1. 資料探勘

讀取資料

import pandas as pd
import numpy as np

df = pd.read_csv('C:/Users/User/OneDrive - student.nsysu.edu.tw/Documents/Python/ML/2019nCov/2019nCov.csv')

計算每日成長人數

成長人數 = [0]
for i in range(len(df)-1):
    成長人數 += [df['確診'][i+1]-df['確診'][i]]
df['確診成長人數'] = 成長人數
df

畫每日確診人數圖

import matplotlib.pyplot as plt
x = df.iloc[:,1:2]

plt.plot(x)
plt.xlabel("Date")
plt.ylabel("Number of people")
plt.title("Wuhan Pneumonia Cumulative Amount Plot")

plt.show()

畫每日肺炎成長率

y = df.iloc[:,4:5]

plt.plot(y)
plt.xlabel("Date")
plt.ylabel("Infected Growth Rate")
plt.title("Wuhan Pneumonia Growth Rate Plot")

plt.show()

計算各項平均數值

print('平均確診成長率：',np.mean(df['確診成長率']))
print('平均死亡率：',np.mean(df['死亡率']))
print('平均治癒率：',np.mean(df['治癒率']))
print('平均確診成長人數：',np.mean(df['確診成長人數']))

平均確診成長率： 0.2659557775454546
平均死亡率： 0.024685260176470585
平均治癒率： 0.09026695652941175
平均確診成長人數： 1759.6176470588234

從數據上來看，死亡率確實很精準地落在百分之二左右，而成長人數因中共的確診方式在2/13時宣稱，因序列檢驗耗時太久，因此只要符合病徵以及MRI確認肺部有發炎症狀即確診，因此人數突然飆升。

2. The SIR epidemic model

一年級有在統計模擬課程上稍微使用過SIR Model，因此這次利用前面的平均數，建立簡單的模擬模型，模擬感染平均狀況的每日人數變化。
這裡僅利用武漢的人口總數計算，因應中國當局以城市當作封閉單位，因此以一個單位內部傳染情況來做推算。
因此利用：
總人口數：\(N\) = 10000000、
平均移除率(平均死亡率+平均治癒率)：\(\gamma\) = 0.024685260176470585+0.09026695652941175 = 0.11495221670588233 假設感染接觸率：\(\beta\) = 0.9、0.6、0.25

這裡假設幾種感染接觸率，這裡的接觸率我們假設三種情況，人口正常移動且無防護意識:0.9、人口減少移動且少部分有防疫意識:0.6、人口低流動且盡可能不接觸可能的感染者:0.25。

import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt

# Total population, N.
N = 100000000
# Initial number of infected and recovered individuals, I0 and R0.
I0, R0 = 1, 0
# Everyone else, S0, is susceptible to infection initially.
S0 = N - I0 - R0
# Contact rate, beta, and mean recovery rate, gamma, (in 1/days).
beta, gamma = 0.9, 0.11495221670588233
# A grid of time points (in days)
t = np.linspace(0, 160, 160)

# The SIR model differential equations.
def deriv(y, t, N, beta, gamma):
    S, I, R = y
    dSdt = -beta * S * I / N
    dIdt = beta * S * I / N - gamma * I
    dRdt = gamma * I
    return dSdt, dIdt, dRdt

# Initial conditions vector
y0 = S0, I0, R0
# Integrate the SIR equations over the time grid, t.
ret = odeint(deriv, y0, t, args=(N, beta, gamma))
S, I, R = ret.T

# Plot the data on three separate curves for S(t), I(t) and R(t)
fig = plt.figure(facecolor='w')
ax = fig.add_subplot(111)
ax.plot(t, S/100000000, 'b', alpha=0.5, lw=2, label='Susceptible')
ax.plot(t, I/100000000, 'r', alpha=0.5, lw=2, label='Infected')
ax.plot(t, R/100000000, 'g', alpha=0.5, lw=2, label='Recovered with immunity')
ax.set_xlabel('Time /days')
ax.set_ylabel('Number (10000000s)')
#ax.set_ylim(0,1.2)
ax.yaxis.set_tick_params(length=0)
ax.xaxis.set_tick_params(length=0)
ax.grid(b=True, which='major', c='w', lw=2, ls='-')
legend = ax.legend()
legend.get_frame().set_alpha(0.5)
for spine in ('top', 'right', 'bottom', 'left'):
    ax.spines[spine].set_visible(False)
plt.show()

beta = 0.5
# Integrate the SIR equations over the time grid, t.
ret = odeint(deriv, y0, t, args=(N, beta, gamma))
S, I, R = ret.T
# Plot the data on three separate curves for S(t), I(t) and R(t)
fig = plt.figure(facecolor='w')
ax = fig.add_subplot(111)
ax.plot(t, S/100000000, 'b', alpha=0.5, lw=2, label='Susceptible')
ax.plot(t, I/100000000, 'r', alpha=0.5, lw=2, label='Infected')
ax.plot(t, R/100000000, 'g', alpha=0.5, lw=2, label='Recovered with immunity')
ax.set_xlabel('Time /days')
ax.set_ylabel('Number (10000000s)')
#ax.set_ylim(0,1.2)
ax.yaxis.set_tick_params(length=0)
ax.xaxis.set_tick_params(length=0)
ax.grid(b=True, which='major', c='w', lw=2, ls='-')
legend = ax.legend()
legend.get_frame().set_alpha(0.5)
for spine in ('top', 'right', 'bottom', 'left'):
    ax.spines[spine].set_visible(False)
plt.show()

beta = 0.25
# Integrate the SIR equations over the time grid, t.
ret = odeint(deriv, y0, t, args=(N, beta, gamma))
S, I, R = ret.T
# Plot the data on three separate curves for S(t), I(t) and R(t)
fig = plt.figure(facecolor='w')
ax = fig.add_subplot(111)
ax.plot(t, S/100000000, 'b', alpha=0.5, lw=2, label='Susceptible')
ax.plot(t, I/100000000, 'r', alpha=0.5, lw=2, label='Infected')
ax.plot(t, R/100000000, 'g', alpha=0.5, lw=2, label='Recovered with immunity')
ax.set_xlabel('Time /days')
ax.set_ylabel('Number (10000000s)')
#ax.set_ylim(0,1.2)
ax.yaxis.set_tick_params(length=0)
ax.xaxis.set_tick_params(length=0)
ax.grid(b=True, which='major', c='w', lw=2, ls='-')
legend = ax.legend()
legend.get_frame().set_alpha(0.5)
for spine in ('top', 'right', 'bottom', 'left'):
    ax.spines[spine].set_visible(False)
plt.show()

3. Benford’s Law

這裡我們來檢查一下偉大黨提供的數據是否有造假的疑慮，以下是excle上面執行的結果截圖：

數字4出現的次數顯得有點突出，若是數據造假，落在數字4難道真的巧合嗎?不，我不這麼認為。
人口清洗的陰謀論、數字6之後出現的次數也相同，難道這是防疫中心想要表達64 64的訊息給廣大的萬萬子民嗎?

總之，希望這次疫情能夠順利結束，大家也能夠配合政府政策，減緩並阻止病毒的擴張，Peace!