A simulation of a COVID-19 epidemic based on a deterministic SEIR model

Carcione, José M.

doi:10.1101/2020.04.20.20072272

Cited by 71 publications

(93 citation statements)

References 24 publications

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“…In the past months a lot of effort has been put into mathematical modelling of the Covid-19 pandemic, see e.g. [15][16][17][18][19][20][21][22][23][24][25][26][27][28] for an extensive, yet incomplete, list. people is plotted against time (in days).…”

Section: Application To the Covid-19 Pandemicmentioning

confidence: 99%

A Divide and Conquer Strategy against the Covid-19 Pandemic?!

Mangat

2020

Preprint

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The concern about (socio-)economic consequences of collective lockdowns in the Covid-19 pandemic calls for alternative strategies. We consider a divide and conquer strategy in which a high risk group (HRG) is put on strict isolation, whereas the remainder of the population is exposed to the virus, building up immunity against Covid-19. The question is whether this strategy may suppress the effective reproduction number below the critical value of R eff < 1 without further lockdown once the HRG is released from isolation. While this proposal appears already rather academic, we show that R eff < 1 can only be obtained provided that the HRG is less than ∼ 20 − 30% of the total population. Hence, this strategy is likely to fail in countries with a HRG larger than the given upper bound. In addition, we argue that the maximum infection rate occurring in this strategy is likely to exceed realistic capacities of most health care systems. While the conclusion is rather negative in this regard, we emphasise that the strategy of stopping the curve at an early stage of the Covid-19 pandemic has a chance to work out. The required duration of the lockdown is estimated to be τ ∼ 14 days/(1 − R eff ) (up to some order one factor) for R eff < 1, provided a systematic tracing strategy of new infections exists for the subsequent relaxation phase. In this context we also argue why R eff remains the crucial parameter which needs to be accurately monitored and controlled.

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Section: Application To the Covid-19 Pandemicmentioning

confidence: 99%

A Divide and Conquer Strategy against the Covid-19 Pandemic?!

Mangat

2020

Preprint

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“…It is important to predict the spread of the disease in the early stages of the outbreak. Many epidemic models were proposed based on dynamic spreading models, agentbased models, the Monte Carlo model, and data-based spreading models [3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…The evolution of the virus was described with a modified susceptible (S), infectious (I), recovered (R) population, the socalled SIR model [3][4][5][6][7]. The prediction of COVID-19 evolution in Brazil was suggested by using the susceptible, infectious, quarantined, recovered (SIQR) model [5].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Infection Rate and Predictions of Disease Spreading Based on Initial Individuals Infected With COVID-19

Chae

Lee

et al. 2020

Front. Phys.

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We consider the pandemic spread of COVID-19 in selected countries after the outbreak of the SARS-CoV-2 coronavirus in Wuhan City, China. We estimated the infection rate and the initial individuals infected with COVID-19 by using officially reported data from the early stages of the epidemic for a model of susceptible (S), infectible (I), quarantined (Q), and officially confirmed recovered (R k) populations (the so-called SIQR k model). In the officially reported data, we know the number of quarantined cases and the officially reported number of recovered cases. We cannot know about recovered cases from asymptomatic patients. In the SIQR k model, we can estimate the parameters and the initial infections (confirmed cases + asymptomatic cases) from fitted values. We obtained an infection rate in the range β = 0.233 ∼ 0.462, a basic reproduction number of R o = 1.8 ∼ 3.5, and the initial number of infected individuals, I (0) = 10 ∼ 8409, for selected countries. By using fitting parameters, we estimated that the maximum time span of the infection was around 50 days in Germany when the government invoked the quarantine policy. The disease is expected to subside about 6 months after the first patients are found.

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“…recently, people implemented the SEIR Model to compute the future response [37] by varying the parameters and initial conditions. The same model has been adopted by one of the researchers for computing the causalities in the United Kingdom [31]. SEIR Model has a great and efficient computational result, therefore, it has been used by many researchers such that India [30], the data record most of the cases are collected from John Hopkin's repository in the multiple time frames.…”

Section: Introductionmentioning

confidence: 99%

“…SEIR Model has a great and efficient computational result, therefore, it has been used by many researchers such that India [30], the data record most of the cases are collected from John Hopkin's repository in the multiple time frames. In addition to this, several research contributions were made in [31][32][33][34] using this SEIR model, therefore, the paper also proposes the same model for computing the casualties in Malaysia.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Impact of Lock-down on COVID-19 Spread in Malaysia

Altahir

Mathur

Thiruchelvam

et al. 2020

Preprint

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After a breakdown notified in Wuhan, China in December 2019, COVID-19 is declared as pandemic diseases. To the date more than 13 million confirmed cases and more than half a million are dead around the world. This virus also attached Malaysia in its immature stage where 8718 cases were confirmed and 122 were declared as death. Malaysia responsibly controlled the spread by enforcing MCO. Hence, it is required to visualize the pattern of Covid-19 spread. Also, it is necessary to estimate the impact of the enforced prevention measures. In this paper, an infectious disease dynamic modeling (SEIR) is used to estimate the epidemic spread in Malaysia. The main assumption is to update the reproduction number Rt with respect to the implemented prevention measures. For a time-frame of five month, the Rt was assumed to vary between 2.9 and 0.3. Moreover, the manuscript includes two possible scenarios: the first will be the extension of the stricter measures all over the country, and the second will be the gradual lift of the lock-down. After implementing several stages of lock-down we have found that the estimated values of the Rt with respect to the strictness degree varies between 0.2 to 1.1. A continuous strict lock-down may reduce the Rt to 0.2 and accordingly the estimated active cases will be reduced to 20 by the beginning of September 2020. In contrast, the second scenario considers a gradual lift of the enforced prevention measures by the end of June 2020, here we have considered three possible outcomes according to the MCO relaxation. Thus, the estimated values of Rt = 0.7, 0.9, 1.1, which shows a rapid increase in the number of active cases. The implemented SEIR model shows a close resemblance with the actual data recorded from 10, March till 7, July 2020.

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A simulation of a COVID-19 epidemic based on a deterministic SEIR model

Cited by 71 publications

References 24 publications

A Divide and Conquer Strategy against the Covid-19 Pandemic?!

A Divide and Conquer Strategy against the Covid-19 Pandemic?!

Estimation of Infection Rate and Predictions of Disease Spreading Based on Initial Individuals Infected With COVID-19

Modeling the Impact of Lock-down on COVID-19 Spread in Malaysia

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