Here’s the complete moodboard and references for the projects, linked to the authors.
You can find all the reports from the Encuesta Nacional de Hospitales here.
this is the website where the offical data was originally posted by the government.
beyond the count
A data visualization project exploring how Venezuela collected its COVID-19 data and why it doesn’t reflect the country’s real situation.
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,can 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.
1. The Official Data
Throughout the course of the pandemic, the Venezuelan government published its COVID-19 data on a now-defunct website that was updated nightly.
this information only listed total infections and deaths. never broken down by region, state, or hospital.
1. The Official Data
To retrieve the official data, I turned to the Johns Hopkins University repository, which archived global COVID data submitted to the WHO.
2. The statistical models
Several organizations raised concerns about the reliability of Venezuela’s official data. Among the most prominent voices was the National Academy of Sciences.
Twice during the pandemic, they published reports questioning the accuracy of government figures.
2. The statistical models
Using statistical modeling, they proposed alternative scenarios that likely painted a more realistic picture of the crisis. These models were essential references throughout this investigation.
3. the ENH data
The Encuesta Nacional de Hospitales is an independent initiative that, since 2018, has gathered weekly data from 40 major public hospitals in Venezuela to monitor the health system’s status.
3. the ENH data
Since March 13, 2020, the ENH expanded its scope to include daily data on pandemic-specific indicators: deaths by Acute Respiratory Infections, availability of protective equipment, and ICU bed usage, among others.
as an independent project, the ENH has limited resources. So the key questions became:
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:
Despite its limitations, this dataset offers a critical view into how the pandemic affected an already fragile healthcare system.
Because data was collected semi-manually, it needed extensive cleaning using tools like OpenRefine, Looker Studio, and, countless hours of meticulous reviewing.
Most errors were minor (typos or misplaced values) but nonetheless, they needed attention.
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:
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.
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(chart exported from flourish vs. edited)
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