type = Research/Review Article, doi = s41095-0xx-xxxx-x, title = A Visual
Modeling Method for Spatiotemporal and Multidimensional Features in
Epidemiological Analysis: Applied COVID-19 Aggregated Datasets, author = Yu
Dong1, Christy Jie Liang1*, Yi Chen2*, and Jie Hua1, runauthor = Yu Dong,
Christy Jie Liang, Yi Chen, and Jie Hua, abstract = The visual modeling method
enables flexible interactions with rich graphical depictions of data and
supports the exploration of the complexities of epidemiological analysis.
However, most epidemiology visualizations do not support the combined analysis
of objective factors that might influence the transmission situation,
resulting in a lack of quantitative and qualitative evidence. To address this
issue, we have developed a portrait-based visual modeling method called
+msRNAer. This method considers the spatiotemporal features of virus
transmission patterns and the multidimensional features of objective risk
factors in communities, enabling portrait-based exploration and comparison in
epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related
datasets in New South Wales, Australia, which combined COVID-19 case number
trends, geo-information, intervention events, and expert-supervised risk
factors extracted from LGA-based censuses. We perfected the +msRNAer workflow
with collaborative views and evaluated its feasibility, effectiveness, and
usefulness through one user study and three subject-driven case studies.
Positive feedback from experts indicates that +msRNAer provides a general
understanding of analyzing comprehension that not only compares relationships
between cases in time-varying and risk factors through portraits but also
supports navigation in fundamental geographical, timeline, and other factor
comparisons. By adopting interactions, experts discovered functional and
practical implications for potential patterns of long-standing community
factors against the vulnerability faced by the pandemic. Experts confirmed
that +msRNAer is expected to deliver visual modeling benefits with
spatiotemporal and multidimensional features in other epidemiological analysis
scenarios. , keywords = Visual Modeling, Epidemiological Analysis,
Spatiotemporal, Multidimensional, COVID-19, copyright = The Author(s),

$1\quad$ |  Yu Dong, Christy Jie Liang and Jie Hua, School of Computer Science, University of Technology Sydney, Sydney, NSW 2007, Australia. E-mail<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
---|---
$2\quad$ |  Yi Chen, Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China. E-mail<EMAIL_ADDRESS>

##  1 Introduction

A growing emphasis on the need for informatics and analytics in public health
over the past 30 years has led to an increasing amount of investment cost in
information systems [23, 24]. Visual modelings in informatics raised
significant roles in big data analysis tasks in public health, especially in
epidemiological analysis as connections tighten up with people. However, many
research challenges and gaps still need to be filled, although many new tools
and algorithms have already been developed to aid experts in analyzing and
visualizing the complex data used in epidemiological analysis [25]. As Lauren
et al. [24] highlighted in a survey, complex epidemiological exploration
requires novel visualization tools, and most of the existing visualizations
applied in analysis tasks suffer from limited adoption. More and more standard
visualizations in epidemiology evolve into visual modeling which aims for
combined multidimensional features such as considerable complexity, dynamism,
and uncertainty analytical tasks rather than simply spatiotemporal features.
[20, 22].

As keywords in epidemiology in the past three years, the global spread of
COVID-19 has overwhelmed health systems and caused widespread social and
economic disruption [42]. Because of the virus’s high transmission ability,
the responses of individual countries and communities to the unfolding
pandemic will be a watershed moment in human history. Although a substantial
body of work has been translated into COVID-19 data visualizations globally,
there hasn’t been much prior work in visualization tools to conduct an
analysis based on the characteristics of communities and the risk factors of
COVID-19. Such intricate analysis raises significant problems related to
pandemic-related uncertainty that must be addressed [41].

To meet these challenges, we optimized collaborative approaches to problem-
solving using data analytics and subject matter expertise. We conducted
interviews with domain experts in health and epidemiology to gather
requirements for further analysis. With the assistance of experts, a visual
modeling method, namely +msRNAer, was developed for spatiotemporal and
multidimensional features in the majority of epidemiological analysis tasks.
This visual modeling method adopts a portrait design inspired by viral anatomy
that can be applied for visualizing each community-based location, allowing
for exploration and comparison of the complex relationships among the
communities’ time-varying case numbers, objective risk factors that may affect
pandemic, and epidemiological data patterns in fundamental geographic,
timeline, and multidimensional visual designs. This creates a vivid
understanding of how objective risk factors are interconnected and contributes
more broadly to building resilient communities, particularly in light of the
effects of pandemic transmission.

To validate the usability of +msRNAer, we collaborated with Australian
Government experts to apply COVID-19 aggregated datasets with processes of (1)
extracted evidence by each local government area (LGA) from the completed 2
years in 742 days (from January 1st, 2020, to January 11th, 2022) of COVID-19
case data compressed to 106 weeks or 53 fortnights for scalability; (2) added
a marked timeline with intervention events extracted by NLP; (3) connected the
most recent census community profiles to each LGA; combining case data with
LGA and postal area geo-locations in New South Wales (NSW), Australia. We then
identified risk factors related to LGAs under expert supervision, including
demographic indicators (e.g., higher-risk populations), social indicators
(e.g., relationships), economic indicators (e.g., rental and mortgage
affordability), infrastructure indicators (e.g., housing), and resident travel
behavior (e.g., using public transportation).

We adjusted the visual designs to include multiple views and interactions,
which facilitated visual exploration in COVID-19-related community portraits.
This was supported by an interactive Control Panel that included event
timelines, a coordinated geographic view, and a multidimensional coordinates
view with a filtering function. Our aim was not only to demonstrate the
effectiveness and scalability of +msRNAer’s visual modeling but also to assist
the government in investigating pre-existing community factors and discovering
practical implications for potential patterns related to vulnerabilities
against COVID-19. We conducted one user study and three subject-driven case
studies using our COVID-19 aggregated datasets. These studies demonstrated how
+msRNAer worked with a prototype among NSW LGAs and postal areas with
spatiotemporal and multidimensional features. In the user study, we not only
pre-evaluated the feasibility and effectiveness of the +msRNAer prototype but
also made iterative improvements to its functions. Furthermore, the three case
studies provided a selected, high-level picture of community resilience in
infrastructure and explored the dimensions of resilience. We evaluated the
COVID-19 exploration results and conducted interviews with domain experts to
collect their feedback for future research.

The paper is structured as follows. Section 2 reviews the related work. We
first discuss the design requirements in Section 3. Based on the extracted
requirements for experts, we elaborate the visual modeling with metaphor and
design in Section 4. We list the data preparation and the prototype
application with multiple views and interactions in Section 5. We conduct a
user study in Section 6 to pre-evaluate the feasibility and effectiveness of
our visual modeling. We further validate the capability of our visual modeling
with applied COVID-19-related case studies in section 7 and interviews with
domain experts in Section 8. Finally, we consult the limitations and future
work in section 9 and make conclusions in Section 10.

##  2 Related Work

In this section, we introduce related works concerning the visualization
perspectives in epidemiological analysis research and accordingly, take into
account how visualization aids COVID-19 spread analysis as specific examples
with this background.

###  2.1 Visualization in Epidemiological Analysis

As early as 2020, a systemic review of COVID-19 epidemiology [11] proved high-
spreading speed when the epidemic broke out. Since then, an increasing number
of visual representations were presented to aid COVID-19 analysis. A novel
study [38] on computational modeling used visualization-centric and algorithm-
assisted for epidemiological modeling that has proved visualization plays a
critical role in epidemiological analysis in 2022. Another ongoing
collaboration [71] between epidemiological modelers and visualization
researchers summarized the concurrent challenges and solutions. They listed
common visualization charts such as heat maps [37, 35] and timelines [36], and
further composite graphics with small multiple views which could support
epidemiological modeling. Wei et al. [32] surveyed and categorized geographic
visual display techniques in epidemiology research into two categories of
Traditional Cartography and Geo-visualization.

From distinct angles of data analysis, certain risk features [26, 33] may
influence the analysis in epidemiology and make the analysis tasks more
challenging. As Chui et al. [34] added human factors (age, gender, etc.) into
the study of infectious diseases in the paper, further analysis of
visualization in epidemiology is beneficial, and it improves the precision of
algorithms like modeling and prediction [31]. According to our classification,
there are two perspectives for visualization in epidemiological analysis
depending on the type of data used: spatiotemporal-based and multidimensional-
based.

Pandemics are geographical in nature, and constitute spatiotemporal phenomena
across large ranges of scales [86]. Improved geographic visualization plays an
important role in pandemic research, that offers an environment to represent
multivariate data by cartographic means, based on its geographical information
effectively and attractively [81, 82, 84], and it is one of the top ten
keywords of IEEE VIS [85] (top conference in visualization field); also 16% of
existing related visualization works adapt maps [80].

Multidimensional-based data analysis makes epidemiological analysis possible
to connect to other factors. The Singapore epidemiology of eye diseases
research is a population-based study where 8,697 adults of Malay, Indian, and
Chinese ethnicity [27]. Steinger et al. [28] used generalized linear models to
investigate the influence of key epidemiological factors on potato virus
infection risk. A prototype has been deployed to demonstrate the impact of
social distancing strategies during the H1N1 (swine flu) outbreak by
Maciejewski et al. [74]. Trajkova et al. analyze relevant Twitter data and
discuss facilitating data interpretation via visualization to avoid the spread
of misconceptions and confusion on social media [72]. Also, multidimensional
data visualization such as Parallel coordinates is commonly employed for
visualizing multidimensional geometry [87, 29, 88, 30]. They could apply
visualization research on multidimensional attributes during the pandemic, to
promote the understanding of how data entries compare to each other.

###  2.2 Visualization in COVID-19 Datasets

Since the COVID-19 pandemic’s initial outbreak, an astounding number of visual
representations or models have been created to reflect the virus’s global
spread and the effects it has had on various nations and regions [44, 39, 45,
47, 37]. A survey study [42] conducted 668 COVID-19 data visualizations to map
the landscape of existing visual works. Another novel research [46] focused on
investigating complex interplay based on COVID-19 datasets between design
goals, tools and technologies, data information, emerging crisis contexts, and
public engagement by a qualitative interview study among dashboard creators.
It could be summarized as two types of data sources in common COVID-19
research: directly-linked data such as infected cases, recovery, and mortality
rates [1, 57], and indirectly-linked data, which contains community
information such as [58], financial impacts [59, 60], etc., which are not
linked to the pandemic directly as objective factors.

In common COVID-19 visualization research, basic techniques include
traditional line charts, bar charts, maps, etc. Kahn’s report reveals that 38%
of related works apply line/area charts, and bar charts take a 29% share [80].
We have collected 48 existing related research works to date in Australia; 10
of them deliver a similar dashboard view, and others either offer graphs or
are still ongoing works; the University of Melbourne conducts an online
prototype that gives a 10-day forecast [79]. Seven projects import data from
aspects such as financial, and LGA details other than only the pandemic case
details. Most works apply traditional bar, stacked bar, line, map, etc.
visualization methods.

In our approach, visual modeling in COVID-19 datasets is separated into three
aspects: data visualization, visual interactions, and intelligent-assistance
visual analytics.

From the first aspect, raw data collected during the pandemic are applied as
inputs to generate graphs. An interactive web-based dashboard [8] to track
COVID-19 in real time was first presented by CSSE at Johns Hopkins University.
It is followed by an online global dashboard by the WHO [6] to show COVID-19
statuses around the globe. Hannah et al. [14] built 207 country profiles with
aggregated cases, testings, vaccinations and etc which allow users to explore
the detailed statistics on the COVID-19 pandemic. In Australia, the State and
Territory governments assist the public with recognizing current statuses by
visual dashboards [57, 61, 62, 40].

From the second aspect, visual modeling methods can combine indirectly linked
data and offer deeper insights by interactively integrating multiple
attributes. Dashboards with adjustable parameters are a common feature from
this perspective; they combine computational analysis techniques with
interactive visualizations [63, 64, 65, 66, 67, 68, 69, 43], which also
emphasize analytical reasoning concerning the pandemic data and other facts
that may affect infection cases or get affected by the pandemic through
interaction techniques. Carson et al. [16] presented a big data visualization
and visual prototype for analyzing COVID-19 epidemiological data. Besides,
more and more research has included objective factors in the COVID-19
analysis. Lan et al. [83] review the usefulness of GIS-based dashboards for
mapping COVID-19 prevalence and propose improved geo-visualization techniques
to incorporate the temporal component in interactive animated maps to analyze
pandemic transmission with COVID-19-related information. Wu et al. develop a
novel Joint Classification and Segmentation (JCS) system to perform real-time
and explainable chest CT diagnosis of COVID-19 infections [70]. Muto et al.
import more facts about gender, age, marriage status, poverty, and
drinking/smoking habits into a matrix to address Japanese citizens’ behavioral
changes and preparedness against the outbreak [75].

From the third aspect, intelligent techniques are introduced to assist in the
exploration and investigation of the COVID-19 pandemic. It takes into account
more relevant yet not directly-linked complex data which includes modeling
[48, 50, 56, 55], predicting [52, 51, 54, 53, 31], and other complex
exploration strategies [49, 19] with AI algorithms [10]. Reinert et al.
developed a framework that enables effective and efficient visual exploration
through interactive, human-guided analytical environments during the pandemic
[76]. Shehzad et al. purposed a decision-making environment [17] for person-
to-person contact modeling in the COVID-19 pandemic, which was based on their
previous works [18, 74] in epidemiology. Bowe et al.’s research indicates that
the pandemic plays out differently across different scales; it is related to
the global supply chain, local dynamics, neighborhood mutual aid networks, and
personal geographies of mitigation and care [77]. Preim and Lawonn describe
visual analytical solutions aiming to provide preventive measures. Prevention
aims at advocating behavior and policy changes likely to improve human health
[78]. Another research proposes a prediction of pandemic viral attack and how
far it is expanding globally by Roy et al. [31]. Guo’s system discovers
spatial interaction patterns, providing valuable insight into designing more
effective pandemic mitigation strategies and supporting visual exploration in
time‐critical situations. An approach by Christopher [15] was conveyed through
visual exploration by similarity comparison and predictions. Yu et al. [19]
provided a user-centered visual explorer that applied COVID-19 datasets for
in-process exploring and comparing spatiotemporal features in portrait-based
perspectives.

##  3 Design Requirements

In 2022, a survey conducted by Jason et al. [71] summarized the challenges,
solutions, reflections, and recommendations of visualization for
epidemiological modeling. The authors categorized the supporting visual
modeling in epidemiological analysis into three stages based on different time
scales:

The initial stage involves the quick application of candidate templates with
visualization tools to establish problems. Data is transferred to preset
combinations of views for simple comparison.

In the short-mid term stage, ongoing research provides a redesign of the
visualization prototype for iteratively redefining the problem, exploring
potential patterns, and providing users insight into complex tasks.

In the long-term stage, a more stable visual system is developed for
widespread application to common usage scenarios, as demonstrated through
multiple cases in epidemiological analysis.

This summary underscores the motivation for our proposed visual modeling
method in epidemiology to fulfill multiple objectives. The visual modeling
method for epidemiological analysis should not only enable quick responses to
basic information trends of a pandemic, such as the infection cases in time-
varying trends but also facilitate the discovery of knowledge concerning
combined analysis tasks, such as identifying the factors that affect infection
cases. Finally, the visual modeling method must be applied to a completed
visual system and validated using real-world cases.

In consultation with experts from the epidemiology and health domains, we
outlined the following user requirements: Experts required information to
assess the profile of communities in terms of their resilience to virus
attacks. Combined with the inspection of the number of infected cases, a few
key factors might affect and help understand the impacts of different pandemic
phases occurring concurrently. They must assess the impact of the government
interventions and measure the resulting pandemic situations in order to
investigate both the community profile and the infection cases concurrently.
They needed to investigate the link between community factors and the number
of infection cases caused by outbreaks, intervention events, and responses.
Usability studies conducted with early existing prototypes identified a
variety of requirements. The desired features, which we distilled into three
progressive categories of peer-to-peer design requirements for our approach
from aspects of visual design, visual analytics, and modeling prototype
application during several design iterations with subject matter expertise.

R1. Provide comparative visual portrait design of the numerical distribution
of consecutively transmitted cases for each community: To perform effective
epidemiological analysis, it is essential to describe consecutive infection
cases in terms of timelines and locations. This involves investigating the
geographic and temporal trends of the epidemic’s spread, along with
qualitative analyses utilizing spatiotemporal features. By providing
comparative visualization portraits of these aspects for each community, we
could better understand the patterns and trends of transmission.

R2. Offer visual exploration for analyzing transmission patterns with
spatiotemporal and multidimensional features among each community: Epidemic
outbreaks are often related to various objective factors in specific
locations, such as socioeconomic or cultural factors. Therefore, it is crucial
to support multidimensional feature exploration, including humanities and
finance, among other fields, in addition to visualizing the spreading
situation with spatiotemporal features. By combining both perspectives in
interactive portraits of location-based risk factors and infection cases over
time, we can provide a more comprehensive understanding of transmission
patterns.

R3. Verify the effectiveness of visual modeling through a prototype system
using actual epidemiological cases: To demonstrate the effectiveness of our
visual modeling approach, the prototype system should offer multiple visual
views with robust interactive functions, such as collaborative filtering and
comparison. By applying this system to real user studies and case studies in
epidemiological tasks, we can show how our approach can help researchers gain
insights and make informed decisions in the field.

##  4 Visual Modeling

To address the design requirements, this section presents our design rationale
through visualization, design considerations, and guidelines for the
components.

###  4.1 Design Metaphor

Figure 1: The cross-sectional simulation of SARS-related coronavirus with its
main components.

Inspired by viral anatomy, our visual design primarily adopts the 2D genome
structure of SARS-related coronavirus particles. Coronaviruses, named for
their ”crown-like” shape observed in the electron microscope, have particles
packed with shells. This illustration became familiar to the public, as shown
in Figure 1, and is widely introduced by the news and media. The coronavirus
particles are organized with +sRNA (positive single-stranded RNA) polymers
packed inside, further surrounded by outer inserted proteins. These outer
proteins derive from the cells in which the virus is last assembled but are
modified to contain specific viral proteins, including the Spike (S), Membrane
(M), and Envelope (E) Proteins. The S Protein allows viruses to enter and
infect other cells. After the virus enters the host cell, the genome was
transcribed, and replication takes place involving coordinated processes of
RNA synthesis. Positive Multiple Strands of RNA Encoder, abbreviated as
”+msRNAer”, is the name of our proposed visual modeling method. Inspired by
this simulation and combined with our previous research [73, 19], we map
different parts of the virus into multiple meanings and design +msRNAer to
apply the aggregated datasets of its inner and outer parts.

###  4.2 Visual Design

We depict a novel visualization that leverages the biological components of
the coronavirus as metaphors to represent and compare communities’
epidemiological characteristics. Specifically, we define the particle symbol
for each community, which we call a ”Portrait” and designed the outer Proteins
and inside RNAs to encode information related to cases (such as actual cases,
cases per 10k/100k population) and transmission trends and each community’s
unique risk factors respectively. Figure 2 is the detailed design for all
components. Notably, we have varied the RNA from positive single strand to
positive multiple strands to capture multiple key risk variables related to
community characteristics. This design choice provides multidimensional
insight into the epidemiological patterns of each community and aimed to
fulfill R1.

Figure 2: Two types of visual portrait and their implementation processes: (e)
Portrait with S and M Proteins, and (f) Portrait with E Proteins. Both
Portrait (e) and (f) include 4 strands of RNAs. The red arrow path represents
the process of (a) combining bars on the timeline and encoding them as S and M
Proteins, while the light gray arrow path represents the portrait with (b) E
Protein in grayscale, depicting different phases of the timeline.

As domain experts suggested, the number of infection cases caused by the
pandemic is related to multiple potential factors among communities, for
example, the population and their percentage in higher-risk groups, including
the aged, lower-income, and lone-person groups. As a result, we could
emphasize community portraits based on the characteristics of the vulnerable
population in high-risk areas. Hence, we assemble all visual elements as the
portrait for each community, as shown in Figure 2. This single aggregated
crown-like portrait describes both the community’s existing characteristics
and also how the community reflects viral infection case numbers information.
The visual portrait consists of three components: the crown, outer designs
with Proteins, and inner designs with RNAs. The circle of a crown represents
one unit of the whole timeline. On the outside, we use multi-segments as time
spans, with S Protein representing case numbers information and M Protein
representing zero cases, and multiple RNAs representing the highlights of
selected factors in higher risk groups inside the crown. The E Protein, unlike
the S and M Proteins embedded on the crown, can represent significant
intervention events rather than case numbers.

Crown Design: We define the central core with radius $R_{c}$ of the Crown to
label the community $C$, as shown in Figure 2(d). The circular loop
surrounding the Crown easily depicts the entire timeline of any transmission
taking place. The circle is evenly divided into continuous segments matching
time spans for S or M Proteins clockwise from the top point to the looped end.
Or, on the other hand, we use E Proteins inserted to denote the categorized
intervention events within the timeline colored in grayscale.

Outer Design: The outer-growing S Proteins are densely packed together. For
each community over each time span, we encode distinct bars with rounded
corners, with their heights representing the case number shown in Figure 2(a).
We use the smallest-sized M Proteins to indicate zero cases in a certain time
span. The S or M Protein height of time span $x$ is calculated as follows:

$H_{timespan_{x}}=\left\\{\begin{array}[]{ll}h+{R^{\prime}_{c}}*(a+\ln
f(x))*b&,f(x)\in N^{*}\\\ h&,f(x)=0\end{array}\right.$ (1)

A base height $h$ appears when there are 0 cases in one time span, and a
growth height is proportional to the number of non-zero infection cases in a
time span $x$, in which $f(x)$ is the function of infection cases
corresponding to this time span. Both $a$ and $b$ are customized parameters,
and $R^{\prime}_{c}$ refers to the initial radius of the Crown.

Inner Design: Although viruses normally only carry one strand of RNA, it is
not ideal to employ only one strand to represent several important elements.
We suggest splitting the RNA strand into numerous strands to show these
parameters in order to conserve space and reduce unnecessary requirements.
Thus, we propose to distribute the four RNA strands across three channels
rather than joining them head-to-tail. Each element’s maximum value is set to
occupy half of a channel when multiple data elements of the same type must be
represented, such as visualizing the numbers of males and females (exclusive
of transgender people). These two can then be combined and allocated to a
single channel. Other whole channels may be assigned to data of a single type.
Figure 2(c) demonstrates the potential of our visual design to share one
channel with two RNAs. We implement three channels of RNA that grow from the
middle point of the circle. Combining two related factors in one shared
channel accommodates all four RNAs placed in three-oriented channels within
the Crown.

To fulfill the visual design, we employ a visual metaphor of spiral genomes
with four cosine wave-shaped RNAs with the same amplitude in three channels.
As shown in Figure 2, the length of RNA is encoded by the exact values (e) of
its categories.

To denote the length of RNA $L_{ij}$ in the arc, we define:

$L_{ij}=\frac{{R^{\prime}_{c}}-{R_{c}}}{m}*({\frac{N_{ij}}{max\\{N_{i}\\}}*\frac{2\pi}{n}+\frac{\theta}{n}})$
(2)

I.e., the arc angle $\Theta_{ij}$ corresponding to the arc length $L_{ij}$ is:

$\Theta_{ij}=\frac{N_{ij}}{max\\{N_{i}\\}}*\frac{2\pi}{n}+\frac{\theta}{n}$
(3)

Where $N_{i}$ represents the set of data from independent variable RNA
category $i$, then ${max\\{N_{i}\\}}$ denotes the maximum value in $N_{i}$.
$N_{ij}$ refers to the value of data from an independent variable community
$j$ in an independent variable RNA category $i$. Parameter
$\frac{{R^{\prime}_{c}}-{R_{c}}}{m}$ represents the channel radius of the
current RNA category $i$ located with parameter $m\in\\{1,2,3\\}$. Parameter
$\theta$ is the minimum arc angle in RNAs, maintaining RNA even if the value
of $N_{ij}$ is tiny. Parameter $n\in\\{1,2\\}$ indicates whether the maximum
length of RNAs occupies a full channel or a half channel.

Further define the cosine wave function $F_{ij}$ for drawing each RNA as:

$F_{ij}=\frac{{R^{\prime}_{c}}-{R_{c}}}{3}*|\cos(\Theta_{ij}*\frac{N_{ij}}{sum\\{N_{i}\\}})|+\frac{{R^{\prime}_{c}}-{R_{c}}}{m}$
(4)

The parameter in equation (4) applies $\frac{{R^{\prime}_{c}}-{R_{c}}}{3}$ as
amplitude, ${sum\\{N_{i}\\}}$ as the sum value of data from all communities in
RNA category $i$, then $\frac{N_{ij}}{sum\\{N_{i}\\}}$ represents the
frequency in cosine waves, which can be compared within the same RNA category
among different communities.

As an illustration, the RNA for selected factor $income$ from community
$Sydney$ should be mapped when allocated in the second channel:
$F_{income,Sydney}=\frac{{R^{\prime}_{c}}-{R_{c}}}{3}*|\cos(\Theta_{income,Sydney}*\frac{N_{income,Sydney}}{sum\\{N_{income}\\}})|+\frac{{R^{\prime}_{c}}-{R_{c}}}{2}$.

Filter Trigger Design: All the portraits are needed to motivate a filter
trigger. We reset the Crown and created a sample portrait with E Proteins for
filtering purposes, which can be used interactively to attach events to the
timeline. On the outer circle, the grays on the circle indicate different
events by timeline. In the inner Crown, three full-circled values of RNAs, as
shown in Figure 2(f) are the indicators for selected factor groups. During
exploration, we intend to interact with the visualization by interacting with
all these elements in Control Panel.

Design for All Colors: Two sets of color scales for Control Panel and Portrait
View have been used in our visual design. To raise awareness of the threat, we
encode bright red for the S Protein design, encoding the infection case
numbers, and light grey for the M Protein, indicating there were no cases this
week. Inside the Crown of the portrait, inner color scales are used to
representatively paint community portraits on RNAs, which include azure blue,
mint pink, gold yellow, and pale purple. In Control Panel, a pre-defined
grayscale is designed for E Protein to differentiate different types of
intervention events along the timeline, initially with normal gray, silver
gray, and dark gray. The darker shade of gray indicates a higher level of
restriction for the events. For interaction, charcoal gray is used in both
Control Panel and the portrait design for selection interaction.

##  5 Data Preparation and Prototype Application

We further developed a web-based visual prototype based on +msRNAer, which
aimed to assist in investigating the pre-existing community factors and
discovering practical implications for potential patterns of established
community characters against the vulnerability faced by epidemiological
analysis.

Although +msRNAer should be suitable for most epidemiological analyses with
community factors, we introduced the aggregated COVID-19 datasets in NSW as
hot issues from epidemiological analysis to better present the design ideas in
the previous section. We then applied +msRNAer to the prototype implementation
by each view and introduced the interaction designs in this application.

Figure 3: The entire workflow of the visual modeling method +msRNAer applied
to NSW COVID-19 aggregated datasets, from raw data collection to prototype
system which embodies one completed pathway. It consists of two types of
arrows, where the tangerine color connects the human-participated actions and
the cyan color means the data flow direction. The pathway starts from (A) data
collection and processing, (B) dealing with datasets summarized COVID-19
cases, GIS information, LGA-based censuses under experts’ supervision, and
intervention events extracted by NLP, and assembling the aggregated datasets
for application in +msRNAer; (C) the prototype interface with (E) Portrait
View depicts four high-risk factors and COVID-19 cases corresponding to the
selected timeline in (D) Control Panel and interacts with (F) and (G); (F) the
GIS View with highlighted polygons of LGAs and postal areas shows their
COVID-19 distribution; (G) the MDC View is a high-level overview of other risk
factors from the census indicators. This +msRNAer prototype is finally
completed by a conducted user study by adding a search function (H) in GIS
View.

###  5.1 Data Sources

We intended to investigate whether there had any pattern or potential
relationship between NSW COVID-19 cases and demographics, geo-information,
infrastructure information, or other factors in the census fields, as
clarified by the requirement of analyzing COVID-19 situations. Multiple
datasets were prepared to aggregate for demonstrating the effectiveness of our
approach, which includes COVID-19 case data with event timelines and selected
columns of NSW census data.

The COVID-19 case data in NSW was collected by the government, and the
pandemic data program led by the Data Analytics Centre (DAC) provides digital
information to improve the coordination of the government’s COVID-19 response.
The datasets provided applicable information about infection cases based on
the location of usual residence since the first infection case; they excluded
189 cases in crew members who tested positive while onboard a ship docked in
NSW at the time of diagnosis; and case aspects include confirmed, tested,
recovered, and death by their notification date, location, age-range, and
likely source of infection. Some of them were no longer released due to
privacy. Plus, Geographic Information System (GIS) map data and event
information were respectively extracted from media releases [2] and NSW
Property Web Service [3] authorized by NSW government websites.

We decided to identify the high-risk factors that may aid in COVID-19
infection from the census data, which involves millions of people and
households and is conducted by the Australian Bureau of Statistics (ABS) every
four years. The data provided a rich snapshot of the nation and informs the
government, communities, and businesses; it contained essential concepts such
as populations, rents, mortgages, incomes, religion, languages, housing, and
more. Besides, based on the definition from ABS, a lone person is classified
as the only person aged 15 or over who lives in a private dwelling. Also,
people in NSW earning more than 50% but less than 80% of the NSW or Sydney
median income are described as earning a lower-income [7]. The next wave of
census data has been released in stages since June 2022, but detailed
information will not be released until mid-2023. Thus, the latest census data
from 2016 was applied in this paper.

###  5.2 Variables Consideration

The COVID-19 case dataset delivers every case recorded by NSW Health and
contains multiple attributes concerning cases by notification date and
postcode, local health district, LGA, and likely source of infection.
Considering the risk of leaking information that could directly identify
individuals, only personal age, gender, and the location of their usual
residence about infection cases are included in this dataset, which is
assessed to measure the risk of identifying an individual and to measure the
information gained if it is known that an individual is in the dataset. LGA is
an official spatial unit that contains multiple postal areas that represent
the whole geographical area, and there are 128 LGAs in NSW in Australia
(Bayside Council was formed on September 9th, 2016 after the 2016 census, and
relevant LGA data is merged from the City of Botany Bay and the Rockdale City
Councils). Moreover, as data journalism may affect the COVID-19 pandemic [21],
relevant news articles, alerts, and ministerial media releases issued by the
NSW government about COVID-19 are attached as events to combine with the
notified date of infection cases.

The timeline selection was intercepted during the COVID-19 pandemic, from
January 2020 to January 2022. We used NLP [9] (Natural Language Processing) to
extract textual information from media data and considered marking the
timeline related to keywords in interventions and social restrictions as three
types of phases: uncontrolled, eased (e.g., keeping social distance, masks
required), and restrict controlled (e.g., curfew, bubble restriction,
lockdown); and the marked timeline was attached to the COVID-19 cases dataset
for two data periods: The long period used 53 fortnights of COVID-19 case data
as a biweekly time span grouped by 106 weeks of data from January 1st, 2020,
to January 11th, 2022; the short period was listed 53 fortnights of case data
as a weekly time span from January 1st, 2020, to January 5th, 2021. Further
imported JSON files combined a single LGA layer for the long period and an LGA
layer with postal areas for the short period of COVID-19 case data.

We further consulted domain experts and selected the four top key factors from
a suite of factors by their supervision that may cause infection in their
communities (LGAs and postal areas) as directed: aged males and females (70+),
lower income groups, and living alone groups. Experts extended four additional
categories of indicators from censuses that may affect the COVID-19 pandemic:
LGA-based demographic indicators, social indicators, economic indicators,
infrastructure indicators, and resident travel behavior. Each category
contained over 30 indicators and descriptions. Some indicators contained
complex hybrid patterns that may influence transmission. For instance, apart
from living alone, the indicator of household is defined as different from a
family, which refers to at least one person over the age of 15 who lives in
the same private dwelling. In this situation, it is necessary to deliberate
details on the comprehension of demographic, social, and economic indicators.
We extracted the data on median age, population, and area size from LGA
demographic information and further calculated population density; median rent
expense, median mortgage, median personal income, median family income, and
median household income from social and economic indicators; the average
bedroom per person and the average bedroom size per household as dwelling
factors from infrastructure indicator; and the public transportation rate for
traveling from resident travel behavior.

Finally, we finalized 96460 rows of COVID-19 case data up until January 11th,
2022, and aggregated both datasets with intervention events and spatiotemporal
features as well as simplified objective factors from the NSW census data
tables weighing 449 MB.

###  5.3 Prototype Implementation

The prototype was implemented to meet requirements that offer extra visual
explorations of interactive community portraits. For common epidemiological
analysis tasks, we assembled to propose +msRNAer prototype with collaborative
visualization views, as shown in Figure 3, including Control Panel (D),
Portrait View (E), Geographic View (F), and Multidimensional Coordinates View
(G), implemented based on D3.js [13] and Mapbox.js [12] as base map.

For better illustration, we applied our data preparation to the NSW aggregated
COVID-19 dataset. The entire workflow of the applied +msRNAer prototype is
demonstrated in Figure 3. In this subsection, +msRNAer prototype is introduced
as being integrated into the pipeline workflow with summarized human-
participated actions and data flow directions by steps, which include
collecting and dealing with raw datasets, processing specific data with NLP,
and extracting information under expert supervision to the aggregated COVID-19
datasets, and applying +msRNAer to the prototype system with several functions
and interactions finalized.

Control Panel and Portrait View. The Portrait View is built containing a
Control Panel based on our visual modeling method with a force-directed layout
to avoid overlapping and improve readability. First, we can select the applied
datasets and inspection of modes - either actual cases or cases per 10k
population - in the Control Panel. Then, we can hover or click on each time
span on the sample portrait to interactively observe the corresponding filter
result on each portrait and inspect all color tooltips among each view.

We can further inspect, explore, and compare infection cases and community key
factors for each in the Portrait View, with aggregated case numbers by time
span on the Crown and overviews of selected risk factors in the inner. We fix
Portrait View as the main view and we set each portrait with the first channel
with azure blue for older males and mint pink for older females, the second
with gold yellow for lower-income groups, and the third inner channel with
pale purple for lone people groups.

Geographic (GIS) View. We added extra visual polygonal layers to the Mapbox-
drawn landscape in order to divide different LGAs and postal areas in NSW.
When an LGA or postal area is selected, it will be highlighted with a charcoal
gray polygonal boundary and will display detailed information about
geographical information and infection cases. Using the GIS View, we can
explore geographical information by zooming in on postal areas within the LGA
and zooming out for an overview. The GIS View also provides a playable
timeline window for enabling the inspections of the spreading situations in
selected time periods. The visual design of GIS View utilizes a red gradient
scale. The darker the red, the more infection cases are represented in each
LGA.

Multidimensional Coordinates (MDC) View. We utilized parallel coordinates-
based visualization for multidimensional variables. The MDC View aims to show
a high-level overview of other factors related to community resilience and
allows users to explore the dimensions of resilience. Each LGA polyline is
distinguished by coordinates, different colors, and different styles. Polyline
uses different dash styles or colors according to the positive proportional to
the infection cases.

###  5.4 Visual Interactions

We can interact with multiple views, including Portrait, GIS, and MDC Views,
triggered by Control Panel. We offered flexible interactions within and among
multiple views collaboratively on this visual prototype system for each visual
exploration from multiple perspectives, summarized as follows.

By combining the Control Panel and the Portrait View, we have implemented
several interactions that enable efficient navigation through the LGA
portraits and visual cues within the core for further exploration and
comparison.

Filtering by Time Spans. The interactive Control Panel serves as a starter to
filter LGAs in all visual views and by time spans based on intervention events
and to address all color legends in the Portrait View. Users can independently
inspect the COVID-19 cases on S Protein or M Protein among each LGA portrait
based on the gray-scale mapped on E Protein, a time period of intervention
events, or a time period before and after the restriction events.

Zoom and Pan. An interactive portrait based on SVG supports zooming and
panning for exploring overviews or detailed visual elements.

Drag and Reposition. The force-directed layout ensures that each portrait does
not overlap. Users can drag and lock any portrait to a new location to
allocate any selected LGA for comparison. In addition, we enhance interactions
for further functionality by left-clicking to mark the cores and canceling
their current location by double-clicking.

Highlight Visual Cues and Switch Contexts. When hovering the mouse over each
type of visual cue in the Portrait View, corresponding visual cues and
associated data information tooltips will be highlighted for further
inspection.

Conjunction with filtering interaction, as shown in Figure 4, it supports
dynamic highlighting of ranking or case numbers in the core based on the cases
or prevalence rate with the average cases per 10k population in the previous
filtered timeline added with the hovering S Protein’s height. Alternatively,
users can hover only on RNA to highlight its ranking among all LGAs.

The GIS and MDC Views collaborate with the Portrait View by highlighting the
selected both LGAs and postal areas. Some other interactions are also
supported.

Playable Timeline Window. The GIS View provides an interactive timeline window
that allows users to investigate the transmission situation over different
time spans. It includes several functions such as auto-play and pause,
enabling users to customize their explorations.

Boundaries Highlighted. The GIS View supports clicking to highlight boundaries
and reflect LGA portraits in the Portrait View. It also supports connecting
with the cores in the LGA selection.

Heatmap Highlighted. The GIS View offers a heatmap layer in LGA or postal
areas by timeline filtering in Control Panel. The colors reflect the number of
COVID-19 cases in the selected time period.

Brush on MDC View. In MDC View, the brush function is used to filter the
portrait factors in multiple dimensions and reflect them to other views.

Figure 4: Four highlighting strategies are displayed when hovering over: (a)
the ranking and tooltip of the selected RNA among all LGA portraits, (b) the
sum of case numbers in the filtered phase with the total cases in the entire
period, (c) the ranking and tooltip of case numbers summed by hovering time
span, and (d) the context is switched to the case numbers per 10k population
by hovering time span.

##  6 User Study

To ascertain the viability and effectiveness of +msRNAer prototype, we
conducted a user study before applied to case studies. In this section, we
presented the details of the study setup and analyzed the obtained results.

###  6.1 Participants and Apparatus

In our user study, we endeavored to recruit a diverse group of participants
with varied backgrounds and levels of research experience in the field of
computer-related disciplines. Ultimately, we successfully recruited 16
volunteers from our campus, comprising an equal representation of 8 male and 8
female individuals. Notably, the age range of our participants was wide,
ranging from 19 to 30 years, with a mean age of 24.06 years. Additionally, we
verified that 5 of our participants were enrolled college students without any
prior research experience, while the remaining 11 were postgraduate candidates
with research experience ranging from one year to eight years, resulting in a
mean of 1.88 years of research experience. Despite their shared interest in
visualization, all participants reported being unfamiliar with visualization
methodologies.

All user studies were planned to be presented in the campus study pods. Our
+msRNAer prototype was supported by an Apple MacBook Pro (15in, 2018) equipped
with 16GB of memory, i7 processors, and a Radeon Pro 555X Graphics Card which
provided participants for visualizing and interacting with +msRNAer prototype
clearly and effectively on a 60-inch LED external monitor with 1920 x 1080
resolution.

###  6.2 Tasks

The tasks included in our user study incorporated quantitative and qualitative
analysis. The quantitative analysis provided objective numerical data, while
the qualitative analysis allowed us to gain subjective insights into
participants’ interactions that aimed to verify the feasibility and
effectiveness of +msRNAer prototype.

To enhance clarity in quantitative analysis, we established specific
exploration tasks for each view as well as collaborative tasks among the views
to test the exploration capabilities of +msRNAer prototype. By recording the
completion time of each task, we intended to conduct analyses to determine
whether +msRNAer prototype improved the exploration capabilities for
portraits’ COVID-19 case trends and risk factors and whether they had positive
impacts from a visual metaphor standpoint. Additionally, we planned to gather
participants’ feedback on their interactions with the prototype as a whole and
each view, using a set of six well-designed questions to assess their personal
experience. Figure 5 lists specific exploration tasks and detailed
questionnaires. Besides, we planned to record their feedback in transcripts
via interview for qualitative analysis.

Figure 5: The quantitative analysis results of the exploration task list
(E1–E5) and the questionnaire (Q1–Q6), where exploration tasks record the
completed time in a box plot and the questionnaire counts participants’
choices in stacked bars.

###  6.3 Procedure

After setting up the exploration tasks and questionnaires, we rehearsed a
tutorial on how to use +msRNAer for exploration, repeated the five exploration
tasks, and recorded completion times. We found that each exploration task
could be completed within four minutes. Although we conducted the design of
+msRNAer, the randomness of a force-directed layout in Portrait View did not
provide significant benefits in terms of saving our completion time. In other
words, every participant could finish each exploration task within four
minutes after tutorials. Therefore, we decided to allocate approximately a
60-minute face-to-face session for each participant, consisting of a 10-minute
tutorial, approximately 20 minutes of exploration, a fixed 10-minute preset
questionnaire, and a 20-minute open-ended interview. Participants were
instructed on how to apply +msRNAer prototype and were required to complete
five specific exploration tasks, with completion times recorded. They were
also asked to complete the questionnaire based on their subjective experiences
during prototype usage. Additionally, their complementary feedback was also
recorded and used for subsequent qualitative analysis.

###  6.4 Results

In this subsection, we discussed the quantitative and qualitative results of
our user study.

Quantitative Results. The quantitative results of our user study were
reflected in two aspects. The first aspect was task completion time. We
observed that the fluctuations in completion time across these five tasks were
within reasonable ranges. The median time to complete the first four
exploration tasks varied from 36 seconds to 61 seconds, while the last, more
complicated task took a median of 181 seconds to complete. This consequence
could be easily explained by the fact that the completion time of tasks E2-E4
was slightly reduced as participants became more familiar with the prototype
during the first exploration task. Additionally, the first four exploration
tasks focused on simple tasks that could be completed within one or two views.
In contrast, the last exploration task required participants to engage with
all collaborative views and gradually decipher three interactive results.
Consequently, this task demanded more time and attention, leading to a higher
median completion time. These results were in line with our expectations, as
all tasks were completed within four minutes.

According to the results of the questionnaire, the majority of participants
expressed satisfaction with the +msRNAer prototype. Specifically, 12 out of 16
participants strongly agreed with its performance, while the remaining four
were satisfied with it overall. All participants were highly impressed with
the prototype’s visual design in Portrait View and expressed varying degrees
of satisfaction with its analysis functions, including trends, rankings, and
risk factors. Two later questions further highlighted the exceptional
integration of both GIS View and MDC View in the prototype. The final question
evaluated the implementation of interaction logic, with one participant
responding neutrally and the other 15 participants expressing varying degrees
of agreement that the interaction implementation was user-friendly.

The quantitative results from two aspects indicated that participants rated
the feasibility and effectiveness of the applied exploration tasks and
prototype designs relatively highly. They also found the visualization and
interaction designs to be intuitive and impressive.

Qualitative Results. As an extension of the quantitative results, we further
employed qualitative analysis during the last 20-minute open-ended discussion.
In particular, we conducted opinion research for individuals who did not
provide the most satisfactory options on the questionnaire. To derive the
qualitative results, we repeatedly reviewed the interview recording,
summarized their critical thinking, and achieved a consensus based on their
complementary feedback during the interview.

Their feedback was mostly positive with high marks, with only a few critical
comments. These comments can be mainly divided into two aspects:

(1) A few participants felt that the current force-directed layout of Portrait
View was a ”double-edged sword”. On the one hand, it provided dynamicity to
avoid overlapping and allowed for easy dragging and relocating, but on the
other hand, its randomness may increase the workload of recognizing specific
portraits in multiple explorations.

(2) Some participants who marked Neutral or Somewhat Agree in Q6 expected to
see more interaction logic among views, such as enabling the inclusion of more
risk factors in Portrait View or providing solutions for swapping key risk
factors with other factors in MDC for higher levels of comparison purposes.

We felt grateful for their feedback as it served as a pre-evaluation before
case studies to domain experts and helped us improve this applied prototype.
Regarding the comments on the applied force-directed layout, we believe that
its advantages outweigh the disadvantages, but we still consider it necessary
to improve. As a result, we implemented the search function in GIS View to
filter any LGAs or postal areas, which also strengthened the interaction
between GIS View and Portrait View to a certain extent.

Since we are proposing a visual modeling method for epidemiological analysis,
any other related applications using +msRNAer in prototypes may vary in
detail. In this situation, with the prior requirements that we consulted with
domain experts, we deemed that locking the highest risk factors in each
portrait is acceptable because aged, lone, lower-income groups have been
proven by domain experts to be high-risk factors, while other objective
factors selected from the census were considered to be indirectly related
categories. Thus, we explained the reasoning to the participants and derived
their understanding and acceptance.

Overall, our user study showed that the design of +msRNAer was creative, and
the application of +msRNAer with the COVID-19 aggregated dataset was also
proven to be feasible and effective, although there were some imperfections in
certain details. According to the user study, we not only improved +msRNAer
with search functions in GIS View but also inspired some interesting cases
that were demonstrated in future subject-driven case studies.

##  7 Case Studies

Coastal areas are considered densely populated areas prone to cluster
infections for transmission, in contrast to the vast land and sparsely
populated areas of NSW, Australia. For other incidents, intermittent spreading
cycles of virus variants, corresponding policies or restrictions, densely
populated residential areas, areas where older people gather, more
impoverished areas, or areas with relatively backward public infrastructure
may affect the infection situation among different LGAs. In this section, we
finalized our +msRNAer prototype in two different data aspects based on the
COVID-19 aggregated dataset in NSW, which consists of a weekly case summary of
each postal area within an LGA in one year and a bi-weekly case summary of
LGAs in two years, to the aggregated dataset to provide three prominent cases:
Overview-driven cases, Event-driven cases, and Portrait-driven cases, which
are based on highlighted driving aspects to compare and explore the
significant connections between COVID-19 issues and detailed factors in each
LGA census, even attempting to discover the relationships and potential
patterns that were really affecting the COVID-19 pandemic behind each LGA
portrait. We also combined our findings with facts and news for verification
and analysis.

Figure 6: Three cohorts of LGA Portrait Collections: (a) A partial overview of
LGA Portraits and geo-locations for 2020 and 2021. (b) Selected LGA Portrait
with cases appearing continuously from July to December 2021 (c) Selected LGA
Portraits with no cases for the full year of 2020.

###  7.1 Overview-driven Cases

We first set up a visual representation for the LGA portraits within the
period from January 1st to January 11th, 2022. Connected to the GIS View, it
showed the severity distribution of COVID-19 cases in geo-polygons. With a
quick glance in Figure 6(a), we could distinguish whether the situations are
severe or not with roughly two types of portrait appearances. The results are
also correspondingly reflected on the map, showing the contrasting
perspectives between coastal and inland areas in case numbers.

When comparing one LGA portrait to another, one can examine variations in the
COVID-19 cases’ spreading situations among LGAs. The S Proteins of each LGA
portrait showed the changing trends of COVID-19 cases by height. The only two
LGA portrait types that can be distinguished based on appearance are those
with COVID-19 cases in both 2020 and 2021 and others with cases only in 2021
but none in 2020. We randomly sorted out a few portraits of each type for
analysis, as shown in Figure 6(b).

The second visual result depicted the trends of COVID-19 cases within an 8-LGA
portrait collection. We gained insights demonstrating that they all suffered
relatively worse situations and caused two typical waves of cases from mid-
year to the end of 2021. Compared to the height of S Proteins in each
portrait, they reached the peak of the first wave in September, dropped back
to low levels in November, and rose sharply to the highest point in two years.
We corroborated the visual result with the actual situation as the NSW
government released Delta concerns on July 30th, 2021[4], and the information
released on November 28th, 2021, of the first Omicron variant case in NSW[5].
From GIS View, we found they are all located in coastal areas surrounding
Sydney City.

The following visual output in Figure 6(c) showed all LGAs with portraits had
COVID-19 cases during 2021, although they did not have any cases in 2020. We
could further observe that there were 7 LGAs located in inland areas despite
the presence of Kyogle LGA near the coastal regions of these 8 LGAs. Compared
with cases appearing frequently in other developed LGAs, this consequence was
most likely related to the sparse population of these LGAs.

###  7.2 Events-driven Cases

We observed that the first wave of COVID-19 spread across NSW from
approximately January to October 2020. Based on the events on the timeline, we
divided this period into three phases: uncontrolled, eased, and restricted
control. Therefore, we further filtered each LGA portrait into weekly time
spans from January 1st to October 20th, 2020. This enabled us to inspect the
COVID-19 spreading situations among LGAs during this period, as tracked with
the details of these events.

Uncontrolled Phase. After interacting on Control Panel, we hovered the mouse
over each E Protein to spot LGAs, which detected the first case occurring in
week 4 in Randwick, Paramatta, Kur-ring-gai, and Burwood.

We hovered the mouse over each E Protein in Control Panel to spot LGA
portraits, which could observe the first COVID-19 case occurring in week 4 of
2020 in Randwick, Parramatta, Ku-ring-gai, and Burwood. Concurrently, we
noticed the first intervention event started in week 13. Thus, we began by
selecting the phase from January 1st, 2020 to March 24th, 2020, to identify
the uncontrolled phase. We further utilized the playable timeline window in
GIS View to explore the spreading pattern in the Greater Sydney Region and
surrounding places. As shown in Figure 7(a), besides the case that occurred in
week 4, there were no other cases during the period from January 1st, 2020 to
February 25th, 2020. But starting from week 9 to the end of week 12, cases
began spreading in the surrounding places of the Greater Sydney Region, and in
a short period, the cases spread severely and finally caused outbreaks in
almost all postal areas, especially in the Waverley LGA in darker polygon
color.

Drawing an overview of this uncontrolled phase, the visualization results in
Figure 7(b) showed that all LGAs were affected by the first wave of weekly
increasing COVID-19 cases, with no restricted events yet. We focused on LGAs
with the highest number of COVID-19 infections and interacted with them in GIS
View. We located them around the main cities, including Sydney. During this
period, Bondi Beach, in the Waverley LGA, had more cases than other LGAs,
peaking in only two weeks. We noted that Bondi Beach was crowded with massive
gatherings of people, as reported in the news, and the health officials
announced a crowd ban on March 21st, 2020. NSW residents were facing physical
and psychological pressures due to the spread of COVID-19 and bushfires across
the state. These factors indirectly contributed to overcrowding in these
tourist hotspots. This situation was also reflected in the Northern Beaches
LGA, which saw a rapid increase in COVID-19 cases in the first wave of the
pandemic. We further examined the other two LGA portraits, Sydney and
Woollahra, and concluded that the consequences were likely due to the
connection to Waverley in GIS View, resulting from the movement of people in
the adjacent LGA. The fifth infection case in the LGA occurred in Sutherland
Shire, located in the southern region of Sydney. We could assume that the
COVID-19 pandemic had spread to the outer LGAs at this phase.

Eased Phase. We adjusted the timeline for the first NSW lockdown events from
March 25th to June 30th, 2020. The visual results of the top 5 LGAs in Figure
7(c) showed that the Northern Beaches had the highest number of cases,
followed by Penrith and Blacktown, with Sydney LGA dropping to fourth place.
Waverley finished fifth. With the special ban in place at Bondi Beach, the
number of infection cases in Waverley and its adjacent LGA, Sydney,
significantly decreased. We also noted that the NSW lockdown event had a
positive impact on LGA cases in the Greater Sydney Region, as reflected by 0
infection cases in the Northern Beaches, Sydney, and Waverley for several
weeks in the second half of the selected timeline. Penrith, Blacktown, and
Blackburn all had a larger number of low-income residents and are located in
the Greater Western Sydney Region, which had more than half the cosine arc of
the lower-income RNA and an intense frequency. As a result, we anticipated
that the pandemic would spread quickly to other LGAs in the Greater Western
Sydney Region.

Restrict Controlled Phase. To test our hypothesis, we further filtered the
timeline from July 15th to October 20th, 2020, which was when a new lockdown
and tighter restrictions were implemented by the NSW government. The visual
results, as shown in Figure 7(d), reveal that all of the LGAs are connected in
the Greater Western Sydney Region. We observed that the most strictly
controlled events showed stable increases in cases, even in Cumberland and
Liverpool, which had continuous growth in cases over the next few weeks. This
confirms our conjecture about the pandemic situation, with a large number of
infection cases in the Greater Western Sydney Region.

We also noticed potential patterns among LGA portraits, particularly in the
lower-income RNA, demonstrating that all five LGAs have significant lower-
income population groups and high population density. In other words, during
the implementation of the tightened lockdown, most people will work from home
or be self-isolated, and the number of infection cases in the CBD and tourist
hotspots will markedly decrease. Thus, the factors in the LGA portraits will
dominate and reflect the number of infection cases. Lower-income groups or
other population factors in the census may be the most crucial factors
affecting the spread of the pandemic.

Figure 7: The event-driven cases encompass explorations of (a) the COVID-19
spreading pattern in the uncontrolled phase and (b)-(d) three phases of
overviews. (a) provides a detailed breakdown by time spans of the COVID-19
spreading pattern in the Greater Sydney Region and surrounding places during
the uncontrolled phase from 2020/01/01 to 2020/03/24. In (b)-(d), each marked
ranking number is arranged according to geo-location, where (b) contains the
top 5 LGA portraits in total infection cases with non-controlled events
applied to the same phase as (a); (c) displays the top 5 LGA portraits in
total in the eased phase from 2020/03/25 to 2020/06/02; and (d) includes the
top 5 LGA portraits in total case numbers during the restricted controlled
phase from 2020/07/15 to 2020/10/20.

###  7.3 Portrait-driven Cases

The highlighted interaction in RNAs facilitates the comparison of four key
factors within each portrait and multiple attached attributes in MDC View. We
selected 12 alternative factors in the census indicators to enable a more
comprehensive comparison of the COVID-19 situations based on their portrait
factors. To conduct these comparisons, we analyzed portrait-driven cases with
different factor influences in collaboration among views.

Prevalence Rate Influence. We began by exploring LGA portraits for the entire
time period from January 1st, 2020 to January 11th, 2022 that aimed to compare
LGA portraits of coastal areas with inland areas across different case modes.
We randomly selected two coastal LGA polygons (Sydney and Mid-coast) and two
inland LGA polygons (Cobar and Hay) as representers and compared them using
actual cases mode and prevalence rate mode, as depicted in Figure 8.

The consequences conveyed that all S Proteins in Sydney and Mid-Coast LGAs
decreased in prevalence rate mode, while all S Proteins in inland LGAs
increased in height. The opposite occurred in actual cases mode. We were able
to make comparisons among all LGA portraits in both modes since they had been
standardized using the same calculation methods in S Proteins’ heights.

These findings suggested that almost all LGAs in inland areas did not have
enough 10k population, resulting in the height of S Protein in their portraits
growing up. We also concluded that the cases per 10k population with
standardization were similar in both coastal and inland areas, which validated
the strong infectiousness of COVID-19.

Figure 8: Four cohorts of LGA portraits in different modes (actual cases and
cases per 10k population) were selected using LGA polygons in GIS View. Two
coastal LGAs (Sydney and Mid-Coast) are marked with white pins, while two
inland LGAs (Cobar and Hay) are marked with black pins.

Case Trends and Lower-income Influences. Based on our previous findings, there
may be potential relationships between COVID-19 cases and lower-income groups
in each LGA. Therefore, we kept the COVID-19 outbreak timeline from the first
year (January 1st, 2020 to January 5th, 2021) in the Control Panel and
selected the top 10 LGAs with the worst COVID-19 pandemic situations (i.e.,
the highest total case numbers).

From the top 10 rankings displayed on each portrait in the first year, as
shown in Figure 9(a), we identified that areas with RNAs longer than half the
range of lengths, or LGAs with the busiest frequency of RNA in one factor,
have comparatively worse COVID-19 situations. For example, Fairfield ranked
8th highest in the number of cases with 143 total cases. This area contains
the 4th longest lower-income RNA length but the highest tightness frequency.
Additionally, the RNA lengths of aged and lone persons in this area do not
approach half the length of the full path.

Given that all lower-income RNAs are highlighted in the visual overview, we
assume that the lower-income RNA in the LGA portrait is the most influential
factor in COVID-19. We selected 9 LGA portraits only based on the appearances
of low-income RNA and categorized them into three layers whose lengths are
longer than one-half, longer than one-quarter but shorter than one-half, and
shorter than one-quarter. As shown in Figure 9(b), it reveals significant
patterns that the COVID-19 situation is getting better by layers, where we
scan the length of lower-income RNAs, and they are getting shorter from left
to right, top to bottom directions.

Combined Influences on LGA portraits, geo-locations, and events. We reset the
time period to encompass both the eased and restricted controlled phases. Upon
analyzing the Greater Sydney Region using GIS View, we observed that adjacent
LGAs exhibited similar heatmap polygons. Using an iterative approach, we
selected the top 5 LGA portraits with the highest case sums during this period
and evaluated their case sums, as well as the total case sums throughout the
year. These five LGAs are highlighted in Figure 9(c), which pin-points
correspond to the colors used in Figure 9(d). With the exception of Waverley
LGA, which had been analyzed in previous cases, the other four LGAs were
adjacent. While all five LGAs had case ratios exceeding 50% during the
selected period, given the duration of our chosen timeframe, which spanned
more than half a year, these eased and restricted events effectively
suppressed the spread of COVID-19 in this densely populated region to a
certain extent.

Household and Dwelling Influence. The LGA portraits were reverse-selected to
MDC View help to discover the consequences of COVID-19 spread in conjunction
with other highlighted census attributes from residents’ perspectives. In each
column, the median age of each LGA is roughly inversely proportional to
financial factors, including the median mortgage, personal income, family
income, household income, and rent cost. In most living environments with more
than two bedrooms in one household, the majority of people do not meet the
standard of one bedroom per person. This indicates that most people still live
with others or families, which must also be considered in the analysis of the
consequences of the pandemic’s spread.

Population Density Influence. The three LGAs with the highest population
densities in the Greater Sydney Region are Sydney, Waverley, and Northern
Sydney. These areas have all been severely affected by COVID-19. Upon
analyzing their trends on coordinates, we discovered that they share
similarities such as a young median age, similar financial profiles, high
living costs, and a propensity for using public transit.

Figure 9: The compiled cohorts of cases are organized according to LGA
portrait factors. Specifically, (a) an LGA portrait collection showcases the
top ten areas with the highest infection case counts across the entire
timeline; (b) a visualization shelf of LGAs is presented in three levels,
filtered by lower-income factors; (c) detailed portraits of filtered LGAs are
provided, and the colors of map pin-points correspond to the colors used in
(d); (d) two portrait cohorts are presented after being filtered by population
density and public transportation rate, displayed in multidimensional
coordinates, and distinguished by colored lines to differentiate LGAs.

##  8 Discussion and Interview with Domain Experts

The +msRNAer prototype applied with our aggregated COVID-19 dataset was
deployed and used in the workplaces of three domain experts from the
Australian government. Through interaction with domain experts, we observed
the following:

###  8.1 Influence of Key Risk Factors

The combined influence of key risk factors has a notable negative relationship
with the community’s resilience profile against the virus spread. According to
infection cases, LGAs with a higher representation in four risk groups are all
at the top of the list. For example, among the entire list of factors in NSW,
the Central Coast has the highest-ranked key factors, ranking #1 in the aged
group, #1 in the lone person, and #2 in the lower-income group; Canterbury
also has the highest-ranked key factors, ranking #2 in the aged group, #1 in
the lower-income group, and #3 in the lone person.

The larger the population in the high-risk group, the higher COVID-19 cases.

Key factor – aged group: The areas with smaller aged male or female groups
among LGAs in NSW have significantly reduced infection cases. In the ranking
according to infection cases, the top LGAs generally have older age groups in
community representation. There is not much difference in the effect caused by
aged male or female groups.

Key factor – lower-income: Among four key risk factors, the lower-income group
has the highest impact on COVID-19 risks. The visualization results show that
infection cases dramatically increase for LGAs in the lower-income group,
roughly occupying one-quarter of the maximum of the lower-income group
population in NSW. For example, Cumberland, Canterbury, Sutherland, Sydney,
Fairfield, Penrith, Blacktown, Liverpool, and Central Coast are the LGAs with
the worst COVID-19 situation, coming with larger population sizes in the
lower-income group from small to large.

Key factor – lone-person: The lone-person group has a certain impact as well.
For example, Sydney CBD ranked 3rd according to infection cases and has key
risk factors ranking, respectively, lone person (#1), low-income (#12), and
age group (Male #25, Female #29). Compared with other factors across all LGAs,
its influence seems the weakest. However, it would also affect LGA’s
resilience if taking into account other risk factors.

Lower risk factors, the higher resilience: According to the domain expert, if
LGAs have a lower presentation of most risk factors, their resilience appears
to be higher. This also showed in the situation where several higher resilient
LGAs would respond quickly and positively to flatten the curve and reduce the
risk, even when they were the areas where these early and severe infections
happened during outbreaks.

Domain experts pointed out that these implications largely support the
guidelines developed by the Australian government during the pandemic.
However, they would also include representation of other under-resourced
people in future work, who may also be more at risk of exposure, including
aboriginal and Torres Strait Islander people, people living in aged care
facilities, and people with disabilities. They plan to share with us more
datasets to increase the diversity of higher-risk groups. For the lone-person
risk group, domain experts explained that an individual or group’s social
relationships should be significantly affected and explained that a lone
person would have much fewer resources and social support compared to a person
living together. Australian and most Western societies encourage young adults
to move out and live alone. However, this situation may not be beneficial
during the pandemic. Apart from living conditions, they would also like to
explore diverse relationship statuses as they believe that residents’
relationships might have a greater impact on social support and mental well-
being.

###  8.2 Influence of Other Factors

The higher population does attract more risk for infection. All LGAs with
larger resident populations should be given more attention during all phases
of the pandemic. For example, Canterbury, Blacktown, and Central Coast ranked
top 3 in population, and have higher infection cases than other outbreak LGAs.

Travel restrictions could effectively control the risk caused by activities
related to public transportation. For instance, North Sydney, Burwood, and
Inner West are the top 3 in terms of the number of people who frequently use
public transit. These LGAs have the earliest cases during outbreaks before the
controlled phase due to the high volume of public transit. However, they
respond positively and quickly when the government increases travel
restrictions.

Geographic factor: From the overview, the total number of infection cases in
coastal areas is generally higher than inland areas for the first 12 months.
In particular, those areas with famous beaches had severe infections, for
example, the Northern Beaches and Waverley (with Bondi Beach). Analyzing the
spreading pattern in Greater Sydney Region during the uncontrolled phase,
domain experts observed the outbreak initially appeared in scattered locations
throughout the LGA and subsequently spread to areas adjacent to existing
cases. During the early stage of the outbreak without control, spreading
patterns were more apparent in residential areas. However, in the latter half
of the phase, the pattern became more prevalent in tourist attractions,
ultimately resulting in the peak of infection cases in Waverley.

Domain experts have also discovered that the combination of key risk factors
and other factors can have a more significant impact. Taking the Northern
Beaches as an example, they had the most infection cases in the first year of
COVID-19 outbreak, with a greater effect on geographic factors and the highest
presentation in lone-person groups, despite a reasonable economic indicator
and a low presentation in the elderly group. Although most of these
implications seem obvious, they would consider strengthening governance based
on these factors, as these implications also prove the effectiveness of
government intervention. The experts also emphasized the crucial need for
clearer regulations about beaches and recreational usage.

After experimenting with experts, we also conducted a follow-up interview.
Overall, experts were impressed by the ”actionable insights” that +msRNAer
obtained. They felt excited about the interface design inspired by the viral
2D structure. They expressed that it supported their memory of each data
object and their representation because of the familiarity of the metaphor
adopted. They commented, ”This would help us reduce the effort and time
involved in training other staff.” They found that ”interaction with humans
and interface-in-the-loop is intuitive and assists them in manipulating
exploration along a timeline and geographic map.” They also used the prototype
system to make several valid assumptions and discover unexpected implications.
For assumption validation, they found the prototype’s capability to help them
target higher-risk groups is ”specifically useful,” and the interactive
portrait was ”the most useful treasure” considering the magnitude of the data
they have.

Experts consistently agreed that the visual modeling of +msRNAer can be easily
applied in most epidemiological analyses due to the similarity of virus
transmission that is often caused by spatiotemporal and objective factors. The
prototype will be reusable because the integrated portrait designs helped them
augment awareness by porting most data features. They were swiftly able to
identify higher risk regions with ”constrained factors and higher presentation
in risk groups using the realistic”. The experts mentioned that they expect
the prototype to enable them to understand how to build profiles and
predictions for communities in the future based on ”reasonable and meaningful
information provided.”

##  9 Limitations and Future Work

###  9.1 Limitations

Our work was constrained by the quality and availability of the data provided,
including the related datasets and the redefined risk factors with domain
experts. These data issues could have led to inaccuracies in our final
results. For example, COVID-19 case data collected by the NSW government is
based on usual residence, but some records did not track location accurately,
which could have compromised the precision of our analysis. Concurrently, the
case data we had access to was recorded by LGAs or postal areas, which limited
our ability to examine more granular infection trends in communities.
Additionally, our use of a weekly time frame, as dictated by the aggregated
datasets, may have limited our ability to uncover more nuanced patterns in the
pandemic’s progression. The Australian Bureau of Statistics conducted the
latest census surveys on communities in 2020, but the complete summary will
not be released until mid-2023. Thus, we have utilized the 2016 census in
aggregated datasets, which may pose bias in accurately assessing the impact of
the pandemic on population demographics and social indicators during this
ongoing period. Moreover, the LGA profiles we used contained a vast amount of
data, with over 15,000 variables each. We could not concentrate on all
variables, necessitating a reduction to only thirteen based on expert
supervision. We considered social and economic indicators and indicators of
vulnerability to COVID-19, such as rental and mortgage affordability. However,
variables in other categories may have directly or indirectly influenced our
results. Furthermore, the intervention events in the COVID-19 dataset were
constrained by the Australian government system, with different levels of
government responsible for administering public policies and programs, which
may have resulted in varying intervention measures implemented across LGAs in
NSW.

###  9.2 Future work

Based on the feedback from participants in the user study and discussions of
case studies with domain experts, we have targeted four aspects to focus on in
our future work.

Firstly, we plan to update our prototype by importing refined COVID-19 case
data and new census data later next year, when community datasets are
collected during the pandemic.

Secondly, to increase the diversity of community profiles, we will include
more variables such as relationships, ethnic groups mentioned by domain
experts, and education level. We will continue to apply machine learning
algorithms, such as Principal Components Analysis (PCA) for dimensionality
reduction to add more categorical indicators and time series predictions for
infectious cases affected by risk factors. We also expect to propose a rating
system for all variables with assistance and evaluation from domain experts,
which would be able to quantify the characteristics of communities. By
designing an appropriate measurement matrix for indicators, we will be able to
create index metrics for future use in other epidemiological analysis
scenarios. These metrics will not only assist decision-makers in making
pandemic prevention measurements but will also educate the public on personal
influence and, eventually, how to work together to tackle this greatest
challenge by putting in their own efforts.

Thirdly, with the improved approach, we intend to conduct a systematic review
with more government staff. As suggested, we will offer an automated reporting
function for storytelling purposes. This function would help disseminate the
right messages to the public.

Finally, we will consider applying our prototype to more transmission datasets
in epidemiological analyses to validate the scalability of +msRNAer.

##  10 Conclusion

With epidemiological analysis as a backdrop, this paper proposes a new visual
modeling method called +msRNAer for exploring and comparing spatiotemporal and
multidimensional features based on requirements in epidemiology. The method
employs a metaphor to assemble portraits that can be used for visualizing each
community by combining the visual encoding of time-varying case numbers with
objective risk factors that may affect transmissions. The method integrates
multiple views, including Control Panel, GIS, and MDC Views, to provide wide-
scope observations on filtering events at different severity levels, geo-based
spreading distributions, and multidimensional risk factors for each community.

To evaluate the feasibility and effectiveness of +msRNAer, we deployed and
applied a two-year-scale aggregated dataset by integrating COVID-19 cases with
geo-information, NLP-extracted events division on timelines, and risk factors
from NSW census Data based on expert supervision. After applying +msRNAer into
this COVID-19 aggregated dataset, we progressively validated the feasibility
and effectiveness of +msRNAer by conducting one user study for iterative
improving the applied prototype and comparing visual portraits from profuse
perspectives in three subject-driven case studies. We summarized how
geography, phases of intervention events, and objective risk factors affected
COVID-19 spreading situations during the pandemic.

In further interviews with domain experts, we identified additional objective
factors that may be influential in the Australian context. We examined pre-
existing community factors and discovered practical implications for potential
patterns of established community characteristics against the vulnerability
facing this pandemic. Despite some limitations and future work, feedback from
domain experts suggests that the +msRNAer can be considered a common visual
modeling method for exploring community-based spatiotemporal and
multidimensional features. This method can be applied to abundant
epidemiological analyses such as investigating case trends and comparisons,
geo-distribution and transmission, risk factors explorations and rankings, and
other related tasks.

##  11 Declarations

### Availability of data and materials

We promise that all datasets used in this work are open-source and accessible.
The source code is available on Github:
https://github.com/YuDong5018/msRNAers.

### Declaration of competing interest

The authors have no competing interests to declare that are relevant to the
content of this article.

### Funding

This work was funded by UTS-CSC scholarship by the University of Technology
Sydney and China Scholarship Council under Agreement No.201908200009.

### Authors’ contributions

All authors participated in conceiving, discussing, designing, and writing
this work. Yu Dong and Jie Hua made contributions to collecting and processing
data. Yu Dong and Christy Jie Liang performed the programming for this visual
modeling. Yu Dong, Christy Jie Liang, and Yi Chen put effort into
proofreading. Christy Jie Liang and Yi Chen behaved to the corresponding
author’s duty.

### Acknowledgements

The authors would like to thank all domain experts from the Australian
government who provided valuable feedback and comments based on their
expertise.

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### Author biography

[Yudong.png]Yu Dong received his M.S. degree from Beijing Technology and
Business University in 2017. He is currently a Ph.D. candidate at the
University of Technology, Sydney. His research interests include data
visualization and visual analytics, machine learning, and human-computer
interaction.

[Christyjieliang.png]Christy Jie Liang received her doctorate in data
visualization from the University of Technology, Sydney, and now leads the
Data Visualization Research Lab in the Visualization Institute at UTS. Her
research interests focus on data visualization and visual analytics.

[YiChen.png]Yi Chen is a professor and director at Beijing Technology and
Business University. She received her Ph.D. degree in Computer Application
Technology from Beijing Institute of Technology in 2002. Her research
interests include visualization, machine learning, and big data technology for
food safety.

[JieHua.png]Jie Hua received a Ph.D. degree in software engineering from the
University of Technology, Sydney in 2014. He is currently working as a
researcher at UTS and Professor at Shaoyang University. His research interests
include graph drawing, visualization, big data, and deep learning.

### Graphical abstract