About four years ago, seven infants and toddlers and a 37-year-old woman were admitted to a hospital in Sibu, a coastal town on the island of Borneo in Malaysia. Records don’t show how many of them arrived together; they came from villages in different parts of the province, lived in different types of housing, and were of at least four different ethnicities. They all were experiencing something like pneumonia, a normal winter respiratory infection. But their symptoms—caused by an array of viruses—concealed a secret. They were also carrying a coronavirus that held a genetic signature indicating it had previously infected cats and dogs.
We know this because swabs of pulmonary secretions taken from the eight Malaysians during their illness were stored as part of a screening project, and then analyzed last year by a Duke University team seeking to validate a new test. What they found, and described two weeks ago in Clinical Infectious Diseases, may show a novel coronavirus in the process of leaping from the animal world into people, as the virus that causes Covid probably did in 2019.
That new virus might have been an accidental bystander in the airways of those Malaysian patients, whose swabs variously also turned up evidence of adenoviruses, rhinoviruses or flu. Or it might have been a cause of their disease. It’s too soon to know. (To be clear: It wasn’t Covid. Canine coronaviruses and the viral cause of Covid belong to separate genera of the coronavirus family.)
Nevertheless, researchers in the world of emerging infections are absolutely sure about this: It should have been found much sooner. Covid is supposed to have demonstrated that we need faster detection. That a possible novel pathogen could lie concealed in a lab freezer since as early as 2017 shows how much work we still have to do.
“A novel coronavirus reached human populations and we did not find out about it until an academic publication?” Colin J. Carlson asks in exasperation. Carlson is an assistant professor at Georgetown University Medical Center and principal investigator for a consortium called the Viral Emergence Research Initiative. “This should show us that the notification system for human disease is broken,” he continues.
As it happens, an effort to repair disease detection was announced just one day after that paper appeared online. On May 21, United Kingdom prime minister Boris Johnson said his government will create a new “global pandemic radar” to track Covid variants and emerging diseases, built on the UK’s known expertise in genomic sequencing for tracking Covid strains within its borders. “We need to build a system of disease surveillance fit for the 21st century, with real-time data sharing and rapid genomic sequencing and response,” Johnson said at the time.
There is little disagreement that better, faster surveillance is needed. In fact, the first independent review of the Covid crisis, published two weeks before Johnson’s announcement, called the global alert system “too slow—and too meek.” In a scathing report, the Independent Panel for Pandemic Preparedness and Response called Covid “the 21st century’s Chernobyl moment” and said a key element of pandemic prevention should be “a new global system for surveillance, based on full transparency by all parties, using state-of-the-art digital tools.”
It’s no accident these announcements were made so close together. May and June are when global governance traditionally attends to global health, with meetings of the health ministers of the G7 and G20 groups of nations and also the World Health Assembly, the gathering of the 194-member nations that jointly set policy for the World Health Organization. The UK currently holds the presidency of the G7; Johnson made his announcement at a summit in Italy before the assembly, as prep for hosting a G7 ministers’ meeting this week.
So, after 17 months of this pandemic, the world is turning its attention to more rapid identification of whichever one comes next. That’s good. And yet: The world already possesses many surveillance systems, some that existed before Covid and others that have been created in response.
Here, have some acronyms. The WHO supervises the 10-year-old GOARN (the Global Outbreak Alert and Response Network), a kind of worldwide listening network, and a new hub for pandemic intelligence in Berlin. It also oversees GISRS (the Global Influenza Surveillance and Response System), a network made up of institutions in 123 nations. Then there are national, philanthropic, and NGO-based surveillance systems, including the National Institutes of Health’s network CREID (Centers for Research in Emerging Infectious Diseases); the new French agency PREZODE (for Preventing Zoonotic Diseases Emergence) and the Geneva-based, foundation-funded I-DAIR (for International Digital Health & Artificial Intelligence Research Collaborative); CORDS (Connecting Organisations for Regional Disease Surveillance), six networks that cover Africa, Asia, and the Middle East; Europe’s version of the CDC, which draws on 27 health ministries, and the US CDC and its international partners. And then (deep breath!) there’s an array of academic and nonprofit detection networks aimed at HIV, malaria, Ebola, tuberculosis, fungal diseases, antibiotic-resistant pathogens, wildlife diseases, and on and on.
In short: The world might not need another surveillance system. The existing plethora is one reason why the Rockefeller Foundation’s recently announced Pandemic Prevention Institute instead proposes to create a hub for aggregating and analyzing data that is held in existing systems, rather than creating a new one. There hasn’t been much detail released about the new British effort, but there are indications that the UK government is thinking along similar lines.
The effort is supported by the philanthropic Wellcome Trust, and in concept documents, that organization recommends creating a super-hub that links existing networks while providing a shared resource for sequencing, data analysis, and computing infrastructure, along with the workforce to operate them. On the day Johnson made his announcement, Jeremy Farrar, Wellcome’s director, stated that the plan would be to create a system that is “locally owned [and] internationally networked.”
But let’s get to details: What should a successful system do? If the world is going to have a new detection and response network—or a network of networks, or a super-network—some decisions need to be made about its scope. That way it doesn’t end up as just another monitoring system, but has the power to pinpoint areas of risk and to spin up predictions about how the risk might play out.
Caitlin Rivers, an epidemiologist and senior scholar at the Johns Hopkins Center for Health Security, has been thinking about this issue for more than a decade, since working on pandemic-prediction initiatives for the Obama administration. (Yes, better pandemic prediction has been under discussion at least that long.) She laid out details last year in a proposal for a national center for epidemic forecasting, published in Foreign Affairs, with Dylan George of the intelligence-focused venture capital firm In-Q-Tel.
They wrote that pandemic prediction suffers from relying on academics who have to justify their research to grantmakers, and who can’t necessarily step away when public service needs their expertise. The authors proposed giving disease modelers financial support to work out their models in advance of emergencies, and creating formal channels between them and federal decisionmakers who could call on their work as needed—similar to what the National Weather Service already does.
Rivers’ and George’s proposal was read by the right people. Five days after President Joe Biden’s inauguration, the new administration committed to creating a National Center for Epidemic Forecasting and Outbreak Analytics. In March, they designated $500 million in funding for it as part of the American Rescue Plan Act.
Here’s where the coming US agency and the hoped-for international effort dovetail: Their successes will hinge on data: more abundant data, more granular data, just more. In the mid-20th century, the inaccuracy of weather forecasting was the butt of late-night TV jokes. What made it a reliable endeavor was deploying data-collection devices—satellites, Doppler radar, weather balloons, automated surface-observing systems—and achieving the supercomputer processing power and graphical systems to understand and represent the results.
The data-collection devices that could help us scan the horizon for pandemics already exist. (You might be reading this on one.) Mobility data, purchase records, search terms, the words you use in tweets—all represent information that can fuel predictive tools. Public health doesn’t yet do a good job of accessing that data, collating it, and analyzing it. The channels for getting to it haven’t been carved out even in rich countries. In the Global South, the problem is worse.
“There’s so much heterogeneity in the underlying capabilities of various countries and places,” Rivers says. Obtaining that data to help a country ring alarm bells, let alone contribute to global forecasting, “might even be a matter of moving from paper reporting to digital reporting,” she adds. “It’s hard to see how you can skip to the end and have an advanced radar system without first attending to those basic pieces, when each of those pieces in each jurisdiction is a big undertaking.”
Take test results, for instance. It would be desirable to plug in the results of any diagnostic tests done during health care visits, to sort out whether a wave of respiratory infections is being caused by a common virus or a new strain. But so many people lack access to health care that diagnostic data might have limited predictive power. On the other hand, most people use sewage systems—where they exist—and wastewater sampling can detect pathogens without intruding on individual privacy or forcing the construction of interoperable record systems.
Animal data is another gap. Structures already exist for reporting cases of human disease and wildlife and livestock diseases, but they are separate, run by different United Nations agencies. Reports in one system won’t ring an alarm bell in another—an oversight, since so many emerging diseases are zoonotic, beginning in animals and then leaping to humans.
That revelation two weeks ago that a coronavirus carried by cats and dogs had been found in old throat swabs from people proves the point. It came to light belatedly, because of an academic project. These detections did not get reported through a notification system, and there is no indication that anyone has set up anything new to track the virus. “We don’t have systems now that could go keep an eye on canine coronaviruses,” Carlson says. “We know that this is a virus that can recombine in such a way that it can transmit to humans. We’ve seen it do it, in a really limited way. We know that is a potential threat to health security. But there is no global monitoring.”
The final question a pandemic radar will face is this: Who benefits? The colonialist model of resource extraction—take a commodity from the Global South, use it to benefit the Global North—has tripped up disease surveillance before. In 2007, in the midst of worldwide concern over the spread of H5N1 avian flu, Indonesia stopped sending viruses collected within its borders into the WHO’s flu surveillance network. The WHO scolded the nation, saying it was endangering the world. The government of Indonesia—which, at the time, had experienced more bird flu deaths than any other country—responded that this was its only possible leverage against inequity. If affluent countries used Indonesian viruses to develop a bird-flu vaccine, Indonesia wanted guaranteed, inexpensive access—to not have to compete to buy a product that would not have existed without its help.
That immediate crisis faded, thanks to a complex negotiation between the country and the WHO, but the underlying issue of viral sovereignty never really went away. It surfaced again after the Ebola epidemic of 2014 and in the early days of Covid. It’s possible that fresh attention to the need for global surveillance could become a moment when Global South countries get the assistance they deserve, not just to collect their own data but to benefit from it as well.
“What we really need is a broadly distributed, high-fidelity, always-on surveillance system that empowers local organizations to collect information on their own populations that is relevant to them, that generates ownership in their data, that empowers them to advocate for their community needs,” says Samuel V. Scarpino, an assistant professor at Northeastern University who directs its Emergent Epidemics Lab. “This is not something that can be built easily. But we have a narrow window right now, where basically the whole planet knows that we need to solve this.”
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