Global dynamics of a multi-strain SEIR epidemic model with general incidence rates: application to COVID-19 pandemic

Khyar, Omar; Allali, Karam

doi:10.1007/s11071-020-05929-4

Cited by 106 publications

(58 citation statements)

References 38 publications

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“…Simulations have been used since the early days of the COVID-19 pandemic. Classic compartmental epidemiological models were first produced (e.g., many SEIR models [21][22][23][24]), with a focus on estimating broad trends and key epidemiological quantities such as the expected number of new cases generated by each infected individual (i.e., the basic reproduction number R 0 ). Such compartmental models provide limited support to study the effect of interventions, for instance by lowering the contact rate to represent the impact of social distancing.…”

Section: Introductionmentioning

confidence: 99%

Returning to a normal life via COVID-19 vaccines in the USA: a large-scale agent-based simulation study

Giabbanelli

2021

Preprint

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BackgroundIn 2020, COVID-19 has claimed more than 300,000 deaths in the US alone. While non-pharmaceutical interventions were implemented by federal and state governments in the USA, these efforts have failed to contain the virus. Following the FDA approval of two COVID-19 vaccines, however, the hope for the return to normalcy is renewed. This hope rests on an unprecedented nation-wide vaccine campaign, which faces many logistical challenges and is also contingent on several factors whose values are currently unknown.ObjectiveWe study the effectiveness of a nation-wide vaccine campaign in response to different vaccine efficacies, the willingness of the population to be vaccinated, and the daily vaccine capacity under two different federal plans. To characterize the possible outcomes most accurately, we also account for the interactions between non-pharmaceutical interventions and vaccines, through six scenarios that capture a range of possible impact from non-pharmaceutical interventions.MethodsWe use large-scale cloud-based agent-based simulations by implementing the vaccination campaign using Covasim, an open-source ABM for COVID-19 that has been used in several peer-reviewed studies and accounts for individual heterogeneity as well as a multiplicity of contact networks. Several modifications to the parameters and simulation logic were made to better align the model with current evidence. We chose six non-pharmaceutical intervention scenarios and applied the vaccination intervention following both the plan proposed by Operation Warp Speed (former Trump administration) and the plan of one million vaccines per day, proposed by the Biden administration. We accounted for unknowns in vaccine efficacies and levels of population compliance by varying both parameters. For each experiment, the cumulative infection growth is fitted to a logistic growth model, and the carrying capacities and the growth rates are recorded.ResultsFor both vaccination plans and all non-pharmaceutical intervention scenarios, the presence of the vaccine intervention considerably lowers the total number of infections when life returns to normal, even when the population compliance to vaccines is as low at 20%. We noted an unintended consequence: given the vaccine availability estimates under both federal plans and the focus on vaccinating individuals by age categories, a significant reduction in non-pharmaceutical interventions results in a counterintuitive situation in which higher vaccine compliance then leads to more total infections.ConclusionsAlthough potent, vaccines alone cannot effectively end the pandemic given the current availability estimates and the adopted vaccination strategy. Non-pharmaceutical interventions need to continue and be enforced to ensure high compliance, so that the rate of immunity established by vaccination outpaces that induced by infections.

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Section: Introductionmentioning

confidence: 99%

Returning to a normal life via COVID-19 vaccines in the USA: a large-scale agent-based simulation study

Giabbanelli

2021

Preprint

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“…This value helps eliminate the disease and expected number of secondary infections produced by an infected individual in a population when all individuals are susceptible to infection. In [12], authors made a multi-strain-modified SEIR epidemic model for COVID-19 and wrote a complete analysis on how to control the value of the reproduction number to control the next upcoming virus peaks.…”

Section: Introductionmentioning

confidence: 99%

Models for COVID-19 Daily Confirmed Cases in Different Countries

et al. 2021

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In this paper, daily confirmed cases of COVID-19 in different countries are modelled using different mathematical regression models. The curve fitting is used as a prediction tool for modeling both past and upcoming coronavirus waves. According to virus spreading and average annual temperatures, countries under study are classified into three main categories. First category, the first wave of the coronavirus takes about two-year seasons (about 180 days) to complete a viral cycle. Second category, the first wave of the coronavirus takes about one-year season (about 90 days) to complete the first viral cycle with higher virus spreading rate. These countries take stopping periods with low virus spreading rate. Third category, countries that take the highest virus spreading rate and the viral cycle complete without stopping periods. Finally, predictions of different upcoming scenarios are made and compared with actual current smoothed daily confirmed cases in these countries.

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“…Lie et al proposed a data-driven COVID-19 model with distributed delay [ 35 ]. Global dynamics of a COVID-19 SEIR epidemic model has been investigated by Khyar et al [ 36 ]. Further, different authors have suggested control strategies for disease transmission of COVID-19 [ 37 – 39 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of COVID-19 transmission with comorbidity: a data driven modelling based approach

et al. 2021

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An outbreak of the COVID-19 pandemic is a major public health disease as well as a challenging task to people with comorbidity worldwide. According to a report, comorbidity enhances the risk factors with complications of COVID-19. Here, we propose and explore a mathematical framework to study the transmission dynamics of COVID-19 with comorbidity. Within this framework, the model is calibrated by using new daily confirmed COVID-19 cases in India. The qualitative properties of the model and the stability of feasible equilibrium are studied. The model experiences the scenario of backward bifurcation by parameter regime accounting for progress in susceptibility to acquire infection by comorbidity individuals. The endemic equilibrium is asymptotically stable if recruitment of comorbidity becomes higher without acquiring the infection. Moreover, a larger backward bifurcation regime indicates the possibility of more infection in susceptible individuals. A dynamics in the mean fluctuation of the force of infection is investigated with different parameter regimes. A significant correlation is established between the force of infection and corresponding Shannon entropy under the same parameters, which provides evidence that infection reaches a significant proportion of the susceptible.

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Global dynamics of a multi-strain SEIR epidemic model with general incidence rates: application to COVID-19 pandemic

Cited by 106 publications

References 38 publications

Returning to a normal life via COVID-19 vaccines in the USA: a large-scale agent-based simulation study

Returning to a normal life via COVID-19 vaccines in the USA: a large-scale agent-based simulation study

Models for COVID-19 Daily Confirmed Cases in Different Countries

Dynamics of COVID-19 transmission with comorbidity: a data driven modelling based approach

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