ReviewNPJ digital medicine2025
Survey, taxonomy, and emerging paradigms of societal digital twins for public health preparedness.
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Transforming Public Health Practice with Artificial Intelligence: A Framework-Driven Approach.Healthcare (Basel, Switzerland) · 2026Article
- Analysis of digital twin applications in nursing practice and education: a scoping review.Journal of educational evaluation for health professions · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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).
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.