewed
Nov 28, 2017
| S=y[0] | ||
| I=y[0] | ||
| R=y[0] | ||
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lyy005
reviewed
Nov 28, 2017
| plot_1=ggplot(simDF_1,aes(x="t", y="S"))+geom_line(color='blue')+geom_line(simDF_1,aes(x="t",y="I"), color='red')+ geom_line(simDF_1,aes(x="t",y="R"), color='green')+theme_classic() | ||
| plot_1 | ||
| #maximum daily incidence is 292.26, occuring between day 0 and 1 | ||
| max(simDF_1.iloc[:,0]) |
Owner
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The simulation is good. The max daily incidence should be:
numpy.max(sim[1:len(sim),1]-sim[0:(len(sim)-1),1])
lyy005
reviewed
Nov 28, 2017
| max(simDF_1.iloc[:,0]) | ||
| #max prevalence is 669.84 | ||
| percent_infected_1=100*(1000-simDF_1.iloc[499,2])/1000 | ||
| print(percent_infected_1) |
Owner
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The percentage infected should be:
numpy.sum(sim[len(sim)-1,1:3])/numpy.sum(sim[len(sim)-1,:])*100
lyy005
reviewed
Nov 28, 2017
| sim=spint.odeint(func=diseaseSim,y0=y0,t=times,args=params) | ||
| simDF_8=pd.DataFrame({"t":times,"S":sim[:,0],"I":sim[:,1],"R":sim[:,2]}) | ||
| plot_8=ggplot(simDF_8,aes(x="t", y="S"))+geom_line(color='blue')+geom_line(simDF_8,aes(x="t",y="I"), color='red')+ geom_line(simDF_8,aes(x="t",y="R"), color='green')+theme_classic() | ||
| plot_8 |
Owner
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In general, you guys did a good job. Just need some minor changes in the model and the calculation of the parameters. Good job!
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