Above photo: COVID-19, healthcare workers wheel the bodies of deceased people from the Wyckoff Heights Medical Center in Brooklyn By Andrew Kelly for Reuters
Emergency health measures implemented in six major countries have “significantly and substantially slowed” the spread of the novel coronavirus, according to research from a UC Berkeley team published today in the journal Nature. The findings come as leaders worldwide struggle to balance the enormous and highly visible economic costs of emergency health measures against their public health benefits, which are difficult to see.
In the first peer-reviewed analysis of local, regional and national policies, the researchers found that travel restrictions, business and school closures, shelter-in-place orders and other non-pharmaceutical interventions averted roughly 530 million COVID-19 infections across the six countries in the study period ending April 6. Of these infections, 62 million would likely have been “confirmed cases,” given limited testing in each country.
Continuation of these policies after the study period has likely avoided many millions more infections, according to lead author Solomon Hsiang, director of Berkeley’s Global Policy Laboratory and Chancellor’s Professor at the Goldman School of Public Policy.
“The last several months have been extraordinarily difficult, but through our individual sacrifices, people everywhere have each contributed to one of humanity’s greatest collective achievements,” Hsiang said. “I don’t think any human endeavor has ever saved so many lives in such a short period of time. There have been huge personal costs to staying home and canceling events, but the data show that each day made a profound difference. By using science and cooperating, we changed the course of history.”
Maps based on detailed data show how emergency policies helped control the pandemic — and what would have happened without those measures.
The study evaluated 1,717 policies implemented in China, South Korea, Italy, Iran, France and the United States in the period extending from the emergence of the virus in January to April 6, 2020. The analysis was carried out by Hsiang and an international, multi-disciplinary team at the Global Policy Laboratory, all working under shelter-in-place restrictions.
Recognizing the historic challenge and potential impact of the pandemic, “everyone on our team dropped everything they were doing to work on this around the clock,” said Hsiang.
Before intervention, exponential growth
The novel coronavirus emerged in late 2019 in Wuhan, China, and in a matter of weeks, spread around the globe. The medical impact was compounded by the devastating economic slowdown. How great was the risk posed by the virus? Without comprehensive data on policy interventions, it would not have been possible to gauge how quickly it would have spread naturally. But according to the new study, infections were growing 38% per day on average — doubling roughly every two days in each country — before crisis policies slowed the spread.
Today, global cases are nearing 7 million, with just over 400,000 dead. But the UC Berkeley research suggests that the toll would have been vastly worse without policy interventions.
“So many have suffered tragic losses already. And yet, April and May would have been even more devastating if we had done nothing, with a toll we probably can’t imagine,” Hsiang said.
“It’s as if the roof was about to fall in, but we caught it before it crushed everyone. It was difficult and exhausting, and we are still holding it up. But by coming together, we did something as a society that nobody could have done alone and which has never been done before.”
The life-saving impact of policy
Large-scale policies were initially deployed based on recommendations from epidemiological modeling teams that simulated the spread of COVID-19 under different sets of assumptions. But since both the pandemic and policy responses are unprecedented, nobody knew in advance which policies would work or how effective they would be.
The UC Berkeley team studied how quickly the number of COVID-19 infections grew within different locations before and after policies were enacted, for the first time measuring how much each policy contributed to flattening the curve.