A Simulation of a Covid-19 Epidemic Based on a Deterministic Seir Model

Dados Bibliográficos

ID22073266
AutoresJosé M Carcione (0000-0002-2839-705X, National Institute of Oceanography and Applied Geophysics), Juan E Santos (0000-0002-8486-1080, Hohai University), Claudio Bagaini (0000-0002-2432-3715, Haywards Heath Hospital), Jing Ba (0000-0002-9861-0186, Hohai University, autor correspondente)
Ano2020
Volume8
Páginas230-230
Data de publicação2020-05-28
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoFrontiers in Public Health (JOURNAL)
Identificadores do periódicoISSN: 2296-2565 • E-ISSN: 2296-2565
EditoraFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2020.00230
PMID32574303
OpenAlexW3029408214
IdiomaEN
Citações recebidas13
Referências citadas26

of 3 initially, 1.36 at day 22, and 0.8 after day 35, indicating different degrees of lockdown. The predicted death toll is approximately 15,600 casualties, with 2.7 million infected individuals at the end of the epidemic. The incubation period providing a better fit to the dead individuals is 4.25 days, and the infectious period is 4 days, with a fatality rate of 0.00144/day [values based on the reported (official) number of casualties]. The infection fatality rate (IFR) is 0.57%, and it is 2.37% if twice the reported number of casualties is assumed. However, these rates depend on the initial number of exposed individuals. If approximately nine times more individuals are exposed, there are three times more infected people at the end of the epidemic and IFR = 0.47%. If we relax these constraints and use a wider range of lower and upper bounds for the incubation and infectious periods, we observe that a higher incubation period (13 vs. 4.25 days) gives the same IFR (0.6 vs. 0.57%), but nine times more exposed individuals in the first case. Other choices of the set of parameters also provide a good fit to the data, but some of the results may not be realistic. Therefore, an accurate determination of the fatality rate and characteristics of the epidemic is subject to knowledge of the precise bounds of the parameters. Besides the specific example, the analysis proposed in this work shows how isolation measures, social distancing, and knowledge of the diffusion conditions help us to understand the dynamics of the epidemic. Hence, it is important to quantify the process to verify the effectiveness of the lockdown

Basic reproduction number · Biology · Case fatality rate · Death toll · Disease · Epidemic model · Geography · Incubation · Incubation period · Population · Social distance · Statistics · Susceptible individual · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Demography · Mathematical and Theoretical Epidemiology and Ecology Models · Mathematics · Medicine