This Trick Could Have Stopped COVID-19 Across a Whole Country

When COVID-19 first appeared in Chile, how it spread gave scientists a unique chance to observe how diseases move through a country with an unusual shape. Chile’s geography—long and narrow—played a big role in how the virus traveled. The way the virus moved wasn’t just about where people went or the rules put in place. It also followed patterns similar to natural systems, where small changes in one area can lead to much bigger impacts across the whole system. Knowing when a disease shifts from local outbreaks to a countrywide crisis is especially important in a country like Chile.

Professor Mauricio Canals from Universidad de Chile studied this process closely. He used a type of mathematical model called a logistic model, which describes how growth starts slowly, speeds up, and then levels off. He also applied something known as percolation theory, which examines how things like fluids or diseases move through connected spaces, to understand how COVID-19 spread throughout Chile. His research, published in the journal Cureus, involved simulating the country’s communes—its local administrative areas—on a grid. This helped him see how the number of infections and the way areas are connected affect the growth of an epidemic.

In the simulations, the virus didn’t spread across the whole country when only a smaller number of communes were infected. The spread stayed patchy. But once most of the communes had cases, the virus could move freely across Chile without any gaps. Real-world data confirmed this: once the virus reached enough places, stopping it from spreading everywhere became extremely difficult.

One important takeaway from this work is how well the logistic model matched what actually happened. It showed that if even a smaller portion of the communes had stayed free of the virus, the countrywide spread might have been avoided. “The logistic model of the spatial spread of an epidemic allows an estimation of the time when the threshold would be reached, which constitutes a window during which mitigation or control measures can be implemented,” said Professor Canals. In this context, a threshold is the critical point at which a small change can lead to a much larger and more widespread effect. His estimates closely matched the moment when the country actually crossed that tipping point, offering a valuable early warning tool.

This isn’t just a theory for researchers. It has real-world importance. It shows how stopping the virus in key areas could prevent it from spreading more widely. Chile’s long shape made it a perfect place to test this idea. If some central areas remained healthy—especially those that stretch across the country from east to west—they could act like natural blocks against the spread of the virus.

Professor Canals also explained how geography affected the early stages of the outbreak. The virus first appeared in the north, center, and south, creating separate clusters. These clusters gradually grew and eventually joined into one large infected zone. Some small, remote communes avoided infection for a while, but because they were small and had people moving in and out, they didn’t stop the virus overall. The grid-based model—a simplified map-like simulation used to represent space and movement—was run thousands of times and showed that the chance of a full-country spread increased sharply when a large share of communes had cases.

In the end, this research supports using models like these to understand and plan for epidemics. They can help predict when a situation will get out of control and show where to focus prevention efforts. “It was useful to show that a significant majority of the communes needed to be infected for the entire country to be affected,” said Professor Canals. This kind of insight helps public health officials develop smart, focused strategies to slow or stop the spread of future diseases.

Journal Reference

Canals M. “Spatiotemporal Analysis of the Spread of the COVID-19 Epidemic in Chile Using a Percolation Model.” Cureus, 2025; 17(3): e80468. DOI: https://doi.org/10.7759/cureus.80468

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