@article{fdi:010082175,
  title = {{M}emory is key in capturing {COVID}-19 epidemiological dynamics},
  author = {{S}ofonea, {M}. {T}. and {R}eyne, {B}. and {E}lie, {B}. and {D}jidjou-{D}emasse, {R}ams{\`e}s and {S}elinger, {C}hristian and {M}ichalakis, {Y}. and {A}lizon, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{SARS}-{C}o{V}-2 virus has spread over the world rapidly creating one of the largest pandemics ever. {T}he 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}). {T}his unprecedented situation calls for rapid and accurate mathematical models to best inform public health policies. {W}e develop an original parsimonious discrete-time model that accounts for the effect of the age of infection on the natural history of the disease. {A}nalysing the ongoing {COVID}-19 in {F}rance 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. {T}his work shows that including memory-effects in the modelling of {COVID}-19 spreading greatly improves the accuracy of the fit to the epidemiological data. {W}e estimate that the epidemic wave in {F}rance started on {J}an 20 [{J}an 12, {J}an 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 {M}ar 17 to 24 [21, 27] of its value. {W}e 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. {T}he present parsimonious discrete-time framework constitutes a useful tool for now-and forecasting simultaneously community incidence and {ICU} capacity strain.},
  keywords = {{M}athematical epidemiology ; {D}iscrete-time modelling ; {N}on-{M}arkovian processes ; {E}pidemiosurveillance ; {R}eproduction number},
  booktitle = {},
  journal = {{E}pidemics},
  volume = {35},
  numero = {},
  pages = {100459 [8 ]},
  ISSN = {1755-4365},
  year = {2021},
  DOI = {10.1016/j.epidem.2021.100459},
  URL = {https://www.documentation.ird.fr/hor/fdi:010082175},
}