Dynamic model of infectious diseases on the coronavirus disease 2019

Under the general trend of globalization, historically and newly discovered infectious diseases are seriously threatening people’s health and lives, including: Avian infl uenza H7N9, AIDS HIV, Infl uenza A H1N1, etc., a new type of corona that is currently spreading in many countries around the world Viral pneumonia (C0VID-19), there is currently no good therapeutic drug, which seriously affects human survival and development. The rapid spread of the new coronavirus in Hong Kong, while starting the epidemic prevention work, uses mathematical modeling methods to construct the propagation model, and then calculates the infl ection point for better prevention and control of the spread of epidemic work. The spread of Hong Kong was analyzed, and the quantitative relationship between the growth rate of the number of new coronavirus infections and time was explored.


Data
The epidemiological data we obtained comes from the Microsoft Bing website, and the data is offi cial and reliable.

The model
According to the collected epidemic data, we try to fi nd out the transmission law of COVID-19 and put forward effective prevention and control methods.
There are generally three methods for systematically studying the spread of infectious diseases. One is to establish a dynamic model of infectious diseases. The second is statistical modeling using statistical methods such as random processes and time series analysis. The third is to use data mining technology to obtain information in the data and discover the epidemic law of infectious diseases. Using the collected data on infection of new coronary pneumonia in Hong Kong, China [11,12], this article mainly uses the fi rst and second methods.
In this paper, the SIR prediction model for the spread of   Table 1.
Because there are suspected cases, it is reasonable for the susceptible population that the infected person can reach without taking the total number of Hong Kong as unknown [13][14][15][16][17][18].
In addition, the number of daily stays in the hospital is defi ned as the number of infected persons, and the cumulative number of discharges and deaths is defi ned as the number of people who are moved out.   [19,20].
Objective function code of genetic algorithm

SIR model estimates
Based on the cumulative number of confi rmed cases in Hong Kong, China, we used Matlab to establish the SIR prediction model and performed linear regression analysis. Using the above processing, we can get the predicted cumulative number of confi rmed cases in Hong Kong, China as shown in Figure 3. Figure 3, we can conclude that when the amount of data is large, it can be seen that the effect of fi tting is not particularly good.

Discussion
In the early stages of the transmission of COVID-19, it is diffi cult to establish a SIR model and parameter estimation and obtain a fairly accurate simulation result, but the initial estimated parameters such as the growth rate of the confi rmed cases and the possible cumulative maximum confi rmed cases can be obtained through existing data. It is helpful to solve important parameters such as infection rate and recovery rate, which will help us to grasp the transmission trend of COVID-19 more accurately. On the other hand, the statistical model of the spread of the new coronavirus in the population analyzed by the SIR model is something that can be done immediately after obtaining the latest data every day. Although this method usually requires enough data to support it, in the early stages of epidemic transmission, this method can still be used to predict the indicators of epidemic transmission, thus providing control departments and policy implementation in the population at various stages provides a short-term Emergency prevention plan [21][22][23][24][25].

Confl ict of interest
We have no confl ict of interests to disclose and the manuscript has been read and approved by all named authors.