AI now used in the fight against global infectious diseases
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A program run by a Canadian university is seeking to improve global health care for the most vulnerable by examining how artificial intelligence (AI) can enhance readiness for infectious disease epidemics in the Global South.
This is according to a report by CTV News published on Wednesday.
Never again
“We’re making locally-relevant data actionable to create common policies that are relevant to the local public,” Jude Kong, the program’s executive director, told the news outlet.
“Our hope is for COVID-19 to never repeat itself.”
The objective is to develop and train AI algorithms to solve specific needs, such as anticipating outbreaks in a given region, and to construct health-care solutions based on regional data acquired from a variety of sources. The initiative is led by York University in Toronto and covers a total of 16 projects throughout the South.
So far, AI has demonstrated considerable potential in a number of areas related to infectious disease, such as epidemiological research, diagnostics, treatment development, and epidemic prediction.
For example, to identify and anticipate epidemics before they spread, AI systems can analyze a massive quantity of data, including posts on social media, news articles, and clinical data. They can also assist policymakers and public health authorities in making well-informed choices about interventions and resource allocation by simulating the spread of infectious diseases.
York’s new program, called the Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), combines knowledge from researchers, decision-makers, and local community experts throughout the globe to construct AI-powered solutions that are customized to individual countries.
A total of 221 project entries from 47 nations were submitted to the Canadian university team. From these, 16 finalists representing Africa, Asia, Latin America, the Caribbean, and the Middle East were chosen.
Kong told CTV News his team wanted to make sure that the projects were distributed both linguistically and geographically and fell into four distinct categories.
According to the news outlet, these include “early detection of diseases, warning systems, response, and mitigation and control of developing epidemics.” Each project is carefully categorized into one of these sections based on the particular requirements of each global location.
Local support
Kong added that although internationally-led, the chosen projects have local support, making them particularly effective.
“People sitting in a university environment think they know better what the problem is in (other) communities, but no,” Kong told CTV News.
“Something that’s working in London will not necessarily work in Ghana or in the interior of the Philippines.”
The new initiative has brought out the best in all participants, said Kong, with people continuing to be involved in the program even when their applications have been rejected.
“They continue to get involved in the network in other aspects, even though their proposal was not chosen. It shows that passion to improve health-care systems,” Kong added.
Examples of the projects undertaken by the program include everything from polio surveillance in Ethiopia to creating easy-to-navigate platforms whose aim is to prevent disease outbreaks in Brazil, reported CTV News. The effort is a testament to what can be achieved when collaboration among health-care professionals, data scientists, and policymakers is encouraged and promoted.
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