Comprehensive Risk Assessment and Spatial Pattern Analysis of

COVID-19 of China

Man Xie

, Xiling Wu

1,2,* b

and Chiping Yuan

1,3 c

School of Economics and Trade, Guangzhou Xinhua University, Dongguan, Guangdong, China

School of Economics and Trade, Guangdong University of foreign studies, Guangzhou, Guangdong, China

Institute of Guangdong Hong Kong and Macao Development Studies, Sun Yat-sen University, Xingang West Road,

Guangzhou, Guangdong, China

Keywords: COVID-19, Risk Assessment, Spatial Agglomeration, Exploratory Spatial Data Analysis.

Abstract: The outbreak of COVID-19 has a certain impact on China, an objective assessment of COVID-19 risk is of

great significance for epidemic preparedness and public health management. In this paper, the spatial

distribution pattern and spatial aggregation pattern of comprehensive risk of COVID-19 are studied by

constructing an index system of COVID-19 with using an exploratory spatial data analysis method. The

results show that the overall Moran's I index of comprehensive risk is 0.2417, indicating that there is a

positive spatial correlation and a significant spatial clustering feature. The comprehensive risk distribution

of COVID-19 in some regions follows the characteristics of geographical proximity, and there is a risk of

transmission between regions. Tianjin, Hubei, Sichuan, Liaoning, Shanghai and Hainan are the areas with

high comprehensive risk of COVID-19, while the low risk areas are Guangdong, Yunnan, Tibet, Shanxi,

Qinghai, Ningxia and Xinjiang. There are 13 regions with low-low clustering pattern (LL), there are 5

regions with high-high clustering pattern (HH). According to the distribution of comprehensive risk, we

should formulate prevention, control and emergency response strategies, strengthen the construction of

public health facilities and training of medical professional and technical personnel, and reduce the level of

epidemic risk.

1 INTRODUCTION

Corona Virus Disease 2019, or COVID-19, was

named by the World Health Organization. Since

December 2019, some hospitals in Wuhan, Hubei

Province have found a number of unexplained

pneumonia cases with a history of exposure to the

seafood market in South China, which has been

confirmed to be an acute respiratory infectious

disease caused by the 2019 novel corona-virus

infection. Chinese government departments quickly

launched a first-level response to major public health

emergencies. The epidemic is highly and fast

infectious, leading to the outbreak of a massive

public health crisis across the country, posing a

certain threat to urban public health safety.

The rapid spread of COVID-19 is a global public

health challenge. As of 24:00 on June30, 2021,

https://orcid.org/0000-0002-0186-3436

https://orcid.org/0000-0001-6118-5377

https://orcid.org/0000-0002-4739-8090

according to 31 provinces (autonomous regions,

municipalities directly under the Central

Government) and Xinjiang Production and

Construction Corps, 421 confirmed cases (including

7 severe cases), a total of 78,479 discharged cases,

4,634 deaths and 83,534 confirmed cases were

reported. After the outbreak of the epidemic, the

government promptly launched an emergency

response and resolutely took a series of public

emergency measures. Many places in China

resolutely took measures such as nucleic acid

testing, community control, and personnel isolation

to prevent the spread of the epidemic. All 31

provinces (autonomous regions and municipalities

directly under the Central Government) have

experienced COVID-infected people, with a wide

range and strong infectivity.

Number of beds in hospitals, medical resources,

rescue teams and other resources exposed regional

gaps, which also showed the difference of the

prevalence and mortality of COVID-19 in different

regions. In addition, the improvement of the

Xie, M., Wu, X. and Yuan, C.

Comprehensive Risk Assessment and Spatial Pattern Analysis of COVID-19 of China.

DOI: 10.5220/0011233000003438

In Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare (ICHIH 2022), pages 107-114

ISBN: 978-989-758-596-8

107

interurban transportation system accelerated the

spread of epidemic among regions, and so did the

spatial proximity. How to evaluate and analyse the

risks of the epidemic in various places objectively

and comprehensively, classify the risk levels in

different regions, provide a theoretical basis for

epidemic prevention has become the focus of

scholars at present. In view of this, it is very

necessary to explore the regional risk index and

pattern of COVID-19 and its spatial distribution

mode deeply, so as to provide theoretical guidance

for the prevention and control of the epidemic, and

draw lessons from experience for public health risk

management.

In this paper, the spatial distribution pattern and

spatial aggregation pattern of comprehensive risk of

COVID-19 are studied by constructing an index

system with using an exploratory spatial data

analysis method, which may deepen the

understanding of the spatial nature of the epidemic

distribution, supplement the lack of ignoring spatial

autocorrelation in the modelling of the distribution

of infectious diseases in traditional studies. At the

same time, this paper may provide an assistance of

resource allocation to response to COVID-19 and

even provide an effective method or perspective for

scientifically understanding the risk of epidemic in

various regions.

2 LITERATURE REVIE

The outbreak of COVID-19 has a phased impact on

China's economy growth. Economic growth rate in

2020 was reversed from low before high in 2020 (Jia

2020). Wang Xuyang et al. (2020) focused on a

prediction analysis in Hubei Province based on the

index smoothing model, and the model fits well and

can be used for COVID-19 epidemic prediction, to

ensure the normal production and operation order of

enterprises, and reduce the negative impact on the

economy.

Pan Jiahua (2020), from the perspective of urban

spatial pattern and urban form, believes that urban

spatial aggregation, pattern differentiation and

spatial planning concept affect the spread and

diffusion of the epidemic to a certain extent, which

is the objective condition for the aggravation of

disaster impact. The spread of COVID-19 epidemic

is in line with the law of geographical proximity

(Zeng et al. 2020), especially in the intercity

communication stage, and the spatial adjacent

diffusion effect is obvious (Liu and Jin 2020). Liu

Yi et al (2020) analyzed the spatial and temporal

diffusion characteristics of COVID-19 cases in

Guangdong Province and the spatial differences in

the spread and change of the epidemic in various

prefecture-level cities, and found that the diffusion

risk of medium-tier cities is high but the

comprehensive risk is at the medium level, and the

diffusion risk of developed cities is low but the

highest of comprehensive risk. Liang Ze et al (2020)

took 282 cities in China as the basic research unit.

They explored the impact of urban population

migration and socio-economic factors on COVID-19

incidence through geographical weighted regression

method, and found that the migration rate in Wuhan

greatly increased the incidence of COVID-19 in

surrounding cities of Wuhan, and the effect presents

spatial attenuation characteristics with the increase

of geographical distance from Wuhan (except in

northeast and southwest regions).

In the literature of COVID-19, most of the

samples were conducted in Hubei Province. For

example, Lv Zhenhua and Cheng Shaowen (2020)

used Crystal Ball and GIS to analyze the space-

temporal characteristics of COVID-19 development,

and found a spatial correlation between cities in the

risk areas of Hubei Province. But Wuhan is the only

area of high-low diffusion in Hubei province. Li

Chenxi et al. (2020), Liu Xun et al. (2020) analyzed

the spatial pattern of COVID-19 epidemic in Hubei

Province and explored its spatial and temporal

aggregation by using the ESDA method and GIS,

respectively. In view of this, from the perspective of

geography, it is very necessary to explore the

regional risk index and pattern of COVID-19 and its

spatial distribution mode deeply, so as to provide

theoretical guidance for the prevention and control

and planning of the epidemic, and draw lessons from

experience for public health risk management.

3 CONSTRUCTION OF COVID-19

COMPREHENSIVE RISK

ASSESSMENT SYSTEM

Risk assessment is the premise of risk management,

and plays a very important role in the whole process

of epidemic prevention control and treatment. Based

on the triangle model of the public safety system, the

risk assessment model is constructed from the three

edges of the triangle, respectively (showed in Fig.1).

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

108

Figure 1: Public safety risk assessment "triangle" model

The first edge of the "triangle" model is

emergency, which refers to virus outbreak risk of

COVID-19. In this article, we use the prevalence

rate of 10,000 and the fatality rate to reflect the risk

of COVID-19. The prevalence rate of 10,000 people

equals the number of confirmed cases divided by the

resident population (10,000), and the fatality rate

equals to the number of deaths divided by the

number of confirmed cases.

The second edge is the Hazard-affected Carriers.

In the field of public safety, we believe that the most

vulnerable disaster carrier that needs protection is

people. According to the early summary of 44,672

cases in the China Centre for Disease Control and

Prevention Weekly, it found that age had a great

impact on mortality, and deaths mainly occurred in

older people. The report revealed that child case

symptoms are relatively mild, children aged 0 to 9

died of infection was 0, and no deaths in cases

younger than 30 basically. Patients under 50 died

below 1%, increasing only in the elderly and with a

poor prognosis. Therefore, the Vulnerability of

Hazard-affected Carriers is reflected by the

proportion of the elderly population over 60 in the

total population at the end of the year. The higher

the proportion indicates that the greater the

vulnerability of Hazard-affected carriers and the

higher comprehensive risk of COVID-19 in the

region.

Due to the characteristics of disaster carrier

mobility, the series of methods adopted after the

COVID-19 outbreak are: detection of sources of

infection, isolation of high-risk people, and traffic

control in high-risk areas. Relevant studies also

show that the spatial distribution of epidemic risk is

not random and is partly affected by spatial

transmission in adjacent areas. Therefore, to a large

extent, measures such as "lockdown cities" and

restricting travel can curb the spatial spread of the

epidemic. The impact of population mobility caused

by the property of convenient transportation

aggravates the spatial risk of the epidemic, which

specifically shows at the two aspects of population

inflow and outflow. Population inflow may increase

the risk of the local epidemic, while population

outflow, especially in the key epidemic areas, may

lead to the risk of external spread and increase the

scope of the spatial spread. The intercity

communication and family-oriented local

communication brought about by population flow

constitute a two-stage model of the spread of the

epidemic in China, shaping the spatial and temporal

pattern of the epidemic (Liu and Jin 2020).

Population migration, transportation, economy and

other factors are internally related to the

transmission of COVID-19 (Qi et al. 2020). So this

paper will uses population mobility to reflect The

vulnerability of Hazard-affected carriers. The

stronger the regional population mobility, the higher

the transmission rate will be, and the new

coronavirus comprehensive risk will be higher.

Population mobility is represented by the number of

travel per capita (passenger volume / average

population) and the per capita travel distance

(passenger turnover / average population).

The third edge is emergency management, which

is designed to ensure public health and safety. The

outbreak of COVID-19 has shown us the importance

of building a strong public health system. Li Liming

(2021) believes that public health is the guard of

national health, and the comprehensive public health

capacity is an important symbol of the government's

modern disease construction system and the fine

social management. This research reveals the

importance of reform and improvement of the public

health system. For example, in early January 2020,

the mortality rate in Wuhan was much higher than in

other rest of China. This distorted mortality rate was

due to the serious lack of hospital care among many

infected people, and the lack of disease control

resources seriously affected the control of the

epidemic.

The study adopts three secondary indicators,

including emergency medical facilities, emergency

command facilities and emergency support effect.

The differences in population base and medical

levels in different regions are the impact of the

epidemic. Emergency medical facilities adopt the

number of beds in medical institutions per 10,000

people(units), the number of medical and health

institutions (units), the number of health personnel

per 10,000(10,000 person), the number of outpatient

and inpatient medical assistance (10,000 person-

times).

Comprehensive Risk Assessment and Spatial Pattern Analysis of COVID-19 of China

109

The emergency command facilities adopt the

number of community service institutions and

facilities per 10,000 people, the total number of real-

name volunteers (10,000), and the number of

emergency equipment (units) as its indicators.

Since the outbreak of the COVID-19 epidemic,

the community has played a role as a "battle

fortress" in the epidemic prevention and control for

external import and internal proliferation. The work

of community service agencies is relatively trivial,

including door-to-door investigation, urging

gatherings, banquets and other gathering activities,

disinfection, providing materials during home

observation or isolation, errands, psychological

counselling and other services. Volunteers in

COVID-19 are a "red barrier" for epidemic

prevention and control, and have become a beautiful

scenery for epidemic prevention and control.

Technical and equipment support is also very

important, which can effectively improve the

response and deal with emergencies. Such as crawler

intelligent disinfection campaign robot, outdoor

epidemic prevention disinfection robot, family

epidemic prevention emergency package, multi-

functional intelligent protection all-in-one machine,

new energy health epidemic prevention elimination

vehicles, epidemic prevention mass fog elimination

robot, fire emergency fire prevention vehicles,

hospital centre attract system emissions inactivation

device, folding aluminium magnesium alloy

equipment box material handling box, unmanned

aerial vehicles, etc.

The effect of emergency support is reflected by

the number of cured and discharged people / the

number of confirmed patients, which reflects the

scientific response and treatment effect of the

epidemic. Considering the difficulty of obtaining

some data and the systematicness and integrity of

the whole index system, based on the principles of

comprehensiveness, typicality, operability and so on,

the selection of indicators at all levels is as shown in

Table 1. The weight of each layer index is obtained

by fuzzy analysis method. The calculation formula

of Novel Coronavirus Comprehensive Risk

Evaluation Index (short for CREI) is:

Comprehensive Risk Evaluation Index of

COVID-19= (Virus outbreak risk

×The vulnerability

of disaster victims) / effectiveness of emergency

guarantee

Table1: Comprehensive risk evaluation index system of COVID-19

First-level Indicators Second-level Indicators Third-level Indicators

Virus outbreak risk

Prevalence rate of 10,000 Number of confirmed cases/Resident Population (10,000)

The fatality rate Number of deaths/confirmed cases

The vulnerability of

Hazard-affected

carriers

Proportion of aged population Population over 60 years old/total population at year-end

Population mobility

travel per capita = passenger volume/average population

Per capita travel distance = passenger turnover / average

ulation

Effectiveness of

emergency support

Emergency medical facilities

Number of beds in medical institutions per 10,000 people

(sheet)

Number of Medical and health institutions

Number of health workers per 10,000 (10,000)

Number of outpatient and inpatient medical assistance

(10,000 person-times)

Emergency command facility

Number of community service agencies and facilities per

10,000 people

Total number of real-name volunteers (10,000)

Number of Emergency equipment (units)

Emergency support effect

Number of cured and discharged patients/number of

confirmed patients

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

110

4 RESEARCH METHODS AND

DATA SOURCES

4.1 Research Methods

This paper uses an exploratory spatial data analysis

method. The Exploratory Spatial Data Analysis

(ESDA) includes global and local auto-correlation

analysis, which focuses on spatial correlation

measures to describe and display the spatial

distribution of the studied objects, and to reveal

spatial connections as well as spatial patterns. And

the global auto-correlation analysis is the

dependence and heterogeneity of a research object

on the regional space, and the formula is as

followed:



jiij

)-)(-(

IMoran

WiS

YYYYW

(1)

In equation (1), variety



=−=

YYYYS

)(

. n

is the total number of study regions, Yi represents

the observation of the I region, that is

Comprehensive Risk Evaluation Index of COVID-19

of the i region, W

is the spatial weight matrix, and

i,j represents the region i and regional j. In this

paper, if Moran's I >0, it indicated that areas with

similar comprehensive risk levels tend to gather

together. If Moran's I <0, it indicates that areas with

high and low comprehensive risk levels exist in the

same region, with great spatial differences. If

Moran's I =0, there is no spatial dependence between

regions.

Local auto-correlation analysis often uses Moran

scatter map (or Moran plot) that can refine the local

characteristics and changes of the analysis space.

The scatter map is divided into four quadrants, the

first of which is high-high agglomeration (HH),

indicating a high comprehensive risk level in both

the region itself and the surrounding areas. The

second quadrant is low-high agglomeration (LH),

indicating that areas with lower COVID-19

comprehensive risk levels are surrounded by areas

with higher peripheral risk levels. The third quadrant

is low-low agglomeration (LL), with the low

comprehensive risk level in the region itself and the

surrounding areas. The fourth quadrant is high and

low agglomeration (HL), where areas with high risk

level of comprehensive coronavirus are surrounded

by areas with lower risk levels. The first and three

quadrants are typical regions, while the second and

four quadrants are atypical regions (spatial outlier).

4.2 Data Source

The sample is from 31 provinces (cities and

autonomous regions) of China, and the data comes

from the National Health Commission, the National

Bureau of Statistics, China Emergency Information

Network, Public Health Science Data Centre, the 7th

National Census Bulletin, and local social statistics

bulletin, China Statistical Yearbook, China

Yearbook of Civil Affairs Statistics and China City

Statistical Yearbook. And data on COVID-19 cases

were available as of June 30, 2021. The proportion

of the elderly population adopts the seventh census

data in 2021. The total number of real-name

volunteers and the number of emergency equipment

are the latest statistical data of the emergency

information network, and the other indicators are the

data of 2020.

5 RESULTS ANALYSIS

5.1 Space and Temporal Distribution

As of June 30, 2021, the region with the largest

number of confirmed cases in China was

concentrated in Hubei (68,162). As showed in Table

2, Wuhan, Hubei, is the most severe city, followed

by Guangdong (2,759), Shanghai (2,222 cases),

Heilongjiang (1,612), Zhejiang (1,386), Henan and

Hebei (all 1,317), Sichuan (1,109), Beijing (1,078),

Hunan (1,061), Anhui (1,008), and the total number

of cases in other regions were below 1,000.

The spread of novel coronavirus is increased due

to its geographical proximity to Hubei Province,

especially the closest cities to Wuhan, such as

Henan Xinyang, Zhengzhou, Nanyang, Zhumadian,

Hunan Changsha, Yueyang, Anhui Hefei, Bengbu,

Bozhou, Fuyang, Jiangxi Nanchang, Shangrao,

Xinyu, Jiujiang and Chongqing Wanzhou district. In

addition, as an economically developed trade centre,

a transportation centre, and a political and cultural

centre, they are often the centre of the spread of the

epidemic. Due to the large number of migrant

workers in Wuhan, Hubei Province, its flow with

Guangzhou, Shenzhen, Wenzhou, Hangzhou,

Ningbo is very frequent. Therefore, the number of

COVID-19 infections in Beijing, Shanghai,

Guangdong, Zhejiang and so forth are larger.

Comprehensive Risk Assessment and Spatial Pattern Analysis of COVID-19 of China

111

Table 2: The cumulative number of confirmed COVID-19 cases (As of June 30,2021).

Region

Number of

Confirmed

cases

（person）

Region

Number of

Confirmed

cases

（person）

Region

Number of

Confirmed

cases

（person）

Region

Number of

Confirmed

cases

（person）

Beijing 1078 Heilongjiang 1612 Shandong 883 Chongqing 601

Tianjin 402 Shanghai 2222 Henan 1317 Sichuan 1109

Hebei 1317 Jiangsu 743 Hubei 68162 Guizhou 147

Shanxi 253 Zhejiang 1386 Hunan 1061 Yunnan 446

Inner

Mon

olia

394 Anhui 1008 Guangdong 2759 Tibet 1

Liaoning 430 Fujian 688 Guangxi 276 Shaanxi 629

Jilin 573 Jiangxi 937 Hainan 188 Gansu 195

Qinghai 18 Ningxia 76 Xinjiang 980

Data sources: Public Health Science Data Center

According to CREI of COVID-19 calculated by

the comprehensive index system, 31 provinces and

autonomous regions in China are divided into 5

levels, with lowest-risk, lower-risk, medium-risk,

higher-risk and highest-risk. Highest-risk areas (risk

index 0.2657-1.0465) include Tianjin, Hubei,

Sichuan, Liaoning, Shanghai and Hainan; The

higher-risk areas (risk index 0.1779-0.2657) include

Gansu, Hunan, Chongqing, Heilongjiang, Jilin and

Beijing; Medium-risk areas (risk index 0.1503-

0.1779) include Shanxi, Inner Mongolia, Anhui,

Jiangxi, Guizhou and Shandong; Lower-risk areas

(risk index 0.1121-0.1503) include Hebei, Jiangsu,

Fujian, Henan, Guangxi and Zhejiang; Lowest-risk

areas (risk index 0.0243-0.1121) are Guangdong,

Yunnan, Tibet, Shaanxi, Qinghai, Ningxia, and

Xinjiang. The COVID-19 risk area at all levels has

the characteristics of spatial agglomeration. In

general, the comprehensive risk in the northwest and

southwest regions is relatively low, and the

comprehensive risk in the eastern and central regions

is high(showed in Fig.2).

Figure 2: Spatial distribution of CREI of COVID-19.

Note: Based on the standard under-drawing

GS(2016) 2892 map review system of Natural

Resources, not modified. Due to data difficulties,

this study does not include Hong Kong, Taiwan and

Macau

Combined with the index system and original

data, the difference between the regions of

comprehensive risk level is related with public

health service system construction, medical service

system organization imbalance and emergency

service supply capacity. For example, some regional

hospitals and community service institutions is less

in quantity and small in scale, and health technical

personnel reserve is backward with talent draining.

Therefore, the areas in the process of the epidemic,

measures such as diagnosis and treatment,

comprehensive isolation and cut off the transmission

channels was unable to meet the demand of

confirmed treatment and isolation.

5.2 Correlation Analysis

The Moran index of CREI is 0.2417 and the P value

is 0.01. As showed in Fig.3, it implies that the

spatial distribution is not completely random state,

but a spatial agglomeration between similar values,

that is, a positive spatial correlation, which shows

that the areas of higher and high-tech comprehensive

risk are close to each other, and the regions with low

high-tech comprehensive risk are also close to the

spatial relationship structure, and the regions are

relatively concentrated.

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

112

Figure 3: Moran plot of CREI of COVID-19.

About 58% of the regions are typical areas in

positive correlation (including HH and LL), among

which the high-high agglomeration mode (HH)

includes Heilongjiang, Jilin, Liaoning, Guangxi and

Yunnan, which have a relatively high comprehensive

risk and the surrounding areas. There are many areas

belonging to the low-low agglomeration mode (LL),

including Xinjiang, Tibet, Qinghai, Gansu, Hebei,

Shaanxi, Shaanxi, Shanxi, Zhejiang, Jiangsu,

Shanghai, Shandong, Henan and Tianjin. The

comprehensive risk level of these regions and

neighbouring areas is low, forming a low-risk

agglomeration belt. Belonging to the low-high

agglomeration mode (LH) are Inner Mongolia,

Fujian, Hunan, Chongqing and Jiangxi, which have

low comprehensive risk and are surrounded by the

high-risk areas. Ningxia, Guangdong, Anhui and

Hubei are the High and Low Cluster Mode (HL).

The comprehensive risk of the new regions is high

and the risk level of the surrounding areas is

relatively low. Sichuan, Beijing and Hainan are

across two quadrants, belonging to the LL and HL

modes, and Guizhou across the HH and HL

quadrants. The HL and LH regions are atypical

regions, namely spatial outlier states, showing that

the degree of spatial difference in the new

coronavirus risk is relatively large (showed in

Table.3).

Table 3: Distribution of Moran scatter plots

e Provincial Administrative Re

ions

Heilongjiang, Jilin, Liaoning, Guangxi,

Yunnan

Inner Mongolia, Fujian, Hunan,

Chon

, Jian

Xinjiang, Tibet, Qinghai, Gansu,

Hebei, Shaanxi, Shanxi, Zhejiang,

Jiangsu, Shanghai, Shandong, Henan,

Tian

HL Nin

xia, Guan

don

, Anhui, Hubei

Across

Quadrants

Sichuan, Beijing, Hainan（LL&HL）,

Guizhou（HH&HL）

6 CONCLUSIONS

Considering the influence of spatial factors, we

established a Comprehensive Risk Evaluation Index

system of COVID-19 with using exploratory spatial

data analysis, and calculated the index of 31

provinces (cities, autonomous regions), to drew the

following conclusions: Tianjin, Hubei, Sichuan,

Liaoning, Shanghai, Hainan are areas with high

comprehensive risk, while Guangdong, Yunnan,

Tibet, Shaanxi, Qinghai, Ningxia and Xinjiang are

the low-risk areas. In general, the comprehensive

risk of COVID-19 in the northwest and southwest is

relatively low, and the risk in the east and central

regions is high. China's CREI presents a significant

positive correlation in space. The CREI in most

regions is a typical region of (HH and LL), forming

high-high agglomeration and low-low

agglomeration modes. The CREI of Shanghai,

Zhejiang, Jiangsu, Anhui, Henan, Hunan and other

places follows the geographical proximity law with

the outbreak centre in terms of geographical and

spatial distribution. The comprehensive risks of

Shanghai, Zhejiang, Jiangsu, Anhui, Henan, Hunan

and other places follow the geographical proximity

to in terms of geospatial distribution.

For high-risk areas, relevant departments should

pay more attention at the epidemic prevention and

emergency response, and implement strict control

strategies such as non-proliferation internally, and

export-prevention externally, especially in the areas

with a developed economy, large personnel mobility

and density, where should also pay attention to the

risk control of the epidemic. In addition, after the

outbreak of COVID-19, it also exposed the

problems in the medical and health service facilities

and public health emergency management system.

We need to learn from it and improve the major

epidemic prevention and control system and

Comprehensive Risk Assessment and Spatial Pattern Analysis of COVID-19 of China

113

mechanism, regular the national public health

emergency management system, and perfect the

incentive mechanism, cultivate more health

technicians, and to the end to improve the response

ability and management level of major public health

emergencies.

The deficiency in this paper is that we just

mainly analyse the spatial distribution pattern and

aggregation pattern of Comprehensive risks of

COVID-19, and some other factors that may affect

COVID-19does not take account into the research

system, such as urbanization process, regional

economic development level, fiscal expenditure,

population migration and migration and adjacent to

other countries and so on. In future research, we will

further consider and incorporate the above relevant

factors to simulate and predict the outbreak risks and

trends of COVID-19.

ACKNOWLEDGEMENTS

This study was supported by the "public

management" construction project of Characteristic

Key Discipline from Guangdong Province, China in

2016(F2017STSD01), Scientific research fund

projects of Guangzhou Xinhua University

(2018ZDXKZZ03), "The COVID-19" epidemic

prevention and control research

project(2020YQYJ06), Guangdong Higher

Education Teaching Reform project (F2018J051).

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