Application of Artificial Intelligence for Early Detection of Pandemic Outbreak

Some random thoughts on how Artificial Intelligence could be used for early identification of pandemic outbreaks

Today I stumbled upon an interesting BBC article titled ‘Treating cancer, stopping violence… How AI protects us‘ and that suddenly sparked a few thoughts on how AI could help us with early prediction of Pandemics/Epidemics in the coming days. A google search helped me with a few initiatives in this regard. I would like to discuss these initiatives first, then talk about my personal views on further opportunities in this direction.

The BBC article mentions two interesting systems in use for early prediction of infectious diseases such as Dengue fever, yellow fever, Zika and chikungunya. The first one named Artificial Intelligence in Medical Epidemiology (AIME) uses case reports pulled in from local hospitals combined with the weather and social factors to predict the outbreak well in advance. Another project from Microsoft called Microsoft Premonition employs scalable monitoring of the environment to detect disease threats early, using robotics and genomics. Their cloud-scale genomic analysis try to identify all the species of organisms and viruses in environmental samples to spot new transmission patterns.

More recently, an article titled ‘AI could help with the next pandemic—but not with this one‘ in MIT Technology Review explains how companies like BlueDot and Metabiota used a range of natural language processing (NLP) algorithms to monitor news outlets and official health care reports in different languages around the world to provide early indications of COVID-19 outbreak. They also effectively applied air travel data analysis for predicting the pace of spread with some reasonable accurate results.

One problem with big data analysis method described above is that not all countries allow transparent sharing of information on news channels or social media platforms. In that case, depending heavily on news/social media analysis for pandemic prediction may draw inconsistent results. We might need to use additional techniques as well to come up with a more reliable result. For example, we could use Computer Vision technology along with Thermal Imaging sensors in crowded places such as Bus stations,Railway stations and Airports to identify abnormal patterns. If we can apply AI techniques such as Natural Language Processing (NLP) , Speech and Computer Vision in multiple areas such as news channels, social media, video/voice communications and crowded places, then we would be in a very good position to track the hints of pandemic at the very early stage itself. Needless to say, early identification and subsequent rapid preventive measures can only ensure the containment of the disease to a small geographical area.

Of course, data privacy is a major concern here. But it is not limited to this case. Most of the AI applications share the same concern due to the heavy volume of data usage. It needs to be mitigated at appropriate levels to ensure the survival of human beings. At some point of time, we need to stop thinking on absolute basis and start perceiving things on a comparative mode to ensure that the rules and regulations do not compromise our existence in this world.