Technology Helps Stem Viral Tide
Looking ahead: As long as humankind remains vulnerable to infectious disease, we need to be prepared. Which technologies and approaches could help us stop the next disease outbreak in its tracks?
When the Covid-19 pandemic is behind us, we will have discovered a great deal about how to combat disease. And we will need that knowledge, because we live in a world where the threat of infectious disease is rising. New mutations of pathogens and zoonotic diseases that can pass at any time from animal to human pose a constant threat that is difficult to predict. Increasing resistance to antimicrobial drugs means medicines are becoming less effective. As Covid-19 has shown, our interconnected globalized world provides an ideal network for the devastating spread of a life-threatening pathogen. How can we bolster our ability to contain the next potential pandemic?
CONTACT TRACING FOR THE 21ST CENTURY
We have learned from SARS, MERS and now Covid-19 that, in the absence of a vaccine, we are reliant on classic epidemiological controls, which include rapid diagnosis, contact tracing, quarantine, physical distancing and hygiene measures. How efficient these interventions are will in turn dictate how extreme and, therefore, how economically damaging an imposed societal quarantine has to be in order to save lives.
Contact tracing has historically been a slow process of detective work carried out by public health officials involving interviewing a patient, identifying people who may have been in contact with them and alerting them as quickly as possible. The problem with a manual response is that viruses travel too fast to keep up. With Covid-19, the race is on to leverage the power of big data to speed up this process. Around the world, developers are working on tracing and tracking apps, and testing their efficacy. South Korea’s ability to cut cases of Covid-19 from 909 on February 29 to 76 on March 24 showed how effective this approach can be. The Chinese app gives citizens a red, amber or green travel code depending on risk of contagion. In South Korea, apps publish the movements of people with Covid-19 so that you can track them and stay away.
THE PRIVACY PROBLEM
Two challenges have been raised around contact tracing apps: data privacy and take up. According to Stefaan Verhulst, co-founder and chief research and development officer for the Governance Laboratory at New York university, this is a symptom of a lack of preparedness. He argues we need to use big data better to reduce uncertainty during a pandemic. This is particularly pertinent when we are forced, as with Covid-19, to take an iterative approach to policy, seeing daily which strategies work and which do not. “One of the tragedies of Covid-19 is that despite having heralded the arrival of big data for the last 15 years, and the fact we are in a so called data age, we have not managed to connect the vast amount of data collected and archived with the demand side to become smarter about the pandemic and our options moving forward,” says Verhulst. He argues that to properly utilize the benefits of data collection and analysis in the future, we must create a framework to use data in an ethical manner. This would involve a governance framework, establishing what data is actually needed, funding for a data infrastructure and a conversation with citizens about the reuse of their data.
ROBOTS OFFER A HELPING HAND
While privacy issues are putting in question the efficacy of contact tracing apps, some other means of containment do not raise such difficulties. China, for example, has explored the use of robots to help contain the spread of Covid-19 and keep human workers away from risk. Robot technology is now advanced enough to be deployed in many settings, from monitoring compliance with quarantine rules to delivering medication and food, measuring vital clinical signs and handling contaminated waste. It can be particularly useful in clinical settings for diagnosis, screening and patient care. Diseases are easily spread through hospital surfaces. Rather than expose workers to this risk, robots can carry out disinfection.
Mobile robots are also being considered for temperature measurement in public areas and for automated disease testing, freeing up frontline medical staff for other duties. All these advantages had already been recognized during the 2014 Ebola outbreak, but funding for development has remained limited. Professor GuangZhong Yang, Dean of the Institute of Medical Robotics at Shanghai Jiao Tong University, and his fellow researchers have called for a more sustainable approach to research so that, in a future outbreak, we have cost-effective robots that can be rapidly deployed in a range of scenarios.
ACTIONABLE INSIGHTS FROM BIG DATA
It is not just frontline services that need to be better prepared for disease outbreak; governments and businesses must be prepared, too, in order to mitigate the risks and minimize the wider societal impact of an epidemic. Nita Madhav is Chief Executive Officer of California-based Metabiota and a leading epidemiologist. Her company curates data from over 400 sources on past and present outbreaks in order to help governments and businesses identify, quantify and mitigate the specific risks they face. “We have developed a historical database that contains over 2,500 epidemics that we’ve painstakingly structured the data for, while also doing full-scale probabilistic modeling using hundreds of thousands of simulations to show on a global scale how diseases can spread from country to country and person to person to help better understand what resources are needed to respond to these events,” she says. “We also track the level of public fear caused by different pandemics, as this is tightly linked to economic loss.”
Preparedness is about understanding a range of potential events.
As growing computer power enables faster computation, and each outbreak brings new understanding, so the modeling improves, and it is possible to create faster and more accurate predictions. But a big challenge is that the warnings can get lost in a surfeit of modeling simulations. “Each group has their own set of modelers they turn to,”says Madhav. “There needs to be some mechanism where multiple models can be compared with assumptions documented, because some models are coming out with wildly different results.” There may still be gaps, and none of these solutions will replace traditional epidemiology. Nevertheless, advances in big data, robotics and AI can bolster our defenses against the next outbreak, enabling us to contain diseases better before they spread and ensure that our key institutions and businesses do not buckle under the pressure.