July 2, 2026
Pandemic math, emotional damage
Modeling the Covid-19 Outbreak with J (2020)
A COVID math post turned into a relatable cry for help over a baffling coding language
TLDR: The post explains, in plain steps, how a math model can simulate COVID spread and why reinfection and delays in symptoms matter. But the community’s loudest reaction was pure intimidation at seeing it all done in J, turning the comments into a very relatable “my brain hurts” moment.
A 2020 blog post set out to do something very serious: explain how people estimate the spread of COVID using a math-based disease model and the programming language J. The writer carefully walks through the basics — people start out able to catch the virus, then get exposed, then become infectious, then recover, and in this version, some can become vulnerable again. It’s all meant to make scary headline numbers, like huge infection estimates, feel less like magic and more like math.
But in the community, the biggest reaction wasn’t “wow, great model.” It was more like: who on earth can read J without developing a headache? The standout comment instantly stole the show with a painfully relatable mood: “gosh i wish i was smart enough to understand J.” And honestly, that became the real plot twist. Instead of fierce debate over virus forecasting, the vibe was equal parts awe, confusion, and self-dragging humor. The strongest opinion on display? That the math might be hard, but J makes it feel like the final boss of already terrifying material.
So yes, the article is about COVID projections, reinfection, and how numbers get estimated. But the comment-section energy is what gives it life: a one-line mini-meltdown that doubles as a universal tech meme. In a moment full of panic and uncertainty, the community found one thing to agree on — pandemic math is intimidating, and doing it in J somehow made it feel even more cursed.
Key Points
- •The article explains COVID-19 outbreak modeling through the SEIRS epidemiological model and a planned discrete-time simulation in J.
- •It contrasts the SIR model with SEIRS, arguing SIR assumptions of permanent immunity and immediate symptoms do not fit COVID-19 as presented in the post.
- •The SEIRS framework in the article uses four compartments: Susceptible, Exposed, Infectious, and Recovered.
- •The post introduces four rate parameters — β, σ, γ, and ξ — and states that in the simplified non-mortality model, R0 = β / γ.
- •The article uses cited values including R0 of about 2.28, incubation of roughly 10 days within a 2–14 day range, symptoms lasting about two weeks, and a 14% reinfection rate to derive simulation variables.