[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120995-en":3,"doc-seo-120995-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120995,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Enhancing Healthcare Facility Resilience - Utilizing Machine Learning Models for Airborne Disease Infection Prediction","Nosocomial airborne infections during COVID-19 exposed limitations in early epidemiological forecasting and in accounting for complex indoor built environments. This paper proposes a joint modelling framework that integrates machine learning risk estimation with building information modelling and agent-based models to evaluate nosocomial airborne infection risk. With limited input data, machine learning provides high-confidence infection risk estimates, which are embedded into agent-based simulations within a comprehensive building model, while also representing human movement across varied indoor conditions. The approach supports deeper investigation of transmission risk and resource-relevant resilience insights for healthcare facilities.","1  \n6  \n11  \n16  \n21  \n26  \n31  \n36  \n41  \n46  \n51  \nQ1  \nJOURNAL OF BUILDING PERFORMANCE SIMULATION [https://doi.org/10.1080/19401493.2024.2395269](https://doi.org/10.1080/19401493.2024.2395269)  \nEnhancing healthcare facility resilience: utilizing machine learning model for airborne disease infection prediction  \nKangkang Tang  \nDepartment of Civil and Environmental Engineering, Brunel University London, London, UK  \nABSTRACT  \nDuring this pandemic, advanced epidemiological models have been widely employed to determine intervention strategies for controlling the spread of the disease in public and healthcare settings. These models play a crucial role by providing predictive insights into disease transmission dynamics, informing resource allocation and guiding policy decisions. However, the accuracy of these predictions depends on a substantial amount of input data, which was not readily available at the onset of the pandemic. Another concern with the existing models is their inability to adequately account for the complex indoor built environments, which has been shown to significantly impact infection risk. To tackle these issues, this paper discusses the potential of developing a joint modelling technique that integrates machine learning models, building information models and agent-based models to assess the risk of nosocomial airborne infections. With limited available data, machine learning models can determine infection risk with high confidence. By incorporating these risk estimates into agent-based models within a more comprehensive building model, we conducted a thorough investigation of nosocomial infections. This approach also considers human movement patterns across various indoor conditions, enhancing the depth of our analysis.  \nARTICLE HISTORY  \nReceived 2 February 2024 Accepted 17 August 2024  \nKEYWORDS  \nArtificial neural network modelling (ANN); Respiratory syndrome coronavirus 2 (SARS-CoV-2); Agent-based modelling (ABM); Building information modelling (BIM); Modern methods of construction (MMC)  \n1. Research background  \nSignificant instances of nosocomial transmissions occurred at the early stage of the COVID-19 pandemic. In the UK, approximately one-seventh to one-fifth ofCOVID- 19 patients, as well as the majority of infected healthcare workers, contracted the disease in healthcare facilities during the first wave (Campbell and Barr 2021; Evans et al. 2020;Readetal. 2021). Rickman et al. (2020) investigated nosocomial COVID-19 transmission cases in London hospitals and reported that 55% of the patient-to-patient infections took place in the same bay; 14% were in different bays but on the same floor; 12% of infections actually happened in the single-occupancy rooms. Besides the serious challenges posed by nosocomial infections, the COVID-19 pandemic has underscored another critical aspect: the resilience of healthcare facilities in coping with a surge of patients. Many hospitals found it challenging to deliver adequate healthcare services while managing the overwhelming patient influx. This pandemic has highlighted the existing issues regarding resilience of healthcare systems on a significant scale, capturing widespread attention.  \nThe readiness and responsiveness of the healthcare system rely on multifaceted strategies and approaches. Preparedness plans, surge capacity planning and multidepartment collaboration form the foundation for effective outbreak response, ensuring the allocation of resources and coordination of efforts. Among these, epidemiological modelling has played a crucial role by providing predictive insights into disease transmission dynamics, informing resource allocation and guiding policy decisions. During this pandemic, advanced epidemiological models have been widely used to determine intervention strategies for controlling the spread of the disease in public and healthcare settings (Currie et al. 2020; Swallow et al. 2022) . It is important to note that the accuracy of these predi","cbCaildk3NvQ0z1C","https://ap.wps.com/l/cbCaildk3NvQ0z1C","pdf",3591635,1,16,"English","en",105,"# Research background\n## Factors influencing human-to-human transmission of COVID-19\n### Primary modes of transmission","[{\"question\":\"为什么需要更强的医疗设施韧性与传播预测能力？\",\"answer\":\"COVID-19期间，医疗设施在患者激增下很难维持足够服务，同时院内传播带来了额外挑战。疫情也放大了医疗体系在大规模情境下韧性不足的问题。\"},{\"question\":\"现有流行病学模型存在哪些主要局限？\",\"answer\":\"预测准确性高度依赖大量输入数据，而疫情早期数据不足且可能相互冲突。同时，现有模型难以充分表征复杂的室内建成环境对感染风险的影响。\"},{\"question\":\"文中提出的联合建模方法如何工作？\",\"answer\":\"将机器学习模型用于在有限数据条件下估计感染风险，再把这些风险估计嵌入到结合建筑信息模型的基于主体（agent-based）模拟中，从而考察人群在不同室内条件下的移动模式并开展更深入的院内传播风险评估。\"}]","Enhancing Healthcare Facility Resilience - Utilizing Machine Learning Models for Airborne Disease Infection Prediction | PDF",1785733231,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"enhancing-healthcare-facility-resilience-utilizing-machine-learning-models-for-airborne-disease-infection-prediction","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/enhancing-healthcare-facility-resilience-utilizing-machine-learning-models-for-airborne-disease-infection-prediction/120995/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"为什么需要更强的医疗设施韧性与传播预测能力？","Question",{"text":75,"@type":76},"COVID-19期间，医疗设施在患者激增下很难维持足够服务，同时院内传播带来了额外挑战。疫情也放大了医疗体系在大规模情境下韧性不足的问题。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"现有流行病学模型存在哪些主要局限？",{"text":80,"@type":76},"预测准确性高度依赖大量输入数据，而疫情早期数据不足且可能相互冲突。同时，现有模型难以充分表征复杂的室内建成环境对感染风险的影响。",{"name":82,"@type":73,"acceptedAnswer":83},"文中提出的联合建模方法如何工作？",{"text":84,"@type":76},"将机器学习模型用于在有限数据条件下估计感染风险，再把这些风险估计嵌入到结合建筑信息模型的基于主体（agent-based）模拟中，从而考察人群在不同室内条件下的移动模式并开展更深入的院内传播风险评估。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]