COVID-19 has significantly impacted the entire world, changing the way of life for everyone. Knowing that the spread of the virus has been affecting almost all aspects of our day-to-day lives, it is critically important that we provide the relevant information, analyses, and predictions that can effectively inform the public so that everyone can make informed decisions during such epidemiological crisis.
The pathogenesis of COVID-19 is increasingly suggesting impairments in the respiratory system. In this light, it is natural to ask – Can sound samples serve as acoustic biomarkers of COVID-19? If yes, an acoustics based COVID-19 diagnosis can provide a fast, contactless, and inexpensive testing scheme, with potential to supplement the existing molecular testing methods, such as RT-PCR and RAT. The present Challenge is an exploration of ideas to find answers to this question.
The traditional drug discovery process is expensive and time-consuming. Accelerating this process a significant challenge, for which taking a ML/AI-based pre-screening approach could assist us with high-throughput virtual pre-screening of huge amount of drug candidates to identify highly potent candidates for experimental testing and further validation.