A Simulation of a COVID-19 Epidemic Based on a Deterministic SEIR Model
TLDR
An SEIR model is implemented to compute the infected population and the number of casualties of an epidemic disease caused by a new coronavirus in Northern Italy with a strong contagion rate and shows how isolation measures, social distancing, and knowledge of the diffusion conditions help to understand the dynamics of the epidemic.Abstract:
An epidemic disease caused by a new coronavirus has spread in Northern Italy with a strong contagion rate. We implement an SEIR model to compute the infected population and the number of casualties of this epidemic. The example may ideally regard the situation in the Italian Region of Lombardy, where the epidemic started on February 24, but by no means attempts to perform a rigorous case study in view of the lack of suitable data and the uncertainty of the different parameters, namely, the variation of the degree of home isolation and social distancing as a function of time, the initial number of exposed individuals and infected people, the incubation and infectious periods, and the fatality rate. First, we perform an analysis of the results of the model by varying the parameters and initial conditions (in order for the epidemic to start, there should be at least one exposed or one infectious human). Then, we consider the Lombardy case and calibrate the model with the number of dead individuals to date (May 5, 2020) and constrain the parameters on the basis of values reported in the literature. The peak occurs at day 37 (March 31) approximately, with a reproduction ratio R 0 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.read more
Citations
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Estimates of the severity of coronavirus disease 2019: a model-based analysis.
Robert Verity,Lucy C Okell,Ilaria Dorigatti,Peter Winskill,Charles Whittaker,Natsuko Imai,Gina Cuomo-Dannenburg,Hayley A Thompson,Patrick G T Walker,Han Fu,Amy Dighe,Jamie T. Griffin,Marc Baguelin,Sangeeta N. Bhatia,A Boonyasiri,Anne Cori,Zulma M. Cucunubá,Richard G. FitzJohn,Katy A. M. Gaythorpe,W Green,Arran Hamlet,Wes Hinsley,Daniel J Laydon,Gemma Nedjati-Gilani,Steven Riley,Sabine L. van Elsland,Erik M. Volz,Haowei Wang,Y Wang,Xiaoyue Xi,Christl A. Donnelly,Christl A. Donnelly,Azra C. Ghani,Neil M. Ferguson +33 more
TL;DR: These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death.
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Management strategies in a SEIR-type model of COVID 19 community spread.
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Modeling the effect of lockdown timing as a COVID-19 control measure in countries with differing social contacts.
Tamer Oraby,Michael G. Tyshenko,Jose Campo Maldonado,Kristina P. Vatcheva,Susie Elsaadany,Walid Q. Alali,Joseph C. Longenecker,Mustafa Al-Zoughool +7 more
TL;DR: In this article, the authors developed a stochastic continuous-time Markov chain (CTMC) model with eight states including the environment (SEAMHQRD-V), and derived a formula for the basic reproduction number, R0, for that model.
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Christopher S. McMahan,Stella Self,Lior Rennert,Corey A. Kalbaugh,David Kriebel,Duane A. Graves,Jessica A. Deaver,Sudeep C. Popat,Tanju Karanfil,David L. Freedman +9 more
TL;DR: The SEIR model provides a robust method to estimate the total number of infected individuals in a sewershed based on the mass rate of RNA copies released per day and thereby provides an additional tool that can be used to better inform policy decisions.
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Modeling the Effect of Lockdown Timing as a COVID-19 Control Measure in Countries with Differing Social Contacts
Tamer Oraby,Michael G. Tyshenko,Jose Campo Maldonado,Kristina P. Vatcheva,Susie Elsaadany,Walid Q. Alali,Joseph C. Longenecker,Mustafa Al-Zoughool +7 more
TL;DR: It is found that well-timed lockdowns can split the peak of hospitalizations into two smaller distant peaks while extending the overall pandemic duration, and a tunneling effect on incidence can be achieved to bypass the peak and prevent pandemic caseloads from exceeding hospital capacity.
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Estimates of the severity of coronavirus disease 2019: a model-based analysis.
Robert Verity,Lucy C Okell,Ilaria Dorigatti,Peter Winskill,Charles Whittaker,Natsuko Imai,Gina Cuomo-Dannenburg,Hayley A Thompson,Patrick G T Walker,Han Fu,Amy Dighe,Jamie T. Griffin,Marc Baguelin,Sangeeta N. Bhatia,A Boonyasiri,Anne Cori,Zulma M. Cucunubá,Richard G. FitzJohn,Katy A. M. Gaythorpe,W Green,Arran Hamlet,Wes Hinsley,Daniel J Laydon,Gemma Nedjati-Gilani,Steven Riley,Sabine L. van Elsland,Erik M. Volz,Haowei Wang,Y Wang,Xiaoyue Xi,Christl A. Donnelly,Christl A. Donnelly,Azra C. Ghani,Neil M. Ferguson +33 more
TL;DR: These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death.
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