Julio simón

A data visualization project exploring how the Venezuelan government collected its COVID-19 data while comparing it with direct report from major hospitals.

When the pandemic hit, data became crucial.

It told us what was happening: how many were infected, how many had died, and whether it was safe to go outside. It also allowed for comparisons: which countries were handling the crisis more effectively?

But in Venezuela, a country where institutional opacity is the norm, (especially in the health sector) and where the public healthcare system can't meet basic needs, could we trust official data?

This project investigates whether the government-reported figures reflect the on-the-ground reality by comparing them with public data and independent records collected directly from hospitals by the Encuesta Nacional de Hospitales (ENH).

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

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

As epidemiological reports and metadata can get technical fast, this project aims to make the investigation accessible and easy to follow.

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There were three main sources of data, in order to get the full 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.

• The statistics models and its conclussion ran by 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, sequential narrative.To structure the story, I focused on four core questions:

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

References:

The design needed to reflect the project’s central concern: a meta-narrative on public health data. But it also had to be clear and digestible, not a dense journal article.

I drew inspiration from early digital UIs and bit-style graphics: simple, high-contrast elements with minimalist palettes, all laid out in an editorial grid.

Structure:

Instead of a long, scrollable article, I chose a “screen-by-screen” slide format to let users explore the content at their own pace.

The layout was based on a 12-column grid (grouped into 4s when needed). Text and visuals were placed intentionally within white space to reduce cognitive load and allow the content to breathe.

Iconography:

The project’s main visual symbol is a wireframe PCR test tube referencing both the pandemic’s data origins and the need for transparency.

The wireframe style serves as a metaphor for the clarity and scrutiny the project seeks to promote.

Data Visualizations:

Visualizations had to be narrative-driven and self-explanatory, even when viewed outside of context. Simplicity and clarity were key.

Most charts were created in Flourish, then edited in Figma to match the overall graphic style.

For interactive graphics, CSV data was exported and converted into Lottie files to enable cursor animations within Webflow.

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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 screen compositions on a unified Figma grid saved hours during Webflow development. I only had to build a base container with the same grid and drop in the content.The challenge came with layering.

To maintain precise placements, content containers weren’t stacked like in a traditional landing page. Instead, they were superimposed within a full-screen body element, allowing for scroll animations.

The final piece is an interactive website that walks users through the investigation. It begins with the broader context of Venezuela’s healthcare crisis and institutional opacity, then outlines the policies that shaped pandemic data collection.

It contrasts official data with hospital reports and statistical models, ultimately arguing that due to methodological flaws, government numbers don’t reflect the true impact of COVID-19 in Venezuela.

But beyond that, the project uses COVID data as a Trojan horse to ask a larger question: If even public, "open" data like pandemic stats was this flawed, what might be happening in less visible areas of institutional reporting?

In the end, this is one more call for institutional transparency. not just in Venezuela, but everywhere.