key: cord-0825493-9yrb5yw1
authors: Challener, Douglas W.; Dowdy, Sean C.; O’Horo, John C.
title: Mayo Clinic Strategies for COVID-19 Analytics and Prediction Modeling During the COVID-19 Pandemic
date: 2020-06-22
journal: Mayo Clin Proc
DOI: 10.1016/j.mayocp.2020.05.040
sha: 6103be02736bb9788aa5022bbdc5ddf898b048c9
doc_id: 825493
cord_uid: 9yrb5yw1

nan

The ongoing coronavirus disease 2019 (COVID-19) worldwide pandemic has generated substantial interest in mathematical and predictive modeling for infectious diseases. These models have been used to predict the course of the epidemic, inform disaster preparedness planning, forecast the economic outlook, and allocate limited resources, such as personal protective equipment (PPE) and testing supplies. COVID-19 has also made the limitations of modeling evident. It is important to understand potential-use cases, current modeling strategies, and underlying assumptions of existing models, as more reliance will be placed on these tools over the coming months.

Since the beginning of the pandemic, multiple groups have published predictive mathematical models for COVID-19, which vary substantially in the method of construction and in predicted outcomes. The Columbia University model, often cited by the New York Times, is based on classic epidemiology theory with a SEIR framework.

This model divides the population into 4 categories: susceptible, preinfectious (or exposed), infectious, and recovered (and presumed immune). 1 Another early model from the Imperial College London, based on traditional epidemiology with Bayesianbase Monte Carlo simulation, forecasted large numbers of British deaths in several projected scenarios and likely prompted a change in United Kingdom policy. 2, 3 The Institute for Health Metrics and Evaluation (IHME) model is based on matching regional and demographic data within the United States to worldwide locations further along in the epidemic and has thus far led to more optimistic models than either of the 2 mentioned above. 4 IHME modelers believe death rates are more accurate than case rates. By using information from countries that have already passed the peak of the pandemic, the IHME model requires fewer starting assumptions. Little agreement exists The most important purpose of the models is to inform institutional and nationwide efforts to ensure patient safety. Models of infection cases can inform the implementation of public policy measures designed to reduce the spread of the infection, such as social distancing and closure of nonessential businesses. They can also help guide institutional efforts to care for patients by ensuring adequate hospital beds, PPE, testing resources, and staffing. Predicting surges in infected caseloads can help determine if outpatient visits and surgeries should be scaled back to ensure adequate resources to care for the infected or allowed to grow to prepandemic levels.

Because of the substantial variation, Mayo Clinic has used multiple models to estimate the future burden of COVID-19 in our practice. This includes the aforementioned Columbia University and IHME models in addition to internally developed models that blend institution-specific data such as length-of-stay and hospitalization rates with state-based data such as rates of testing. In general, the models have shown a great deal of agreement for the near future despite different underlying assumptions. We have largely relied on models to help plan for a 2-week horizon, as the considerable divergence after this point and the rapid rate of change have made longer-term epidemic modeling less reliable.

We have also used modeling to estimate resource requirements, such as PPE Models are useful tools as long as the underlying assumptions and reasons for substantial divergence are understood. Policymakers and physicians must understand the basic assumptions underlying predictive models to use them effectively. Continued validation, recalculation, and, critically, education will allow mathematical modeling to assist in the response to COVID-19.

Flattening the curve before it flattens us: hospital critical care capacity limits and mortality from novel coronavirus (SARS-CoV2) cases in US counties

Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)

Impact of nonpharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand

IHME COVID-19 health service utilization forecasting team, Murray CJL. Forecasting the impact of the first wave of the COVID-19 pandemic on hospital demand and deaths for the USA and European Economic Area countries

Spread of SARS-CoV-2 in the Icelandic Population

Estimating epidemic exponential growth rate and basic reproduction number

Editing, proofreading, and reference verification were provided by Scientific Publications, Mayo Clinic.