Addressing the Public Health Misinformation Challenge with Real-Time Data Fusion

Misinformation is at an all-time peak across the globe, it wouldn't be considered an exaggeration if we describe 'misinformation' as a billion-dollar industry where bad actors profit off the chaos generated by misinformation. The public health industry has been at the receiving end of this challenge for way too long, leading to a high mortality rate in the public health industry. The number of resources invested into misinformation has made this phenomenon complex through its adoption of new and sophisticated deception techniques. The impact of misinformation on democracy and human rights can be damning and have severe consequences, also counter-misinformation will prove to be counter-productive, further denting the integrity of democracy and human rights; the COVID-19 pandemic illustrates what the war of misinformation looks like, an unending wave of misinformation and impeding crack on democracy as we know it. On numerous levels, effective responses to tackle disinformation are required, some of which will include laws and regulations, civil actions, and corporate measures among other practices. The early months of 2020 undeniably altered humanity's lifestyle in several ways, some of which humans could not have imagined. Of all the changes brought upon humanity by the 2020 COVID- 19 pandemic, the fusion of aggregated data using technological solutions is paramount. The adoption of AI in several fields was fast-tracked by the occurrence which is the COVID-19 pandemic; AI was already in the works, but the pandemic accelerated its wide-scale adoption by compressing digital transformation that would have taken two years into a few months. Here we review the misinformation challenge encountered by the public health sector and identify major gaps in research, we also propose asolution that leverages the IT techniques in AI, deep learning, and semantic technologies. The place of IT techniques in addressing the public health misinformation challenge with realtime data fusion will be explored in this paper.