There’s no shortage of models attempting to forecast COVID-19 cases and deaths. Here on CivicMeter, we’ve covered a few of the most prominent ones for our Modelpedia. But the numbers vary wildly, and it raises the question: why is it so hard to model a pandemic?
FiveThirtyEight has written a great explainer showcasing why pandemics are uniquely difficult to model.
Maggie Koerth, Laura Bronner and Jasmine Mithani write:
Every variable is dependent on a number of choices and knowledge gaps. And if every individual piece of a model is wobbly, then the model is going to have as much trouble standing on its own as a data journalist who has spent too long on a conference call while socially isolated after work.
Consider something as basic as data entry. Different countries and regions collect data in different ways. There’s no single spreadsheet everyone is filling out that can easily allow us to compare cases and deaths around the world. Even within the United States, doctors say we’re underreporting the total number of deaths due to COVID-19.
The same inconsistencies apply to who gets tested. Some countries are giving tests to anyone who wants one. Others are … not. That affects how much we can know about how many people have actually contracted COVID-19, versus how many people have tested positive.
And the virus itself is an unpredictable contagion, hurting some groups more than others — meaning that local demographics and health care access are going to be big determinants when it comes to the virus’ impact on communities.