Breaking Down COVID-19 Models – Limitations and the Promise of Machine Learning

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Every major news outlet offers updates on infections, deaths, testing, and other metrics related to COVID-19. They also link to various models, such as those on HealthData.org, from The Institute for Health Metrics and Evaluation (IHME), an independent global health research center at the University of Washington. Politicians, corporate executives, and other leaders rely on these models (and many others) to make important decisions about reopening local economies, restarting businesses, and adjusting social distancing guidelines. Many of these models possess a shortcoming—they are not built with machine learning and AI.
Predictions and Coincidence
Given the sheer numbers of scientists and data experts working on predictions about the COVID-19 pandemic, the odds favor someone being right. Like the housing crisis and other calamitous events in the U.S., someone took credit for predicting that exact event. However, it’s important to note the number of predictors. It creates a “multiple hypothesis testing” situation where the higher number of trials increases the chance of a result via coincidence.
This is playing out now with COVID-19, and we will see in the coming months many experts claiming they had special knowledge after their predictions proved true. There is a lot of …

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