Mathematical Model Shows Heterogeneous Approach Might Be Best for Reducing COVID-19 Deaths
By Jocelyn DuffyMedia Inquiries
As people worldwide are practicing social distancing to help stop the spread of the novel coronavirus, COVID-19, mathematicians from Carnegie Mellon University and the University of Pittsburgh School of Medicine have developed a mathematical model to evaluate the potential impact different levels of mitigation efforts will have on mortality rates from the disease.
In their models, Carnegie Mellon’s Wesley Pegden and the University of Pittsburgh’s Maria Chikina found that a heterogeneous approach that focuses efforts on decreasing transmission of the virus among the most at-risk populations, specifically those over 65, would result in the fewest number of deaths.
“First and foremost, we want to be clear that we are not saying that mitigation efforts for COVID-19 are not needed. On the contrary, the worst-case scenarios for our models — those with the most mortalities — are those where no action is taken,” said Pegden, associate professor of mathematical sciences.
“Our models showed that we could minimize the number of deaths by enacting mitigation strategies that selectively focus most of their effort on reducing transmission rates in people over the age of 65.”
COVID-19 is a highly transmittable infectious disease that impacts age groups differently. It is much more lethal among older adults and those with certain health conditions.
In their model, Pegden and Chikina looked at how strategies, including those that focus on the entire population and those that focus on at-risk groups, would impact overall mortality rates during an outbreak of a virus with a profile similar to that of COVID-19. Their parameters included information about not only deaths caused by the virus, but also deaths caused when the medical infrastructure becomes overwhelmed by cases of the virus.
Their models showed that when mitigation strategies for the over-65 population were increased, the number of deaths were reduced by 50-60% over when no interventions were taken.
“Our models suggest that by focusing on the most vulnerable, we can reduce deaths not only by reducing the number of cases of an infection, but by lessening the burden on the health care system,” said Pegden.
The researchers are careful to point out that their data does not make a case against mitigation efforts. They also note that their models can’t predict the future and that their work doesn’t determine the effectiveness of any specific mitigation policies.
The researchers’ report can be found online: https://math.cmu.edu/~wes/covid.html