Metaverse in Dynamic Viral-shedding Surveillance for Emerging Infectious Disease: A Digital Twin Approach to COVID-19 Epidemic

Abstract

Metaverse in effective surveillance of outbreaks of emerging infectious diseases such as COVID-19 opens a new avenue for precision and efficient contact tracing, quarantine, and isolation. We adopted a digital twin model to generate digital threads for tracing and tracking virtual data on the cycle threshold (Ct) values of the repeated RT-PCR with parameters learned from real-world (physical) data fitted with Markov machine learning algorithms. Such a digital twin method is demonstrated with COVID-19 community-acquired outbreaks of the Alpha and Omicron Variants of Concern (VOCs) in Taiwan. The personalized dynamics of Ct-defined transitions were derived from the digital threads of the two community-acquired outbreaks to guide precision contact tracing, quarantine, and isolation of both Alpha and Omicron VOCs outbreaks. Metaverse surveillance with such a Ct-guided digital twin model is supposed to be useful for timely containing the spread of emerging infectious diseases in the future.