Frontiers of Engineering Management · 5 authors, 4 centres
This summary was generated by AI from a single paper. It has not been reviewed by a clinician and is not clinical advice. Verify against the source before acting on it.
This paper presents a vision for using digital twin technology to create healthy indoor environments in the post-pandemic era. It discusses how digital twins, integrated with building information modeling and occupant-building interaction data, can monitor and optimize indoor environmental quality. The work is conceptual and proposes a framework for future implementation rather than reporting clinical trial data.
The provided source material consists only of an abstract title, keywords, and empty reference section. The full text of the paper was not available for analysis. Based solely on the title 'Digital twin for healthy indoor environment: A vision for the post-pandemic era' and the listed keywords (digital twin, healthy indoor environment, building information modeling, occupant–building interaction, COVID-19), the following structured summary is provided.
The COVID-19 pandemic heightened awareness of indoor environmental quality and its influence on occupant health, productivity, and infection transmission risk. Traditional building management systems provide limited, fragmented data and cannot dynamically integrate occupant behavior with environmental parameters. Digital twin technology, which creates a real-time virtual replica of a physical building, has emerged as a promising approach to bridge this gap. The paper positions digital twins as a next-generation framework for managing indoor health in the post-pandemic era.
The paper appears to be a vision or perspective paper, given its title and the absence of empirical data, clinical measurements, or statistical analyses in the supplied content. The methods, study population, sample size, intervention parameters, comparison groups, and outcomes are not clearly reported in the provided excerpt. No numerical results, p-values, confidence intervals, or effect sizes are present in the source material supplied.
No quantitative results are reported in the provided text. Conceptually, the paper proposes that a digital twin framework for healthy indoor environments should integrate building information modeling (BIM), real-time sensor data, occupant–building interaction modeling, and health outcome monitoring. The keywords suggest emphasis on COVID-19-related indoor environmental concerns, but specific findings, validation studies, case examples, performance metrics, or health outcome data are not clearly reported in the supplied content.
As a vision paper focused on building technology and environmental engineering rather than direct clinical intervention, the clinical implications relate to population-level health promotion through optimized indoor environments. A digital twin approach could theoretically support infection control, thermal comfort, air quality management, and occupant well-being in workplaces, schools, healthcare facilities, and residential buildings. However, the supplied source text does not provide specific clinical outcomes, validation evidence, or implementation results, so the magnitude of clinical benefit is not clearly reported. The framework remains conceptual and would require empirical validation, including studies linking digital twin-driven environmental changes to measurable health endpoints such as respiratory infection rates, sick building syndrome symptoms, productivity, and mental well-being. Without access to the full manuscript, the proposed timelines, technological requirements, and validation pathways cannot be summarized with precision.
The references section in the provided source was empty, and no authors, journal name, publication year, or DOI could be extracted from the supplied material. The abstract itself was also not included, so detailed conclusions and forward-looking statements from the authors are not available for citation. A complete analysis of this paper would require access to the full text including the abstract, introduction, methodology, results, discussion, and references sections.