Exploring the Fucntion of the SIR Model

Infectious diseases have had a great impact on the history of mankind. Spanish diseases such as colds and plague have historically played an important role. The disease transmission model is used to predict the outcome of the epidemic. These models are used to calculate the impact of infectious diseases, funds needed for mass vaccination, and data from the public health department. The earliest mathematical model of infection was created by Daniel Bernoulli in 1766. This model was used to predict the outcome of vaccination against smallpox.

In the previous article I talked about the SIR model, the simplest epidemiological model of simple infection. Basically, this model is a series of mathematical functions. Given the number of people infected with the virus at a given time step, you can predict the number of people expected in the next step. The SIR model runs iteratively from start to finish, and if there are inputs that may cause a pandemic, it looks like this: At first, only a few people are sick. No recovery (SIR is an acronym for these three states). As time goes by, infected people burn slowly the affectable population, then during the rapid transition the infection will soon play a role in the entire population.

One way to simulate infection is to use the SIR model. The premise of this model is that the entire population can be categorized into three different categories, susceptibility, infection and recovery. "Susceptible" means a person who is currently healthy but may be infected with a disease. "Infectious" means all people who are suffering from or having the disease. In other words, they can spread the disease to everyone susceptible to the disease. Finally, the category of "recovery" refers to those who are considered immune to this disease; this can be either natural or vaccinated