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Memory is key in capturing COVID-19 epidemiological dynamics
Memory is key in capturing COVID-19 epidemiological dynamics
- Source :
- Epidemics, Epidemics, Elsevier, 2021, 35, pp.100459. ⟨10.1016/j.epidem.2021.100459⟩, Epidemics, 2021, 35, pp.100459. ⟨10.1016/j.epidem.2021.100459⟩, Epidemics, Vol 35, Iss, Pp 100459-(2021)
- Publication Year :
- 2020
-
Abstract
- International audience; 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.
- Subjects :
- Epidemiology
Reproduction number
Basic Reproduction Number
Infectious and parasitic diseases
RC109-216
[SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy
law.invention
0302 clinical medicine
law
Pandemic
030212 general & internal medicine
Hospital Mortality
Mathematical epidemiology
[SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE]
Incidence
[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Epidemiosurveillance
3. Good health
Intensive Care Units
Infectious Diseases
Geography
Transmission (mechanics)
[SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology
Epidemiological Monitoring
France
medicine.medical_specialty
030231 tropical medicine
Microbiology
Article
Mathematical modelling of infectious disease
03 medical and health sciences
[SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems
Virology
Intensive care
medicine
Humans
Non-Markovian processes
[SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health
[SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE]
SARS-CoV-2
Public health
Public Health, Environmental and Occupational Health
COVID-19
Models, Theoretical
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Discrete-time modelling
Communicable Disease Control
Parasitology
[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
Basic reproduction number
Demography
[SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Forecasting
Subjects
Details
- ISSN :
- 18780067 and 17554365
- Volume :
- 35
- Database :
- OpenAIRE
- Journal :
- Epidemics
- Accession number :
- edsair.doi.dedup.....9dc0c6c1b4d81c5b5c9831acaa85223c
- Full Text :
- https://doi.org/10.1016/j.epidem.2021.100459⟩