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Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.评估美国 COVID-19 情景建模中心在不确定情况下为大流行应对提供信息的能力。
Nat Commun. 2023 Nov 20;14(1):7260. doi: 10.1038/s41467-023-42680-x.
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Public health impact of the U.S. Scenario Modeling Hub.美国情景建模中心对公共卫生的影响。
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Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England.英格兰地区 SARS-CoV-2 传染性和严重程度的流行病学驱动因素。
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The interactions of SARS-CoV-2 with cocirculating pathogens: Epidemiological implications and current knowledge gaps.SARS-CoV-2 与同时流行的病原体的相互作用:流行病学意义和当前的知识空白。
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Leveraging Responsible, Explainable, and Local Artificial Intelligence Solutions for Clinical Public Health in the Global South.在全球南方地区,利用负责任、可解释且本地化的人工智能解决方案促进临床公共卫生。
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Pandemic modelling for regions implementing an elimination strategy.实施消除策略地区的大流行建模。
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Model-Based Analysis of SARS-CoV-2 Infections, Hospitalization and Outcome in Germany, the Federal States and Districts.基于模型的德国、联邦州和地区 SARS-CoV-2 感染、住院和结局分析。
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重新定义大流行防范:传染病CERP建模研讨会的多学科见解,研讨会报告

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report.

作者信息

Nunes Marta C, Thommes Edward, Fröhlich Holger, Flahault Antoine, Arino Julien, Baguelin Marc, Biggerstaff Matthew, Bizel-Bizellot Gaston, Borchering Rebecca, Cacciapaglia Giacomo, Cauchemez Simon, Barbier-Chebbah Alex, Claussen Carsten, Choirat Christine, Cojocaru Monica, Commaille-Chapus Catherine, Hon Chitin, Kong Jude, Lambert Nicolas, Lauer Katharina B, Lehr Thorsten, Mahe Cédric, Marechal Vincent, Mebarki Adel, Moghadas Seyed, Niehus Rene, Opatowski Lulla, Parino Francesco, Pruvost Gery, Schuppert Andreas, Thiébaut Rodolphe, Thomas-Bachli Andrea, Viboud Cecile, Wu Jianhong, Crépey Pascal, Coudeville Laurent

机构信息

Center of Excellence in Respiratory Pathogens (CERP), Hospices Civils de Lyon (HCL) and Centre International de Recherche en Infectiologie (CIRI), Équipe Santé Publique, Épidémiologie et Écologie Évolutive des Maladies Infectieuses (PHE3ID), Inserm U1111, CNRS UMR5308, ENS de Lyon, Université Claude Bernard Lyon 1, Lyon, France.

South African Medical Research Council, Vaccines & Infectious Diseases Analytics Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.

出版信息

Infect Dis Model. 2024 Feb 23;9(2):501-518. doi: 10.1016/j.idm.2024.02.008. eCollection 2024 Jun.

DOI:10.1016/j.idm.2024.02.008
PMID:38445252
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10912817/
Abstract

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

摘要

2023年7月,呼吸道病原体卓越中心组织了一场为期两天的关于传染病建模以及从新冠疫情中吸取的经验教训的研讨会。本报告总结了研讨会期间进行的丰富讨论。研讨会参与者讨论了多源数据整合,并强调了将传统监测与移动数据、社交媒体和废水监测等更新颖的数据源相结合的好处。在预测模型的开发方面取得了重大进展,来自不同国家的实例展示了机器学习和人工智能在检测和监测疾病趋势中的应用。强调了各利益相关方在建模中的开放合作的作用,主张在疫情之后继续保持这种伙伴关系。确定的一个主要差距是缺乏数据共享的通用国际框架,这对全球疫情防范至关重要。总体而言,研讨会强调了强大、适应性强的建模框架以及不同数据源的整合和跨部门合作的必要性,这些是加强未来疫情应对和防范的关键要素。