This article has participated in 「 Digging force Star Program 」

Preface

First of all, it must be stated that this is only a fictional example , Not a real event .

Before that zulko See some examples of dynamic visualization of data , It's fun , So there is today's article .

Part of the content is from maxberggren.

Let's start happily ~~~

Effect display ：

The population density map of Nordic countries is used as an example , Stockholm was the first source of infection .

For the specific implementation process, see the introduction of the home page to obtain the source code in the relevant files .

development tool

**Python edition ：**3.6.4

Related modules ：

numpy modular ;

matplotlib modular ;

PIL modular ;

As well as some Python Built in modules .

Environment building

install Python And add to environment variable ,pip Install the relevant modules required .

Introduction of the principle

Virus transmission model ：

This paper adopts SIR Model , The core formula is ：

Parameter interpretation ：

among ,S Represents susceptible people ;I Represents the number of infected groups or zombies ;R Represents the amount of removal , That is, those who die or return to health .

β Indicates the infectious degree of the disease ;γ Indicates the rate of progression from infection to death .

S' Tell us the rate at which healthy people become zombies ;I' Tell us how infected people increase and the rate at which zombies enter the removal state ;R' Just add parameters γ Of I.

consider S/I/R After spatial distribution, it is modified as follows ：

Eulafa ：

Now we know u', So the prediction function u The calculation of can be approximated by Euler method , The following is derived ：