# -*- coding: utf-8 -*-

"""

Created on Thu Mar 26 22:55:37 2020

@author: Travis Czechorski tjcze01@gmail.com

Data and Model from:

A conceptual model for the coronavirus disease 2019 (COVID-19)

outbreak in Wuhan, China with individual reaction and

governmental action

DOI:https://doi.org/10.1016/j.ijid.2020.02.058

https://www.ijidonline.com/article/S1201-9712(20)30117-X/fulltext

"""

import matplotlib.pyplot as plt

from scipy.integrate import solve_ivp

import numpy as np

import math

import pandas as pd

import os

import time

start = time.time() #Real time when the program starts to run

clear = lambda: os.system('cls')

cwd = os.getcwd()

dir_path = os.path.dirname(os.path.realpath(__file__))

path_fol = "{}\SEIR Model for Spread of Disease".format(dir_path)

try:

os.mkdir(path_fol)

except:

pass

def R0(α, β, μ, γ):

# R_0 = (α/(μ + α))*(β/(μ + λ))

R_0 = (β/γ)*(α/(α + μ))

return R_0

def R0b(β, γ, σ, μ):

return (β*σ)/((γ + μ)*(μ + σ))

def aplha(day):

return 0.8478

def Beta(α,β0, D, N, k):

B = β0*(1 - α)*((1 - D/N)**k)

return B

def SEIR(t, y, *args):

return [dsdt, dedt, didt, drdt, dndt, dDdt, dcdt]

def jacobian(t, y, *args):

[ 0, σ, 0, 0, 0, 0, 0]]

def roundup(x, places):

return int(math.ceil(x / int(places))) * int(places)

Λ = 0.0 # Birth rate

μ = 0.0 # Death rate

# Λ = 0.01 # Birth rate

# μ = 0.0205 # Death rate

Tc = 2.0 # Typical time between contacts

# β = 0.5944 #1.0/Tc

β = 1.68

# Tr = 11.1 # Typical time until recovery

Tinfs = [2.9, 2.3, 2.3, 2.9, 10.0, 1.5]

# Tr = sum(Tinfs)/len(Tinfs) #5.0

# Tr = 11.1

Tr = 14.0

γ = 1.0/Tr

Tincs = [5.2, 5.2, 6.1, 5.5, 4.8, 5.0, 6.5, 4.8]

Tinc = sum(Tincs)/len(Tincs)

σ = Tinc**-1

# σ = 3.0**-1

F = 10

α = 0.0

# α = 0.05

# α = 0.4239

# α = 0.8478

d = 0.05

# k = 1117.3

# k = 200

k = 0

λb = 11.2

λ = λb**-1

Infi = 10 # Initial infected

Daysnn = 150

NP = 329436928 # 1437904257

S0 = NP - Infi

its = 10000

itern = Daysnn/its

Days = [0.0, Daysnn]

Time = [i for i in range(0, int(Daysnn + 1), 1)]

tt = list(range(0,its,1))

Time_f = [i*itern for i in tt]

Y0 = [NP, 0.0, Infi, 0.0, NP, d, Infi]

Ro = R0b(β, γ, σ, μ)

# print(Ro)

# print('Λ')

# print('μ')

# print(α)

# print(β)

# print(Ro, 1.68**-1)

# print(λ)

# print(σ)

answer = solve_ivp(SEIR, Days, Y0, t_eval=Time_f, method = 'Radau', args=(σ, β, γ, μ, Λ, F, α, d, k, λ), jac=jacobian, rtol=1E-10, atol=1E-10)

ts = answer.t

Bs = [Beta(σ, β, i, j, k) for i,j in zip(answer.y[5],answer.y[4])]

Sn = answer.y[0]

En = answer.y[1]

In = answer.y[2]

Rn = answer.y[3]

Nn = answer.y[4]

Dn = answer.y[5]

Cn = answer.y[6]

Spb = answer.y[0]/NP

Epb = answer.y[1]/NP

Ipb = answer.y[2]/NP

Rpb = answer.y[3]/NP

Npb = answer.y[4]/NP

Dpb = answer.y[5]/NP

Cpb = answer.y[6]/NP

Sp = [i*100.0 for i in Spb]

Ep = [i*100.0 for i in Epb]

Ip = [i*100.0 for i in Ipb]

Rp = [i*100.0 for i in Rpb]

Np = [i*100.0 for i in Npb]

Dp = [i*100.0 for i in Dpb]

Cp = [i*100.0 for i in Cpb]

m = max(In)

mi = (In.tolist()).index(max(In))

mip = mi/its

peakn = round(Daysnn*mip)

my = max(Ip)

myi = (Ip).index(max(Ip))

myp = myi/its

peakyn = round(Daysnn*myp)

PEAK = [int(round(Daysnn*(mi/its)))]

nPEAK = np.array(PEAK, ndmin=2)

Tdata = np.array((Time_f, Sn, En, In, Rn))

TTdata = np.array((Time_f, Spb, Epb, Ipb, Rpb, Sp, Ep, Ip, Rp))

Tdatal = Tdata.tolist()

if its <= 16384:

writer = pd.ExcelWriter(r'{}\SIR Population.xlsx', engine='xlsxwriter')

writerp = pd.ExcelWriter(r'{}\SIR Percent.xlsx', engine='xlsxwriter')

indexes = ['Time [Days]', 'Susceptible', 'Exposed', 'Infected', 'Recovered', 'Peak [Day]']

indexest = ['Time [Days]', 'Susceptible', 'Exposed', 'Infected', 'Recovered' , 'Susceptible [%]', 'Exposed [%]', 'Infected [%]', 'Recovered [%]', 'Peak [Day]']

df = pd.DataFrame([Time_f, Sn, En, In, Rn, PEAK], index=[*indexes])

dft = pd.DataFrame([Time_f, Spb, Epb, Ipb, Rpb, Sp, Ep, Ip, Rp, PEAK], index=[*indexest])

df.to_excel(r"{}\SIR Population.xlsx".format(path_fol), sheet_name="SIR Population.xlsx", header=True, startrow=1)

dft.to_excel(r"{}\SIR Percent.xlsx".format(path_fol), sheet_name="SIR Percent.xlsx", header=True, startrow=1)

elif its > 16384 and its <= 1048576:

writer = pd.ExcelWriter(r'{}\SIR Population.xlsx', engine='xlsxwriter')

writerp = pd.ExcelWriter(r'{}\SIR Percent.xlsx', engine='xlsxwriter')

indexesb = ['Time [Days]', 'Susceptible', 'Exposed', 'Infected', 'Recovered']

indexestb = ['Time [Days]', 'Susceptible', 'Exposed', 'Infected', 'Recovered' , 'Susceptible [%]', 'Exposed [%]', 'Infected [%]', 'Recovered [%]']

df = pd.DataFrame(Tdata.T, columns=[*indexesb])

df.T

df.loc[:,'Peak [Day]'] = pd.Series([PEAK])

dft = pd.DataFrame(TTdata.T, columns=[*indexestb])

dft.T

dft.loc[:,'Peak [Day]'] = pd.Series([PEAK])

df.to_excel(r"{}\SIR Population.xlsx".format(path_fol), sheet_name="SIR Population.xlsx", header=True, startrow=1)

dft.to_excel(r"{}\SIR Percent.xlsx".format(path_fol), sheet_name="SIR Percent.xlsx", header=True, startrow=1)

else:

print("To many data points, couldn't save to excel file")

print('Maximum excel dimensions are 1,048,576 rows by 16,384 columns')

fig = plt.figure()

plt.plot(Time_f, Sn, 'b-', label=r'$\it{Susceptible}$')

plt.plot(Time_f, En, 'c-', label=r'$\it{Exposed}$')

plt.plot(Time_f, In, 'r-', label=r'$\it{Infected}$')

plt.plot(Time_f, Rn, 'g-', label=r'$\it{Recovered}$')

plt.axvline(x=int(Time_f[mi]), color='k', linestyle='--')

plt.legend([r'$\it{Susceptible}$', r'$\it{Exposed}$', r'$\it{Infected}$', r'$\it{Recovered}$', r'$\it{}$'.format("{}{}{}".format('{',"Peak \ = {}{}{} \ Days".format('{',r'\ {}'.format(peakn),'}'),'}'))],loc="best", fontsize=15)

plt.xlim((0,Daysnn))

plt.ylim((0,NP))

plt.yticks([roundup(i*(NP/10.0), 1000000) for i in range(11)])

plt.gca().get_yaxis().get_major_formatter().set_scientific(False)

plt.gca().set_yticklabels([r'{:,}'.format(int(x)) for x in plt.gca().get_yticks()])

plt.xlabel(r'$\bf{Time \ [Days]}$', fontsize=15)

plt.ylabel(r'$\bf{Number \ of \ people}$', fontsize=15)

plt.title(r'$\bf{SEIR \ Method \ for \ Spread \ of \ Disease}$', fontsize=18)

plt.grid()

fig.savefig(r"{}\SEIR Population.pdf".format(path_fol), bbox_inches='tight')

fig.savefig(r"{}\SEIR Population.svg".format(path_fol), bbox_inches='tight')

fig = plt.figure()

plt.plot(Time_f, Sp, 'b-', label=r'$\it{Susceptible}$')

plt.plot(Time_f, Ep, 'c-', label=r'$\it{Exposed}$')

plt.plot(Time_f, Ip, 'r-', label=r'$\it{Infected}$')

plt.plot(Time_f, Rp, 'g-', label=r'$\it{Recovered}$')

plt.axvline(x=int(Time_f[myi]), color='k', linestyle='--')

plt.legend([r'$\it{Susceptible}$', r'$\it{Exposed}$', r'$\it{Infected}$', r'$\it{Recovered}$', r'$\it{}$'.format("{}{}{}".format('{',"Peak \ = {}{}{} \ Days".format('{',r'\ {}'.format(peakn),'}'),'}'))],loc="best", fontsize=15)

plt.xlim((0,Daysnn))

plt.ylim((0,100))

plt.yticks([i*10 for i in range(11)])

plt.gca().set_yticklabels([f'{int(y)}%' for y in plt.gca().get_yticks()])

plt.gca().set_xticklabels([f'{int(x)}' for x in plt.gca().get_xticks()])

plt.xlabel(r'$\bf{Time \ [Days]}$', fontsize=15)

plt.ylabel(r'$\bf{Percentage \ of \ population}$', fontsize=15)

plt.title(r'$\bf{SEIR \ Method \ for \ Spread \ of \ Disease}$', fontsize=18)

plt.grid()

fig.savefig(r"{}\SEIR Percent.pdf".format(path_fol), bbox_inches='tight')

fig.savefig(r"{}\SEIR Percent.svg".format(path_fol), bbox_inches='tight')

plt.show()

end = time.time() #Time when it finishes, this is real time

def timer(start,end):

hours, rem = divmod(end-start, 3600)

minutes, seconds = divmod(rem, 60)

print("Completion Time: {} Hours {} Minutes {} Seconds".format(int(hours),int(minutes),int(seconds)))

timer(start,end) # Prints the amount of time passed since starting the program