Prevention Trumps Cure
In our fight against infectious disease, new technologies and lessons learnt promise to help us contain the spread of infectious disease more effectively. But what if we could prevent disease outbreaks altogether?
Public health experts had been warning for years that a new pandemic was long overdue, but when Covid-19 finally arrived it still found most governments unprepared. This global outbreak, if nothing else, will have focused minds on how best to prevent the next one. One of the basic elements is deciding which diseases to focus on. The World Health Organization (WHO) maintains a list of priority diseases for research and development. There is a vast number of potential pathogens but only limited resources, so the list includes only those thought to pose the greatest potential public health threat, whether through their epidemic potential or the fact that there are still few countermeasures in place.
Beside Covid-19, it currently includes SARS, MERS, Ebola, Lassa fever and Rift Valley fever. With this list as its base, the WHO’s R&D Blueprint is a global strategy and preparedness plan that provides a research roadmap and target product profiles for each disease. “The roadmaps identify key interventions, as well as important R&D gaps,” says WHO spokesperson Christian Lindmeier. WHO regularly brings together experts from a wide range of disciplines to revise which diseases need investment and more R&D. The experts include microbiologists, clinical experts, epidemiologists, public health policy experts, veterinarians, anthropologists, bioethicists, and biological weapons experts, among others.
THE UNKNOWN THREAT
At the end of WHO’s priority disease list sits Disease X. This represents the knowledge that a serious international epidemic could result from a pathogen currently unknown to cause human disease. It could be a new pathogen such as the coronavirus that causes Covid-19, a known pathogen that does not usually cause human disease epidemics, or one that is suddenly subject to a change in epidemiology or pathogenicity, like Zika. “Although we have experienced a series of these threats, we cannot predict when or how Disease X will strike,” says Lindmeier. “We need to be ready with basic capabilities for multidisciplinary research and product deployment in the affected countries so that they reach the populations who need them. Access considerations must always be at the heart of all R&D efforts.”
Among the most effective tools to prevent an epidemic are vaccines. They bolster the body’s immune system and help it fight disease. The problem is that each vaccine only works against a specific pathogen, and development take years. This means that in the case of a previously unknown or a rapidly mutating virus like Disease X, producing a vaccine ahead of an outbreak or in its early stages has, up to now, been impossible. Platform technologies could be the solution and are being deployed in the search for vaccine for Covid-19. The Coalition for Epidemic Preparedness Innovation (CEPI) is leading these efforts. It was founded in 2017 to advance vaccines against both known threats and previously unknown pathogens. A platform technology uses the same basic components as a backbone and can be adapted for use against different pathogens. The new pathogen’s genetic or protein sequence is simply “slotted in,” like a video game cartridge, to produce the vaccine.
AI AGAINST RESISTANCE
Disease X also refers to pathogens that acquire resistance to treatment. Overuse of antibiotics and intensive farming methods have contributed to the growth in resistance, and it is now a significant global threat. A growing number of infections, from TB to pneumonia, are becoming harder to treat. Given the pace at which microbes are able to evolve, the number of infections resistant to almost any available antibiotic is going to grow. The UN’s Interagency Coordination Group on Antimicrobial Resistance has warned that the number of annual deaths as a result of drug-resistant infections could reach 10 million by 2050. One tool that shows considerable promise for preventing drug-resistant outbreaks is AI. Databases containing the genomes from different strains of pathogen are growing, along with information about whether they were susceptible to antibiotics. Using this data, AI can allow scientists to identify the DNA sequences that indicate resistance. This can speed up treatment of diseases like TB.
Infectious diseases can spread fast in a globalized world. BlueDot’s risk software uses flight and mobile phone data to predict their dispersion and impact.
Access considerations must always be at the heart of all R&D efforts.
Normally it takes a series of time-consuming tests to determine whether a patient has multidrugresistant TB, but if the genetic code of the bacterium is known, the patient could be prescribed the right drugs more quickly. Machine learning can also speed up the discovery of new antibiotic compounds. Using a machine learning algorithm, researchers at the Massachusetts Institute of Technology have identified a new antibiotic compound that kills disease-causing bacteria that are resistant to known antibiotics. The computer model screens more than 100 million chemical compounds in days and selects potential antibiotics that can kill bacteria using mechanisms different to those of existing drugs.
AI can also help alert the world to the threat of a disease outbreak. We were first made aware of Covid-19 in December 2019 by BlueDot, a company in Toronto. It used an algorithm to trawl notifications, disease networks, global news stories and even airline ticketing information to accurately predict how the outbreak would spread. BlueDot founder and CEO, Kamran Khan, is an infectious disease and public health physician. His career as a doctor began during the SARS epidemic of 2003, during which he saw colleagues become infected and die. “What we experienced in Toronto was a microcosm of what we’re now witnessing around the world,” he says. “That virus crippled our city; this virus has crippled the planet.”
BlueDot’s team of data engineers, physicians and health experts has built algorithms that can read text in 65 languages, 24 hours a day, looking for more than 150 diseases and syndromes, and organize and structure this vast amount of text data. “It is about having a machine play to its strengths, and humans play to theirs,” says Khan. “AI relies on large amounts of historical data to train a machine to understand patterns. But with many of the things we’re dealing with there are no historical patterns. We do not have 10,000 of these outbreaks that we can train a machine on – we have a handful.” This means we still rely on human knowledge of history and context. “Human intelligence is augmented by artificial intelligence. The two are complementary; one does not replace the other.”
Human intelligence is augmented by artificial intelligence. The two are complementary; one does not replace the other.
THE HUMAN-ANIMAL INTERFACE
Like other experts, Kahn believes that, to get ahead of the game, we have to look more carefully at what triggers this type of outbreak. “Some of the biggest drivers are the mass consumption of wildlife, industrialization of agriculture and the disruption of wildlife ecosystems,” he says. “While the life and health of every person is more connected than ever to those of everyone else, it is also more connected to the health of every living system on the planet.” Covid-19 has inevitably put the focus on zoonoses – diseases that originate in animal populations. These account for some 70 percent of all new emerging diseases. One idea is to track pathogens that have the potential to leap over into humans. The Global Virome Project was founded to do just that. It aims to identify the estimated 500,000 as yet undiscovered animal viruses capable of transmission to people, and build a global atlas of zoonotic viruses. These diseases are opportunistic, thriving where there is change to the environment, to animal or human hosts, or in the pathogen itself. “The mechanisms are complex and vary among diseases,” says Doreen Robinson, Chief of Wildlife at the UN Environment Programme. “This means we need to understand the ecological dimensions much better.”
As humans encroach on forests and other natural habitats, they increase their risk of exposure to potential pathogens. Understanding the relationship between environmental degradation and the spread of disease is likely to be key to preventing future outbreaks. It will mean taking action on hugely challenging areas like animal welfare, intensive farming, rapid urbanization, overcrowding, sanitation and climate change.
When we protect our planetary health, we are protecting ourselves.
Robinson argues that now is the time to improve our monitoring and risk assessment for zoonotic diseases, while also improving sanitary measures for wild and domestic animals consumed as food. As our economies return and lockdowns ease, she sees an opportunity to launch a robust and accountable post2020 global biodiversity framework to be adopted by all countries, with enough resources to take the necessary action. But that remains only part of the picture. “Equally, we cannot lose momentum on setting new targets to reduce greenhouse gas emissions. We need to work more closely across human, animal and environmental health to find systemic, holistic solutions and mitigate future risks,” says Robinson. “When we protect our planetary health, we are protecting ourselves.”