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Coronavirus: The Role of AI in the ‘War’ Against Epidemics and Pandemics

Andy Favell, Computer Weekly
2.4.2020

Data science is stepping up to back up medical science and to prepare humanity better for future pandemics.

In the fight against epidemics, including the current Covid-19 coronavirus, medical staff are on the front line, risking their own lives to save the lives of others. But behind the lines the war is fought – or should be fought – by authorities, medical researchers, statisticians and computer scientists using an array of artificial intelligence (AI) and data science technologies.

Preparedness

The SARS-CoV2 or Covid-19 virus, which surpassed 500,000 confirmed cases and 23,000 deaths within three months of first detection (WHO statistics), appears to have taken most national governments by surprise – but it shouldn’t have.

Since 2000, there has been warning after warning: SARS (SARS-CoV) in 2003, H1N1 “swine flu” influenza in 2009, MERS (MERS-CoV) in 2012, West African Ebola in 2014, Zika in 2015, and numerous re-emergences of diseases such as cholera, dengue, yellow fever and, even, plague.

Global leaders have ignored repeated warnings from experts and organisations, such as Dennis Carroll (in the early 2000s) and Bill Gates. The head of the World Health Organisation (WHO), Tedros Ghebreyesus, warned in 2018: “A devastating epidemic can start in any country at any time, and kill millions of people, because we are not prepared.”

Artificial intelligence can help governments prepare their readiness for the next epidemic with computer modelling and simulations in the same way AI helps prepare nations for war through AI for military simulation and AI for military readiness.

In a 2015 TED Talk titled The next outbreak? We’re not ready, Bill Gates used computer models to predict that a pathogen as virulent as the 1918 Spanish flu would kill 33 million people worldwide in just nine months. Gates laments that governments regularly conduct war simulations to test their preparedness, “war games”, but not pandemic simulations, “germ games”.

The international community has belatedly started assessing countries’ readiness for coping with pandemics. The first Global health security index was published in October 2019. Data collection was largely manual, with researchers asking yes or no questions. Countries were scored between zero and 100, with higher scores denoting better health conditions. The US came top, with a score of 83.5, and the UK second, scoring 77.9. Retrospective evaluation of each country’s readiness for Covid-19 will highlight if pandemic readiness testing needs to be more sophisticated than this in the future.

Prediction

In the past 50 years, more than 1,500 new pathogens have been discovered, 70% of which have proved to be of animal origin, according to WHO (2018) statistics. Virus or bacterial infections that “spillover” from animals to humans are called “zoonotic”. Spillover might occur when an infected animal is eaten, trafficked, farmed or bites a human, and where human activity encroaches on or destroys habitats.

Artificial intelligence can help predict the conditions and locations where spillovers of known and unknown pathogens might occur. This allows governments and agencies to plan ahead and ban or educate against high-risk activities.

The leading force in identifying zoonotic threats was Predict, set up by Dennis Carroll in 2009. It estimated that there are 1.6 million unknown viral species in animals, of which 700,000 could infect humans. Predict’s funding was withdrawn by the US government in October 2019.

Prompted by the emergence of the Zika virus in 2015, Predict started developing machine learning to help predict possible hosts for emerging Flavivirus (the family containing Zika, dengue and yellow fever), says Pranav Pandit, a researcher at the One Health Institute based at University of California School, who helped develop the tools for Predict.

AI has been used by other researchers to predict when and where spillover will cause re-emergences of known pathogens, including Zika (see Han Lab), dengue (see AIME) and Ebola.

Spillover is very rare, stresses Kate Jones, professor of ecology and biodiversity at UCL. It takes a unique cocktail of bad luck for a human to interact with a particular animal that is contagious with a virus that is capable of infecting a human and being passed human to human.

This is what makes AI useful for predicting what, when, why and where these rare events might occur.

Jones’s team has built machine learning models to predict where animals pinpointed as likely carriers of Ebola are likely to exist, where human behaviour, such as deforestation, brings animals and humans into dangerous proximity, and where population density and mobility risks greater spread. Jones is also experimenting with AI-enabled sensors and cameras that can detect the presence of animals – including potential hosts or “reservoirs” of zoonotic diseases – in close proximity to humans.

Detection

The first stage of outbreak analytics is detection. Quick detection is crucial because it enables early intervention – including patient isolation, contact tracing, treatment and vaccination (if available) – and the delivery of local and global alerts to prevent spread.

In a perfect world of ubiquitous, connected, affordable global healthcare – as advocated by the WHO – an infected person quickly receives medical attention and details of the illness are shared into a global, AI-enabled data system that can provide advice, summon assistance and issue warnings in real time.

The handling of the outbreak of SARS-CoV2 in Wuhan in December 2019 was a long way off this scenario. Chinese authorities – despite their developed health system – were too slow to detect, recognise or publicise the threat.

Regardless of the secrecy, news of the new pathogen emerged. Several AI systems picked up on the internet chatter about a cluster of unidentified pneumonia cases in Wuhan and issued alerts, regardless of silence from the Chinese authorities. Dataminr claims to have been first to issue an alert (only to its clients) on 30 December 2019, having picked up chatter on social media, including an image of deep cleaning taking place at the now-notorious Wuhan market. Other paid-for services such as BlueDot and Metabiota also claim that their natural language processing (NLP) algorithms were quick to pick up on the news, according to reports.

The first public alerts were also issued on 30 December, according to Associated Press. First was an automated alert from HealthMap, based at Boston Children’s Hospital, which mines numerous feeds for information. The other was a more considered alert issued by ProMED, after New York epidemiologist Marjorie Pollack had been notified by talk of the “unexplained pneumonia” cases via old-school email from China.

Healthmap and BlueDot helped to predict the spread of the virus internationally by mining data of flights leaving Wuhan during the crucial period after outbreak and before travel restrictions were brought in.

Forecasting

A great deal of focus has been given to forecasts of spread, rates of infection, incubation, recovery and death, and peaks and decline of the Covid-19 coronavirus. Notably, predictions by the team at Imperial College, London, are credited for rapidly changing the UK government’s strategy from “wait and see” to introducing intervention, such as social distancing. These models have traditionally been mathematical and do not tend to use AI.

However, researchers from Fudan University in Shanghai have used the Covid-19 outbreak in China as a case study to test and prove that AI makes better real-time predictions for transmission than traditional epidemiological forecasting models. Their first study used a stacked auto-encoder for modelling the transmission dynamics of the epidemic in China. A second paper used AI to predict the consequences of governments delaying making interventions on the spread of the virus.

Read the full story on Computer Weekly

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