AMidlige Tutorial 10

tutorial10part1.py

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+import pandas
+import numpy
+import scipy
+import scipy.integrate as spint
+from plotnine import *
+
+r_list=[-0.1,0.1,0.4,0.8,1]
+
+def growthRate(y,to,r,K):
+    N=y[0]
+    dNdt=r* (1-N/K)* N
+
+    return [dNdt]
+
+for i in r_list:
+    params=(i,100)
+    N0=10
+    times=range(0,1000)
+
+modelSim=spint.odeint(func=growthRate,y0=N0,t=times,args=params)
+modelOutput=pandas.DataFrame({"t":times,"N":modelSim[:,0]})
+a=ggplot(modelOutput,aes(x="t",y="N"))+geom_line()+theme_classic()
+a.draw()
+
+K_list=[10,50,100]
+def carryingCapacity(y,to,r,K):
+    N=y[0]
+    dNdt=r* (1-N/K)* N
+
+    return [dNdt]
+
+for i in K_list:
+    params=(0.2,i)
+    N0=1
+    times=range(0,1000)
+
+modelSim=spint.odeint(func=carryingCapacity,y0=N0,t=times,args=params)
+modelOutput=pandas.DataFrame({"t":times,"N":modelSim[:,0]})
+b=ggplot(modelOutput,aes(x="t",y="N"))+geom_line()+theme_classic()
+b.draw()
+
+N_list=[1,50,100]
+def popSize(y,to,r,K):
+    N=y[0]
+    dNdt=r* (1-N/K)* N
+
+    return [dNdt]
+
+for i in N_list:
+    params=(0.1,50)
+    N0=i
+    times=range(0,1000)
+
+modelSim=spint.odeint(func=popSize,y0=N0,t=times,args=params)
+modelOutput=pandas.DataFrame({"t":times,"N":modelSim[:,0]})
+c=ggplot(modelOutput,aes(x="t",y="N"))+geom_line()+theme_classic()
+c.draw

tutorial10part2.py

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+import pandas
+import numpy
+import scipy
+import scipy.integrate as spint
+from plotnine import *
+
+beta=[0.0005,0.005,0.0001,0.00005,0.0001,0.0002,0.0001]
+gamma=[0.05,0.5,0.1,0.1,0.05,0.05,0.06]
+params_dict = {}
+for i in range(len(beta)):
+    params_dict[beta[i]] = gamma[i]
+
+def disTrans(y, t, beta, gamma):
+    S=y
+    I=y
+    R=y
+    N=1000
+    dSdt = -beta * S * I / N
+    dIdt = beta * S * I / N - gamma * I
+    dRdt = gamma * I
+
+    return dSdt
+    return dIdt
+    return dRdt
+
+for i in params_dict.keys():
+    params=(i,params_dict[i])
+    S0=999
+    I0=1
+    R0=0
+    times=range(0,500)
+
+    modelSim=spint.odeint(func=disTrans,y0=S0,t=times,args=params)
+    modelSim2=spint.odeint(func=disTrans,y0=I0,t=times,args=params)
+    modelSim3=spint.odeint(func=disTrans,y0=R0,t=times,args=params)
+    modelOutput=pandas.DataFrame({"t":times,"S":modelSim[:,0],"I":modelSim2[:,0],"R":modelSim3[:,0]})
+
+print modelOutput
+
+a=ggplot(modelOutput,aes(x="t",y="y0"))+geom_line(aes(x="t",y="S"),color='blue')+geom_line(aes(x="t",y="I"),color='red')+geom_line(aes(x="t",y="R"),color='green')+theme_classic()
+a.draw()
+
+for i in I.modelOutput:
+    incidence=i-(i-1)
+
+for i in modelOutput:
+    prevalence= I(i) / (S(i)+ I(i)+ R(i))
+
+for i in modelOutput:
+    percentAffected= (I(i)+ S(i)) / (S(i)+ I(i)+ R(i))
+
+for i in modelOutput:
+    reproductionNumber= beta[i]* (S(i)+ I(i)+ R(i)) / gamma[i]
+