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Public Health Policies Have Prevented Hundreds of Millions of Coronavirus Infections
Posted on by Dr. Francis Collins
The alarming spread of coronavirus disease 2019 (COVID-19) last winter presented a profound threat to nations around the world. Many government leaders responded by shutting down all non-essential activities, implementing policies that public health officials were hopeful could slow the highly infectious SARS-CoV-2, the novel coronavirus that causes COVID-19.
But the shutdown has come at a heavy cost for the U.S. and global economies. It’s also taken a heavy personal toll on many of us, disrupting our daily routines—getting children off to school, commuting to the office or lab, getting together with friends and family, meeting face to face to plan projects, eating out, going to the gym—and causing lots of uncertainty and frustration.
As difficult as the shutdowns have been, new research shows that without these public health measures, things would have been much, much worse. According to a study published recently in Nature [1], the implementation of containment and mitigation strategies across the globe prevented or delayed about 530 million coronavirus infections across six countries—China, South Korea, Iran, Italy, France, and the United States. Take a moment to absorb that number—530 million. Right now, there are 8.8 million cases documented across the globe.
Estimates of the benefits of anti-contagion policies have drawn from epidemiological models that simulate the spread of COVID-19 in various ways, depending on assumptions built into each model. But models are sophisticated ways of guessing. Back when decisions about staying at home had to be made, no one knew for sure if, or how well, such approaches to limit physical contact would work. What’s more, the only real historical precedent was the 1918 Spanish flu pandemic in a very different, much-less interconnected world.
That made it essential to evaluate the pros and cons of these public health strategies within a society. As many people have rightfully asked: are the health benefits really worth the pain?
Recognizing a pressing need to answer this question, an international team of scientists dropped everything that they were doing to find out. Led by Solomon Hsiang, director of the University of California, Berkeley’s Global Policy Laboratory and Chancellor’s Professor at the Goldman School of Public Policy, a research group of 15 researchers from China, France, South Korea, New Zealand, Singapore, and the United States evaluated 1,717 policies implemented in all six countries between January 2020, when the virus began its global rise, and April 6, 2020.
The team relied on econometric methods that use statistics and math to uncover meaningful patterns hiding in mountains of data. As the name implies, these techniques are used routinely by economists to understand, in a before-and-after way, how certain events affect economic growth.
In this look-back study, scientists compare observations before and after an event they couldn’t control, such as a natural disaster or disease outbreak. In the case of COVID-19, these researchers compared public health datasets in multiple localities (e.g., states or cities) within each of the six countries before and several weeks after lockdowns. For each data sample from a given locality, the time period right before a policy deployment was the experimental “control” for the same locality several weeks after it received one or more shutdown policy “treatments.”
Hsiang and his colleagues measured the effects of all the different policies put into place at local, regional, and national levels. These included travel restrictions, business and school closures, shelter-in-place orders, and other actions that didn’t involve any type of medical treatment for COVID-19.
Because SARS-CoV-2 is a new virus, the researchers knew that early in the pandemic, everyone was susceptible, and the outbreak would grow exponentially. The scientists could then use a statistical method designed to estimate how the daily growth rate of infections changed over time within a location after different combinations of large-scale policies were put into place.
The result? Early in the pandemic, coronavirus infection rates grew 38 percent each day, on average, across the six countries: translating to a two-day doubling time. Applying all policies at once slowed the daily COVID-19 infection rate by 31 percentage points! Policies having the clearest benefit were business closures and lockdowns, whereas travel restrictions and bans on social gatherings had mixed results. Without more data, the analysis can’t specify why, but the way different countries enacted those policies might be one reason.
As we continue to try to understand and thwart this new virus and its damage to so many aspects of our personal and professional lives, these new findings add context, comfort, and guidance about the present circumstances. They tell us that individual sacrifices from staying home and canceled events contributed collectively to a huge, positive impact on the world.
Now, as various communities start cautiously to open up, we should continue to practice social distancing, mask wearing, and handwashing. This is not the time to say that the risk has passed. We are all tired of the virus and its consequences for our personal lives, but the virus doesn’t care. It’s still out there. Stay safe, everyone!
Reference:
[1] The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Hsiang S, Allen D, Annan-Phan S, et al. Nature. 2020 June 8 [published online ahead of print].
Links:
Coronavirus (NIH)
Global Policy Lab: Effect of Anti-Contagion Policies (University of California, Berkeley)
Video: How much have policies to slow COVID-19 worked? (UC Berkeley)
Hsiang Lab (UC Berkeley)
“Global Policy Lab Rallies for COVID-19 Research,” COVID-19 News, Goldman School of Public Policy, June 5, 2020.
Can Smart Phone Apps Help Beat Pandemics?
Posted on by Dr. Francis Collins
In recent weeks, most of us have spent a lot of time learning about coronavirus disease 2019 (COVID-19) and thinking about what’s needed to defeat this and future pandemic threats. When the time comes for people to come out of their home seclusion, how will we avoid a second wave of infections? One thing that’s crucial is developing better ways to trace the recent contacts of individuals who’ve tested positive for the disease-causing agent—in this case, a highly infectious novel coronavirus.
Traditional contact tracing involves a team of public health workers who talk to people via the phone or in face-to-face meetings. This time-consuming, methodical process is usually measured in days, and can even stretch to weeks in complex situations with multiple contacts. But researchers are now proposing to take advantage of digital technology to try to get contact tracing done much faster, perhaps in just a few hours.
Most smart phones are equipped with wireless Bluetooth technology that creates a log of all opt-in mobile apps operating nearby—including opt-in apps on the phones of nearby people. This has prompted a number of research teams to explore the idea of creating an app to notify individuals of exposure risk. Specifically, if a smart phone user tests positive today for COVID-19, everyone on their recent Bluetooth log would be alerted anonymously and advised to shelter at home. In fact, in a recent paper in the journal Science, a British research group has gone so far to suggest that such digital tracing may be valuable in the months ahead to improve our chances of keeping COVID-19 under control [1].
The British team, led by Luca Ferretti, Christophe Fraser, and David Bonsall, Oxford University, started their analyses using previously published data on COVID-19 outbreaks in China, Singapore, and aboard the Diamond Princess cruise ship. With a focus on prevention, the researchers compared the different routes of transmission, including from people with and without symptoms of the infection.
Based on that data, they concluded that traditional contact tracing was too slow to keep pace with the rapidly spreading COVID-19 outbreaks. During the three outbreaks studied, people infected with the novel coronavirus had a median incubation period of about five days before they showed any symptoms of COVID-19. Researchers estimated that anywhere from one-third to one-half of all transmissions came from asymptomatic people during this incubation period. Moreover, assuming that symptoms ultimately arose and an infected person was then tested and received a COVID-19 diagnosis, public health workers would need at least several more days to perform the contact tracing by traditional means. By then, they would have little chance of getting ahead of the outbreak by isolating the infected person’s contacts to slow its rate of transmission.
When they examined the situation in China, the researchers found that available data show a correlation between the roll-out of smart phone contact-tracing apps and the emergence of what appears to be sustained suppression of COVID-19 infection. Their analyses showed that the same held true in South Korea, where data collected through a smart phone app was used to recommend quarantine.
Despite its potential benefits in controlling or even averting pandemics, the British researchers acknowledged that digital tracing poses some major ethical, legal, and social issues. In China, people were required to install the digital tracing app on their phones if they wanted to venture outside their immediate neighborhoods. The app also displayed a color-coded warning system to enforce or relax restrictions on a person’s movements around a city or province. The Chinese app also relayed to a central database the information that it had gathered on phone users’ movements and COVID-19 status, raising serious concerns about data security and privacy of personal information.
In their new paper, the Oxford team, which included a bioethicist, makes the case for increased social dialogue about how best to employ digital tracing in ways the benefit human health. This is a far-reaching discussion with implications far beyond times of pandemic. Although the team analyzed digital tracing data for COVID-19, the algorithms that drive these apps could be adapted to track the spread of other common infectious diseases, such as seasonal influenza.
The study’s authors also raised another vital point. Even the most-sophisticated digital tracing app won’t be of much help if smart phone users don’t download it. Without widespread installation, the apps are unable to gather enough data to enable effective digital tracing. Indeed, the researchers estimate that about 60 percent of new COVID-19 cases in a community would need to be detected–and roughly the same percentage of contacts traced—to squelch the spread of the deadly virus.
Such numbers have app designers working hard to discover the right balance between protecting public health and ensuring personal rights. That includes NIH grantee Trevor Bedford, Fred Hutchinson Cancer Research Center, Seattle. He and his colleagues just launched NextTrace, a project that aims to build an opt-in app community for “digital participatory contact tracing” of COVID-19. Here at NIH, we have a team that is actively exploring the kind of technology that could achieve the benefits without unduly compromising personal privacy.
Bedford emphasizes that he and his colleagues aren’t trying to duplicate efforts already underway. Rather, they want to collaborate with others help to build a scientifically and ethically sound foundation for digital tracing aimed at improving the health of all humankind.
Reference:
[1] Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Ferretti L, Wymant C, Kendall M, Zhao L, Nurtay A, Abeler-Dörner L, Parker M, Bonsall D, Fraser C. Science. 2020 Mar 31. [Epub ahead of print]
Links:
Coronavirus (COVID-19) (NIH)
COVID-19, MERS & SARS (National Institute of Allergy and Infectious Diseases/NIH)
NextTrace (Fred Hutchinson Cancer Research Center, Seattle)
Bedford Lab (Fred Hutchinson Cancer Research Center)
NIH Support: National Institute of General Medical Sciences
