Data Visualization

Blog of the Data Visualization & Communication Course at OSB-AUB

This is my favorite part about analytics: Taking boring flat data and bringing it to life through visualization” John Tukey

Where You Live Determines Your Risk: How Uneven COVID-19 Infection Patterns Reveal Disparities in Health Care Systems Across Lebanon

Where You Live Determines Your Risk: How Uneven COVID-19 Infection Patterns Reveal Disparities in Health Care Systems Across Lebanon

The Healthcare Scene in Lebanon
Rami spent the majority of his life in Aley, Choueifat El Aamrousiyeh, a quiet town where people know each other all throughout the area. When Covid-19 began spreading in Lebanon, he assumed that his location would be relatively safe in terms of health implications. After all, the news was primarily focused on Beirut.

During peak months, Rami started hearing about his neighbors testing positive at a pace he certainly did not expect. Meanwhile, his cousin Leila, who lives close by in Kahhaleh, hardly knew anyone infected. They were both in the same region, but faced entirely different risks.

Rami’s worry and stress levels grew a lot, especially for his elderly parents with chronic conditions. If Covid spread in his town at a fast pace, would they be able to get help in time? Would testing and vaccination centers be available in close proximity to where they live? Would nearby hospitals be overwhelmed with full capacities?  

Leila and Rami’s experiences reflect what many Lebanese families endure. Two households in the same region, but different towns, had completely different stress levels regarding the readiness of healthcare emergency responses. 

Health Patterns in Lebanon: What the Data Reveals
We tend to think of public health at the regional level, but covid behaved more so at a town level per region. This exposed imbalances that are not usually explored. Top town per region with the highest contribution to the total national case count revealed unexpected results:

  • In Aley (region), Choueifat Aamrousiyeh alone accounted for 2.75% of all cases in the country.
  • In Baalbek-Hermel, Baalbek alone stood out with 1.33%, which is much higher than surrounding towns
  • The remaining regions showed similar patterns: one or two towns carried the majority of cases

What Does This Mean Exactly?
People like Rami, who happen to live in a high risk town, experienced a completely different pandemic from people in towns just a few kilometers away. This is likely to repeat in the future if another major healthcare crisis hits the country.

Moving Forward, What Can Be Done?

  1. Prioritize hotspot towns: testing centers, clinics, and awareness campaigns should start where case data shows concentration, not where population is highest.
  2. Build local readiness plans: Instead of generic region level plans, towns with higher infection percentages need specific preparation steps (rapid testing, temporary isolation centers, and community awareness). 
  3. Use data driven action plans: Covid case percentages help identify where outbreaks are likely to happen again. If regions plan smarter, hospitals and clinics face less chaos.
  4. Strengthen communication and public awareness: Towns with consistently high rates should receive ongoing health messaging to prevent repeat scenarios.

The Key Takeaway
By understanding how Covid-19 was not distributed proportionately across towns, we can finally design smarter, more effective responses. This applies not only to pandemic/epidemics, but to any future public health threat in Lebanon.