One of the key goals of science is to create theoretical models that are useful at describing the world we see around us. However, no model is perfect. The inability of models to replicate observations is often called the “synthetic gap.” For example, it may be too computationally expensive to include a known effect or to vary a large number of known parameters. Or, there may be unknown instrumental effects associated with variability in conditions during the data acquisition.
In responding to a pandemic, time is of the essence. As the COVID-19 pandemic has raged on, it has become evident that complex decisions must be made as quickly as possible, and quality data and statistics are necessary to drive the solutions that can prevent mass illness and death. Therefore, it is essential to outline a robust and generalizable statistical process that can not only help to diminish the current COVID-19 pandemic but also assist in the prevention of potential future pandemics.