Professors in Texas Tech University's Department of Mechanical Engineering updated their predictive model to include three additional states.
Professors in Texas Tech University's Department of Mechanical Engineering through the Edward E. Whitacre Jr. College of Engineering, who developed a new epidemiological model for predicting COVID-19 spread, have updated their predictions to now include eight representative U.S. states: Arizona, California, Florida, Illinois, Louisiana, New Jersey, New York and Texas. These major states together now constitute 43% of the entire U.S. population, while the projection is recently calculated separately for all 50 U.S. states.
Fazle Hussain, the President's Endowed Distinguished Chair in Engineering, Science & Medicine; Zeina Khan, a research assistant professor of mechanical engineering; and Frank Van Bussel, a postdoctoral researcher, recently wrote, "A Predictive Model for COVID-19 Spread Applied to Eight U.S. States." Reopening being a significant episode in the trajectory of any state, the update now accounts for the notable evolving consequences, namely rapid proliferation, of cases in the reopening phases of several states.
New predictions the paper makes include:
• COVID-19 will become endemic, circulating for more than two years, subject to availability of a proven vaccine.
• Most states may experience a secondary peak in 2021 if states reopen.
• The number of COVID-19 deaths would be significantly lower in most states if lockdowns had been kept in place.
• Decreasing contact rate by 10%, or increasing testing by 15% or doubling lockdown compliance from the current 15% could eradicate infections in the state of Texas within a year.
"Our motivation was to formulate some analytical and quantitative scenarios of the evolving pandemic," Hussain said. "We combined our expertise in bioengineering, mathematical modeling and computer simulation and developed a comprehensive model of COVID-19 spread and infection rates consisting of relevant and important parameters.
"Identifying both generic and COVID-specific model parameters, as well as the different population groups in the model and their interactions, was the first step where we had to draw on significant judgement. While choices of these parameters and the number of equations can vary, having an optimum choice is desirable, which we have been successful in identifying and, also, extracting the predictions from the computer solutions for seven variables. Quantitatively evaluating the parameters by fitting the model with available, reliable case and death data was the first hurdle; next finding the projections by solving the model equations, and determining the sensitivity of the model to the model parameters were some of the major steps."