Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report.
| Title: | Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report. |
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| Authors: | Nunes MC; 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.; Thommes E; New Products and Innovation (NPI), Sanofi Vaccines (Global), Toronto, Ontario, Canada.; Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada.; Fröhlich H; Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, Sankt Augustin, Germany.; University of Bonn, Bonn-Aachen International Center for IT (b-it), Bonn, Germany.; Flahault A; Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland and Swiss School of Public Health, Zürich, Switzerland.; Arino J; Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada.; Baguelin M; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.; Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.; Biggerstaff M; National Center for Immunization and Respiratory Diseases (NCIRD), US Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA.; Bizel-Bizellot G; Departement of Computational Biology, Departement of Global Health, Institut Pasteur, Paris, France.; Borchering R; National Center for Immunization and Respiratory Diseases (NCIRD), US Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA.; Cacciapaglia G; Institut de Physique des Deux Infinis de Lyon (IP2I), UMR5822, IN2P3/CNRS, Université Claude Bernard Lyon 1, Villeurbanne, France.; Cauchemez S; Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, UMR2000 CNRS, Paris, France.; Barbier-Chebbah A; Decision and Bayesian Computation, Institut Pasteur, Université Paris Cité, CNRS UMR 3571, France.; Claussen C; Fraunhofer-Institute for Translational Medicine and Pharmacology, Hamburg, Germany.; Choirat C; Institute of Global Health, Faculty of Medicine, University of Geneva, Switzerland.; Cojocaru M; Mathematics & Statistics Department, College of Engineering and Physical Sciences, University of Guelph, Guelph, Ontario, Canada.; Commaille-Chapus C; Impact Healthcare, Paris, France.; Hon C; Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau, China.; Kong J; Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada.; Lambert N; Quinten Health, Paris, France.; Lauer KB; Airfinity Ltd, London, UK.; Lehr T; Clinical Pharmacy, Saarland University, Saarbrücken, Germany.; Mahe C; Sanofi Vaccine, Lyon, France.; Marechal V; Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, Paris, France.; Mebarki A; Kap Code, Paris, France.; Moghadas S; Agent-Based Modelling Laboratory, York University, Toronto, Ontario, Canada.; Niehus R; European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden.; Opatowski L; UMR 1018, Team 'Anti-infective Evasion and Pharmacoepidemiology', Université Paris-Saclay, UVSQ, INSERM, France.; Epidemiology and Modelling of Antibiotic Evasion, Institut Pasteur, Université Paris Cité, Paris, France.; Parino F; Sorbonne Université, INSERM, Pierre Louis Institute of Epidemiology and Public Health, Paris, France.; Pruvost G; Lifen, Paris, France.; Schuppert A; Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany.; Thiébaut R; Bordeaux University, Department of Public Health, Inserm UMR 1219 Bordeaux Population Health Research Center, Inria SISTM, Bordeaux, France.; Thomas-Bachli A; BlueDot, Toronto, Ontario, Canada.; Viboud C; Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.; Wu J; York Emergency Mitigation, Engagement, Response, and Governance Institute, Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario, Canada.; Crépey P; EHESP, Université de Rennes, CNRS, IEP Rennes, Arènes - UMR 6051, RSMS - Inserm U 1309, Rennes, France.; Coudeville L; Sanofi Vaccine, Lyon, France. |
| Source: | Infectious Disease Modelling [Infect Dis Model] 2024 Feb 23; Vol. 9 (2), pp. 501-518. Date of Electronic Publication: 2024 Feb 23 (Print Publication: 2024). |
| Publication Type: | Journal Article; Review |
| Language: | English |
| Journal Info: | Publisher: KeAi Communications Co., Ltd Country of Publication: China NLM ID: 101692406 Publication Model: eCollection Cited Medium: Internet ISSN: 2468-0427 (Electronic) Linking ISSN: 24680427 NLM ISO Abbreviation: Infect Dis Model Subsets: PubMed not MEDLINE |
| Imprint Name(s): | Original Publication: Beijing : KeAi Communications Co., Ltd., [2016]- |
| 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.; (© 2024 The Authors.) |
| Competing Interests: | MCN reports grants from the Bill & Melinda Gates Foundation, European & Developing Countries Clinical Trials Partnership, Pfizer, AstraZeneca, and Sanofi; and consultation fees outside the work reported here from Sanofi. TL received funding from the Government of the Saarland for the maintenance and development of the COVID Simulator. SMM reports advisory roles for Janssen Canada and Sanofi for cost-effectiveness of their vaccine products, and received consultation fees outside the work reported here. JW acknowledges support from NSERC-Sanofi Industrial Research Chair program and the NSERC Alliance program. PC reports consulting fees from Sanofi, Pfizer, and Seqirus. Fraunhofer-Institute declares various national and international public and private grants which are in line with its status as a non-for-profit research organization. |
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| Contributed Indexing: | Keywords: Covid-19; Infectious diseases; Modelling; Pandemic preparedness; Workshop |
| Entry Date(s): | Date Created: 20240306 Latest Revision: 20241031 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC10912817 |
| DOI: | 10.1016/j.idm.2024.02.008 |
| PMID: | 38445252 |
| Database: | MEDLINE |
Journal Article; Review