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改进的SIR差分模型及三个模型的应用

 收录于 数学建模
   约 4324 字   预计阅读 9 分钟 

前言

感谢组员的共同协作，做组长的有些东西帮不上实在抱歉。

改进的SIR差分模型

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%% SIR差分模型
frame=importdata('美国covid19疫情数据l-history改.csv');
date_row=size(frame.data,1);
data=frame.data;
date=frame.textdata(:,1);
real_date=date(2:date_row+1,:);
E=zeros(2,90);
l=0.000125;    %日接触率
m=0.01;   %日治愈率0.0005
E(1,1)=data(345,12)/300000000;
E(2,1)=1-E(1,1);
for i=1:90
    E(1,i+1)=l*E(2,i)-m*E(1,i)+E(1,i);
    E(2,i+1)=E(2,i)-l*E(1,i)*E(2,i);
end
X=flip(data(311:345,12)');% 5.31  4.2 
rate=flip(data(311:345,12)')%现阳性率
rate(isnan(rate))=0;
n=length(rate);
rt=0:1:n-1;
%a0=[100,10]; %初值
figure(1)
h1 = plot(rt,X/300000000,'*'); %画点
hold on;
a=E(1,:);
b=E(2,:);
h2 = plot(a,'r')
xlabel('日期');         %设置横坐标名
ylabel('感染人数');   %设置纵坐标名
legend([h1 h2],'3~5月感染人数','SIR模型迭代曲线','Location','NorthWest');
hold on
%%  预测
X=flip(data(255:345,12)');%311行后为第一题
rate=flip(data(255:345,12)')%现阳性率
rate(isnan(rate))=0;
n=length(rate);
rt=0:1:n-1;
figure(2)
h3 = plot(rt,X/300000000,'*'); %画点
hold on;
a=E(1,:);
b=E(2,:);
h4=plot(a,'r')
xlabel('日期');
ylabel('感染人数'); 
legend([h3 h4],'3~6月感染人数','SIR模型迭代曲线','Location','NorthWest');
hold on
%% 接种疫苗前
frame=importdata('usc.csv');
date_row=size(frame.data,1);
data=frame.data;
date=frame.textdata(:,1);
real_date=date(2:date_row+1,:);
E=zeros(3,150);
l=0.0018;    %日接触率 0.0018
m=0.01;   %日治愈率 0.01
E(1,1)=data(320,1)/300000000;
E(3,1)=28664448/300000000%43714928  33714928  28664448
E(2,1)=1-E(1,1)-E(3,1);

for i=1:150
    E(1,i+1)=E(1,i)+l*E(2,i)-m*E(1,i);
    E(2,i+1)=E(2,i)-l*E(1,i)*E(2,i);
    E(3,i+1)=1-E(1,i+1)- E(2,i+1);
end
X=data(320:486,1)';%80-482  3.1-7.1
rate=data(320:486,1)'%
rate(isnan(rate))=0;
n=length(rate);
rt=0:1:n-1;
figure(6)
h11 = plot(rt,X/300000000,'*'); %画点
hold on;
a=E(1,:);
b=E(2,:);
c=E(3,:);
t=1:151;
h1=plot(t,a,'r')%感染人数
%plot(t,b,'b')%健康人数
plot(t,c,'r')%移除者
%legend('i(t)','s(t)','r(t)')
hold on
%% 接种疫苗后
E=zeros(3,150);
l=0.0018;    %日接触率 0.0018
m=0.01;   %日治愈率 0.01
E(1,1)=data(320,1)/300000000;
E(3,1)=53714928/300000000%43714928  33714928  28664448
E(2,1)=1-E(1,1)-E(3,1);
for i=1:150
    E(1,i+1)=E(1,i)+l*E(2,i)-m*E(1,i);
    E(2,i+1)=E(2,i)-l*E(1,i)*E(2,i);
    E(3,i+1)=1-E(1,i+1)- E(2,i+1);
end
X=data(320:486,1)';%80-482  3.1-7.1
rate=data(320:486,1)'%
rate(isnan(rate))=0;
n=length(rate);
rt=0:1:n-1;
figure(6)
%h11 = plot(rt,X/300000000,'*'); %画点
hold on;
a=E(1,:);
b=E(2,:);
c=E(3,:);
t=1:151;
h2=plot(t,a,'b')%感染人数
%plot(t,b,'b')%健康人数
plot(t,c,'b')%移除者
%legend('i(t)','s(t)','r(t)')
hold on
xlabel('日期');         %设置横坐标名
ylabel('r(t)与i（t）');   %设置纵坐标名
legend([h1 h2 h11],'接种疫苗前','接种疫苗后','感染人数原始数据','Location','NorthWest');

logistic模型预测美国人口

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%改进的指数增长模型
x=flip([324985536 322941312 320635168 318300992 315993728 313830976 311556864 309321664 306771520 304093952 301231200 298379904 295516608 292805312 290107936 287625184 284968960 282162400 279040000 275854016 272656992])
y=flip([0.006330017,0.007192424,0.007333235,0.007301613,0.006891455,0.007299188,0.007226135,0.008312845,0.008805068,0.009503504,0.009555925,0.00968912,0.009259723,0.009297836,0.008631901,0.009321099,0.009946612,0.011189794,0.011549529,0.011725443,0.012112401])
n=length(x);
t=0:1:n-1;
rk=zeros(1,n);
rk(1)=(-3*x(1)+4*x(2)-x(3))/2;
rk(n)=(x(n-2)-4*x(n-1)+3*x(n))/2;
for i=2:n-1
rk(i)=(x(i+1)-x(i-1))/2;
end
rk=rk./x;
p=polyfit(t,rk,2);
r0=p(1);
r1=p(2);
r2=p(3);
x0=x(1);
R=r0*t.^2+r1*t+r2;
X=x0*exp((r0*t.^3)/3+(r1*t.^2)/2+r2*t);
figure(1)
hold on;
xlabel('year');         %设置横坐标名
ylabel('rate');         %设置纵坐标名
grid on                 %网格线
plot(t,y,'r*')
figure(2)
hold on;
xlabel('year');         %设置横坐标名
ylabel('population');   %设置纵坐标名
grid on                 %网格线
plot(t,x,'r*',t,X)
figure(3)
hold on;
xlabel('year');         %设置横坐标名
ylabel('rate');         %设置纵坐标名
grid on                 %网格线
plot(t,y,'r*',t,R')
%2018
t1=t(21)+1;
x2018=x0*exp((r0*t1.^3)/3+(r1*t1.^2)/2+r2*t1)
y2018=r0*t1.^2+r1*t1+r2
%2019
t1=t(21)+2;
x2019=x0*exp((r0*t1.^3)/3+(r1*t1.^2)/2+r2*t1)
y2019=r0*t1.^2+r1*t1+r2

药物中毒急救建模

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clc;
clear all;
syms x t;
f(t)=6*(exp(-0.1155*t)-exp(-0.1386*t));
h(t)=exp(-0.1386*t);
figure(1);
fplot(f,[0 25]);
hold on;
fplot(h,[0,25]);
grid on;
xlabel('时间t/h');
ylabel('药量');
legend('血液中的药量y/mg','胃肠道中的药量x/mg');
figure(2)
fplot(f,[0,25]);
hold on;
fplot(h,[0,25]);
f(t)=-(exp(-0.693*t)-exp(-0.1386*t))/4;
fplot(f,[0,25]);
xlabel('时间t/h');
ylabel('药量');
legend('血液中的药量y/mg','胃肠道中的药量x/mg','施救后血液中的药量y/mg');

热传导差分偏微分模型应用

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clear;
close all;
clc;
pho=[300;862;74.2;1.18];
c=[1377;2100;1726;1005];
lamda=[0.082;0.37;0.045;0.028];
a=lamda./(pho.*c);
 
d=[0.6;6;3.6;5]*10^-3;
 
TT=273.15;
T_in=37;
T_out=75;
T_s=48.08;
 
xmin=0;
xmax=sum(d);
 
N=5400;
h=0.05*10^-3;
k=1;
r=k/h^2;
I=round((xmax-xmin)/h);
 
A=zeros(1,I);
B=zeros(1,I+1);
C=zeros(1,I);
 
N1=round(d(1)/h);
N2=round(d(2)/h);
N3=round(d(3)/h);
N4=round(d(4)/h);
 
for i=1:N1
    A(i)=-a(1)*r;
    B(i)=2+2*r*a(1);
    C(i)=-r*a(1);
end
for i=N1+1:N1+N2
    A(i)=-a(2)*r;
    B(i)=2+2*r*a(2);
    C(i)=-r*a(2);
end
for i=N1+N2+1:N1+N2+N3
    A(i)=-a(3)*r;
    B(i)=2+2*r*a(3);
    C(i)=-r*a(3);
end
for i=N1+N2+N3+1:N1+N2+N3+N4
    A(i)=-a(4)*r;
    B(i)=2+2*r*a(4);
    C(i)=-r*a(4);
end
 
T=zeros(I+1,N+1);
T(:,1)=(T_in+TT)*ones(I+1,1);
 
T_xt=xlsread('CUMCM-2018-Problem-A-Chinese-Appendix.xlsx');
 
h_min=110;
h_max=120;
delta_h=0.1;
H1=h_min:delta_h:h_max;
delta=zeros(1,length(H1));
 
for j=1:length(H1)
    h1=h_min+(j-1)*delta_h;
    k1=lamda(1);k2=lamda(2);k3=lamda(3);k4=lamda(4);
    x1=d(1);x2=d(1)+d(2);x3=d(1)+d(2)+d(3);x4=d(1)+d(2)+d(2)+d(4);
    t1=T_out+TT;t2=T_in+TT;t3=T_s+TT;
    
    h5=-((h1*k2*k3*k4*t1)/(k1*k2*k3*k4-h1*k1*k2*k3*x3-h1*k1*k2*k4*x2 ...
        -h1*k1*k3*k4*x1+h1*k1*k2*k3*x4+h1*k1*k2*k4*x3+h1*k1*k3*k4*x2+h1*k2*k3*k4*x1)-(h1*k2*k3*k4*t3)...
        /(k1*k2*k3*k4-h1*k1*k2*k3*x3-h1*k1*k2*k4*x2-h1*k1*k3*k4*x1+h1*k1*k2*k3*x4+h1*k1*k2*k4*x3+h1*k1*k3*k4*x2+h1*k2*k3*k4*x1))...
        /(t2/k1-t3/k1);
    
    AA=diag(B)+diag(A,1)+diag(C,-1);
    AA(1,1)=lamda(1)/h+h1;
    AA(1,2)=-lamda(1)/h;
    AA(I+1,I)=-lamda(4)/h;
    AA(I+1,I+1)=lamda(4)/h+h5;
    
    AA(N1+1,N1)=-lamda(1);
    AA(N1+1,N1+1)=lamda(1)+lamda(2);
    AA(N1+1,N1+2)=-lamda(2);
    
    AA(N1+N2+1,N1+N2)=-lamda(2);
    AA(N1+N2+1,N1+N2+1)=lamda(2)+lamda(3);
    AA(N1+N2+1,N1+N2+2)=-lamda(3);
    
    AA(N1+N2+N3+1,N1+N2+N3)=-lamda(3);
    AA(N1+N2+N3+1,N1+N2+N3+1)=lamda(3)+lamda(4);
    AA(N1+N2+N3+1,N1+N2+N3+2)=-lamda(4);
    
    for n=1:k:N
        D=zeros(I+1,1);
        D(1)=h1*(T_out+TT);
        D(I+1)=h5*(T_in+TT);
        for i=2:1:N1
            D(i)=r*a(1)*T(i-1,n)+(2-2*r*a(1))*T(i,n)+r*a(1)*T(i+1,n);
        end
        for i=N1+1:1:N1+N2
            D(i)=r*a(2)*T(i-1,n)+(2-2*r*a(2))*T(i,n)+r*a(2)*T(i+1,n);
        end
        for i=N1+N2+1:1:N1+N2+N3
            D(i)=r*a(3)*T(i-1,n)+(2-2*r*a(3))*T(i,n)+r*a(3)*T(i+1,n);
        end
        for i=N1+N2+N3+1:1:N1+N2+N3+N4
            D(i)=r*a(4)*T(i-1,n)+(2-2*r*a(4))*T(i,n)+r*a(4)*T(i+1,n);
        end
        D(N1+1)=0;
        D(N1+N2+1)=0;
        D(N1+N2+N3+1)=0;
        T(:,n+1)=AA\D;
    end
   delta(j)=sqrt(sum((T_xt(:,2)-T(end,:)'+TT).^2)/length(T_xt(:,1)));
end
%图二 
figure(1);
mesh(0:k:N,1000*(0:h:sum(d)),(T-TT));
%图三
T_problem1=zeros(N+1,4);
T_problem1(:,1)=T(1,:)';
T_problem1(:,2)=T(N1+1,:)';
T_problem1(:,3)=T(N2+N1+1,:)';
T_problem1(:,4)=T(N3+N2+N1+1,:)';
T_problem1=T_problem1-TT;
figure(2);
plot(0:k:N,T_problem1(:,1)',0:k:N,T_problem1(:,2)',0:k:N,T_problem1(:,3)',0:k:N,T_problem1(:,4)',0:k:N,T_xt(:,2)');

python数据处理(写的很乱)

为了写第一个模型整合各种数据。。。有点乱，将就看吧。

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#导入需要的数据库和文件
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pyplot import MultipleLocator
odata=pd.read_csv(r'us_state_vaccinations.csv')
plt.rcParams['font.sans-serif'] = ['SimHei'] 
plt.rcParams['axes.unicode_minus'] = False 

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r_hex = '#dc2624'     # red,       RGB = 220,38,36
dt_hex = '#2b4750'    # dark teal, RGB = 43,71,80
tl_hex = '#45a0a2'    # teal,      RGB = 69,160,162
r1_hex = '#e87a59'    # red,       RGB = 232,122,89
tl1_hex = '#7dcaa9'   # teal,      RGB = 125,202,169
g_hex = '#649E7D'     # green,     RGB = 100,158,125
o_hex = '#dc8018'     # orange,    RGB = 220,128,24
tn_hex = '#C89F91'    # tan,       RGB = 200,159,145
g50_hex = '#6c6d6c'   # grey-50,   RGB = 108,109,108
bg_hex = '#4f6268'    # blue grey, RGB = 79,98,104
g25_hex = '#c7cccf'   # grey-25,   RGB = 199,204,207

1

odata

date location total_vaccinations total_distributed people_vaccinated people_fully_vaccinated_per_hundred total_vaccinations_per_hundred people_fully_vaccinated people_vaccinated_per_hundred distributed_per_hundred daily_vaccinations_raw daily_vaccinations daily_vaccinations_per_million share_doses_used
0 2021-01-12 Alabama 78134.0 377025.0 70861.0 0.15 1.59 7270.0 1.45 7.69 NaN NaN NaN 0.207
1 2021-01-13 Alabama 84040.0 378975.0 74792.0 0.19 1.71 9245.0 1.53 7.73 5906.0 5906.0 1205.0 0.222
2 2021-01-14 Alabama 92300.0 435350.0 80480.0 NaN 1.88 NaN 1.64 8.88 8260.0 7083.0 1445.0 0.212
3 2021-01-15 Alabama 100567.0 444650.0 86956.0 0.28 2.05 13488.0 1.77 9.07 8267.0 7478.0 1525.0 0.226
4 2021-01-16 Alabama NaN NaN NaN NaN NaN NaN NaN NaN 7557.0 7498.0 1529.0 NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
11328 2021-06-30 Wyoming 421749.0 516025.0 226911.0 34.38 72.87 198958.0 39.21 89.16 55.0 913.0 1578.0 0.817
11329 2021-07-01 Wyoming 423238.0 516325.0 227741.0 34.51 73.13 199743.0 39.35 89.21 1489.0 1113.0 1923.0 0.820
11330 2021-07-02 Wyoming 424025.0 516865.0 228162.0 34.59 73.26 200184.0 39.42 89.31 787.0 847.0 1463.0 0.820
11331 2021-07-03 Wyoming 431008.0 517365.0 230914.0 35.34 74.47 204522.0 39.90 89.39 6983.0 1680.0 2903.0 0.833
11332 2021-07-04 Wyoming 431101.0 517365.0 230993.0 35.35 74.49 204598.0 39.91 89.39 93.0 1682.0 2906.0 0.833

11333 rows × 14 columns

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states = list(set(odata['location']))
len(states)

65

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date = list(set(odata['date']))
date.sort()
date

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date[0]

'2020-12-20'

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US = odata.loc[odata['date'] == '2021-01-12']
US

date location total_vaccinations total_distributed people_vaccinated people_fully_vaccinated_per_hundred total_vaccinations_per_hundred people_fully_vaccinated people_vaccinated_per_hundred distributed_per_hundred daily_vaccinations_raw daily_vaccinations daily_vaccinations_per_million share_doses_used
0 2021-01-12 Alabama 78134.0 377025.0 70861.0 0.15 1.59 7270.0 1.45 7.69 NaN NaN NaN 0.207
174 2021-01-12 Alaska 35838.0 141600.0 22486.0 0.74 4.90 5400.0 3.07 19.36 NaN NaN NaN 0.253
348 2021-01-12 American Samoa 2124.0 10650.0 842.0 0.47 3.81 260.0 1.51 19.12 NaN NaN NaN 0.199
522 2021-01-12 Arizona 141355.0 563025.0 95141.0 0.11 1.94 8343.0 1.31 7.74 NaN NaN NaN 0.251
696 2021-01-12 Arkansas 40879.0 274400.0 39357.0 0.00 1.35 8.0 1.30 9.09 NaN NaN NaN 0.149
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
10463 2021-01-12 Virginia 190607.0 797150.0 NaN NaN 2.23 NaN NaN 9.34 NaN NaN NaN 0.239
10637 2021-01-12 Washington 195567.0 567725.0 162105.0 0.23 2.57 17689.0 2.13 7.46 NaN NaN NaN 0.344
10811 2021-01-12 West Virginia 103330.0 160975.0 NaN NaN 5.77 NaN NaN 8.98 NaN NaN NaN 0.642
10985 2021-01-12 Wisconsin 137253.0 429500.0 125895.0 0.19 2.36 11343.0 2.16 7.38 NaN NaN NaN 0.320
11159 2021-01-12 Wyoming 16467.0 47800.0 13577.0 0.37 2.85 2116.0 2.35 8.26 NaN NaN NaN 0.344

65 rows × 14 columns

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US['people_vaccinated'].sum()

15746663.0

1

data=[]

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for i in date:
    temp = odata.loc[odata['date'] == i]
    data.append(temp['people_vaccinated'].sum())
data

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dataframe=pd.DataFrame(data=data, index=date, columns=['vaccinations'], dtype=None, copy=False)
dataframe

vaccinations
2020-12-20 0.0
2020-12-21 0.0
2020-12-22 0.0
2020-12-23 0.0
2020-12-24 0.0
... ...
2021-06-30 366017545.0
2021-07-01 367355455.0
2021-07-02 367983174.0
2021-07-03 368905977.0
2021-07-04 369524458.0

197 rows × 1 columns

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dataframe.dropna(subset=['vaccinations'],inplace=True)
dataframe.columns = ['vaccinations']
dataframe.drop(dataframe[dataframe['vaccinations']<0.01].index)
vus = dataframe
vus

vaccinations
2020-12-20 0.0
2020-12-21 0.0
2020-12-22 0.0
2020-12-23 0.0
2020-12-24 0.0
... ...
2021-06-30 366017545.0
2021-07-01 367355455.0
2021-07-02 367983174.0
2021-07-03 368905977.0
2021-07-04 369524458.0

197 rows × 1 columns

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fig = plt.figure( figsize=(16,6), dpi=100)
ax = fig.add_subplot(1,1,1)
x = dataframe.index
y = dataframe['vaccinations']
ax.plot( x, y, color=dt_hex, linewidth=2, linestyle='-' )
ax.set_title('接种疫苗人数(/千万)',fontdict={
      'color':'black',
      'size':24
})
ax.set_xticks( range(0,len(x),30))

[<matplotlib.axis.XTick at 0x208ab850c18>,
 <matplotlib.axis.XTick at 0x208ab850be0>,
 <matplotlib.axis.XTick at 0x208ab8426a0>,
 <matplotlib.axis.XTick at 0x208ad89a588>,
 <matplotlib.axis.XTick at 0x208ad89aa20>,
 <matplotlib.axis.XTick at 0x208ad89aeb8>,
 <matplotlib.axis.XTick at 0x208ad89a978>]

output_12_1.png

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cdata=pd.read_csv(r'DXYArea.csv')

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cdata

continentName continentEnglishName countryName countryEnglishName provinceName provinceEnglishName province_zipCode province_confirmedCount province_suspectedCount province_curedCount province_deadCount updateTime cityName cityEnglishName city_zipCode city_confirmedCount city_suspectedCount city_curedCount city_deadCount
0 北美洲 North America 美国 United States of America 美国 United States of America 971002 33718538 0.0 29096816 605526 2021-07-05 19:13:09 NaN NaN NaN NaN NaN NaN NaN
1 欧洲 Europe 法国 France 法国 France 961002 5921696 0.0 353370 111190 2021-07-05 19:12:06 NaN NaN NaN NaN NaN NaN NaN
2 南美洲 South America 巴西 Brazil 巴西 Brazil 973003 18769808 0.0 17082876 524475 2021-07-05 19:12:06 NaN NaN NaN NaN NaN NaN NaN
3 欧洲 Europe 瑞典 Sweden 瑞典 Sweden 962005 1090880 0.0 4971 14631 2021-07-05 19:12:06 NaN NaN NaN NaN NaN NaN NaN
4 欧洲 Europe 俄罗斯 Russia 俄罗斯 Russia 964006 5635294 0.0 5083441 138579 2021-07-05 19:12:06 NaN NaN NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
554367 亚洲 Asia 中国 China 辽宁省 Liaoning 210000 0 1.0 0 0 2020-01-22 03:28:10 NaN NaN NaN NaN NaN NaN NaN
554368 亚洲 Asia 中国 China 台湾 Taiwan 710000 1 0.0 0 0 2020-01-22 03:28:10 NaN NaN NaN NaN NaN NaN NaN
554369 亚洲 Asia 中国 Hongkong 香港 Hongkong 810000 0 117.0 0 0 2020-01-22 03:28:10 NaN NaN NaN NaN NaN NaN NaN
554370 亚洲 Asia 中国 China 黑龙江省 Heilongjiang 230000 0 1.0 0 0 2020-01-22 03:28:10 NaN NaN NaN NaN NaN NaN NaN
554371 亚洲 Asia 中国 China 湖南省 Hunan 430000 1 0.0 0 0 2020-01-22 03:28:10 NaN NaN NaN NaN NaN NaN NaN

554372 rows × 19 columns

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cus = cdata.loc[cdata['countryName'] == '美国']
comfirm=cus.copy()
cus

continentName continentEnglishName countryName countryEnglishName provinceName provinceEnglishName province_zipCode province_confirmedCount province_suspectedCount province_curedCount province_deadCount updateTime cityName cityEnglishName city_zipCode city_confirmedCount city_suspectedCount city_curedCount city_deadCount
0 北美洲 North America 美国 United States of America 美国 United States of America 971002 33718538 0.0 29096816 605526 2021-07-05 19:13:09 NaN NaN NaN NaN NaN NaN NaN
106 北美洲 North America 美国 United States of America 美国 United States of America 971002 33716933 0.0 29096816 605526 2021-07-05 11:05:16 NaN NaN NaN NaN NaN NaN NaN
308 北美洲 North America 美国 United States of America 美国 United States of America 971002 33716933 0.0 29096816 605526 2021-07-05 10:01:19 NaN NaN NaN NaN NaN NaN NaN
437 北美洲 North America 美国 United States of America 美国 United States of America 971002 33716933 0.0 29096816 605526 2021-07-05 09:39:02 NaN NaN NaN NaN NaN NaN NaN
728 北美洲 North America 美国 United States of America 美国 United States of America 971002 33714928 0.0 29087421 605524 2021-07-04 21:41:13 NaN NaN NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
541769 北美洲 North America 美国 United States of America 美国 United States of America 971002 9 0.0 0 0 2020-02-03 09:28:34 NaN NaN NaN NaN NaN NaN NaN
543505 北美洲 North America 美国 United States of America 美国 United States of America 971002 8 0.0 0 0 2020-02-02 07:41:43 NaN NaN NaN NaN NaN NaN NaN
545557 北美洲 North America 美国 United States of America 美国 United States of America 971002 6 0.0 0 0 2020-02-01 02:48:13 NaN NaN NaN NaN NaN NaN NaN
547090 NaN NaN 美国 United States of America 美国 United States of America 971002 6 0.0 0 0 2020-01-31 07:17:36 NaN NaN NaN NaN NaN NaN NaN
550409 NaN NaN 美国 United States of America 美国 United States of America 971002 5 0.0 0 0 2020-01-27 17:20:43 NaN NaN NaN NaN NaN NaN NaN

3241 rows × 19 columns

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cus['updateTime'] = pd.to_datetime(cus.updateTime,format="%Y-%m-%d",errors='coerce').dt.date
cus.drop_duplicates(subset=['provinceName', 'updateTime'], keep='first', inplace=True)
cus.drop(['provinceName','continentName','continentEnglishName','countryEnglishName','cityName','province_deadCount','province_suspectedCount','city_deadCount','city_curedCount','city_suspectedCount','city_confirmedCount','province_confirmedCount','countryName','provinceEnglishName','province_zipCode','cityEnglishName','city_zipCode'],axis=1,inplace=True)
cus.dropna(subset=['province_curedCount'],inplace=True)
cus = cus.drop(cus[cus['province_curedCount']<0.01].index)
#cus = cus.drop(cus[cus['updateTime']<0.01].index)
cus = cus.sort_values(by='updateTime', ascending=True)
cus

province_curedCount updateTime
520054 3 2020-02-12
517924 3 2020-02-13
515650 3 2020-02-14
514566 3 2020-02-15
511738 3 2020-02-16
... ... ...
3425 29026688 2021-07-01
2131 29052087 2021-07-02
1499 29072881 2021-07-03
728 29087421 2021-07-04
0 29096816 2021-07-05

477 rows × 2 columns

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cus['province_curedCount']

520054           3
517924           3
515650           3
514566           3
511738           3
            ...   
3425      29026688
2131      29052087
1499      29072881
728       29087421
0         29096816
Name: province_curedCount, Length: 477, dtype: int64

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#cus.set_index('updateTime',inplace=True)
#cus.index.name = None
temp = cus['updateTime'].copy()
cout=0
temp_list = []
for i in temp:
    temp_list.append(str(i))
    cout+=1
cus["updateTime"]=temp_list
cus.set_index('updateTime',inplace=True)
cus

province_curedCount
updateTime
2020-02-12 3
2020-02-13 3
2020-02-14 3
2020-02-15 3
2020-02-16 3
... ...
2021-07-01 29026688
2021-07-02 29052087
2021-07-03 29072881
2021-07-04 29087421
2021-07-05 29096816

477 rows × 1 columns

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9

fig, axes = plt.subplots(1,1,figsize=(16, 4))
x=cus.index
y=cus.values
plot=axes.plot(x,y,color=dt_hex,linewidth=2,linestyle='-',label='治愈')
axes.set_xticks(range(0,len(x),40))
plt.xlabel('日期',fontsize=10)
plt.ylabel('人数',fontsize=10)
axes.legend(loc=0,frameon=True)
plt.show()

output_19_0.png

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v_cus = cus.loc[cus.index > '2021-03-01']
v_cus.columns = ['curedCount']
v_cus

curedCount
updateTime
2021-03-02 19817532
2021-03-03 19905322
2021-03-04 19997983
2021-03-05 20093442
2021-03-06 20183329
... ...
2021-07-01 29026688
2021-07-02 29052087
2021-07-03 29072881
2021-07-04 29087421
2021-07-05 29096816

106 rows × 1 columns

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v_vus=vus.loc[vus.index > '2021-03-01']
v_vus.index.name = "updateTime"
v_vus

vaccinations
updateTime
2021-03-02 105774893.0
2021-03-03 108010927.0
2021-03-04 110417023.0
2021-03-05 113492161.0
2021-03-06 117171344.0
... ...
2021-06-30 366017545.0
2021-07-01 367355455.0
2021-07-02 367983174.0
2021-07-03 368905977.0
2021-07-04 369524458.0

125 rows × 1 columns

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result = pd.merge( v_cus, v_vus, how='left',on='updateTime')
result.dropna(subset=['curedCount'],inplace=True)
result.dropna(subset=['vaccinations'],inplace=True)
result

curedCount vaccinations
updateTime
2021-03-02 19817532 105774893.0
2021-03-03 19905322 108010927.0
2021-03-04 19997983 110417023.0
2021-03-05 20093442 113492161.0
2021-03-06 20183329 117171344.0
... ... ...
2021-06-30 29007495 366017545.0
2021-07-01 29026688 367355455.0
2021-07-02 29052087 367983174.0
2021-07-03 29072881 368905977.0
2021-07-04 29087421 369524458.0

105 rows × 2 columns

1

#result.to_csv("cvus.csv")

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pusdata=pd.read_csv(r'美国covid19疫情数据l-history改.csv')
pusdata['date'] = pd.to_datetime(pusdata.date,format="%Y-%m-%d",errors='coerce').dt.date
pusdata = pusdata.sort_values(by='date', ascending=True)
pusdata

date death deathIncrease inIcuCumulative inIcuCurrently hospitalizedIncrease hospitalizedCurrently hospitalizedCumulative negative negativeIncrease onVentilatorCumulative onVentilatorCurrently positive positiveIncrease Unnamed: 14 states totalTestResults totalTestResultsIncrease
419 2020-01-13 NaN 0 NaN NaN 0 NaN NaN NaN 0 NaN NaN NaN 0 NaN 1 0 0
418 2020-01-14 NaN 0 NaN NaN 0 NaN NaN NaN 0 NaN NaN 0.0 0 NaN 1 0 0
417 2020-01-15 NaN 0 NaN NaN 0 NaN NaN NaN 0 NaN NaN 0.0 0 NaN 1 0 0
416 2020-01-16 NaN 0 NaN NaN 0 NaN NaN NaN 0 NaN NaN 0.0 0 NaN 1 0 0
415 2020-01-17 NaN 0 NaN NaN 0 NaN NaN NaN 0 NaN NaN 0.0 0 NaN 1 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4 2021-03-03 508665.0 2449 45214.0 9359.0 2172 45462.0 873073.0 73857281.0 267001 4260.0 3094.0 28520365.0 66836 0.002349 56 357888671 1406795
3 2021-03-04 510408.0 1743 45293.0 8970.0 1530 44172.0 874603.0 74035238.0 177957 4267.0 2973.0 28585852.0 65487 0.002296 56 359479655 1590984
2 2021-03-05 512629.0 2221 45373.0 8634.0 2781 42541.0 877384.0 74307155.0 271917 4275.0 2889.0 28654639.0 68787 0.002406 56 361224072 1744417
1 2021-03-06 514309.0 1680 45453.0 8409.0 503 41401.0 877887.0 74450990.0 143835 4280.0 2811.0 28714654.0 60015 0.002094 56 362655064 1430992
0 2021-03-07 515151.0 842 45475.0 8134.0 726 40199.0 878613.0 74582825.0 131835 4281.0 2802.0 28756489.0 41835 0.001457 56 363825123 1170059

420 rows × 18 columns

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temp = pusdata['date'].copy()
cout=0
temp_list = []
for i in temp:
    temp_list.append(str(i))
    cout+=1
pusdata["date"]=temp_list
pusdata.set_index('date',inplace=True)
pusdata=pusdata[['positive']]
pusdata.index.name = "updateTime"
pusdata

positive
updateTime
2020-01-13 NaN
2020-01-14 0.0
2020-01-15 0.0
2020-01-16 0.0
2020-01-17 0.0
... ...
2021-03-03 28520365.0
2021-03-04 28585852.0
2021-03-05 28654639.0
2021-03-06 28714654.0
2021-03-07 28756489.0

420 rows × 1 columns

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#pd.merge( result,pusdata, how='left',on='updateTime').dropna(subset=['positive'],inplace=True)

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#confirm
comfirm['updateTime'] = pd.to_datetime(comfirm.updateTime,format="%Y-%m-%d",errors='coerce').dt.date
comfirm.drop_duplicates(subset=['provinceName', 'updateTime'], keep='first', inplace=True)
comfirm.drop(['provinceName','continentName','continentEnglishName','countryEnglishName','cityName','province_deadCount','province_suspectedCount','city_deadCount','city_curedCount','city_suspectedCount','city_confirmedCount','province_curedCount','countryName','provinceEnglishName','province_zipCode','cityEnglishName','city_zipCode'],axis=1,inplace=True)
comfirm.dropna(subset=['province_confirmedCount'],inplace=True)
comfirm = comfirm.drop(comfirm[comfirm['province_confirmedCount']<0.01].index)
comfirm = comfirm.sort_values(by='updateTime', ascending=True)
temp = comfirm['updateTime'].copy()
cout=0
temp_list = []
for i in temp:
    temp_list.append(str(i))
    cout+=1
comfirm["updateTime"]=temp_list
comfirm.set_index('updateTime',inplace=True)
comfirm.columns = ['confirmedCount']
comfirm

confirmedCount
updateTime
2020-01-27 5
2020-01-31 6
2020-02-01 6
2020-02-02 8
2020-02-03 9
... ...
2021-07-01 33665047
2021-07-02 33679489
2021-07-03 33709176
2021-07-04 33714928
2021-07-05 33718538

486 rows × 1 columns

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result = pd.merge( result,comfirm, how='left',on='updateTime')
result.dropna(subset=['confirmedCount'],inplace=True)
result

curedCount vaccinations confirmedCount
updateTime
2021-03-02 19817532 105774893.0 28664448
2021-03-03 19905322 108010927.0 28719624
2021-03-04 19997983 110417023.0 28771556
2021-03-05 20093442 113492161.0 28827140
2021-03-06 20183329 117171344.0 28894787
... ... ... ...
2021-06-30 29007495 366017545.0 33653426
2021-07-01 29026688 367355455.0 33665047
2021-07-02 29052087 367983174.0 33679489
2021-07-03 29072881 368905977.0 33709176
2021-07-04 29087421 369524458.0 33714928

105 rows × 3 columns

1

#result.to_csv("cvus.csv")

完整文档详见：博客相关资源-常微分偏微分建模练习

更新于 2021-07-09
 matlab, 数学建模, python
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