Metaverse-based Digital Twin Model for Monitoring Emerging Infection Using Dynamic Cycle Threshold Values (Preprint)

BACKGROUND Leveraging the metaverse concept to stop the spread of the emerging infectious disease offers novel insights into immersive experiences of precision contact tracing, isolation, and quarantine guided by dynamic viral shedding. OBJECTIVE To develop a metaverse surveillance model for monitoring emerging infectious diseases guided by dynamic viral shedding illustrated with an example of COVID-19 and to assess the effectiveness of various immersive experiences in contact tracing, isolation, and quarantine of all suspected infected persons. METHODS Markov machine learning techniques were leveraged to train parameters from physical cycle threshold (Ct) data. A virtual cohort of digital threads was created from dynamic Ct values obtained by repeated RT-PCR, using a digital twin strategy. A new cut-and-paste editing method integrated virtual and physical data to produce the metaverse dataset. The dataset, illustrated by COVID-19 community-acquired outbreaks of the Alpha and Omicron variants of concern (VOCs) in Changhua, central Taiwan, was utilized to evaluate the effectiveness of immersive precision isolation and quarantine schedules. The Ct-defined dynamic transitions were derived from the digital threads of two small community-acquired outbreaks to guide precision contact tracing, quarantine, and isolation for both Alpha and Omicron VOCs. RESULTS The precision intervals required to trace, quarantine, and isolate contact persons to achieve the effectiveness of stopping a further large outbreak were quantitatively estimated according to the Ct-defined level collected from a small early outbreak. Days tracing back to stop 90% spread via contact persons with Alpha infectives were 15, 27, and 34 days for low, medium, and high viral load levels, respectively. To prevent 90% spread of Omicron infection, precise days for quarantine and isolation were 5 and 7 days for individuals with and without booster vaccination. CONCLUSIONS A Ct-guided digital twin metaverse model shows potential for timely and precise containment of emerging infectious diseases. The study highlights the transformative impact of advanced digital technologies in healthcare, setting the stage for enhanced disease surveillance and management of emerging infectious diseases.