Assessing the Risks of Spatial Spread of the New
Coronavirus COVID-19

Jiang X; Cao J; Zhao B

Research Article

J Bacteriol Mycol. 2020; 7(3): 1131.

Assessing the Risks of Spatial Spread of the New Coronavirus COVID-19

Jiang X¹, Cao J² and Zhao B¹*

¹Department of Science, Hubei University of Technology, Wuhan, Hubei, China

²Department of Information and Mathematics, Yangtze University, Jingzhou, Hubei, China

*Corresponding author: Bin Zhao, School of Science, Hubei University of Technology, Wuhan, Hubei, China

Received: May 12, 2020; Accepted: May 28, 2020; Published: June 04, 2020

Abstract

With the spread of the new coronavirus around the world, governments of various countries have begun to use the mathematical modelling method to construct some virus transmission models assessing the risks of spatial spread of the new coronavirus COVID-19, while carrying out epidemic prevention work, and then calculate the inflection point for better prevention and control of epidemic transmission. This work analyses the spread of the new coronavirus in China, Italy, Germany, Spain, and France, and explores the quantitative relationship between the growth rate of the number of new coronavirus infections and time.

Background: In December 2019, the first Chinese patients with pneumonia of unknown cause is China admitted to hospital in Wuhan, Hubei Jinyintan, since then, COVID-19 in the rapid expansion of China Wuhan, Hubei, in a few months’ time, COVID-19 is Soon it spread to a total of 34 provincial-level administrative regions in China and neighboring countries, and Hubei Province immediately became the hardest hit by the new coronavirus. In an emergency situation, we strive to establish an accurate infectious disease retardation growth model to predict the development and propagation of COVID-19, and on this basis, make some short-term effective predictions. The construction of this model has Relevant departments are helpful for the prevention and monitoring of the new coronavirus, and also strive for more time for the clinical trials of Chinese researchers and the research on vaccines against the virus to eliminate the new corona virus as soon as possible.

Methods: Collect and compare and integrate the spread of COVID-19 in China, Italy, France, Spain and Germany, record the virus transmission trend among people in each country and the protest measures of relevant government departments. According to the original data change law, Establish a Logistic growth model.

Findings: Based on the analysis results of the Logistic model model, the Logistic model has a good fitting effect on the actual cumulative number of confirmed cases, which can bring a better effect to the prediction of the epidemic situation and the prevention and control of the epidemic situation.

Interpretation: In the early stage of the epidemic, due to inadequate anti-epidemic measures in various countries, the epidemic situation in various countries spread rapidly. However, with the gradual understanding of COVID-19, the epidemic situation began to be gradually controlled, thereby retarding growth.

Keywords: New Coronavirus; Logistic Growth Model; Infection Prediction and Prevention

Introduction

After the outbreak of COVID-19 in China, COVID-19 has also erupted in other countries in the world. Among the countries where new pneumonia outbreaks, Spain, Italy, France and Germany are more serious [1]. As of April 27, Spain, Italy, France and Germany have each accumulated diagnosed 229842 cases, 199414 cases, 165,842 cases, 158758 cases, the new crown pneumonia spread and various measures of everyday life and people’s social normal operation had not Estimated impact [2].

In fact, there are some urgent problems to be solved regarding the spread of COVID -19. Can existing interventions effectively control COVID-19? Can you elaborate on the changes and development characteristics of each epidemic situation? Can you combine the conclusions found in the comparison of the city/region, actual national population, medical level, traffic conditions, geographic location, customs and culture, and anti-epidemic measures? What mathematical model can we build to solve the problem?

COVID-19? Can you elaborate on the changes and development characteristics of each epidemic situation? Can you combine the conclusions found in the comparison of the city/region, actual national population, medical level, traffic conditions, geographic location, customs and culture, and anti-epidemic measures? What mathematical model can we build to solve the problem? emergency response is and how to invest medical resources more scientifically in the future. On this basis, this article aims to study the shortcomings of this part [3-5].

Methods

Data

We obtained epidemiological data from the Aminer website, the People’s Republic of China from January 22 to April 3, and Spain, Italy, France, Germany from February 15 to April 27. This includes data such as cumulative confirmed cases, cumulative deaths, newly diagnosed cases per day, cumulative number of cured cases, and existing confirmed cases. The relevant input is shown in the Figure:

Figure 1: Cumulatively confirmed cases.

    
    
    Figure 1: Cumulatively confirmed cases.

Figure 2: Cumulatively cured cases.

    
    
    Figure 2: Cumulatively cured cases.

Figure 3: Daily new cases.

    
    
    Figure 3: Daily new cases.

Figure 4: Cumulative deaths.

    
    
    Figure 4: Cumulative deaths.

Figure 5: Existing confirmed cases.

    
    
    Figure 5: Existing confirmed cases.

The model

Based on the collected epidemic data, we tried to find the propagation law of COVID-19 and proposed 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 modelling 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 from various countries, this article mainly uses the third method.

In this paper, the growth model of COVID-19 transmission is established, and the prediction effect of the mathematical model on the spread of COVID-19 epidemic is compared.

Based on logistic estimated square law

The traditional SEIR model cannot describe the different developments of the epidemic well. After analysing the actual situation and the existing data, we have established a more effective infectious disease transmission model. According to the actual situation of the epidemic, we will analyze the relevant data indicators of the five countries (cumulatively diagnosed cases, cumulative deaths, newly diagnosed cases per day, cumulative number of cured cases, existing confirmed cases) to adapt to the current situation of the new coronary pneumonia epidemic in the world propagation.

As can be seen from the data graph, the change in cumulative death toll in Italy over time is a non-linear process. Considering the shape of the scatter plot and the model generally involving the Logistic curve model, here we use the Logistic curve model for fitting. The basic form of the logistic curve model is:

y=1/(a + be ^(-t))

Therefore, we need to transform this nonlinear process into a linear model after data processing. Take x0=e^(-t), y0=1/y; Then the original model is converted to a linear model y0=a+bx0.

Simulation

Since COVID-19 has been developing in Italy for a long period of time, and the cumulative number of confirmed cases is relatively large, the data is more convincing, so here we take the cumulative number of confirmed cases in Italy from February 15th to May 3rd The nonlinear model becomes a linear model, and matlab is used for fitting linear regression analysis. Matlab source code is as follows [6- 9]:

x=[1: 1: 27];

y=[3,3,21,229,655,1701,3089,5883,10149,17660,27980,41035,591 38,74386,92472,105792,119827,132547,143626,156363,165155,17592 5,183957,192994,199414,205436 , 210717];

plot (x,y,’r*’); xlabel (‘time’)

ylabel (‘population’) x0=exp(-x);

y0=1./y;

y fit =1./(f (1).*exp (-0.338.* x) + f(2)); plot (x,y fit* 1000);

hold on

plot (x, y, ‘r *’); xlabel (‘time’) ylabel (‘population’)

Results

Logistic model estimates

On the basis of the cumulative number of confirmed cases in Italy from February 15^th to May 3^rd, we used Matlab to establish a Logistic model and performed linear regression analysis. Using the above processing, we can get the predicted cumulative number of confirmed cases in Italy as shown in Figure 6.