Pennsylvania Governor Tom Wolf's COVID-19 restrictions saved thousands of lives, according to a new study from researchers at the University of Pittsburgh.
University of Pittsburgh physician Mark S. Roberts and his team developed a model to evaluate the impacts of closing and reopening schools, offices, restaurants, and stores. In the study, Roberts and his team considered the biology of how the virus is transmitted, as well as local data on restrictions and reopening dates.
Pennsylvania has reported over 152,000 COVID-19 cases and nearly 8,000 confirmed deaths across the state to date. In a ruling on Monday, U.S. District Judge William S. Stickman IV ruled that components of Wolf's COVID-19 response were unconstitutional, including his decision to limit indoor and outdoor gatherings, stay-at-home orders, and decisions to temporarily close businesses. But studies show that without these restrictions, COVID-19’s impact on Pennsylvania could have been several times higher.
Roberts and his team found that, on average, Pennsylvania engaged in a 50% level of social distancing for the first six months of the pandemic, with 0% indicating no restrictions and 100% representing a complete lockdown. The percentage hit its high of around 80% between March and April but has since decreased to under 50%.
The team predicted around 6,000 additional deaths, just under the current number of reported deaths, would have occurred if there had been no restrictions put in place. If the state maintained an average of only 30% distancing over the first six months, the number of deaths would have been at least twice as high, according to the study.
“It clearly has saved lives, no question at all,” Roberts told The Philadelphia Inquirer. “It’s easy to project that there would be two to three times the deaths, at a minimum, with less social distancing.”
Further evidence has supported the finding that social-distancing restrictions have saved lives. Researchers have found that earlier lockdowns have prevented more deaths but additional studies are needed to determine the best combination of restrictions.