A data driven analysis and forecast of an SEIARD epidemic model for COVID-19 in Mexico

León, Ugo Avila-Ponce de; Pérez, Ángel; Ávila-Vales, Eric

doi:10.3934/bdia.2020002

Cited by 20 publications

(13 citation statements)

References 9 publications

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“…Since we do not have a reliable report of the number of recoveries by state in Mexico, we choose not to fit the values of the recovery-related parameters ( γ 0 , γ 1 and τ γ ) and optimize only the values of the other parameters ( β 0 , β 1 , τ β , δ 0 , δ 1 , τ δ , w and p ). For parameters γ 0 , γ 1 and τ γ we use the same fixed values for all states, which were obtained previously in a best fit optimization using the recovered data for all Mexico [15].…”

Section: Implementation To Estimate the Parametersmentioning

confidence: 99%

An SEIARD epidemic model for COVID-19 in Mexico: mathematical analysis and state-level forecast

León

Pérez

Ávila-Vales

2020

Preprint

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We propose an SEIARD mathematical model to investigate the current outbreak of coronavirus disease in Mexico. Our model incorporates the asymptomatic infected individuals, who represent the majority of the infected population (with symptoms or not) and could play an important role in spreading the virus without any knowledge. We calculate the basic reproduction number (R 0 ) via the next-generation matrix method and estimate the per day infection, death and recovery rates. The local stability of the disease free equilibrium is established in terms of R 0 . A sensibility analysis is performed to determine the relative importance of the model parameters to the disease transmission. We calibrate the parameters of the SEIARD model to the reported number of infected cases and fatalities for several states in Mexico by minimizing the sum of squared errors and attempt to forecast the evolution of the outbreak until August 2020.

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Section: Implementation To Estimate the Parametersmentioning

confidence: 99%

An SEIARD epidemic model for COVID-19 in Mexico: mathematical analysis and state-level forecast

León

Pérez

Ávila-Vales

2020

Preprint

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“…For this part, we considered system (1) with v = 0 and the vaccinated subpopulations V 1 , V 2 , E V , A V and I V equal to zero. We regarded as fixed parameters w = 0.25, which corresponds to a latent period of 4 days [31], and a proportion p 1 = 0.12 of symptomatic infections [28]. The set of differential equations was solved using Matlab 2016b with the ode45 solver.…”

Section: Data Fitting and Estimation Of Parametersmentioning

confidence: 99%

“…In this paper, we propose a differential equation model to simulate the application of a two-dose vaccine against COVID-19, considering the possibility of vaccine leakiness and asymptomatic infections. The motivation of this study is derived from the work of the authors in [27,28], who considered an SEIARD mathematical model to investigate the outbreak of COVID-19 in Mexico. Therefore, in the present work, we incorporate the vaccination component to the model in [28] to derive an extended SEIARD model to examine the effectiveness of the COVID-19 jabs which are currently being deployed to many countries to help combat the raging pandemic situation.…”

Section: Introductionmentioning

confidence: 99%

An extended SEIARD model for COVID-19 vaccination in Mexico: analysis and forecast

Pérez

Oluyori

2021

Preprint

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In this study, we propose and analyze an extended SEIARD model with vaccination. We compute the control reproduction number Rc of our model and study the stability of equilibria. We show that the set of disease-free equilibria is locally asymptotically stable when Rc<1 and unstable when Rc>1, and we provide a sufficient condition for its global stability. Furthermore, we perform numerical simulations using the reported data of COVID-19 infections and vaccination in Mexico to study the impact of different vaccination, transmission and efficacy rates on the dynamics of the disease.

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“…While studies for Mexico based on mathematical models exist [20] , they differ from ours in several significant ways. Some ignore social-distancing and other mitigation measures [21] , others focus on the estimation of the basic reproduction number

and infections using a Bayesian hierarchical model [22] , and yet others have since become outdated [23] . Mexico faces a unique challenge, due to the prevalence of COVID-19 risk factors, such as obesity, diabetes and hypertension, among its population [24] .…”

Section: Introductionmentioning

confidence: 99%

Lockdown measures and their impact on single- and two-age-structured epidemic model for the COVID-19 outbreak in Mexico

Cuevas–Maraver

Kevrekidis

Chen

et al. 2021

Mathematical Biosciences

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The role of lockdown measures in mitigating COVID-19 in Mexico is investigated using a comprehensive nonlinear ODE model. The model includes both asymptomatic and presymptomatic populations with the latter leading to sickness (with recovery, hospitalization and death possibilities). We consider the situation involving the imposed application of partial social distancing measures in the time series of interest and find optimal parametric fits to the time series of deaths (only), as well as to that of deaths and cumulative infections. We discuss the merits and disadvantages of each approach, we interpret the parameters of the model and assess the realistic nature of the parameters resulting from the optimization procedure. Importantly, we explore a model involving two sub-populations (younger and older than a specific age), to more accurately reflect the observed impact as concerns symptoms and behavior in different age groups. For definitiveness and to separate people that are (typically) in the active workforce, our partition of population is with respect to members younger vs. older than the age of 65. The basic reproductive number of the model is computed for both the single-and the two-population variant. Finally, we consider what would be the impact on the number of deaths and cumulative infections upon imposition of partial lockdown (involving only the older population) and full lockdown (involving the entire population).

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A data driven analysis and forecast of an SEIARD epidemic model for COVID-19 in Mexico

Cited by 20 publications

References 9 publications

An SEIARD epidemic model for COVID-19 in Mexico: mathematical analysis and state-level forecast

An SEIARD epidemic model for COVID-19 in Mexico: mathematical analysis and state-level forecast

An extended SEIARD model for COVID-19 vaccination in Mexico: analysis and forecast

Lockdown measures and their impact on single- and two-age-structured epidemic model for the COVID-19 outbreak in Mexico

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