Ryan McCorvie

 During the past six years statistician Ryan McCorvie, has been researching random processes with cascades, crashes or contagion. McCorvie came to disease modeling in a fairly circuitous route — before pursuing his Ph.D. in statistics his background was in finance. “It turns out that a number of the same techniques used to calibrate forecasts for currency exchange rates may also be applied if you are wanting to forecast ICU bed demand,” based on McCorvie. “You can find general statistical principles which apply to many situations.”

One of many first goals in epidemiology is to gather the basic data about the illness, such as the amount of cases, demographic data about those afflicted, and the quantity of testing being done. Modeling takes the next thing, integrating the raw data with mathematical models to gain additional insights. Models outputs have now been featured prominently in policy discussions, such as Trump's Coronavirus Task Force briefing which incorporated projections from the University of Washington's IHME group.

In accordance with Ryan McCorvie, you can find three major areas where modeling provides an advantage. First is in determining the current growth rate of the illness, something which may also be called a “nowcast” to tell apart it from forward-looking models. For infectious disease, the current growth rate is captured employing a statistic called “R.” “This represents the speed at that the disease is spreading. ‘R'could be the amount of people infected by an individual. In this way, an ‘R'value above ‘1'corresponds to exponential growth, whereas an ‘R'below ‘1'corresponds to a disease that will be dissipating,” states McCorvie.

Secondly, forecasting can aid in near-term planning. Models can project the utilization of limited resources, like hospital beds or personal protective equipment. These kind of projections are precise and quantitative, since policy makers have to know what kind of planning and reallocation is needed. Finally, model results can be utilized to simulate different policy approaches and provide needed insights. Usually these planning models are long run, and the answers are considered holistically as opposed to as precise quantitative estimates. Like, one can simulate different approaches to contact tracing, or different schemes for vaccination, to judge their relative effectiveness.

“One of many hardest problems is how to support and communicate uncertainty,” says Mr. McCorvie. Forecasting is really a tricky business. To be accurate, forecast modeling depends on two primary criteria: accurate data and some historical behavioral perspective. Neither are completely available with COVID-19 modeling. While there is an abundance of data it's not gathered and stored in consistent formats. Collected data can vary country-by-country and even state-by-state. That is exacerbated by a lack of historical information. Simply speaking, we should count on an extremely short history and many unknowns when it comes to how a virus behaves and is transmitted. Any model must try to compensate for these uncertainties and inconsistencies. Weather forecasters are long familiar with this dilemma, as most everyone can be used to the cones of probability that accompany forecasts about hurricanes. We've all seen the spaghetti art diagrams that be a consequence of variations between hurricane path forecast models. Similar principles connect with forecasts of infectious diseases. Sourced elements of uncertainty, including the truth that key model inputs are estimates instead of being precisely known, and uncertainty stemming from the intrinsic random nature of several models, must certanly be contained in any forecast.

By bringing together experts from various fields, and asking them to collaborate with similar data groups around the country, the hope is that predictive modeling can become more accurate. “That's why I needed to have involved,” McCorvie says. “I know how important accurate forecasting will be to how exactly we end the pandemic. I hope our efforts help people see their way through this and possibly even save lives.”