Can we stop the next pandemic before it happens?
Meet the pioneers already battling ‘Disease X’
On 29 June, the death toll for Covid-19 passed 500,000 people globally. That same day, as the southern United States battled a resurgence in new cases, Iran announced its highest 24-hour fatality rate and the World Health Organization cautioned that “the worst is yet to come”. If the situation wasn’t grim enough, in the evening a troubling paper was published in the US journal Proceedings Of The National Academy Of Sciences warning about the dangers of a different disease altogether. A novel strain of swine flu was spreading on Chinese farms – and had the potential to become another pandemic.
Known by the kind of alphanumeric sequence that was once bewildering but now ominous, G4 EA H1N1 had already infected humans. While it hadn’t yet shown the ability to jump from human to human, a mutation could change that. “G4 viruses have all the essential hallmarks of a candidate pandemic virus,” wrote the scientists. Close monitoring “should be urgently implemented”.
It might sound like a freak occurrence, but the worrying truth is it’s no surprise that yet another viral threat has emerged from animals. Almost all new contagious diseases in humans originate in wildlife. We have discovered less than one per cent of the estimated 1.6 million viruses that lurk, often symptomlessly, in the likes of rodents, camels and bats. A 2018 paper predicted that up to 840,000 of these pathogens are potentially “zoonotic” – that is, able to spill over to people. These novel viruses can be especially devastating. Not only do we lack antibodies, but when faced with a new threat our immune systems can over-respond, producing a “cytokine storm” that can lead to organ damage and, ultimately, death.
In 2018, the World Health Organization came up with a name for the hypothetical unknown virus that could cause a future pandemic: “Disease X”. It turned out to be Covid-19 – this time. Suzan Murray, head of the Smithsonian Conservation Biology Institute’s Global Health Program, tells GQ that she is worried it’s only a matter of time before another materialises. “Spillovers are occurring and they’re occurring more and more frequently,” she says. That’s because population expansion is driving people further into animal habitats. “Unless we do something, the chances are higher that this will happen again.”
The question is: how do we better equip ourselves to fight the next Disease X?
The conventional battlegrounds of education, regulation and community engagement are gaining renewed importance – instilling safe practices in deforestation work, for example, where people may come into contact with remote species, is vital.
But around the world experts are also developing new, innovative weapons in the war on viruses.
GQ spoke to the pioneers behind four that could prove transformative...
The virus vault
Jonna Mazet is a virus hunter, and those 1.6 million undiscovered viruses are her quarry. She is part of an Indiana Jones-like effort to track down the pathogens wherever they may lurk – in impenetrable forests, deep caves, at the top of rainforest canopies – and map their genome. It’s an extraordinary endeavour that may take ten years and cost $1.2 billion. “The big audacious goal is to basically know where and how we could get infected by all the viruses that are out there,” she says, “so that we don’t get into this situation again. We can be ready.”
Its name, the Global Virome Project (GVP), might sound familiar. It echoes that of the Human Genome Project – the $5bn international effort to decode our DNA – in a deliberate move to give a sense of its ambition and also its difficulty. Still, the GVP’s leadership team, which includes Mazet as implementation director, is well qualified to take on the challenge. She previously worked with the project’s chair, Dennis Carroll, as director of the US government’s pandemic early warning system Predict, which was shut down in March by the Trump administration. As part of Predict, the team gathered samples from 74,000 wild animals over seven years and detected 820 novel viruses. Think of it as a proof of concept for the GVP, which is now raising funds to do the same but on an altogether grander scale.
The plan is almost inconceivably vast in scope: it would involve taking samples from between 1,000 and 2,000 individual animals for every species in every bioregion in which it exists. Sometimes that might involve simply swabbing a mouth; other times, says Mazet, it’s more complex. “I’ve just joined on with a group for the Amazon rainforest that wants to use drones. They want to swab the fluid that gets into orchids at the top of the canopy that birds drink out of. Their saliva gets into that and so they want the drones to go and take that.”
Once this encyclopaedia of viruses is complete, there could be a range of benefits. If you know what is likely to emerge where, local physicians can be primed to look for symptoms of these otherwise rare diseases. “This is basically the answer to what could have helped with West Africa Ebola. If we had a better handle on the host species, we would have said, ‘You’re going to have Ebola because you have those hosts,’” says Mazet. Crucially, it would also help rank viruses for pre-emptive vaccine development. “If viruses are in [multiple] species, that dramatically increases their risk of getting into people and being able to go human to human. So if we find them in five bats, that’s bad. But if we find them in five species that include a tiger and a pangolin and a bat and a monkey, then we‘re like, ‘Uh oh, that’s a jumper.’”
Still, a question hangs over the project. Is it a good use of resources? The $1.2bn cost is forecast to be enough to discover 71 per cent of zoonotic viral threats to humans (to catalogue 100 per cent would require $3.7bn). The GVP points out that the UN estimates the global economic cost of Covid to have exceeded $2tn. But detractors suggest that the money would be better spent on working out how to disrupt disease transmission mechanisms. Mazet disputes the premise. “My strong feeling about this is you are only nibbling at the surface of understanding those [transmission] mechanisms when you only have the handful of viruses that have caused this disease in the past,” she says. The GVP will provide the data and viruses required to do all kinds of virology work more effectively. “I don’t see it as a zero-sum game.”
The digital sentinel
On 5 January, the WHO alerted the world to a cluster of pneumonia cases of unknown cause in Wuhan City, Hubei Province of China. A team in Massachusetts, however, was already well aware – in fact, they had sounded the alarm six days prior.
Based at Boston Children’s Hospital, HealthMap is an automated system that monitors the internet for signals of disease. Using machine learning algorithms and natural language processing, it crunches data such as search engine queries, social media posts, government reports and local news – in as many as nine different languages – and notifies a dedicated group of researchers and epidemiologists if anything concerning is detected. Local news can be especially valuable. “It’s very attuned to things that are happening in the community,” says HealthMap cofounder John Brownstein. “Time and time again, it is the source that tells us the first time that something happened.” And so it was with Covid-19. On 30 December, HealthMap’s algorithms scraped a Chinese-language news alert. The translated headline was “Unexplained Pneumonia In Wuhan Cannot Be Judged To Be SARS, Seven Cases Are Critically Ill”. HealthMap reported this to its partners, including the WHO. “We started recognising the importance in the first week of January when things kept escalating.”
It might seem strange that such a vital tool is based at a children’s hospital. Its origins go back 14 years to when Brownstein joined the institution to work on his epidemiology PhD about Lyme disease and West Nile virus. “I was collecting ticks in the field and got Lyme disease. And that made me think, ‘You know what, I think I’m done with field work.’” He decided to create a tool that could mine data from the web instead. This evolved into HealthMap and its users would come to include not only the WHO, but also the European Centre For Disease Prevention and its US equivalent. The Ebola outbreak in West Africa six years ago? HealthMap spotted it nine days before it was formally announced.
Clearly, HealthMap is already advanced in its ability to sift through the overwhelming noise of the internet, overcome language barriers and produce meaningful results, but Brownstein wants it to perform even better next time a would-be pandemic arrives. After all, in the case of Covid, local news had got there first. How might HealthMap detect virus outbreaks even earlier than that and do so with more specificity? His plan is to tap into what he calls the new "information economy”. “Now there’s the advent of things like chatbots and telemedicine, which are generating incredible amounts of data; wearables are generating lots of interesting data,” he says. A concentration of people showing raised heartbeats and temperatures – perhaps combined with a spike in chatbot enquiries about flu-like symptoms – would be a valuable alert.
HealthMap is developing partnerships in those spaces, and also hopes to incorporate the emerging field of home diagnostic devices such as internet-connected thermometers. But how would they do so without compromising privacy? “We have to clearly focus on privacy protection through information de-identification and aggregation. As we get more detailed individual level data, public health has to keep pushing on methods that ensure the data can’t be used to reversely engineer sensitive data about users.”
Arguably, incorporating those rich new sources of information can't happen fast enough. “It’s inevitable that we’re going to see [something like Covid-19] again,” he says. “And I think it’ll be sooner rather than later.”
The AI vaccine
Back in January, the biotech company BenevolentAI was troubled by news of the coronavirus outbreak in China. Its CEO, Joanna Shields, formerly UK minister for internet safety and security, asked the team: is there anything we can do about this? The company was set up because creating novel drugs using conventional methods can be glacially slow. “Five to ten years is not by any means unusual,” says Olly Oechsle, BenevolentAI’s lead application engineer. “And then when you pair that with the chances of failure, you start to multiply out quite long times by high cost.” BenevolentAI's solution is to use artificial intelligence to speed up the process. However, in the case of Covid-19, regulatory hoops would mean getting a completely new drug to market would take too long given the urgency. Instead, they decided to use their computers to pinpoint a pre-existing drug that could be repurposed.
Benevolent’s system uses machine learning to read the ever-growing mass of scientific literature, which is estimated to increase by around a million new research papers per year. It then creates billions of data points and intelligently makes connections that would otherwise remain hidden. Its vice president of pharmacology, Peter Richardson, asked the system to find commonalities in the ways that coronaviruses enter human cells, and then tasked the AI with finding a drug that would target the DNA sequences involved. The answer? Baricitinib, normally used to treat rheumatoid arthritis.
“This was quite a surprising result; it wasn’t one that was anticipated,” says Oechsle. Baricitinib turned out to have further benefits for Covid-19 sufferers. “It will also dampen down the body’s own response to the virus which can actually cause very bad side effects and fatalities.” Trials in more than 800 patients began in April with promising results; data from randomised trials are expected shortly.
BenevolentAI's Covid work marks a notable milestone within the nascent world of using AI to fast-track medicine. Crucially, AI may soon help speed up the creation of vaccines too. For Covid-19, it is already having a small role: the biotech company Moderna Therapeutics used machine learning to train the algorithms that helped create its vaccine candidate. By the time we encounter the next Disease X, it is forecast that these tools will be more sophisticated. They may help us find holy-grail “panviral vaccines” which are effective against whole families of viruses.
But one of the big obstacles in bringing vaccines to market remains the length of time it takes to conduct trials and get regulatory approval. Here too AI may be able to help. As the engineer Ray Kurzweil wrote recently in Wired, AI should soon be able to simulate how thousands of patients respond to therapies over the course of many simulated years – and complete the task in perhaps just a few hours. The French company Novadiscovery is already working on this. Kurzweil’s prediction is bold: “By the end of the decade we will be able to realistically model all biology and simulate interventions for diseases without the need for human trials.”
The blood observatory
That we know about the new swine flu in China is cause for celebration: our existing monitoring systems are working. Clearly, however, they could be better – hence Michael Mina's idea for a new kind of virus surveillance network. It has been called “an extraordinary and exciting concept” in Science – “an example of the kind of fresh new thinking we need in public health”. Its name reflects its ambition: the Global Immunological Observatory.
The idea is to turn human beings into sensors. Blood would be routinely collected daily from a host of sources such as donor banks and tested with existing, innovative tools that can scan for hundreds of thousands of antibodies simultaneously at low cost. “Our immune system is essentially like having a camera that’s always on and watching and recording information,” says Mina, who is an assistant professor at Harvard School Of Public Health. If you’ve been exposed to SARS, your antibodies will say so – as they would a new virus – and the GIO would detect this and issue an alert. “Every other serological study is just a snapshot in time – they sample 1,000 people and that’s it. I really want to just have something that constantly, every single day is collecting tens of thousands of samples and churning out tens of thousands of results.”
A perfect version of this “observatory” would have labs around the world and also encourage people to submit finger-prick blood spots through the post. Today, it is at a more embryonic stage – but picking up momentum. Mina is running a pilot study financed by the Open Philanthropy Project, working with thousands of blood plasma specimens collected through the major pharmaceutical company Octapharma and processing them for Covid-19 antibodies.
Before the pandemic, external organisations had relatively little interest in his idea. “Most of them said, ‘This is a pretty expensive endeavour you’re working on. It’s not really needed right now.’” As you might imagine, things have changed. “Now we’re seeing major players willing to talk to us and explore options for what this looks like. There’s been discussions with NIH [National Institutes Of Health] and certainly the largest philanthropic donors.”
The truth is, he says, if the Observatory had existed prior to this pandemic, we could have nipped it in the bud much earlier – even though it takes people around one to two weeks to develop antibodies. “We would have detected Covid more or less as soon as it entered New York City and said to Cuomo or somebody in New York, ‘Hey, you need to shut down New York City.’”
A recent study shows that the virus may have arrived in the area as early as January. No action was taken until March, says Mina. “And, of course, you saw what happened.”
Written by Charlie Burton
