Understanding epidemiology models - Ars Technica

The models, some of which produce eye-catching estimates of fatalities, have driven headlines in addition to policy responses.

And it's fair for the public to ask why different models—or even the same model run a few days apart—can produce dramatically different estimates of future fatalities.

What's much less fair is that the models and the scientists behind them have come under attack by people who don't understand why these different numbers are an expected outcome of the modeling process.

So why have models produced so many different numbers, and why have the numbers seemingly changed so often.

Instead, many researchers have developed models, motivated by the desire to solve different problems or because they felt that a different approach would produce more accurate numbers.

And, to address the limitations, the people behind this model made a major change in early May, merging in a more traditional epidemiological model to their statistical estimator.

A completely different approach to modeling—one that takes into account things like mosquito control—may be required for these diseases.

The Imperial College model that helped drive policy in the US and UK early in the pandemic is incredibly sophisticated, taking into account things like average classroom and office sizes to estimate likely transmission opportunities.

Naturally, the different approaches will produce different numbers?

Should they take the numbers for something like a cruise ship, where the small, contained population can help provide a degree of precision to the estimates.

Do they take numbers from a country like Korea, where contact tracing was done efficiently.

Finally, data from a country like Italy may provide some good overall estimates of the disease's progression, but that data will suffer from limited overall testing and a likely undercount of the total fatalities.

But again, the different choices will almost certainly produce somewhat different numbers.