ModelsCoronavirus Cases: Mathematical Modeling Draws More Accurate Picture

Published 14 April 2020

Mathematical modeling can take what information is reported about the coronavirus, including the clearly underreported numbers of cases, factor in knowns like the density and age distribution of the population in an area, and compute a more realistic picture of the virus’ infection rate, numbers that will enable better prevention and preparation, modelers say. “Actual pandemic preparedness depends on true cases in the population whether or not they have been identified,” says one researcher. “With better numbers we can better assess how long the virus will persist and how bad it will get. Without these numbers, how can health care systems and workers prepare for what is needed?”

Mathematical modeling can take what information is reported about the coronavirus, including the clearly underreported numbers of cases, factor in knowns like the density and age distribution of the population in an area, and compute a more realistic picture of the virus’ infection rate, numbers that will enable better prevention and preparation, modelers say.

“Actual pandemic preparedness depends on true cases in the population whether or not they have been identified,” says Dr. Arni S.R. Srinivasa Rao, director of the Laboratory for Theory and Mathematical Modeling in the Division of Infectious Diseases at the Medical College of Georgia at Augusta University. “With better numbers we can better assess how long the virus will persist and how bad it will get. Without these numbers, how can health care systems and workers prepare for what is needed?”

Better numbers also are critical to better protecting the population and overall pandemic preparedness, Rao and his colleague Dr. Steven G. Krantz, professor of mathematics at Washington University in St. Louis, Missouri write in the journal Infection Control and Hospital Epidemiology. (see also these articles: here , here, and here),

“We wanted to provide info on the real magnitude of the problem, not just the tip of the iceberg,” corresponding author Rao says.

Augusta says that they used their mathematical model, which takes COVID-19 numbers from sources like the World Health Organization, then used factors like an area’s population density, proportion of population living in urban areas where people tend to live in closer proximity, and populations in three age groups  — ages zero to 14, 15 to 64 and 65+ —  to grow more accurate numbers. Because this virus is so infectious, they also considered “transmission probability,” Rao says.

They also looked at the number of new cases daily above 10 and up to the first reported peak, and the date ranges for those peaks as an indicator of the trend in reported case numbers. Emerging information about how long the virus survives on a variety of surfaces and in the air will further refine their model, Rao says. The cutoff date for this study was March 9.