|
|
|
|
LEADER |
03959nam a2200733Ia 4500 |
001 |
10.1016-j.mbs.2021.108671 |
008 |
220427s2021 CNT 000 0 und d |
020 |
|
|
|a 00255564 (ISSN)
|
245 |
1 |
0 |
|a Dynamics of partially mitigated multi-phasic epidemics at low susceptible depletion: phases of COVID-19 control in Italy as case study
|
260 |
|
0 |
|b Elsevier Inc.
|c 2021
|
856 |
|
|
|z View Fulltext in Publisher
|u https://doi.org/10.1016/j.mbs.2021.108671
|
520 |
3 |
|
|a To mitigate the harmful effects of the COVID-19 pandemic, world countries have resorted – though with different timing and intensities – to a range of interventions. These interventions and their relaxation have shaped the epidemic into a multi-phase form, namely an early invasion phase often followed by a lockdown phase, whose unlocking triggered a second epidemic wave, and so on. In this article, we provide a kinematic description of an epidemic whose time course is subdivided by mitigation interventions into a sequence of phases, on the assumption that interventions are effective enough to prevent the susceptible proportion to largely depart from 100% (or from any other relevant level). By applying this hypothesis to a general SIR epidemic model with age-since-infection and piece-wise constant contact and recovery rates, we supply a unified treatment of this multi-phase epidemic showing how the different phases unfold over time. Subsequently, by exploiting a wide class of infectiousness and recovery kernels allowing reducibility (either to ordinary or delayed differential equations), we investigate in depth a low-dimensional case allowing a non-trivial full analytical treatment also of the transient dynamics connecting the different phases of the epidemic. Finally, we illustrate our theoretical results by a fit to the overall Italian COVID-19 epidemic since March 2020 till February 2021 i.e., before the mass vaccination campaign. This show the abilities of the proposed model in effectively describing the entire course of an observed multi-phasic epidemic with a minimal set of data and parameters, and in providing useful insight on a number of aspects including e.g., the inertial phenomena surrounding the switch between different phases. © 2021 Elsevier Inc.
|
650 |
0 |
4 |
|a aging
|
650 |
0 |
4 |
|a analytic method
|
650 |
0 |
4 |
|a Analytical treatment
|
650 |
0 |
4 |
|a Article
|
650 |
0 |
4 |
|a communicable disease control
|
650 |
0 |
4 |
|a Communicable Disease Control
|
650 |
0 |
4 |
|a coronavirus disease 2019
|
650 |
0 |
4 |
|a COVID-19
|
650 |
0 |
4 |
|a COVID-19
|
650 |
0 |
4 |
|a COVID-19
|
650 |
0 |
4 |
|a Delayed differential equations
|
650 |
0 |
4 |
|a Disease control
|
650 |
0 |
4 |
|a disease incidence
|
650 |
0 |
4 |
|a epidemic
|
650 |
0 |
4 |
|a epidemic
|
650 |
0 |
4 |
|a Epidemics
|
650 |
0 |
4 |
|a epidemiology
|
650 |
0 |
4 |
|a Harmful effects
|
650 |
0 |
4 |
|a health policy
|
650 |
0 |
4 |
|a hospital sector
|
650 |
0 |
4 |
|a human
|
650 |
0 |
4 |
|a Humans
|
650 |
0 |
4 |
|a infection control
|
650 |
0 |
4 |
|a infection sensitivity
|
650 |
0 |
4 |
|a Italy
|
650 |
0 |
4 |
|a Italy
|
650 |
0 |
4 |
|a Italy
|
650 |
0 |
4 |
|a Linear-chain trick
|
650 |
0 |
4 |
|a Low dimensional
|
650 |
0 |
4 |
|a mass immunization
|
650 |
0 |
4 |
|a Mass vaccination
|
650 |
0 |
4 |
|a mathematical model
|
650 |
0 |
4 |
|a Multi-phasic age-structured epidemics
|
650 |
0 |
4 |
|a Non-pharmaceutical interventions
|
650 |
0 |
4 |
|a Ordinary differential equations
|
650 |
0 |
4 |
|a pandemic
|
650 |
0 |
4 |
|a Pandemics
|
650 |
0 |
4 |
|a Piece-wise constants
|
650 |
0 |
4 |
|a policy implementation
|
650 |
0 |
4 |
|a population outbreak
|
650 |
0 |
4 |
|a prevention and control
|
650 |
0 |
4 |
|a reproducibility
|
650 |
0 |
4 |
|a SARS-CoV-2
|
650 |
0 |
4 |
|a SIR epidemic model
|
650 |
0 |
4 |
|a Social distancing
|
650 |
0 |
4 |
|a Stable age distributions
|
650 |
0 |
4 |
|a therapy effect
|
650 |
0 |
4 |
|a Transient dynamics
|
700 |
1 |
|
|a d'Onofrio, A.
|e author
|
700 |
1 |
|
|a Iannelli, M.
|e author
|
700 |
1 |
|
|a Manfredi, P.
|e author
|
773 |
|
|
|t Mathematical Biosciences
|