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

IF 8.8 3区 医学 Q1 Medicine Infectious Disease Modelling Pub Date : 2024-02-23 DOI:10.1016/j.idm.2024.02.008
Marta C. Nunes , Edward Thommes , Holger Fröhlich , Antoine Flahault , Julien Arino , Marc Baguelin , Matthew Biggerstaff , Gaston Bizel-Bizellot , Rebecca Borchering , Giacomo Cacciapaglia , Simon Cauchemez , Alex Barbier--Chebbah , Carsten Claussen , Christine Choirat , Monica Cojocaru , Catherine Commaille-Chapus , Chitin Hon , Jude Kong , Nicolas Lambert , Katharina B. Lauer , Laurent Coudeville
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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.

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重新定义大流行病防备:CERP 传染病建模研讨会的多学科见解,研讨会报告
2023 年 7 月,呼吸道病原体高级研究中心组织了一次为期两天的研讨会,主题是传染病建模以及从 Covid-19 大流行中吸取的经验教训。研讨会与会者讨论了多源数据整合问题,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多新型数据源相结合的益处。与会者指出,在开发预测模型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方面的应用。与会者强调了各利益攸关方在建模方面开展公开合作的作用,并主张在大流行病过后继续保持这种伙伴关系。总之,研讨会强调,需要建立健全、适应性强的建模框架,整合不同的数据源,并开展跨部门合作,这是加强未来大流行病应对和防备工作的关键因素。
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来源期刊
Infectious Disease Modelling
Infectious Disease Modelling Mathematics-Applied Mathematics
CiteScore
17.00
自引率
3.40%
发文量
73
审稿时长
17 weeks
期刊介绍: Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.
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