





































Clinical Medicine Insights
Received 10 Dec 2020 | Revised 15 Jan 2021 | Accepted 20 Feb 2021 | Published Online 30 Mar 2021

DOI: https://doi.org/xx.xxx/xxx.xx 
CMI 2 (1), 59−64 (2021) ISSN (O) 2694-4626 IF:1.6

RESEARCH ARTICLE

Biostatistical Analysis of the risks of spatial spread during the
COVID-19 pandemic

Bin Zhao 1∗ Xia Jiang 2 Jinming Cao 3

1School of Science, Hubei
University of Technology, Wuhan,
Hubei, China.

2Hospital, Hubei University of
Technology, Wuhan, Hubei,
China.

3School of Information and
Mathematics, Yangtze University,
Jingzhou, Hubei, China.

Abstract
With the spread of the new coronavirus around the world, governments of various 
countries have begun to use the mathematical modeling 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 analyzes 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 coro-navirus. 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 trans-mission 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 COVI D -19, the epidemic 
situation began to be gradually controlled, thereby retarding growth.

MRERP LTD

controlled, thereby retarding growth.
Keywords: New coronavirus, Logistic growth model, Infection predic-
tion and prevention

Copyright : © 2021 The Authors. Published by MRERP LTD. This is
an open access article under the CC BY-NC-ND license
(https://creativecommons.org/licenses/by-nc-nd/4.0/).

CMI 2 (1), 59−64 59

https://creativecommons.org/licenses/by-nc-nd/4.0/


1 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 serious1. 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 impact2.
In fact, there are some urgent problems to be solved
regarding the spread of COVID -19 . Can existing in-
terventions effectively control COVID-19? Can you
elaborate on the changes and development character-
istics 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 mathe-
matical model can we build to solve the problem?
COVID-19 is a new coronavirus discovered in De-
cember 2019. The epidemic data is not sufficient,
and clinical methods such as clinical trials are still in
the exploration stage. So far, the epidemic situation
data is difficult to apply directly to the existing
mathematical model. The problems to be solved are:
how effective the existing 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 part3-5.

2 METHODS

2.1 Data
We obtained epidemiological data from the Aminer
website, the People’s Republic of China from Jan-
uary 22 to April 3, and Spain, Italy, France, Germany
fromFebruary 15 toApril 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: Cumula vely confirmed cases

FIGURE 2: Cumula vely cured cases

FIGURE 3: Daily new cases

FIGURE 4: Cumula ve deaths

FIGURE 5: Exis ng confirmed cases

Supplementary information The online version of
this article (https://doi.org/xx.xxx/xxx.xx) contains
supplementary material, which is available to autho-
rized users.

Corresponding Author: Bin Zhao
School of Science, Hubei University of Technology,
Wuhan, Hubei, China.

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BIOSTATISTICAL ANALYSIS OF THE RISKS OF SPATIAL SPREAD DURING THE COVID-19
PANDEMIC
2.2 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 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 epi-
demic 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 trans-
mission is established , and the prediction effect of
the mathematical model on the spread of COVID-19
epidemic is compared.
2.3 Based on Logistic estimated square law
The traditional SEIR model can not describe the
different developments of the epidemic well. After
analyzing the actual situation and the existing data,
we have established a more effective infectious dis-
ease transmission model. According to the actual sit-
uation of the epidemic, we will analyze the relevant
data indicators of the five countries (cumulatively di-
agnosed 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 pro-
cess 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.
2.4 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, andmatlab is used for fitting linear regression
analysis. Matlab source code is as follows6-9:
x = [1: 1: 27];

y = [3,3,21,229,655,1701,3089,5883,10149, 17660,27980, 
41035,59138,74386,92472,105792,119827,132547,1436

26,156363,165155,175925,183957,192994,199414,2054
36 , 210717];
plot (x, y, ’r *’); xlabel (’time’)
ylabel (’population’) x0 = exp (-x);
y0 = 1. / y;
f = polyfit (x0, y0,1);
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’)

3 RESULTS

1.1 Logistic model estimates
On the basis of the cumulative number of confirmed
cases in Italy from February 15th to May 3rd, we
used Matlab to establish a Logistic model and per-
formed linear regression analysis. Using the above
processing, we can get the predicted cumulative
number of confirmed cases in Italy as shown in
Figure 6.
As shown in Figure 6, we can conclude that the
Logistic model has a good fitting effect on the actual
cumulative number of confirmed cases, thus provid-
ing reference value for departments and hospitals
at all levels to effectively intervene and prevent the
spread of new coronavirus in the next few days.

4 DISCUSSION

The spread of COVID-19 is affected by many com-
plex factors. In the early stage of the transmission

CMI 2 (1), 59−64(2021) MRERP LTD 61



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BIN ZHAO, XIA JIANG AND JINMING CAO

FIGURE 6: The significance of each parameter under theconstruc on of Logis c model

FIGURE 7: : Comparison of actual cumula ve
confirmed cases and simulatedcumula ve
confirmed cases

of COVID-19, it is difficult to establish a Logistic
model and parameter estimation and obtain a fairly
accurate simulation result, but the initial estimated
parameters such as the growth rate of the confirmed
cases and the possible cumulative maximum con-
firmed 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.

5 LIMITATIONS

1. Promotion of the model: The SEIRmodel based
on 2019-nCoV can be established. The SEIR
model is superior to the logistic model in trend
prediction, but due to the many parameters to be
considered, the calculation error is greater than
the logistic model

2. A dynamic growth rate model based on 2019-
nCoV can be established. The dynamic growth
rate model has a good fitting effect, but has a
certain

3. You

4. After the turning point of the epidemic situation,
that is, the fitting effect of the reducer and the
saturation period is poor, and even a large error
occurs

Conflict of interest
We have no conflict of interests to disclose and the
manuscript has been read and approved by all named
authors.
Acknowledgement
This work was supported by the Philosophical and
Social Sciences Research Project of Hubei Edu-
cation Department (19Y049), and the Staring Re-
search Foundation for the Ph.D. of Hubei University
of Technology (BSQD2019054), Hubei Province,
China.

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BIOSTATISTICAL ANALYSIS OF THE RISKS OF SPATIAL SPREAD DURING THE COVID-19
PANDEMIC
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How to cite this article: B.Z., X.J., J.C. Biostatisti-
cal Analysis of the risks of spatial spread during the 
COVID-19 pandemic. Clinical Medicine Insights. 
2021;59−64. https://doi.org/xx.xxx/xxx.xx

MRERP LTD CMI 2 (1), 59−64 (2021) 64


	Introduction
	Methods
	Results
	Discussion
	 Limitations
	 References



