Artificial Intelligence for Precision Viral Surveillance of Emerging Infection Disease (EID): The Metaverse-envisioned Study (Preprint)
Authors: Lin Ting-Yu , Yen Ming-Fang , Chen Li-Sheng , Hsu Chen-Yang , Yeh Yen-Po , Chen Tony Hsiu-Hsi 
Published: November 2024

BACKGROUND

Precision containment measures guided by dynamic viral shedding are essential for effective control of emerging infectious diseases (EID) though the methodology is complex. The advent of metaverse applications in healthcare introduces a data-driven digital twin model—integrating physical and virtual spaces through augmented reality (AR) and mixed reality (MR)—as a promising solution to enhance disease control strategies.

OBJECTIVE

To develop an AI-based viral surveillance model for monitoring EID through dynamic viral shedding data and to evaluate the effectiveness of precision contact tracing, isolation, and quarantine schedules within the metaverse.

METHODS

A digital twin thread design, consisting of a temporal data pipeline, was proposed to support various twin functions. We began with the physical twin, containing dynamic cycle threshold (Ct) data of viral shedding collected through repeated RT-PCR tests. This data trained parameters governing the infectious disease process using Markov machine learning techniques. In virtual reality (VR), an avatar was created to represent these digital threads, forming the virtual thread cohort. Analytical twins were further developed through AR to overlay virtual data onto the physical twin. Finally, a series of decision twins, implemented in MR, were proposed to evaluate the effectiveness of immersive, precision-guided contact tracing, isolation and quarantine schedules. This metaverse-envisioned surveillance model was illustrated using data from COVID-19 community-acquired outbreaks of the Alpha and Omicron VOCs in Changhua, Taiwan.

RESULTS

Based on physical twin data from 269 COVID-19 cases infected with the Alpha VOC, a virtual thread cohort of 1,000,000 cases were spawned. Analytic twins enabled through AR provided information not only from the physical twin but also captured real-time daily changes in the virtual twin that were unavailable from the physical twin. Accordingly, adjacent transitions such as from normal to Ct≤18 pre-symptomatic state occurred more frequently in the analytic twin than the physical twin. Using the first cluster of Alpha VOC infection from the analytic twins to calculate a series of indicators related to the spread of Alpha VOC. A series of decision twins identified optimal days for Ct-guided precision contact tracing and to evaluate the effectiveness of infection control. For individuals with Ct values between 18-25, the optimal days ranged from 7 days of retrospective tracing for reaching 30% effectiveness, 13 days for 60%, and 24 days for reaching 90%. For Omicron VOC, among boosted individuals, 77% were effectively protected after three days of quarantine, and rising to 94% after seven days while those without a booster shoed 39% effectiveness after three days and 76% after seven days.

CONCLUSIONS

A Ct-guided, metaverse-envisioned surveillance model demonstrates potential for timely and precise containment of EID. This approach has significant implications for extending 4P medicine into metaverse healthcare, enhancing precision surveillance and containment for EID in the future.