# A Web-Based Prototype Dashboard for the Visualization of Spatiotemporal Trends of Respiratory Syncytial Virus

Jingyi Liang, Saturnino Luz, You Li, Harish Nair

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Jingyi Liang, Saturnino Luz, You Li, Harish Nair. A Web-Based Prototype Dashboard for the Visualization of Spatiotemporal Trends of Respiratory Syncytial Virus. 19th IFIP Conference on Human-Computer Interaction (INTERACT), Aug 2023, York, United Kingdom. pp.102-112, 10.1007/978-3-031-61698-3_10. hal-05305336

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# A Web-Based Prototype Dashboard for the Visualization of Spatiotemporal Trends of Respiratory Syncytial Virus

Jingyi Liang<fnref n="1" />✉, Saturnino Luz<fnref n="1" />✉, You Li<fnref n="1" />,<fnref n="2" />✉, and Harish Nair<fnref n="1" />✉

**Abstract.** The Coronavirus (COVID-19) pandemic has underscored the importance of visualization tools in healthcare for monitoring infectious diseases. Geographic Information Systems (GIS) facilitate data comprehension by employing visual representation and analysis to convey disease-related information in a clear and accessible manner. Its increasing utilization in public health has been instrumental in enhancing disease surveillance and response planning efforts.

Respiratory Syncytial Virus (RSV) is a ubiquitous viral infection that is prevalent in both children and adults, and it is the most common pathogen identified in lower respiratory tract infections in infants. However, current RSV surveillance lacks an integrated visualization platform utilizing GIS techniques.

This research aims to address this gap by developing a web-based prototype dashboard that visualizes and analyzes RSV prevalence trends. The prototype dashboard is meticulously designed to provide comprehensive information about RSV, emphasizing a user-centric approach and interactive features. By leveraging GIS and visualization techniques, this study can contribute to the advancement of RSV surveillance and enhance our understanding of the disease.

**Keywords:** Respiratory Syncytial Virus · Geographic Information Systems · Visualization

## 1 Introduction

Since the emergence of the Coronavirus (COVID-19) pandemic, there has been a growing emphasis on the utilization of visualization tools in the healthcare area to facilitate surveillance and monitoring of infectious diseases [1, 2]. One particular approach gaining momentum is the integration of disease mapping with Geographic Information System (GIS) tools. GIS is a robust tool that enhances data comprehension through visualization and analysis [3–7]. Compared to presenting data in the format of tables or figures, visualizing data in map form offers a more intuitive means of conveying

<footnote n="1">¹ Usher Institute, Edinburgh Medical School, The University of Edinburgh, Edinburgh, UK
{s2401514,S.Luz,Harish.Nair}@ed.ac.uk</footnote>

<footnote n="2">² School of Public Health, Nanjing Medical University, Nanjing 211166, China
you.li@njmu.edu.cn</footnote>
disease-related information. This approach facilitates easier comprehension by the general public, as it provides a clear and accessible representation of disease-related information. Its usage among public health experts for planning and surveillance purposes is increasingly prevalent.

The application of GIS in disease studies has advanced the understanding of the intersection between person, place, and time in infectious disease outbreaks, as well as the underlying social and cultural factors, with substantial potential for testing plausible hypotheses about the causes of disease [8, 9]. The combination of epidemiological mapping and GIS techniques has greatly contributed to the understanding of the social and cultural dimensions associated with the spread of specific infectious diseases. The successful control and eradication of malaria in Jamaica following its reintroduction to the country in 2006 proved that using geospatial tools can reveal the foci of infection, allowing for targeted interventions and swift containment of the outbreak [10].

Respiratory syncytial virus (RSV) is associated with nearly 34 million lower respiratory tract infections (LRTI) and 3.4 million hospitalizations per year in infants and children under 5 years of age [11, 12]. The infection of RSV typically begins with the inoculation of the nose or eyes via large particles or direct contact, with an incubation period of 2–8 days [13]. It primarily infects the epithelial cells lining the respiratory tract, leading to the development of bronchiolitis, pneumonia, and asthma exacerbations [13]. The activity of RSV is known to vary geographically, with seasonal patterns varying significantly across different regions worldwide. Environmental factors such as temperature, rainfall, humidity, UV-B radiation, nitrogen dioxide ($NO_2$), particulate matter with a diameter of 2.5 microns or less ($PM_{2.5}$), particulate matter with a diameter of 10 microns or less ($PM_{10}$), etc., have a potential epidemiological role in the transmission of RSV [14–19].

Consequently, the application of GIS and data visualization techniques offers valuable prospects for the analysis and monitoring of RSV transmission. Presently, only a limited number of dashboards for the surveillance of RSV combine visualization platforms with GIS techniques. The Respiratory Syncytial Virus Network (ReSViNET) has developed a global RSV surveillance dashboard, aiming to provide insights into RSV data from 20 different countries [20].

The ReSViNET dashboard effectively visualizes and presents weekly RSV data in a systematic manner. However, one notable limitation of that is the absence of information regarding RSV-related influencing factors. Integrating meteorological factors into the analysis could significantly enhance the general public's understanding of RSV transmission from various perspectives. However, to the best of our knowledge, no RSV surveillance dashboard that combines RSV data with key determinants such as meteorological factors exists. Such factors include, for instance, temperature, precipitation, relative humidity, wind speed, and visibility.
This paper describes a web-based prototype dashboard (dashboard) for visualizing and analyzing prevalence trends for RSV. Using Japan's National Institute of Infectious Diseases (NIID) surveillance data from 2013 to 2019 as the pilot data, this study visualizes the spatiotemporal distribution of RSV transmission through three major modules: basic information visualization, descriptive visualization, and analysis visualization. Building on these analyses, a dashboard has been designed that conveys information about RSV as comprehensively as possible while also focusing on improving users' experiences by adding interactive elements [21]. Thus, we aim to make the dashboard more user-oriented, providing informative content not only at an academic level but also to raise broader public awareness and engagement.

The materials and methods section will provide a detailed description of the over-
all design, analysis methods, and development process of the web-based dashboard.
Furthermore, specific features and functionalities incorporated into the dashboard to
enhance the user experience and accessibility will be discussed in this section. The
results section will illustrate key findings in detail, while the discussion section will
delve into the results obtained from the dashboard and their implications.

2 Materials and Methods

2.1 Data Source

The data consists of two parts: RSV-related data and meteorological data. Weekly RSV-
confirmed cases by prefecture from the 1st week of 2013 to the 52nd week of 2019
were obtained from Japan's National Institute of Infectious Diseases (NIID). Meteo-
rological data, including average temperature (°C), precipitation (in), relative humidity
(%), visibility (m), and wind speed (m/s), were obtained from the National Oceanic and
Atmospheric Administration (NOAA) for the period between the 1st of January 2013
and the 31st of December 2019.

## 2.2 Dashboard Design and Development

We developed a user-oriented accessible dashboard that can be accessed on both desktop and smartphone devices using a web browser. To better design and refine this dashboard, a focused group meeting was convened with our collaborating public health experts. Following this initial input, a high-fidelity prototype map-based dashboard was developed which will be used to iteratively gather further feedback from relevant stakeholders as the project progresses. The dashboard was developed using Shiny [22], a package in the R programming language that enables the creation of interactive web applications [23]. This dashboard is designed for end-users to interact with and visualize the data according to their specific requirements.
The framework of the dashboard is illustrated in Fig. 1, providing an overview of its structure and components. Figure 2 provides a detailed understanding of the layout and design of the dashboard.

**Module 1: Basic Information Visualization.** This module presents essential information about RSV, including its pathogenic characteristics, disease burden, influencing factors, transmission patterns, and vaccination status.

**Module 2: Descriptive Visualization.** This module focuses on presenting time-series trends and spatial patterns of RSV-confirmed cases. The time-series trends present plots depicting cases and cumulative cases organized by region and prefecture. Cases denote the number of new RSV-confirmed cases reported every week in a selected time period.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Workflow of an interactive web-based dashboard for spatiotemporal patterns of RSV epidemics in Japan from 2013 to 2019</figcaption></figure>

In contrast, cumulative cases refer to the accumulated total number of RSV-confirmed cases that have been reported within the selected time period. Users can compare trend plots for different prefectures at various time intervals. The spatial patterns comprise interactive maps displaying cases, cumulative cases, cases exceeding a certain threshold, and cumulative cases exceeding a threshold. Users can adjust the time points and case thresholds to create customized maps. By interacting with the maps using a mouse, users can obtain geographical information about prefectures and access time-series plots for cases and cumulative cases at the prefecture level.

**Module 3: Analysis Visualization.** This module comprised two subsections, the visualization of spatiotemporal clusters of RSV-confirmed cases, and the visualization of associations between RSV-confirmed cases and meteorological factors. Regarding the visualization of spatiotemporal clusters, users are able to select the time-level to be visualized. An overview description of clusters, time-series plots of clusters for a selected year, and maps visualizing the spatial distribution of clusters at country-level for a
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Detailed layout and design of an interactive web-based dashboard for spatiotemporal patterns of RSV epidemics in Japan from 2013 to 2019</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** The interactive map provided by the dashboard and the time-series plots of the RSV-confirmed cases at prefecture-level in 2015</figcaption></figure>

selected year can be provided. As for the visualization of associations, users can gain information from a thematic map for displaying associations.

## 2.3 Analysis

**Spatiotemporal Clusters of RSV-Confirmed Cases.** Functional data analysis methods have been utilized to investigate time-series patterns of RSV-confirmed cases [24]. Smoothed functional curves (B-splines) are used to convert the time-series data into curves. Principal component analysis (PCA) is applied to the smoothed curves to explore
modes of variation in the data. Functional clustering analysis has been performed using the K-means clustering method to identify patterns in RSV-confirmed cases.

**Associations Between the Trajectory of RSV-Confirmed Cases and Meteorological Factors.** Generalized additive models (GAM) and generalized linear models (GLM) are widely employed by researchers for modelling and analyzing respiratory disease infections [25–27]. In this study, four different models, namely mixed and fixed-effect generalized additive models (GAM) and mixed and fixed-effect generalized linear models (GLM), are employed to investigate and quantify the associations. Time is incorporated as either a fixed or random effect in both the GAM and GLM models, respectively. Model performance is assessed by the root-mean-square error (RMSE). Mixed-effect negative binomial regressions with first-order autoregression outperform other models, as they yield the lowest RSME. Consequently, we utilize this model to fit the data at the prefecture-level.

$$Y_{ijt} \sim \text{Negative Binomial Distribution} \qquad (1)$$

$$\ln(E(Y_{ijt})) = \alpha_i + b_{ij} + c_{ij(t)} + \beta_{RH}X_{RH(ijt)} + \beta_{AT}X_{AT(ijt)} + \beta_V X_{V(ijt)} \\ + \beta_P X_{P(ijt)} + \beta_{WS} X_{WS(ijt)} + \varepsilon_{ijt}, \quad \varepsilon_{ijt} \sim AR_1(\rho) \qquad (2)$$

where $Y_{ijt}$ is the number of RSV-confirmed cases in prefecture $i$, year $j$ and week $t$, $\alpha_i$ is the overall mean of prefecture $i$,

$b_{ij} \sim N(0, \delta_b^2)$ is the random main effect of year $j$ of prefecture $i$,

$c_{ij(t)} \sim N(0, \delta_c^2)$ is the random main effect of week $t$ at year $j$ of prefecture $i$,

$\beta_k$ is the fixed main effect of the meteorological factor $k$,

where RH denotes relative humidity, AT denotes average temperature, V denotes visibility,

P denotes precipitation, WS denotes wind speed,

$\varepsilon_{ijt} \sim N(0, \delta_e^2)$ is the error term,

follows the stationary auto regressive AR(1) $\sim cov(\varepsilon_{ijt}, \varepsilon_{ijt*})$.

# 3 Results

## 3.1 Basic Information Visualization

The background information page can provide informative background knowledge about RSV for the general public, raising awareness and enhancing prophylaxis effects.

## 3.2 Descriptive Visualization

Figure 3 depicts the main page of this dashboard. It presents an interactive response map, enabling users to visualize RSV disease in accordance with their specific requirements. Fundamental cartographic functionalities, such as zooming and clicking are seamlessly integrated into this page. The interactive framework empowers users to specify temporal intervals, the threshold of cases, and preferred map overlays, to facilitate the creation
of customized visualization maps. Notably, by clicking on the map, the geographic information and the time-series trend of RSV cases in the chosen prefecture will be displayed.

Figure 4 presents time-series plots of cases and cumulative cases organized by region and prefecture. The interactive framework on this page enables users to define temporal intervals and district granularity, to visualize the temporal patterns of cases and cumulative cases of RSV.

<figure><img src="image_6.png" /><figcaption>Fig. 4. Time series plots of weekly RSV-confirmed cases in prefectures from 2013 to 2016</figcaption></figure>

## 3.3 Analysis Visualization

Figure 5 shows the visualization of the analysis, including the association of meteorological factors and RSV-confirmed cases in selected years, and spatiotemporal clusters in a selected year.

# 4 Discussion

We have developed a web-based dashboard that effectively visualizes and analyzes the prevalence trends of RSV. The design of this dashboard focuses on providing comprehensive information about RSV, with an emphasis on a user-centric approach and interactive features.

To cater to the general public, the dashboard adopts a simplistic and easily understandable design theme that optimizes the transmission of information. Figures and maps are preferred over tables and text, as they are easier to understand and convey information more effectively and intuitively. While three-dimensional figures and maps have
the potential to convey more information, they can pose challenges for non-experts in interpretation. Therefore, the platform opts for two-dimensional figures to illustrate temporal trends of RSV infections and two-dimensional maps to display spatial patterns of RSV infections. Additionally, interactive design plays a crucial role in enhancing the user experience. The platform primarily utilizes interactive features to visualize the preloaded data based on the user's selections. The interactive design allows users to actively engage with the data and customize their exploration according to their preferences, resulting in a more immersive and personalized experience.

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Visualization of the analysis. The association map displays the associations between precipitation and RSV-confirmed cases (upper left pane). The spatiotemporal map shows the spatial distribution of four types of clusters in 2014 (upper right pane). Time-series plots of mean value show trends of weekly RSV-confirmed cases and cumulative RSV-confirmed cases of each cluster in 2014 (bottom left pane), detailed information about each cluster and time-series trends of prefectures within each cluster in 2014 are also shown (bottom right pane).</figcaption></figure>

The design strives to strike a balance between spatial and temporal elements, as well as between the analysis and communication of scientific information [28]. Overall, we discussed the basic requirements of a disease surveillance dashboard and presented a dashboard for a map-based interface and epidemiological dashboard to address the need for a comprehensive and user-oriented surveillance dashboard for RSV.

While this study contributes to the advancement of RSV surveillance and the general public's understanding of RSV through the use of GIS and visualization techniques, it is important to acknowledge that certain limitations still exist in the current version of the dashboard. Firstly, the granularity of RSV data in the dashboard is limited to weekly basis, lacking the precision of daily data. Incorporating data with higher granularity could provide a more accurate representation of RSV prevalence trends. Secondly, the availability of high-quality population-related data, such as population, population density,
and population mobility, as well as testing-related data like the number of sentinel sites and the number of people tested at sentinel sites, is currently lacking. Access to these data would greatly enhance the analysis and interpretation of RSV trends. Thirdly, the modelling analysis in this study only considered the individual effects of meteorological factors in one multivariate model, without considering potential interactions between these factors. Exploring the interactions between meteorological variables could provide a more comprehensive understanding of their synergic impact on RSV transmission.

Future efforts should focus on improving the precision of RSV data, optimizing capacity of the dashboard to incorporate additional epidemiological indicators, including prevalence, incidence, positivity rate, and mortality, and improving the accuracy of the analysis results. By addressing the limitations mentioned above, the dashboard can offer a more comprehensive understanding to the general public and facilitate informed decision-making in public health.

Visualization tools play a crucial role in translating complex and specialized data into easily understandable maps, figures, and tables for the general public. From a public health perspective, these tools help deepen public knowledge and reduce the risk of widespread pandemics. However, it is important to note that while complex and precise maps may facilitate the understanding of various aspects of a disease, they may easily mislead users into concluding that they have known something regarding the underlying causes of a disease [2]. Therefore, achieving more accurate visualization of medical data remains an important area for future exploration.

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