This summer, when bars, restaurants and shops began to open across the United States, people set off despite the continuing threat of COVID-19.
As a result, in many areas, including the St. Louis, an increase in cases was recorded in July.
Using mathematical modeling, a new interdisciplinary research from the laboratory of Arye Nehorai, Professor of Electrical Engineering Eugene & Martha Lohman at the Department of Electrical Engineering and Systems Engineering Preston M. Green at the University of Washington at St. Louis. the boundary between economic stability and the best possible health outcomes is crossed.
The group – which also includes David Schwartzman, PhD in business economics at Olin Business School and Uri Goldsztejn, PhD in biomedical engineering at McKelvey School of Engineering – announced its findings on December 22 in PLOS ONE.
The model suggests that from the scenarios considered by communities, they could maximize economic productivity and minimize disease transmission if, while the vaccine was not available, older people generally stayed at home, while younger people gradually returned to the workforce.
“We have developed a predictive model for COVID-19 that takes into account for the first time its interrelated effect on economic and health outcomes for different quarantine policies,” Nehorai said. “You can have an optimal quarantine policy that minimizes the impact on both health and the economy.”
The paper is an extended version of the Sensitive, Exposed, Infectious, Recovered (SEIR) model, a commonly used mathematical tool for predicting the spread of infections. This dynamic model allows people to move between groups known as sections and that each section in turn affects the other.
Basically, these models divide the population into four sections: Those who are susceptible, exposed, contagious, and recovered. Innovating this traditional model, Nehorai’s team included infected but asymptomatic people, taking into account the state-of-the-art understanding of how transmission can work differently among them, as well as how their behavior may differ from people with symptoms. This has been shown to greatly influence the model outcomes.
People were then divided into different “subdivisions,” for example age (those older than 60) or by productivity. This was a measure of a person’s ability to work from home in the event of quarantine measures. To do so, college diplomas were viewed as a proxy who could continue to work during the quarantine period.
Then they started working, developing equations that modeled the ways people moved from one section to another. The movement was influenced by politics as well as the decisions made by the individual.
Interestingly, the model included a dynamic mortality rate – one that declined over time. “We had a mortality rate that over time represented improvements in medical knowledge,” said Uri Goldstein, a doctorate in biomedical engineering. “And we see that now; mortality rates have dropped.”
“For example,” Goldstein said, “if the economy shrinks, there will be a greater incentive to leave quarantine,” which could appear in the model as people move from an isolated compartment to a sensitive compartment. On the other hand, the transition from infectious to recovery is based less on a person’s actions and can be better determined by recovery or mortality rates. In addition, the researchers modeled the mortality rate as it decreased over time, due to medical knowledge of how to treat COVID-19, becoming better over time.
According to Schwartzman, the team studied three scenarios. In all three scenarios, the deadline was 76 weeks – it was then assumed that the vaccine would be available – and the older ones remained largely quarantined until then.
- If strict isolation measures were maintained at all times.
- If after straightening the curve there was a rapid relaxation of the isolation measures by younger people to normal movement.
- If, after straightening the curve, insulation measures for younger people were slowly abolished.
“The third case was the best in terms of economic damage and health results,” he said. “Because there has been a spread of another disease in the rapid relaxation scenario, the restrictions will be restored.”
Specifically, they found in the first scenario, there are 235,724 deaths and the economy is shrinking by 34%.
In the second scenario, where there was a rapid easing of isolation measures, there is a second outbreak for a total of 525,558 deaths, and the economy shrinks by 32.2%.
With gradual relaxation, as in the third scenario, there are 262,917 deaths, and the economy is shrinking by 29.8%.
“We wanted to show that there is a compromise,” Nehorai said. “And we wanted to mathematically find where the sweet spot is?” As with so many things, the “sweet spot” was not at any extreme – completely locking or continuing as if there were no viruses.
Another key finding was one that no one should be surprised about: “People’s susceptibility to contagion is linked to the precautions they take,” Nehorai said. “It’s still important to use precautions – masks, social distancing, avoiding crowds and hand washing.”