Covid Geographic Diffusion Interactive

How Does a Pandemic Travel? | Do Geography Lab

Do Geography Lab · Spatial Diffusion

How Does a Pandemic Travel?

Follow COVID-19 across the United States, test whether distance predicts its arrival, and discover why a pandemic moves through networks, institutions, and unequal systems of surveillance—not simply across empty space.

A geographical question

Which connections matter when something contagious moves among places?

Read every map twice

Reported cases measure surveillance as well as infection.

01

Prediction

Choose your model before seeing the evidence

Do not search for a pattern after you already know the answer. Make a prediction that the map can disappoint.

Choose one. Your prediction will remain visible while the evidence changes.

02

Mapping diffusion

One pandemic, several geographical waves

Move through eight archived weeks. Switch the denominator and watch the apparent center of the pandemic move again.

—
03

Distance test

Did COVID spread outward from an early center?

Choose an early anchor and a cumulative case threshold. Each dot shows how far a state lies from the anchor and when it crossed the threshold.

NortheastMidwestSouthWestgold ring = major air hub

What would a ripple look like?

A strong upward pattern would mean distant states generally reached the threshold later. A scattered cloud means distance alone leaves much of the timing unexplained.

04

Temporal diffusion

Build the S-curve

An S-curve can describe the diffusion of a threshold through places: slow emergence, rapid acceleration, then saturation. It does not mean every person followed the same path.

The cartographic trap returns

A smooth national S-curve can conceal a disorderly map: multiple introductions, regional surges, new variants, changing policy, unequal vulnerability, and shifting measurement. A tidy graph may summarize complexity without explaining it.

05

Interpretation

What did the model actually show?

Choose the strongest conclusion—not the loudest one.

Exit prompt

Explain one pattern—and one limitation.

Use evidence from the map, scatterplot, or S-curve to identify a diffusion process. Then name one feature of the data or model that prevents you from claiming too much.

Data and model notes

Case counts come from the CDC’s archived Weekly United States COVID-19 Cases and Deaths by State. Rates use 2020 Census resident populations. The air-hub overlay identifies states containing one of the top 15 airports in the FAA’s 2019 passenger-enplanement ranking.

Reported cases were shaped by testing availability, reporting practices, historical revisions, home testing, and unequal access to care. State-centroid distances are approximate. Correlation is descriptive, not causal. This is a teaching model, not an epidemiological reconstruction or medical guidance.

An original Rand-McKay “Do Geography” lab · Reviving the durable idea behind Tracking the AIDS Epidemic for a different pandemic and a different century

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