Julio simón

A data visualization project exploring how the Venezuelan government collected COVID-19 data, comparing official reports with independent hospital records.

This project demonstrates how information design and strategic narrative structure can make complex institutional data accessible to multiple audiences.

The approach was to understand the data, identify the story, then build the visual system to communicate it clearly.

For this projectI led the full process: data collection, cleaning and analysis using multiple sources (official reports, hospital data, statistical models), narrative structure to make complex information accessible, visual system design and iconography, data visualization creation (Flourish + Figma), and web development in Webflow including scroll-triggered animations.

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When the pandemic hit, data became crucial.

It told us what was happening: infection rates, deaths, safety measures. It also enabled comparisons between countries and responses.

But in Venezuela—where institutional opacity is standard, especially in healthcare—could official data be trusted?

This project investigates that question by comparing government-reported figures with independent records collected directly from hospitals by the Encuesta Nacional de Hospitales (ENH). The goal was to understand if official numbers reflected reality.

The strategic goal was to build a visual narrative that unpacks the policies and processes behind official data collection, then contrasts that with statistical models from Venezuela's Academy of Sciences and real hospital data.

In short: comparing what was reported, what should have been reported (according to models), and what actually happened in hospitals.

The challenge came as epidemiological reports get technical fast. The solution to it was a visual system that makes complex information accessible without oversimplifying

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The analysis required three data sources to get the complete picture:

• The official data reported by the government, colllected through the JHU's databse given that the official government website has since been taken offline.

• Statistical models and conclusions from the Venezuelan academy of sciences.
• Data colllected directly from hospitals by the encuesta nacional de hopistales.

As an independent project, the ENH has limited resources. So the key questions became:

1. What portion of the population is covered by the hospitals monitored by the ENH?
2. How consistently was the data reported throughout the pandemic?

By cross-referencing geospatial hospital data with Venezuela’s latest available census (2011), we estimate ENH’s reach covers roughly 60% of the population.

In terms of reporting frequency:

1. During the first 365 days of the pandemic, hospitals reported data an average of 272 days (74.5%).
2. Over the full 981-day period, the average was 709 days (72.3%).

Despite its limitations, this dataset offers a critical view into how the pandemic affected an already fragile healthcare system.

The biggest challenge was turning three disparate datasets—each with different scopes and methodologies—into a coherent narrative, so I structured the story around four core questions:

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• If Venezuela's healthcare system is in crisis, why wasn't the pandemic worse?
• Do official numbers reflect what actually happened?
• Why doubt the government's data?
• Can the true impact of COVID-19 in Venezuela be measured?

Visual Strategy:

The design needed to reflect the project's core concern—transparency in public health data—while remaining accessible. Not a dense journal article, but a clear visual investigation.

I drew from early digital UIs and bit-style graphics: high-contrast elements, minimalist palettes, structured editorial grids.

Structure:

Instead of a long scroll, I chose a screen-by-screen slide format. This lets users explore the content at their own pace and creates natural breaks for complex information.

The layout uses a 12-column grid (grouped into 4s when needed). Text and visuals sit within intentional white space to reduce cognitive load.

Iconography:

The main visual symbol is a wireframe PCR test tube—a reference to both the pandemic's data origins and the transparency this project demands.

The wireframe style works as a metaphor: showing the internal structure, making the invisible visible.

Data Visualizations:

Visualizations had to be narrative-driven and self-explanatory, even out of context. Simplicity and clarity over complexity.

Most charts were built in Flourish, then refined in Figma to match the visual system.

For interactive elements, I exported CSV data and converted it to Lottie files for cursor-driven animations in Webflow.

After putting everything together and multiple iterations of designs, charts and narratives, this is how the figma prototype looked like before heading into webflow for development.

Designing all screens on a unified Figma grid streamlined development. I built a base Webflow container matching that grid, then dropped content in systematically.

The challenge was layering. To maintain precise positioning, content containers aren't stacked like a traditional landing page—they're superimposed within a full-screen body element, enabling scroll-triggered animations.

The final piece is an interactive website that walks users through the full investigation. It starts with Venezuela's healthcare crisis and institutional opacity, then outlines the policies shaping pandemic data collection.

It contrasts official data with hospital reports and statistical models, ultimately showing that government numbers don't reflect COVID-19's true impact in Venezuela.

But the project uses COVID data as a way into a larger question: if even public, "open" data like pandemic statistics was this flawed, what's happening in less visible areas of institutional reporting?

This is a call for transparency—not just in Venezuela, but everywhere.