Rehan Muhammad Waqas, Rehan Muhammad Maaz
University of Lübeck, Institute for Software Engineering and Programming Languages (ISP), 23562, Lübeck, Germany.
University of Lancashire, School of Engineering and Computing, Department of Computer Science, PR1 2HE, Preston, Lancashire, UK.
NPJ Digit Med. 2025 Aug 13;8(1):520. doi: 10.1038/s41746-025-01737-5.
The emergence of SARS-CoV-2 (COVID-19) has demonstrated the severe impact of infectious diseases on global society, politics, and economies. To mitigate future pandemics, preemptive measures for effectively managing infection outbreaks are essential. In this context, Societal Digital Twin (SDT) technology offers a promising solution. To the best of our knowledge, this survey is the premier to conceptualize an SDT framework for infection containment under a novel systematic taxonomy. The framework categorizes infection management into five stages, namely infection initiation, spread, control, combat, and recovery. It provides an overview of SDT approaches within each category, discussing their validation strategies, generalizability, and limitations. Additionally, the survey examines applications, data-driven design issues, key components, and limitations of DT technology in healthcare. Finally, it explores key challenges, open research directions, and emerging paradigms to advance DT applications in the healthcare domain, highlighting smart service paradigms such as SDT as a Smart Service (SDTaaSS) and Healthcare Metaverse as a Smart Service (HMaaSS).
严重急性呼吸综合征冠状病毒2（SARS-CoV-2，即新冠病毒）的出现，已证明了传染病对全球社会、政治和经济的严重影响。为减轻未来的大流行，有效管理感染爆发的预防措施至关重要。在此背景下，社会数字孪生（SDT）技术提供了一个很有前景的解决方案。据我们所知，本次调查首次在一种全新的系统分类法下，为感染控制概念化了一个社会数字孪生框架。该框架将感染管理分为五个阶段，即感染起始、传播、控制、对抗和恢复。它概述了每个类别中的社会数字孪生方法，讨论了它们的验证策略、通用性和局限性。此外，本次调查还研究了数字孪生技术在医疗保健中的应用、数据驱动的设计问题、关键组件和局限性。最后，它探讨了推进数字孪生技术在医疗保健领域应用的关键挑战、开放研究方向和新兴范式，突出了诸如作为智能服务的社会数字孪生（SDTaaS）和作为智能服务的医疗元宇宙（HMaaS）等智能服务范式。
