Generalized SEIR Epidemic Model for COVID-19 in a Multipatch Environment

Meng, Lan; Zhu, Wei

doi:10.1155/2021/5401253

Cited by 10 publications

(8 citation statements)

References 29 publications

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“…SEIR (Susceptible, Exposed, Infectious and Removed) model [3-8] (Figure 1, top panel) is best suited to model pandemics in a closed community. As per an SEIR model, the community members belong to one of the four compartments: S usceptible (S) compartment where a subset of the community is not immune and not exposed to the virus in the current cycle, Exposed (E) compartment where another subset of members are infected with the virus but are not infectious yet, Infectious (I) compartment where infected members are infectious, Recovered/Removed (R) compartment holds the members who are infected and recovered from the infection (and become immune to the virus in this article) or succumbed to the infection.…”

Section: Methodsmentioning

confidence: 99%

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Adaptive sentinel testing in workplace for COVID-19 pandemic

Chen

George

et al. 2022

Preprint

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Testing and isolation of infectious employees is one of the critical strategies to make the workplace safe during the pandemic for many organizations. Adaptive testing frequency reduces cost while keeping the pandemic under control at the workplace. However, most models aimed at estimating test frequencies were structured for municipalities or large organizations such as university campuses of highly mobile individuals. By contrast, the workplace exhibits distinct characteristics: employee positivity rate may be different from the local community because of rigorous protective measures at workplace, or self-selection of co-workers with common behavioral tendencies for adherence to pandemic mitigation guidelines. Moreover, dual exposure to COVID19 occurs at work and home that complicates transmission modelling, as does transmission tracing at the workplace. Hence, we developed bi-modal SEIR model and R-shiny tool that accounts for these differentiating factors to adaptively estimate the testing frequency for workplace. Our tool uses easily measurable parameters: community incidence rate, risks of acquiring infection from community and work-place, workforce size, and sensitivity of testing. Our model is best suited for moderate-sized organizations with low internal transmission rates, no-outward facing employees whose position demands frequent in-person interactions with the public, and low to medium population positivity rates. Simulations revealed that employee behavior in adherence to protective measures at work and in their community, and the onsite workforce size have large effects on testing frequency. Reducing workplace transmission rate through workplace mitigation protocols and higher sensitivity of the test deployed, though to a lesser extent. Furthermore, our simulations showed that sentinel testing leads to only marginal increase in the number of infections even for high community incidence rates, suggesting that this may be a cost-effective approach in future pandemics. We used our model to accurately guide testing regimen for three campuses of The Jackson Laboratory.

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Section: Methodsmentioning

confidence: 99%

“…Compartmental models [1][2]15], especially the variants of SEIR models [3][4][5][6][7][8]15], have been shown to be effective at estimating trends of epidemics. For dynamic populations, the floating population models or multi-group models [9][10][11][12] have been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Adaptive sentinel testing in workplace for COVID-19 pandemic

Chen

George

et al. 2022

Preprint

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“…Then the applicability of the proposed software is demonstrated with examples [21]. An n-patch SEIR epidemic model of novel coronavirus disease 2019 (COVID-19) was presented in [22]. The impact of isolation rate and population migration rate on the spread of the new coronavirus was investigated through real data simulations.…”

Section: Epidemic Dynamical Modellingmentioning

confidence: 99%

Long-term prediction of the COVID-19 epidemics induced by Omicron-virus in China based on a novel non-autonomous delayed SIR model

Pei

Liu

2023

J. Phys. A: Math. Theor.

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Since the outbreak of COVID-19, the severe acute respiratory syndromecoronavirus 2 (SARS-CoV-2) genome is still mutating. Omicron, a recently emergingvirus with a shorter incubation period, faster transmission speed, and stronger immuneescape ability, is soaring worldwide and becoming the mainstream virus in the COVID-19 pandemic. It is especially critical for the governments, healthcare systems, andeconomic sectors to have an accurate estimate of the trend of this disaster. Byusing different mathematical approaches, including the classical susceptible-infectedrecovered (SIR) model and its extensions, many investigators have tried to predict theoutbreaks of COVID-19. In this study, we employed a novel model which is basedupon the well-known susceptible-infected-removed (SIR) model with the time-delayand time-varying coefficients in our previous works. We aim to predict the evolutionof the epidemics effectively in nine cities and provinces of China, including A City, BCity, C City, D City, E City, F City, G City, H City and I Province. The resultsshow it is effective to model the spread of the large-scale and sporadic COVID-19 induced by Omicron virus (Omicron-COVID-19) by the novel non-autonomousdelayed SIR compartment model. The significance of this study is that it canprovide the management department of epidemic control with theoretical referencesand subsequent evaluation of the prevention, control measures, and effects.

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“…In addition, the authors in [12] have introduced a two-patch SIS epidemic model to investigate the influence of the travel rate from one patch to the other. Multicity epidemic models are also proposed in [13][14][15][16] to examine the effect of population transmission. The authors in [13] have introduced a two-city epidemic model to investigate transportrelated infection and computed the basic reproduction number.…”

Section: Introductionmentioning

confidence: 99%

“…Also, authors in [14] have described the propagation of a disease in a population of individuals who travel between n cities, along with computing the basic reproduction number. Furthermore, an n-patch SEIR epidemic model has been introduced in [15] for COVID-19 to investigate the behaviour of this disease by computing equilibrium points and the basic reproduction number. Interested readers are also referred to the literature [17][18][19] to learn more about the impact of considering patchy environments to make the epidemic models closer to the real world.…”

Section: Introductionmentioning

confidence: 99%

Leader‐following consensus and qualitative analysis of a new multi‐agent‐based epidemic model

Amiri Mehra,

Shafieirad,

Zamani

2023

IET Control Theory & Appl

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This paper addresses an investigation into a leader‐following consensus problem in a novel multi‐agent‐based SIS (Susceptible‐Infected‐Susceptible) epidemic model. As infectious diseases are easily spread through travel, the model considers each agent to represent a city in an infected country, interacting with neighbouring cities towards achieving disease eradication through consensus. To make the model more realistic, the Effect Terms (ETs) arising from the effects of agent states on each other are also considered. The differential equations' positivity and existence of solutions are examined, and the proposed model's equilibrium points are obtained, with stability analysed using new local and global basic reproduction numbers defined in this study. Additionally, a leader‐following consensus control for the multi‐agent‐based system is proposed to drive each agent to follow a virtual leader agent. Finally, numerical simulations are included to validate the theoretical findings corresponding to the proposed scheme.

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Generalized SEIR Epidemic Model for COVID-19 in a Multipatch Environment

Cited by 10 publications

References 29 publications

Adaptive sentinel testing in workplace for COVID-19 pandemic

Adaptive sentinel testing in workplace for COVID-19 pandemic

Long-term prediction of the COVID-19 epidemics induced by Omicron-virus in China based on a novel non-autonomous delayed SIR model

Leader‐following consensus and qualitative analysis of a new multi‐agent‐based epidemic model

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