Producing a Vaccine Requires More Than a Patent
Intellectual Property Is Just One Piece of an Elaborate Process
In 1900, the deadliest hurricane in U.S. history hit Galveston, Texas. The storm, estimated to have been a Category 4, all but washed the city away. An estimated 8,000 people died, and even more lives would likely have been lost if Isaac Cline, the chief of the Texas section of the U.S. Weather Service, had not spent the day before the hurricane’s arrival walking around and urging people to seek higher ground. He did so on little more than a hunch based on the fact that a bad storm had recently passed over Cuba. The science of weather forecasting had yet to emerge; guesswork was the best anyone could do.
Over a century later, hurricane forecasts are a central feature of summer and fall for millions of Americans. Such forecasts, along with those predicting winter storms, tornadoes, and floods, have saved an untold number of lives and many billions of dollars. Even fair-weather forecasts play an important role in modern life. Take the airline industry, for example. “Even when you’ve got clear skies, that has an economic benefit,” explained Greg Romano of the National Weather Service, “because then you don’t necessarily need to plan to reroute as much, so you can perhaps take on less fuel; you know that you can have tighter schedules.”
With the world moving quickly toward an age of pandemics, the story of how weather forecasting in the United States improved deserves attention. Because when it comes to modeling the likely course of contagious outbreaks, the country is in some ways closer to the bad old days of deadly, no-notice hurricanes than to the current era of precision storm tracking and multiday weather forecasts.
Today, if a hurricane was barreling down on Florida, no one would suggest that an arbitrary assortment of academics gather data, quickly develop models to predict the storm’s course, anticipate its landfall, tell agencies where to preposition response teams, and craft warnings for the public. That, however, is essentially what the U.S. government has done during the COVID-19 pandemic.
Of course, sophisticated disease modeling has contributed a great deal to the response to the pandemic. Early in the outbreak, modelers estimated that the new coronavirus would prove more deadly than influenza. Others concluded that rapid containment of the virus in China was unlikely, based on the unusually high number of infected international travelers. And when the United States was in the throes of the worst outbreak in the world, it was modelers whose work informed the White House task force that coordinated the federal response, including the decision to begin (and later extend) the “Slow the Spread” campaign of social-distancing measures and business closures.
Unfortunately, however, the full force of epidemiological expertise in the United States is not being brought to bear. Despite some successes, serious gaps remain in the ability of infectious disease models to inform public health policy. That is because the country has no centralized system for disease forecasting; there exists no epidemiological equivalent of the National Weather Service. That is precisely what the country needs to succeed in the fight against COVID-19 and to avoid future failures and missteps of the sort that has marred the U.S. response to the current pandemic.
This is hardly the first time that academics have filled the disease-modeling vacuum. In 2009, during the H1N1 pandemic, officials in Pennsylvania consulted modelers as they deliberated about whether to shut down schools. During the Ebola outbreak of 2014, modelers helped the U.S. Centers for Disease Control and Prevention (CDC) and the Department of Health and Human Services calculate the risk that travelers would import cases into the United States, how many hospital beds would be needed domestically, and how many future cases could be expected under varying scenarios.
But after those outbreaks ended, the ad hoc and informal ties that had developed between modelers and decision-makers were not institutionalized; no formal system emerged for centralizing expertise within the government. So it should have been no surprise that the few modeling experts who do this work within government agencies were quickly overwhelmed by the enormous number of consequential decisions demanded by the COVID-19 pandemic. And once again, academic modelers have stepped in to fill the gap.
This is part of a familiar pattern. During contagion crises, modeling becomes a priority. However, as the sense of urgency fades, attention shifts to other issues and relationships between experts and decision-makers fade. The chief danger is that as the results of modeling shift, those changes won’t get on the radar of policymakers. “The projections provide information at a particular moment but unfortunately will change all the time. It’s important to communicate that uncertainty,” said Jeffrey Shaman, a professor of environmental health sciences at Columbia University. Shaman’s research has been consulted by the White House task force on COVID-19, but Shaman says that the working relationship has been limited.
During contagion crises, modeling becomes a priority. However, as the sense of urgency fades, attention shifts to other issues.
The CDC’s epidemiology task force has convened weekly meetings with pandemic experts. These meetings are a marked improvement in coordination and information exchange over such efforts in previous outbreaks. However, the connection to key decision-makers, including the White House task force, has not been clear.
One result has been a proliferation of models without enough people to make sense of them. “It’s really hard to separate the wheat from the chaff, without someone or a group of people evaluating the assumptions and form of each model,” said Shaman. “There are a lot of teams who want to contribute to modeling efforts, but we don’t have a clear game plan for separating and combining the various projections.”
To better integrate disease forecasting into decision-making, the CDC and other public health agencies should look to the history of the National Weather Service for inspiration. Americans take for granted the elaborate, highly detailed weather information they can easily access. But such tools developed only after decades of sustained public investment.
Numerical weather forecasting emerged in the 1920s in the wake of mathematical advances that helped describe atmospheric physics. Decades later, computers allowed meteorologists to apply more elaborate models. Other new technologies—aircraft, weather balloons, satellites, and digital communications—made it possible for scientists to add ever more data to the models, creating a virtuous cycle that led to progressive improvements in forecasting over time.
But technological change is only part of the story; the other part involves organizational change. During the Civil War era, tracking and anticipating weather was a largely academic effort. Later, disparate independent efforts emerged in multiple federal agencies, including the Department of War, the Department of Agriculture, and the Navy. Finally, in the late 1960s, what would eventually become the National Weather Service was established as an integrated office in the Department of Commerce. This unification and centralization was crucial: it allowed for significant investment over time and created a stable point of contact between forecasters and policymakers by giving weather experts a permanent seat at the table.
Outbreak modeling and analytics should be centralized in a similar way. Over the last decade or so, public investments in disease forecasting have yielded some progress. Programs funded by the National Institutes of Health, the National Science Foundation, and the Department of Homeland Security have advanced modeling capabilities, generated excellent research, and helped to develop a cadre of well-trained experts.
These programs, however, are largely focused on developing basic science, as opposed to helping public health officials navigate difficult decisions about outbreak control. And they rarely receive the kind of reliable, sustained funding that has long supported weather forecasting. Generally, funding takes the form of grants to principal investigators that typically last only three to five years and must result in academic publications—the kind of research that is necessary but not sufficient to the task at hand.
“Everyone is on grants,” noted Cécile Viboud, a staff scientist at the National Institutes of Health. “So if you don’t have a particular dedicated line of funding to work on the [outbreak], then you do it on your own time. You just run those estimates because maybe it can help.”
Making matters worse, private-sector innovation and advanced data technologies have hardly made an appearance when it comes to contagion modeling, in part because there is usually not a great deal of market demand, public health agencies are ill-equipped to evaluate new data technologies, and new technologies must pass through a torturous regulatory path before gaining approval.
Health data are fragmented across the U.S. health-care system in silos that cannot talk to one another.
Roni Rosenfeld, a professor of computer science at Carnegie Mellon University, pointed out another problem. “By far the biggest obstacle is the data,” he lamented. Health data are fragmented across the U.S. health-care system in silos that cannot talk to one another. And public health agencies are not known for being forward leaning on data technology. This hampers their ability to acquire, process, share, analyze, and communicate data that would inform an outbreak response.
“There is no systematic report format for individual locations to provide data in,” said Lauren Gardner, an engineering professor at Johns Hopkins University who led the team that built the university’s coronavirus map, which has become one of the most reliable sources of data on the spread of COVID-19. “Even when data is provided, it may not be in a usable format. Together these issues substantially limit the capabilities of disease models to be able to inform policy in real time.”
Unfortunately, the pandemic is laying bare these data challenges. The White House task force announced last month that all 4,700 or so hospitals in the United States would be asked to send daily emails to the task force with data on their capacities. It’s baffling that in the country that sparked the digital revolution, hospitals must rely on such a rudimentary, unreliable tool in order to transmit vital information.
It’s also striking that the most timely and detailed data on new cases of COVID-19, diagnostic testing, and personal protective equipment have been available mostly through websites set up and run by volunteers or journalists. It is wonderful that so many skilled individuals have been motivated to help out in a time of crisis. But the country’s pandemic response cannot rely on volunteers. And although journalism is crucial during an outbreak, responsibility for tracking the spread of contagions can hardly be outsourced to media organizations.
The best way to address these vulnerabilities would be the establishment of a National Center for Epidemic Forecasting and Analytics (CEFA). The center would handle research and development in outbreak science, develop technology for producing disease forecasts, and provide guidance for preparedness and response to outbreaks. The center’s structure should resemble that of the National Weather Center, located at the University of Oklahoma. The NWC has a unique structure that enables academics to work closely with federal employees and develop a mutual understanding of respective challenges and capabilities. In the same manner, the new center would work with the CDC, nonprofit organizations such as the Council of State and Territorial Epidemiologists, public health departments in all 50 states, and the National Governors Association to improve outbreak science and analytics, decision-making during outbreaks, and the data and technologies needed to support these efforts.
During an epidemic outbreak, CEFA scientists could help to forecast how many cases might emerge in a given place during a given period, which interventions would be most effective, and where to direct resources, and would assess progress in the fight against the pathogen.
During the COVID-19 pandemic, each jurisdiction has been on its own when it comes to finding modelers to help make those kinds of determinations. Sadly, those jurisdictions without access to world-class modelers or epidemiologists were left out in the cold. CEFA would change that by providing all governors and mayors access to high-level insights.
The pandemic is a stark reminder that Americans are vulnerable to the forces of nature.
Between outbreaks, CEFA scientists would develop new models and advance the science of forecasting and analyzing outbreaks, just as atmospheric scientists work constantly to improve the accuracy of weather models. Initially, the center would focus on improving the predictive analytics needed to produce forecasts of new cases and deaths. It would also work to improve visualizations and communication approaches to make as clear as possible the implications and uncertainties involved in all modeling.
To be successful, CEFA scientists would need the ability to directly advise decision-makers on preparedness and response, as forecasters do in other countries. In the United Kingdom, for example, the Scientific Advisory Group for Emergencies brings together experts in various fields, including epidemiology and modeling, to advise the government. The United Kingdom has not always managed to successfully bring scientific expertise to bear on emergencies, but the fact that it has a formalized way of bringing experts together puts it in a better position than the United States. Other countries have similar mechanisms. In the Netherlands, for example, the Infectious Diseases Modelling team at the National Institute for Public Health and the Environment is chaired by Jacco Wallinga, an esteemed modeling expert who also holds a prominent academic position.
In addition to working with officials, just as local meteorologists translate the complex weather models into forecasts that are easily understood by the public, CEFA science communicators could bring forecasts and analyses to the public to help individual decision-making. Just as people rely on weather reports to know when to bring an umbrella, CEFA forecasts could make it easier to decide whether to telework during flu season or whether to wear a face covering. People want to know how to keep themselves and their families safe, and providing information to help them do so would be an enormous advance for public health.
To meet its mission, the center would need to be able to identify, adapt, and adopt cutting-edge technology and attract the best data scientists, engineers, and technologists. That would require significant, sustained funding: approximately $1 billion over the next ten years. But it would be money well spent.
The COVID-19 pandemic should serve as the epidemiological equivalent of the Galveston hurricane. It is a stark reminder that Americans are vulnerable to the forces of nature. But as with weather forecasting, with sufficient vision and investment, the United States can develop the ability to anticipate and respond to such outbreaks. The country can do better—and it must do better, so that this never happens again.
Learning From the COVID-19 Failure—Before the Next Outbreak Arrives