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Large-scale frequent testing and tracing to supplement control of Covid-19 and vaccination rollout constrained by supply

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Infectious Disease Modelling 2021年第1期6卷 955-974页

作者： Lia Humphrey Edward W.Thommes Roie Fields laurent coudeville Naseem Hakim Ayman Chit Jianhong Wu Monica G.Cojocaru Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Vaccine Epidemiology and Modeling Sanofi PasteurCanada Founder ***Canada Sanofi Pasteur&Leslie Dan School of Pharmacy University of TorontoCanada Department of Mathematics and Statistics York UniversityCanada

Non-pharmaceutical interventions(NPI)were implemented all around the world in the fight against COVID-19:Social distancing,shelter-in-place,mask wearing,*** mitigate transmission,together with testing and contact-tracing to identify,isolate and treat the *** majority of countries have relied on the former measures,followed by a ramping up of their testing and tracing *** present here the cases of South Korea,Italy,Canada and the United States,as a look back to lessons that can be drawn for controlling the pandemic,specifically through the means of testing and *** fitting a disease transmission model to daily case report data in each of the four countries,we first show that their combination of social-distancing and testing/tracing have had a significant impact on the evolution of their first wave of pandemic *** then consider the hypothetical scenario where the only NPI measures implemented past the first pandemic wave consisted of isolating individuals due to repeated,country-scale testing and contact tracing,as a mean of lifting social distancing measures without a resurgence of *** give estimates on the average isolation rates needed to occur in each *** find that testing and tracing each individual of a country,on average,every 4.5 days(South Korea),5.7 days(Canada),6 days(Italy)and 3.5 days(US),would have been sufficient to mitigate their second pandemic *** also considered the situation in Canada to see how a frequent large-scale asymptomatic testing and contact tracing could have been used in combination with vaccination rollout to reduce the infection in the *** could offer an alternative approach towards preventing and controlling an outbreak when vaccine supply is limited,while testing capacity has been increasingly enhanced.

关键词： Pandemic modelling Pandemic evolution scenarios Testing frequency policy modelling

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

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： 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 Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey laurent coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

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 *** report summarizes the rich discussions that occurred during 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 *** 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 *** 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 ***,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.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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