ArticleEpidemics2021
Memory is key in capturing COVID-19 epidemiological dynamics.
Article in Epidemics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 58 citations in OpenAlex.
- Review
- Leaky or polarised immunity: Non-Markovian modelling highlights the impact of immune memory assumptions.PLoS computational biology · 2025Article
- Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model.PLoS computational biology · 2024Article
- Memory effects in disease modelling through kernel estimates with oscillatory time history.Journal of mathematical biology · 2024Article
- Estimating time-dependent contact: a multi-strain epidemiological model of SARS-CoV-2 on the island of Ireland.Global epidemiology · 2023Article
- Understanding the role of mask-wearing during COVID-19 on the island of Ireland.Royal Society open science · 2023Article
- Heterogeneity is a key factor describing the initial outbreak of COVID-19.Applied mathematical modelling · 2023Article
- Data-driven multiscale dynamical framework to control a pandemic evolution with non-pharmaceutical interventions.PloS one · 2023Article
- Coupling the Within-Host Process and Between-Host Transmission of COVID-19 Suggests Vaccination and School Closures are Critical.Bulletin of mathematical biology · 2022Article
- From individual-based epidemic models to McKendrick-von Foerster PDEs: a guide to modeling and inferring COVID-19 dynamics.Journal of mathematical biology · 2022Article
- Analyzing and Modeling the Spread of SARS-CoV-2 Omicron Lineages BA.1 and BA.2, France, September 2021-February 2022.Emerging infectious diseases · 2022Article
- Epidemic models: why and how to use them.Anaesthesia, critical care & pain medicine · 2022Article
- Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening.Journal of the Royal Society, Interface · 2022Article
- Back to the Roots: A Discrete Kermack-McKendrick Model Adapted to Covid-19.Bulletin of mathematical biology · 2022Article
- Principles of mathematical epidemiology and compartmental modelling application to COVID-19.Anaesthesia, critical care & pain medicine · 2022Article
- Epidemiological and clinical insights from SARS-CoV-2 RT-PCR crossing threshold values, France, January to November 2020.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2022Article
- Analysing different exposures identifies that wearing masks and establishing COVID-19 areas reduce secondary-attack risk in aged-care facilities.International journal of epidemiology · 2022Article
- The Predictive Value of Mutation Screening for Anticipating COVID-19 Waves.Pathogens (Basel, Switzerland) · 2021Article
- Article
- Predicting COVID-19 incidence in French hospitals using human contact network analytics.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2021Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
SARS-CoV-2 virus has spread over the world rapidly creating one of the largest pandemics ever. The absence of immunity, presymptomatic transmission, and the relatively high level of virulence of the COVID-19 infection led to a massive flow of patients in intensive care units (ICU). This unprecedented situation calls for rapid and accurate mathematical models to best inform public health policies. We develop an original parsimonious discrete-time model that accounts for the effect of the age of infection on the natural history of the disease. Analysing the ongoing COVID-19 in France as a test case, through the publicly available time series of nationwide hospital mortality and ICU activity, we estimate the value of the key epidemiological parameters and the impact of lock-down implementation delay. This work shows that including memory-effects in the modelling of COVID-19 spreading greatly improves the accuracy of the fit to the epidemiological data. We estimate that the epidemic wave in France started on Jan 20 [Jan 12, Jan 28] (95% likelihood interval) with a reproduction number initially equal to 2.99 [2.59, 3.39], which was reduced by the national lock-down started on Mar 17 to 24 [21, 27] of its value. We also estimate that the implementation of the latter a week earlier or later would have lead to a difference of about respectively -13k and +50k hospital deaths by the end of lock-down. The present parsimonious discrete-time framework constitutes a useful tool for now- and forecasting simultaneously community incidence and ICU capacity strain.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.