Overview

Background

This dashboard uses ebola_simulated.rds, a fictional linelist dataset created for PHW251B to illustrate data visualization principles in outbreak analytics. Each record represents an individual with suspected or confirmed Ebola virus disease, including demographic characteristics, dates of symptom onset and hospitalization, clinical outcomes, and the geographic location of the treating hospital. The dataset simulates the progression of an Ebola outbreak over several weeks across multiple health facilities in a defined region.

The research question guiding this dashboard is:

How were Ebola cases distributed across hospitals, and how did the outbreak evolve over time in this simulated scenario?

Understanding both spatial and temporal patterns is central to outbreak response. Spatial mapping identifies facilities that experience the highest burden and informs surge support. Temporal trends reveal whether cases are accelerating, peaking, or declining, enabling rapid public health decision-making.

Results Summary

Figure 1 illustrates the geographic distribution of cases across hospitals, while Figure 2 presents the temporal epidemic curve. Together, these visuals depict both the burden placed on individual hospitals and the overall shape of the epidemic wave. Figure 3 shows the distribution of simulated Ebola cases by clinical outcome.

Accessibility Statement

Colorblind-friendly palettes, clear labels, and unmodified axes are used to support ethical and inclusive visualization. Interactive components are provided to allow users to explore data without distorting underlying patterns.

Hospital Case Distribution (Figure 1)

Figure 1. Spatial distribution of simulated Ebola cases across hospitals, with dot size representing caseload and colors representing individual facilities.

Epidemic Curve Over Time (Figure 2)

This visualization shows the progression of the simulated Ebola outbreak over time, based on the date of symptom onset.

Figure 2. Temporal trend in Ebola cases across hospitals during the simulated outbreak.

Interpretation

During this simulated Ebola outbreak, hospital caseloads varied substantially, with some facilities treating significantly more patients than others. The temporal curve demonstrates a clear epidemic wave, with an initial rise, peak, and gradual decline. Differences in timing and magnitude across hospitals may reflect localized transmission patterns, differential reporting, or variation in patient movement. Together, these visualizations provide a cohesive overview of outbreak dynamics in this fictional scenario.

Cases by Outcome (Figure 3)

This visualization shows the distribution of clinical outcomes among simulated Ebola patients.

Figure 3. Distribution of simulated Ebola cases by clinical outcome.