AUGUST - 20209intervention that a community should implement. These models have driven national social distancing and have saved somewhere between thousands and millions of lives. The fact that the detail is uncleardoes not diminish the value of the model, when used for its purpose. However, many organizations have begun to draw more detailed resource planning decisions from these models and this is costly mistake. Healthcare delivery networks are at particular risk for misusing these models. At the moment there is a lot of medical literature being rapidly produced to estimate what percentage of patients infected with COVID-19 will be hospitalized or need intensive care.When these percentages are applied to SEIR models they suggest many extreme scenarios and a frightening outlook for the average person. However, these models were not designed for this purpose and therefore it is a poor use of the model. In contrast local healthcare systems, businesses, and the average consumer would benefit from supplementing long term SEIR models with the COVID-19 equivalent of looking out your window; statistical models. Short term decisions should be made based on what real data is telling us. A commonly cited COVID-19 model that primarily uses statisticsis published by The Institute for Health Metrics and Evaluation (IHME -). This model has been particularly popular because it shows a much more positive outlook in many areas of the country. The statistical approach of this model has drawn a lot of ire from the epidemiologists who have spent a century developing SEIR disease models. They note that by only looking at the emerging trends it ignores the possible effects of a new outbreak or the future loosening of social restrictions. This is all true, but it does not make the model any less useful and fundamentally important to a particular audience, for a particular purpose. The CDC has recognized the value of reviewing a mixed set of models but little conversation seems to have occurred about the comparative limitations, and fundamentally different applications of the two modelling approaches.As the COVID-19 crises started, the initial issue that gained global attention was supply shortages. Supply chains can be susceptible to erratic purchasing behavior. When hospitals, nationwide, grew concerned about impending shortages they sought to order every personal protective item they could get. This had the potential to collapse the supply chain (you experienced this with your toilet paper). However, had we been looking at statistical growth rather than SEIR curves, some areas of the country may have had more logical ordering behavior which would ensure more equitable distribution of supplies. As hospitals and other industries start to re-open non-essential services they should manage resources by using statistical models to assess likely patterns for the next 1-2 weeks rather than rely on SEIR models which claim to forecast resource need. Similarly as a consumer trying to decide whether to go to the grocery store today or in two days, you are better served by a short term statistical model which may show that both days are equally "safe" rather than a long term model which may incorrectly show cases as skyrocketing in just a few days.As the first COVID-19 wave passes over us, the SEIR models are clearly forecasting future waves of outbreak. With those in mind, now is the time for health systems, state and local governments, and businesses to engage their statistical capabilities. Many sectors of our society have mastered statistically related models that trigger responses based on constant monitoring; whether it beseismic activity or pharmacovigilance. As we move into the marathon portion of COVID-19 we need more statistical models to help us rapidly identify when a pocket of COVID-19 is emerging and generate accurate, short term,resource needs for local systems to respond in an effective coordinated way. All of this should be done in tandem to policy makers periodically checking back to the SEIR models in order to plan for our longer day at the park. Jordan S. Peck
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