COVID-19 Pandemic Risk Assessment: Systematic Review

Abstract

Background
The COVID-19 pandemic presents the possibility of future large-scale infectious disease outbreaks. In response, we conducted a systematic review of COVID-19 pandemic risk assessment to provide insights into countries’ pandemic surveillance and preparedness for potential pandemic events in the post-COVID-19 era.

Objective
We aim to systematically identify relevant articles and synthesize pandemic risk assessment findings to facilitate government officials and public health experts in crisis planning.

Methods
This study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and included over 620,000 records from the World Health Organization COVID-19 Research Database. Articles related to pandemic risk assessment were identified based on a set of inclusion and exclusion criteria. Relevant articles were characterized based on study location, variable types, data-visualization techniques, research objectives, and methodologies. Findings were presented using tables and charts.

Results
Sixty-two articles satisfying both the inclusion and exclusion criteria were identified. Among the articles, 32.3% focused on local areas, while another 32.3% had a global coverage. Epidemic data were the most commonly used variables (74.2% of articles), with over half of them (51.6%) employing two or more variable types. The research objectives covered various aspects of the COVID-19 pandemic, with risk exposure assessment and identification of risk factors being the most common theme (35.5%). No dominant research methodology for risk assessment emerged from these articles.

Conclusion
Our synthesized findings support proactive planning and development of prevention and control measures in anticipation of future public health threats.

Full text

REVIEW
COVID-19 Pandemic Risk Assessment: Systematic
Review
Amanda MY Chu
1
, Patrick WH Kwok
1
, Jacky NL Chan
2
, Mike KP So
2
1
Department of Social Sciences and Policy Studies, The Education University of Hong Kong, Tai Po, Hong Kong;
2
Department of Information Systems,
Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Correspondence: Amanda MY Chu, Department of Social Sciences and Policy Studies, The Education University of Hong Kong, Tai Po, Hong Kong,
Email amandachu@eduhk.hk
Background: The COVID-19 pandemic presents the possibility of future large-scale infectious disease outbreaks. In response, we
conducted a systematic review of COVID-19 pandemic risk assessment to provide insights into countries’ pandemic surveillance and
preparedness for potential pandemic events in the post-COVID-19 era.
Objective: We aim to systematically identify relevant articles and synthesize pandemic risk assessment ndings to facilitate
government ofcials and public health experts in crisis planning.
Methods: This study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and
included over 620,000 records from the World Health Organization COVID-19 Research Database. Articles related to pandemic risk
assessment were identied based on a set of inclusion and exclusion criteria. Relevant articles were characterized based on study
location, variable types, data-visualization techniques, research objectives, and methodologies. Findings were presented using tables
and charts.
Results: Sixty-two articles satisfying both the inclusion and exclusion criteria were identied. Among the articles, 32.3% focused on
local areas, while another 32.3% had a global coverage. Epidemic data were the most commonly used variables (74.2% of articles),
with over half of them (51.6%) employing two or more variable types. The research objectives covered various aspects of the COVID-
19 pandemic, with risk exposure assessment and identication of risk factors being the most common theme (35.5%). No dominant
research methodology for risk assessment emerged from these articles.
Conclusion: Our synthesized ndings support proactive planning and development of prevention and control measures in anticipation
of future public health threats.
Keywords: meta-analysis, coronavirus, pandemic risk management, WHO COVID-19 research database, data visualization
Introduction
The outbreak in 2019 of the novel coronavirus disease (COVID-19), which the World Health Organization (WHO)
ofcially declared a global pandemic on 11 March 2020,
1
is currently the most detrimental worldwide public health event
of the twenty-rst century. The disease’s rapid transmission not only has imposed tremendous pressures on the public
health systems, but it also has severely disrupted the nancial markets,
2,3
our society and the global economy,
4
and our
environment.
5
Furthermore, this threatening pandemic caused drastic harms to people’s mental health. People affected by
COVID-19 showed relatively higher rates of adverse psychiatric outcomes like anxiety, depression, stress, and psycho-
logical distress.
6
Another gloomy impact of COVID-19 to the society was that misinformation and fake news about
transmission, prevention, and medical treatment
7,8
were spread within and across online communities broadly and
swiftly, causing the prevalence of incorrect knowledge about COVID-19. These wide-scale deadly effects made research
works relating to COVID-19 pandemic risk assessment so important that governments, public health professionals, and
scientists could gain insights from research ndings for disease prevention and control strategies.
Since its rst appearance, COVID-19 has been a hot topic of research in many elds, and especially in health-related
disciplines. Even now, the research enthusiasm for COVID-19 has not abated, because the virus has continued to
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open access to scientific and medical research
Open Access Full Text Article
Received: 12 October 2023
Accepted: 5 January 2024
Published: 11 April 2024

transform itself into new variants
9
and has caused successive waves of large-scale transmission with exponential
increases in new infections globally for the past 3 years.
As we have stepped into the post-COVID-19 era, enormous volumes of extant research studies on the various aspects
of the COVID-19 pandemic have been published. During COVID-19 pandemic, people often used online social media
platform to search for information on recent development, communicate their views, and express their feelings. The
analysis conducted by Chandrasekaran et al
10
on COVID-19–related tweets from Twitter data indicated that the contents
could be broadly classied into 10 different themes. Four commonly concerned themes were spread and growth
(15.45%), treatment and recovery (13.14%), impact on the health-care sector (11.40%), and government response to
the pandemic (11.19%). In light of the above observations, it is noteworthy to work out some statistics describing the
coverage distribution of the current COVID-19 pandemic risk assessments such that their diversity and applicability
could be demonstrated to the concerned parties for acquiring a more comprehensive and detailed understanding on how
to prevent, manage, treat, and address the issues. Nevertheless, staying vigilant to the spread of infectious disease and
getting more well prepared are essential. Thus, the objectives of our study were to provide a systematic review of the
COVID-19 pandemic risk assessment.
The articles in this review were selected from the World Health Organization (WHO) COVID-19 Research
Database,
11
which is a centralized database that pools publications from different health-care research databases such
as Ovid, PubMed, Scopus, Web of Science, and others. At the time that we searched the articles, there were already more
than 620,000 records, and the size of the pool is continually growing because it is updated weekly and new publications
are added regularly. The WHO was recognized for its outstanding work in building the COVID-19 Research Database
and for the excellence of its content.
12
The relevant articles were characterized using the following six research
questions (RQs).
RQ1: Study Location
RQ2: Types of Variables Used
RQ3: Availability of the Materials Used to Generate Research Outcomes
RQ4: Use of Data-Visualization Techniques
RQ5: Research Objectives
RQ6: Research Methodologies
This paper is subsequently organized as follows: In the Methods section, we describe thoroughly how the nal
eligible list of articles was selected in order to provide the best possible answers to our research questions. In the Results
section, we develop our classication framework for the research questions and present the summary statistics of the
eligible articles, with the aid of tables and charts. In the Discussion section, we provide our key ndings, along with some
recommendations for policymakers and health-care experts and researchers to deal with the potential for any future
outbreak of disease. Finally, the Conclusions section gives a brief recap of the key conclusions that can be drawn from
our ndings.
By synthesizing insightful ndings with the help of tables and charts, this study aids policymakers, health-care
experts, and researchers in creating preparedness and surveillance efforts for possible new waves of COVID-19 and/or
the emergence of new infectious diseases in the future.
Methods
Overview and Selection Process
We conducted a systematic review of literature reviews on COVID-19 pandemic risk assessment sourced from the World
Health Organization (WHO) databases, according to the Preferred Reporting Items for Systematic review and Meta-
Analysis (PRISMA) guidelines.
13
Two researchers worked independently to select the nal eligible articles in this review.
First, one of them used the electronic search engine available in the WHO database to generate a list of potentially
eligible articles. Next, each article in the potentially eligible list was retrieved either directly from the WHO database or
from the journal website on which that article was published. Finally, the other researcher manually screened each article
to ensure that those in the nal eligible list satised our inclusion criteria and did not meet our exclusion criteria. All
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disagreements between the two researchers over the eligibility of particular articles were resolved through discussion
with a third researcher.
Information Source and Search Strategy
We identied the relevant articles for this review by searching the World Health Organization (WHO) COVID-19
Research Database from its inception to 12 July 2022. This electronic database is freely and publicly accessible online. It
searches, on a frequent basis, a vast number of popular databases to obtain current articles reporting global research on
the coronavirus disease (COVID-19). During the time that we were searching the articles, the three largest sources of
articles, in terms of the quantity in the WHO COVID-19 Research Database, were MEDLINE, Scopus, and Web of
Science.
The search strategy was straightforward, because nearly all articles in the WHO COVID-19 Research Database are
within the domain of the COVID-19 pandemic. No lters or limits were placed in the rst screening process – we just
screened out articles that lacked a title, name, or abstract, and then we removed duplicate records. Approximately 56% of
the records remained and moved forward to the next screening process.
Eligibility Criteria
In our criteria, we included articles that related to one of three main scopes of study: (1) COVID-19, using the keywords
“COVID” or “Coronavirus disease 2019”, (2) pandemic risk, using the keywords “pandemic risk”, and (3) risk
assessment, using the keywords “risk assessment.”
The language of each article, the nature of the article, the research eld of study, and the accessibility of each article
was the four lters in our exclusion criteria. We excluded (1) non-English-language articles, (2) articles with a nature
equivalent to letters/comments/abstracts, and (3) elds of study belonging to “clinical”, “medical”, “virology”, “nance”,
“business”, “logistics”, “supply chain”, and “pharmacy”. For criterion (4), the accessibility of the article, inaccessible or
nonidentiable articles, including non-open-access papers, full-text pdfs, unavailable papers, and papers without a valid
DOI (Digital Object Identier) were excluded because we could not examine the entire papers to determine whether they
were within our research focus.
After the screening, we conducted an additional manual scan of the eligible articles. Meta-analyses and articles not
relating to our research questions were then excluded, leaving a nal eligible list of 62 articles. A description of the
inclusion and exclusion criteria of the articles for this study is presented in Table 1. The quality of the included articles
was assessed using the Effective Public Health Practice Project (EPHPP) Quality Assessment Tool.
14
Each included
article was assessed by two reviewers, who conducted the assessment independently.
Results
The article search and selection process is shown in Figure 1. Initially, there were 626,900 records in the WHO COVID-19
Research Database. Only 410 records were entered into our “Eligibility” phase. In the last phase, “Included”, the number of
records was further reduced to 62. These 62 articles were identied as the nal candidates for analysis in this review.
Table 1 Summary of the Inclusion and Exclusion Criteria
Criteria Inclusion Exclusion
Article type Topic relevant to COVID-19, pandemic risk or risk
assessment
Letters, comments, abstracts, non-journal, systematic review and
meta-analysis
Field of study Any eld except those elds in the Exclusion column Clinical, medical, virology, nance, business, logistic, supply chain
and pharmacy
Language English language All other non-English language
Accessibility Open access Article with non-valid DOI or no full text pdf available
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We developed our classication framework based on six research questions. After the classication framework was
developed, one of the reviewers performed a preliminary allocation by assigning each of the 62 included articles
according to the respective classication types from each research question. To mitigate the risk of bias, another reviewer
validated the preliminary allocation by assessing the individual classication of each article in each research question.
Figure 1 PRISMA ow diagram of the article selection process.
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Finally, a third reviewer examined all of the classication discrepancies between the rst two reviewers and arrived at the
nal summary statistics for the six research questions, which we then described and visualized in the tables and gures
shown below.
RQ1: Study Location
We divided the articles into four different sizes of the geographical areas in which the COVID-19 pandemic risk was
assessed: regional areas, specic areas, local areas, and global coverage. The study location of one article was not
classiable because that article was a pandemic risk modelling evaluation and no location was indicated. Descriptions of
the four sizes of geographical areas, with some examples, are given in Table 2.
Approximately one-third (32.3%) of the articles had conducted COVID-19 pandemic risk assessment in a local area,
and another one-third had assessed the risk globally. Regional areas had attracted the least attention of researchers,
constituting just 6.5% of the 62 included articles (Figure 2).
Among the 17 articles concerned with risk assessment in specic areas, ve articles focused on a single province in
China, such as Qingdao
17
and Hubei.
18
India followed China as the second most popular specic area to have been
studied, but the frequency was just two. The remaining 10 specic areas were each at a different location (Figure 2).
Regarding the 20 local-area articles, the countries of interest were quite diverse, with 12 different countries, only two
of which had been studied in more than two articles. China (seven articles) was again the top country to arouse interest
and to have been studied in a countrywide risk assessment, followed by the USA (three articles).
RQ2: Types of Variables Used
We classied the nature of the variables used in the included articles into six different types of data that the variables
represented: (1) epidemic data, (2) population/demographic data, (3) mobility/transportation data, (4) socioeconomic
data, (5) survey data, and (6) environmental data. Table 3 gives examples of each data type.
Table 2 Description of the Four Sizes of Geographical Areas
Geographical Area Description Example Geographical Coverage
Regional Areas Two or more countries in one continent African countries;
15
European countries
16
Specic Areas One region or a small number of region(s) in one
country, OR a specic event
Region(s) in one country:
Qingdao, China;
17
Hubei, China;
18
Ontario, Canada;
19
Jammu and Kashmir in the northern Himalayan region of India
20
Specic event:
A concert at the Royal Albert Hall
21
Local Areas Multiple cities/regions in one country Twenty regions in Italy;
22
Seventeen metropolitan cities in USA;
23
Whole country of Japan;
24
Whole country of Nepal;
25
Multiple provinces in China
26,27
Global coverage At least ve countries from at least two continents 154 countries studied;
28
Italy, Germany, Spain, France, US, China;
29
China, Switzerland, Japan, Austria, the United States, Brazil, and
Russia;
30
Canada, France, India, South Korea, and the UK;
31
France, Germany, Italy, Spain & USA
32
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As shown in Figure 3, epidemic data was the most common type of variable used, appearing in nearly three-quarters
(74.2%) of the 62 included articles, whereas the percentage was less than 50% for each of the other ve types of variables.
Environmental data were a relatively unpopular variable type and were used in only 9.7% of the 62 included articles.
Figure 4 measures how broadly the different types of variables were used in the 62 included articles (ie, how many
types of variables in Table 2 were used). The minimum breadth (the use of only one type of variable) and maximum
Figure 2 Distribution of the four sizes of geographical area studied (The numbers shown inside/outside the pie chart are the frequency count and the percentage of the 62
articles, respectively.).
Table 3 Examples of the Six Types of Data Represented by the Variables
Data Type Example
Epidemic Data Daily & cumulative number of conrmed cases; daily number of new cases; daily and cumulative number of death cases;
number of conrmed and death cases per a certain number of people; n-day moving averages of conrmed cases and death
cases; case fatality rates; number of sporadic cases imported from other infected areas; clusters of cases detected in well-
dened clusters; number of re-emergent cases
Population/
Demographic Data
Total residents living in the study area; population density per km
2
; ratio of aging population to total population; percentage of
black population/minority population/immigrants; gender; age; income; employment status
Mobility/
Transportation
Data
Ratio between commuting ows and employed population; people’s mobility patterns; hotspot locations of conrmed cases;
mobile phone data on user location information; daily ight-booking data; daily number of passengers on ights from one
country to another; inter-city multichannel transportation information; Daily Baidu Mobility Indexes (dBMIs); Tencent-
Yichuxing location data
Socioeconomic
Data
GDP; public and private debt to GDP; government expenditures to GDP; tourism (contribution of tourism to GDP); ination
rates; unemployment rates (% of the total labor force); percentages of main workers and percentages of literates; prevalence
of low income; poverty index; literacy rate; human development index
(Continued)
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breadth (the use of ve types of variables) were 1 and 5, respectively. Four articles were grouped into “Others” mainly
because they lacked enough relevant information to precisely identify the variable types used in their studies.
Approximately 41.9% of the 62 included articles used only one of the six types of variables, as is shown in the upper
half of Figure 4. The types of variables used by these 26 articles were epidemic data, mobility/transportation data, and
survey data. Epidemic data variables (19 articles) were the distinctly most popular type of variable among the articles
using a single type of variable, compared with mobility/transportation data (ve articles) and survey data (two articles).
More than half (51.6%) of the 62 included articles used two or more types of variables. Use of two types of variables
(29%) followed use of a single type of variable as the second most common number of types of variables used in the risk
assessment.
Among the 18 articles using two types of variables, the lower half of Figure 4 shows that epidemic data and
population/demographic data (eight articles) were the most popular pair, followed by population/demographic data and
survey data (four articles).
RQ3: Availability of the Materials Used to Generate Research Outcomes
Every study’s data collected and computing codes used to realize research outcomes are essential materials during the
development of an article reporting on that research. The left side of Figure 5 summarizes the availability of the data and
codes for the 62 articles we reviewed. More than half (54.8%) of the articles we analyzed did not mention whether their
data and/or codes had open access. Approximately 11% of them quoted in their data availability statement that interested
scholars could request data and codes from the authors. The remaining one-third of the 62 articles provided specic
hyperlinks for downloading their materials.
Figure 3 Penetration rate by the different types of variables (= number of articles that used this type of variable/62).
Table 3 (Continued).
Data Type Example
Survey Data Online questionnaires with which participants were recruited via weblink, social network, or e-mail; qualitative surveys with
in-depth interviews and focus-group discussions; natural survey data of a country
Environmental
Data
Annual average of PM10 daily mean concentration; average winter daily mean temperature; daily temperature; night-time light
intensity; water sanitation and hygiene; virus concentration in wastewater and river water; ecological footprint (human
demand on natural capital)
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The right side of Figure 5 shows the breakdown of the 21 articles that made their data and/or codes available: 20
articles made their data available; nine articles made their codes available; and eight articles made both their data and
their codes available.
Figure 4 Numbers of the types of variables used by the 62 included articles (Numbers shown inside the pie chart are the frequency count and the percentage based on 62
articles, respectively.).
Figure 5 Accessibility of data and codes (Numbers shown inside the pie chart are the frequency counts and the percentages based on 62 articles, respectively.).
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RQ4: Use of Data-Visualization Techniques
All 62 of the articles we reviewed used tables or charts or both to present their research ndings and outcomes.
Approximately 80% of them (50 articles) included tables. The percentage for those using charts was even higher, at
90.3% (56 articles). Forty-four of the articles (71%) displayed both tables and charts in their presentation. There are
many different data-visualization techniques, and we found that seven types of graphic representation of data were used
in the 62 articles: (1) time-series plots, (2) bar charts, (3) scatter plots, (4) box plots, (5) 3D plots, (6) network graphs, and
(7) heat maps. These seven data-visualization techniques are detailed in Figure 6.
Of the seven data-visualization techniques mentioned above, three were used in at least half of the 62 included
articles: time-series plots (38 articles or 61.3%), scatter plots (33 articles or 53.2%), and bar charts (31 articles or 50%).
Box plots and network graphs were less popular data-visualization techniques in our reviewed articles, having been used
in only nine articles (14.5%) and eight articles (12.9%), respectively. The relatively low number of articles that presented
a network graph was expected because not many of the articles had conducted a network analysis as their research
methodology. Figure 7 shows the relative popularities of the different data-visualization techniques.
Figure 8 measures the breadth of the data-visualization techniques usage by the 56 included articles that used charts (ie, it
shows how many types of data-visualization each article used). Of those 56 articles, the minimum (using one type) and maximum
(using six types of visualization) breadth were 1 and 6, respectively. Minority groups made up two extremes: those using just one
type of data visualization technique (7 articles or 12.5%) and those using more than four types (5 articles or 8.9%).
The most common breadth was 2, occupying approximately one-third (19 articles) of the 56 included articles. As
shown in the upper half of Figure 8, out of those 19 pairs, only four different pairs of visualization type were used by
more than one article. The most common pairs were “Time series plot & Bar chart” and “Time series plot & Scatter plot”,
with six articles using each of those pairs. The less common pairs, with two articles using them, were “Time series plot &
Heat map” and “Scatter plot & Heat map”.
The breadth value 3 followed the breadth value 2 and was the second most common size of data visualization
techniques used by the 56 articles that used charts. As is shown in the lower half of Figure 8, the most popular triples
were “Bar chart & Heat map & Time series plot” and “Bar chart & Heat map & Scatter plot”, with three articles using
each of those triplets. Another three articles with the breadth of 3 used two of same techniques, “Time series plot &
Scatter plot”, but their third techniques were different.
RQ5: Research Objectives
We found that the primary research objective of each of the articles could be classied into ve major themes (see also Figure 9):
(1) COVID-19 risk exposure assessments using risk indicators/indexes or identifying risk factors (22 articles or 35.5%); (2)
reviews on the effectiveness of policy measures for COVID-19 control and prevention (11 articles or 17.7%); (3) predictions/
estimations of COVID-19-related parameters (10 articles or 16.1%); (4) investigations on the patterns of COVID-19 transmis-
sion/geographical spread of COVID-19 (eight articles or 12.9%), and (5) specic-focus articles (11 articles or 17.7%).
Table 4 gives further descriptions of the (1) COVID-19 risk exposure assessments that used risk indicators/indexes or
identifying risk factors; (2) reviews on the effectiveness of policy measures for control and prevention; (3) predictions/
estimations of COVID-19 related parameters, (4) patterns of transmission/spread of COVID-19 and (5) specic focuses.
RQ6: Research Methodologies
Generally, the articles we reviewed used more than one research method to produce their research outcomes. In each
article, we focused on the core aspects of the various methods used, and we identied six core statistical research
methodologies from 54 of the included articles: (1) exploratory data analysis (eight articles or 12.9%); (2) network
analysis (ve articles or 8.1%); (3) time-series analysis (four articles or 6.5%); (4) Susceptible, Infected, and Recovered
(SIR)/Susceptible-Exposed-Infectious-Removed (SEIR) Models (seven articles or 11.3%); (5) proposed frameworks/
systems (nine articles or 14.5%); and (6) special models/techniques (21 articles or 33.9%). The remaining eight articles
(or 12.9%) either provided insufcient information to determine which core methods were used or they used a narrative
description/qualitative analysis/context analysis as their core method (Figure 10).
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Figure 6 Description of seven common data-visualization techniques.
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Methodology outlines for the research methodologies for exploratory data analysis, network analysis, time-series
analysis, and SIR/SEIR models are summarized as group levels in Table 5. The proposed framework/system and special
model/technique approaches are described individually in Tables 6 and 7, respectively, because they are quite unique in
nature.
Figure 7 Penetration rate by different data-visualization techniques (= number of articles that used the specic data visualization technique/62).
Figure 8 The number of types of data-visualization techniques used by each of the 56 articles that used charts. (Numbers shown inside the pie chart are, respectively, the
frequency count and the percentage of the 56 articles that used charts.).
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Discussion
Principle Findings
From our classication, which we derived solely from observing the nature of the various research questions, we found
that the 62 included articles reected a wide variety of research focuses on different aspects of the COVID-19 pandemic
risk assessment. With the exception of the research question “Availability of Materials to Generate Research Outcomes”,
each research question classication contained at least four class types. Except epidemic data (74.2% or 46/62) in “Types
Table 4 Further Descriptions of the Research Objectives
Research Objective Number of
Articles
Index of Articles
Risk exposure assessments by using risk indicators/indexes or identifying risk factors
For an individual country 11 [22,25,27,33–40]
For multiple countries 5 [28,41–44]
For public health care system/staff 3 [15,45,46]
Related to building usage in conned spaces 1 [47]
Regarding overseas imported COVID-19 on ocean-going ships 1 [48]
For the human risk of infection due to inadvertent ingestion of water during swimming in a river 1 [49]
Total 22
Reviews of the effectiveness of policy measures for control and prevention
Evaluation of the effectiveness of intervention strategies (eg, one-way movement versus unrestricted
movement, frequency of leaving designated work locations for breaks, distance learning in primary and
secondary schools, and so on)
2 [50,51]
Evaluation of the control and prevention policies by government/policymakers 8 [18,24,29,37,52–55]
(Continued)
Figure 9 Breakdown of the 62 articles by research objective. (Numbers shown inside the pie chart are respectively the frequency count and the percentage of the 62
articles.).
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of Variables Used” and time-series plot (61.3% or 38/62) in “Use of Data-Visualization Techniques”, the distribution of
class types was quite diversied, with no distinct class type that was prominent.
The study locations examined by these 62 included articles comprised worldwide coverage. Four class types, based
on the size of the geographic areas studied, were identied: Local areas (32.3% of articles), Global coverage (32.3% of
articles), Specic areas (27.4% of articles), and Regional areas (6.5% of articles). Seven out of the 20 local-area articles
and ve out of the 17 specic-areas articles focuses in China. No other single country has such a high frequency of
appearance. The study locations of the remaining, much larger proportion of articles were scattered across the globe,
either in a single country other than China or in a mix of different countries.
In the era of big data, we are not surprised that as many as six different types of data were used in the 62 articles. As
was suggested by the titles of the articles, epidemic data were the most widely used type of data (in 74.2% of articles),
while environmental data were the least frequently retrieved type of data (9.7% of articles). Articles using a mixture of
types of data (32 articles) did not substantially outnumber those using just one single data type (26 articles), thus
Table 4 (Continued).
Research Objective Number of
Articles
Index of Articles
Review of existing health security capacities (in light of the COVID-19 outbreak) against public health
risks and events
1 [56]
Total 11
Predictions/estimations of COVID-19-related parameters
Number of new infections/growth of infections 2 [21,57]
COVID-19 attributable mortality/risk of death among conrmed cases 2 [58,59]
Future trends of conrmed cases 2 [20,31]
Time lag between peak days of cases and deaths 1 [30]
Probability of occurrence of extreme epidemics 1 [60]
Probability of COVID-19 resurgence caused by work resuming (and schools reopening) 1 [61]
Potential risk associated with releasing travel restriction measures between countries 1 [62]
Total 10
Investigations on the patterns of COVID-19 transmission/geographical spread of COVID-19
Statistical model development to investigate the pattern of transmission/geographical spread of
COVID-19
5 [19,26,63–65]
Study on the geographic risks of the COVID-19 transmission by countries/regions 3 [66–68]
Total 8
Specic focus
Impacts of vaccination/face masks 3 [16,32,69]
Visualization of the risks from the COVID-19 pandemic 3 [17,70,71]
Narrative description of the development of COVID-19, and lessons learnt 3 [72–74]
Performance level of detecting early-warning signs 2 [23,75]
Total 11
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suggesting that fully utilizing the diversity of available types of data might not be a prevalent phenomenon in COVID-19
pandemic research work.
It goes without saying that thorough documentation, such as making one’s research data and codes available to
readers and other researchers, is essential in published papers in order to facilitate the understanding and the
Figure 10 Types of research methodologies used by the 62 included articles. (Numbers shown inside the pie chart are the frequency count and the percentage of the 62
articles, respectively.).
Table 5 Methodology Outlines of the Exploratory Data Analysis, Network Analysis, Time-Series Analysis, and SIR/SEIR Models
Methodology Outline Number of
Articles
Index of
Articles
Exploratory data analysis
Variables in the research datasets were converted into different categorized data, new indicators were
developed using ratios, percentages, and sums, and the results were presented through descriptive tables and
charts
3 [56,66,76]
Graphical trajectory analysis was used to estimate the time lag between peak days of cases and of deaths. 1 [30]
Probability distribution was used to estimate the probability of occurrence of extreme epidemics /COVID-19
resurgence
2 [60,61]
In addition to descriptive tables and charts, other statistical analysis were also used, such as ANOVAs,
regressions, and the like.
2 [39,51]
Total 8
Network analysis
A co-occurrence matrix was constructed of policy-issuing agencies to sketch the network structure, then
a collaborative network was drawn to track the role changes of agencies, and nally an “agency–topic”
network was built to reveal the policy focus of each agency
1 [52]
A dynamic pandemic network was constructed of connections/graphs by linking two geographical areas if the
correlation of changes in the number of conrmed cases was greater than a threshold value
3 [53,62,71]
A multilayer transportation network was constructed with cities as nodes, connected by four means of inter-
city transportation: Air, Bus, Rail, and Sail
1 [27]
Total 5
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Table 5 (Continued).
Methodology Outline Number of
Articles
Index of
Articles
Time series analysis
The autocorrelation at-lag-1 and standard deviations of rolling windows were examined for use in early
warning signal detection
1 [23]
Time correlations between air trafc and COVID-19 transmission and mortality were compared, where time
correlations were performed using Pearson correlation coefcients compatible with the linear relationships
visually observed with scatterplots
1 [69]
An autoregressive integrated moving average model ARIMA(p, d, q) was adopted where p and q were the
order of the AR model and the MA model, respectively, and d was the level of differencing
2 [31,75]
Total 4
SIR/SEIR models
A contact tracing/network method with a SIR (Susceptible, Infected, and Recovered) model was incorporated
to develop an enhanced spatio-SIR model/spatial agent-based SIR model
2 [36,50]
A Susceptible, Exposed, Infectious, Recovered (SEIR) model was constructed and differential equations were
used to obtain the relationships of model parameters
2 [32,54]
The basic SIR model (Susceptible, Infectious, Recovered) was modied by considering more parameters, such
as having been vaccinated, and whether a recovered person was reinfected, and using additional techniques
such as differential equations and the Kendall ranking method
3 [16,20,65]
Total 7
Table 6 Methodology Outline of the Proposed Frameworks/Systems Approach
Proposed Frameworks/Systems Structure Outline Index of
Articles
A novel data-driven framework was created to assess the
a-priori epidemic risk of a geographical area
Risk index was evaluated as a function of Hazard (H), Exposure
(E), and Vulnerability (V)
[22]
A framework was created to model environmental exposure at
the population level
Three stages were used: (1) individual vector elds were
dened, (2) these individual vector elds were accumulated, and
(3) indicators to evaluate the environmental exposure were
proposed
[34]
A COVID-19 risk-based assessment (CRAM) framework was
created for analyzing COVID-19 risk in various geographical
areas
Three steps were identied: (1) GIS layers of various data were
generated, (2) hazard and vulnerability maps were integrated,
and (3) risk mapping for decision making was conducted to
prioritize COVID-19 risk areas
[35]
A three-stage machine-learning strategy was used to classify
country-level risk based on three types of risk: risk of
transmission, risk of mortality, and risk of inability to test
First stage: four risk groups of countries were created, based on
country-level COVID-19 information
Second stage: country-level geopolitical and demographic
attributes were selected for the prediction of three types of risk
Third stage: leave-one-country-out cross-validation was
employed to nd the strongest model for each type of risk
[42]
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Table 6 (Continued).
Proposed Frameworks/Systems Structure Outline Index of
Articles
A decision-making scheme was created to assess the risk of
continuing transmission for African countries
First, a country was assigned to a transmission scenario and the
health system response’s capacity of that country was assessed.
Then, a matrix combining the transmission scenario and health
system’s response capacity was used to estimate the level of risk
[15]
A new privacy-preserving and inclusive system (PanCast) was
created for epidemic risk assessment and notication
The system components included hardware devices, installation
and collection, testing and uploading, and risk notication.
A spatiotemporal epidemic model was used to generate
notication for contact-tracing actions
[38]
A new Multi-Criteria Decision Making (MCDM) method,
AHPSort II-SW, was created to assess internet public opinion
risk levels for public health emergencies
First step: a multistage risk classication model of Internet public
opinion was built to monitor the risk levels of Internet public
opinion for public health emergencies, with long time extensions
Second step: AHPSort II and Swing Weighting (SW) and
a proposed AHPSort II-SW method were combined to grade
the risk levels of Internet public opinion in public health
emergencies
Third step: The new method was applied to the public opinion
risk rating of Microblog platform
[46]
A framework for the COVID-19 risk assessment was created by
incorporating the COVID-19 cases, exposure, immigration
(quarantined data), public health facility, and population density
data
The framework included personal risk and regional risk
assessment. Personal risk was calculated by an equation
consisting of COVID-19 transmission risk, public health risk, and
socioeconomic risk. Regional risk focused on food productivity
and supply chain network in a region
[25]
A framework was generated to dynamically assess the infection
risk on board ships, based on a data-driven approach
First step: ship “stop” events were detected with the ST-
DBSCAN algorithm
Second step: hoteling stops were extracted from detected stops,
based on distances between their locations and land boundaries
Third step: hoteling stops were mapped to their nearest ports
and countries based on AIS data
Fourth step: a COVID-19 exposure index was calculated to
evaluate the risk of a ship being infected by COVID-19
Fifth step: the infection risk of a ship was categorized into high,
middle, and low levels
[48]
Table 7 Methodology Outline of the Special Models/Techniques Approach
Special Models/Techniques Outline of the Special Models/Techniques Index of
Articles
Pandemic Risk Exposure Measurement (PREM)
model
Exploratory factor analysis was used to develop the model, and Cronbach’s
Alpha assessed the model’s reliability
[28,41]
Optimized gravity models and spatiotemporal risk
modelling
Geographically and temporally weighted regressions (GTWR) were used to
build the models, and kernel density estimations (KDE) based on the
Gaussian kernel function were used to spatially smooth the epidemic data
[17]
Multidimensional item response theory, conrmatory
factor analyses, and structural equation modelling
These techniques were used to construct and assess the quality of the
proposed pandemic-risk-perception scale
[33]
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Table 7 (Continued).
Special Models/Techniques Outline of the Special Models/Techniques Index of
Articles
Semi-quantitative risk assessment model The methods of Brainstorm, Literature study and Analytic Hierarchy
Process (AHP) were used for risk factors selection and model construction
A nonparametric statistical method Weighted Rank Sum Ratio (WRSR)
were used for risk level evaluation
[45]
Total Risk Assessment (TRA) evaluation tool and
Infected Patient Ratio (IPR) tool
Seven indicators with a 5-point scale were used for each indicator to
develop TRA scores
The number of conrmed cases resulting from one primary infector were
calculated during the incubation period, to develop IPR values
[29]
ST (seeding time) and DT (doubling time) Model A 2D plane was divided into four quadrants by using the mean ST and mean
DT, with ST on the x-axis and DT on the y-axis to construct the model
Sensitivity analyses were conducted to verify and validate the model
[43]
Conceivable mathematical model - Accelerated Phase
Modelling
The generic framework of the Stockholm Environment Institute (SEI)
Epidemic–Macroeconomic Model was considered in the model
development stage
The least-squares method, nonlinear regression (eg, low-degree
polynomial), derivation (function) method, and the tangent method were
used to obtain the estimated parameters
[63]
Attributable Mortality Model (AttMOMO) A time series regression model on the total number of deaths was
decomposed into those attributable to one infectious disease with no
excess temperature (base model) or other infectious disease circulating,
and those attributable to deaths due to excess temperatures and benign
effects of other infectious diseases
[58]
Static and dynamic risk assessment models The gravity model was used to develop the static model
The Cox proportional hazards framework with a time-varying hazard
function was used to replace the constant parameters of the static model to
build the dynamic model
[18]
Occupant exposure model (EXPOSED) The crowd model was used to develop the model in eight steps, with the
rst step to dene the crowd movement scenarios and the nal step to
calculate global assessment of occupant exposure G
[47]
Rasch model and Bayes’ theorem The online Rasch rating scale model (developed codes available online) was
used to obtain Rasch scores
The Bayes theorem was applied to estimate the adjusted case fatality rate
(CFR) for countries/regions
[67]
Bayesian hierarchical spatiotemporal model Four models were built – one that was space-time separable and three that
were space-time inseparable
The Poisson distribution was used as the likelihood function in the data
model
A multivariable logistic regression was used as the process model
The Markov Chain Monte Carlo method with different initial values was
used to t each model
The Joinpoint Regression Program, which uses the least-squares regression
method, was used to nd the best-t line from the temporal (weekly)
pattern
[19]
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interpretation of one’s research outcomes. Approximately one-third of the 62 articles we reviewed (21 articles) gave clear
instructions for open access to the data and the codes for their studies. Even if counting as open access the seven articles
that offered possible accessibility to their data and codes upon request from the corresponding authors, more than half of
the 62 included articles (34 articles) still did not provide this option. We understand that full transparency of data and
codes may not always be possible, due to competing interests or other sensitivity issues, but it is worthwhile for authors
to consider at least a limited disclosure of their research materials in order to improve the reliability and the appropriate
use of research outcomes by policymakers and healthcare-related professionals.
Use of tables and charts certainly helps explain the process of the research work clearly and effectively to the readers.
From the initial stage of data exploration to the later stage of presenting the research ndings, we saw many tables and
Table 7 (Continued).
Special Models/Techniques Outline of the Special Models/Techniques Index of
Articles
Mixed-effect models The generalized form of linear regression was used to analyze experimental
outcomes of within-subjects (time points) and between-subjects conditions
(pre- and post-visualization exposure)
[70]
Analysis of covariance (ANCOVA) An ANCOVA was used to examine any signicant difference in average
county death rates by one variable, while adjusting for other variables
[68]
TVP-VAR model Dynamic net pairwise dynamic directional connectedness, based on the
TVP-VAR model, was used to construct dynamic contagion indexes across
countries
[64]
Multiplicative exponential model and Spatio-temporal
model
First, a multiplicative exponential model was used to model the effect of
outow on infection
Next, a nonlinear least-squares method (Levenberg–Marquardt algorithm)
was applied to estimate the parameters of the model, with conrmed cases
as the dependent variable
Last, a Cox proportional hazards framework was used to replace the
constant scaling parameter of the model with a time-varying hazard rate
function to develop another model: a spatiotemporal model
[26]
Model-based approach The nonparametric k-nearest-neighbor (kNN) approach was used to
estimate the number of infectious participants
A compound Poisson distribution was used to calculate the effective
number of participants at risk in an event
[21]
Dynamic infection model Well-known statistical physics that was fundamentally different from classic
infectious disease theory was used with seven conventional and physically
reasonable assumptions on rate and distribution of disease infections to
develop the model
[57]
Time-delay distribution from illness onset to
reporting and death
The gamma distribution was used to t the time-delay distribution from
illness onset to reporting
An exponential growth model and lognormal distribution were used to
model the time-delay distribution from onset to death
[59]
GeoDetector model and the decision-tree model A set of statistical methods were used to detect the spatial heterogeneity/
consistency of the spatial distribution patterns between the dependent
variable and the independent variable
The machine-learning method of the decision tree was used to calculate the
exposure risk of infection
[40]
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charts in different forms and types. Approximately 80% of the articles (50 articles) used tables and even more articles (56
articles or 90.3%) used charts, whereas 44 articles (71%) used both. We observed seven different types of charts, and as
expected, time-series plots were the most common type (used by 38 articles or 61.3%) because the data under study were
the time patterns of several waves of COVID-19 transmission. Two special chart types that may not be found commonly
in most other studies are particularly useful for visualizing the dissemination of the COVID-19 pandemic: heat maps
(used by 28 articles or 45.2%), which displayed the severity of the infection by areas, and network graphs (used by eight
articles or 12.9%), which showed the COVID-19 connectedness using straight lines between different places. For the 56
articles using charts, a vast majority (49 articles) used more than one type of chart in order to broaden their visualization
effects.
The presumably hot objective of “risk exposure assessment by using risk indicators” did not draw overwhelming
interest in the 62 included articles. Although it had the largest proportion (22 articles or 35.5%) of articles, the
proportion was smaller than 50%. In addition to risk exposure assessment by using risk indicators, four other research
objectives (each with a greater than 10% proportion of the articles) were as follows: effectiveness of policy measures
(17.7%), prediction/estimation of COVID-19-related parameters (16.1%), patterns of COVID-19 transmission (12.9%),
and specic focuses (17.7%). Given the variety of research objectives in the 62 included articles, policymakers, public
health ofcials, and health-care professionals are urged to rely on the synthesized ndings of this systematic review to
meet various purposes, such as evaluating the effectiveness of current public health measures, making informed
decisions on policies for prevention and control, clinical practices and further research
77
for early detection of an
outbreak, better preparation, and burden reduction on public health systems in the event of new waves of infectious
disease transmission.
No research methodology was dominant in the 62 articles – in contrast, many different methods were employed, as
shown in Figure 10. One common observation was that, no matter which methodology was employed (except for the six
articles using either a narrative description or context analysis), most articles applied inferential statistics analyses such as
factor analyses, time-series analyses, regressions, Bayesian inferences, and the like, to generate their research outcomes.
Their process ows were clearly outlined, and their research methodologies were well documented. Such thorough
documentation denitely increases the credibility of articles,
78
giving full knowledge of what has already been done,
79
and facilitating others’ ability to replicate research outcomes, with high condence for the appropriate use by interested
parties.
Systematic Review
For policymakers having an interest in topics, which requires reviewing lots of primary papers and articles in
a standardized manner, we suggest the following ve key stages. First of all, setting up the objectives by clearly pre-
dening specic research questions in the context of what are already known. Second, identifying an explicit and
reproducible methodology describing eligibility criteria and search strategy for nding relevant research and collecting
data. Third, specifying the methods used to assess the validity of the selected information such that they meet the
eligibility criteria and how to identify potential risk of bias such as selection bias on target population, performance bias
on treatments and reporting bias on result ndings. Fourth, providing pre-planned methodological and analytical
approach on how to analyze quantitative data and synthesize qualitative evidence. Lastly, describing how to interpret
the results, summarize the ndings and recommend actionable plans.
Limitations and Future Research
Some limitations apply to this systematic review. First, it is possible that some relevant studies could not be included.
Even though we used a comprehensive and highly relevant source, WHO COVID-19 research database, there may still be
chances that some relevant articles were not captured. Another possibility for missing some relevant articles is that
certain articles were screened out by the exclusion criteria such as non-English language articles and articles with no
open/free access. In addition, the number of included articles in this systematic review might be considered to be not
sufcient because of the limited number of articles that met the eligibility criteria. Unlike survey sampling, there is no
universal measurement to determine the appropriate size for systematic review. When retrieving published studies of
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systematic review, it is common to nd that the size of the nal list is usually less than 100, some may even be less than
30. So, we believe that this limitation does not affect the validity of our ndings.
Future research should continue to track the latest development of COVID-19 as it progresses. Two new variants
(Omicron and Arcturus) have emerged after the date of searching relevant studies for this systematic review. In addition
to capturing more recent relevant articles that studied the new waves of transmission, a critical appraisal tool should be
developed in order to assess the quality of the included articles from different assessment criteria such as study design,
statistical analysis, and outcomes. This helps to quantify the strengths and weaknesses of the included articles and hence
facilitate more in-depth discussion and better interpretation of the ndings.
Conclusions
The impact of the COVID-19 pandemic has been enormous, presenting unprecedented challenges to public health.
Therefore, researchers conduct risk assessment based on available data and methods to identify risk factors and/or study
their effects and consequences. Although we have entered the post-pandemic period for COVID-19, history tells us that
we should continue to stay vigilant against both the emergence of a new variant of COVID-19 and also of a new
infectious disease.
This systematic review gathered relevant research works about the global COVID-19 pandemic risk assessment by
conducting an extensive systematic search in the WHO COVID-19 Research Database, and we here provide useful
synthesized ndings of what has been done to evaluate the COVID-19 pandemic risks. Policymakers and those who are
responsible for public health can refer to our detailed summary of the various research objectives, which we have
classied in this systematic review, and can learn from one or more of them depending on the priorities of their country.
This information can support informed decisions and plans for informed actions to analyze and monitor the spread of
new infectious diseases that are likely to arise in the future.
Abbreviations
PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; WHO, World Health Organization.
Data Sharing Statement
The authors conrm that all articles identied through searching WHO database: Global research on coronavirus disease
(https://search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/) from its inception to 12 July 2022 and all
screened articles are freely available to public at the time of writing by accessing the website (assessed on 20 July 2022).
Acknowledgments
This work was supported by the Research Impact Case Grant from the Department of Social Sciences and Policy Studies,
The Education University of Hong Kong, and The Hong Kong University of Science and Technology research grant
“Risk Analytics and Applications” (grant number SBMDF21BM07). The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Disclosure
The authors report no conicts of interest in this work.
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