SEIR model for COVID-19 dynamics incorporating the environment and social distancing

Mwalili, Samuel; Kimathi, Mark; Ojiambo, Viona; Gathungu, Duncan; Mbogo, Rachel Waema

doi:10.1186/s13104-020-05192-1

Cited by 219 publications

(111 citation statements)

References 6 publications

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“…The parameter values may be improved when more information is available. Nevertheless, our model is a preliminary conceptual model, intending to lay a foundation for further modelling studies, but we can tune our model so that the outcomes of our model are in line with previous studies [ 3 – 5 , 8 , 15 , 26 ].…”

Section: Introductionmentioning

confidence: 84%

COVID-19 outbreak, social distancing and mass testing in Kenya-insights from a mathematical model

Mbogo

Odhiambo

2021

Afr. Mat.

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Section: Introductionmentioning

confidence: 84%

COVID-19 outbreak, social distancing and mass testing in Kenya-insights from a mathematical model

Mbogo

Odhiambo

2021

Afr. Mat.

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“…The contact matrix were computed using [35] methods. Figure 3 shows the contact matrix for Kenya for each NPI.…”

Section: Methodsmentioning

confidence: 99%

“…Suppression, which aims at reversing the epidemic growth by driving the reproductive ratio below one, could help in lowering and attening of the epidemic peak, thereby reducing the acute pressure on the health-care system. However, premature and sudden lifting of NPIs could lead to an earlier secondary peak, which could be attened by relaxing the interventions intermittently [26,35]. A previous study in Kenya also predicted the risk of epidemic rebound after the social distancing measures are lifted prematurely [5].…”

Section: Introductionmentioning

confidence: 99%

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Age-structured Impact of Mitigation Strategies on COVID-19 Severity and Deaths in Kenya

Mwalili¹,

Kimathi²,

Ojiambo³

et al. 2020

Preprint

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Introduction: COVID-19, a coronavirus disease 2019, is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There have been a lot of attempts to model this pandemic from a global perspective. The Novel Coronavirus is still spreading quickly in several countries and the peak has not yet been reached in many countries. We developed age-structured model for describing the COVID-19 pandemic in Kenya under different non-pharmaceutical interventions. The first case in Kenya was identified in March 13, 2020 with the pandemic increasing to 465 confirmed cases by end of 3rd May, 2020. We fitted an age-structured deterministic mathematical model in Kenyan context.Methods: We model the COVID-19 situation in Kenya using Age-structured Susceptible Exposed Infectious Recovered compartmental model. These compartments follow a cascade of the disease from the Susceptible to Exposed individuals who in return are either symptomatic or asymptomatic. The symptomatic depict mild signs, which can develop to severe symptoms warranting hospitalization or can otherwise recover. The severe cases can recover with some developing critical condition. The critical are admitted at intensive care units. The resulting age-dependent ordinary differential equations from the model are solved using fourth order Runge-Kutta methods. We controlled for school closure, social distancing and lockdown in terms of movement restrictionsResults: The model shows varying epidemic peak by age-structure and the mitigation scenarios. The peak dates for unmitigated (UM), the 45% NPI (M45) and School closure-curfew-partial lockdown NPI (SCL) are May 21st, October 17th and December 13th 2020, respectively. Their respective cumulative infections peaks are 43M, 24M and 25M. The daily reported severe cases, critical cases and death proportionately increased with age. Conclusions: The cumulative number of infections reduces greatly with introduction of school closure, social distancing and restricted movement in highly affected counties. The degree of COVID-19 severity increases with age. However, it is not immediately clear when these restrictions can be lifted.

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“…Mwalilli et al. [21] discussed a modified SEIR model incorporating the environment and social distancing. The next generation matrix approach was used to obtain the reproduction ratio

.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of SEIR epidemic model by optimal auxiliary functions method

Marinca

Bogdan

2021

Chaos, Solitons & Fractals

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The aim of the present work is to establish an approximate analytical solution for the nonlinear Susceptible, Exposed, Infected, Recovered (SEIR) model applied to novel coronavirus COVID-19. The mathematical model depending of five nonlinear differential equations, is studied and approximate solutions are obtained using Optimal Auxiliary Functions Method (OAFM). Our technique ensures a fast convergence of the solutions after only one iteration. The nonstandard part of OAFM is described by the presence of so-called auxiliary functions and of the optimal convergence-control parameters. We have a great freedom to select the auxiliary functions and the number of optimal convergence-control parameters which are optimally determined. Our approach is independent of the presence of small or large parameters in the governing equations or in the initial/boundary conditions, is effective, simple and very efficient.

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SEIR model for COVID-19 dynamics incorporating the environment and social distancing

Cited by 219 publications

References 6 publications

COVID-19 outbreak, social distancing and mass testing in Kenya-insights from a mathematical model

COVID-19 outbreak, social distancing and mass testing in Kenya-insights from a mathematical model

Age-structured Impact of Mitigation Strategies on COVID-19 Severity and Deaths in Kenya

Dynamics of SEIR epidemic model by optimal auxiliary functions method

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