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

Abstract

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.

Epidemiological Modeling Analysis Reveals the Transmission Potential of COVID-19 Asymptomatic Patients:A Prospective Study of Epidemiological Transmission in America

“…Our model shows that asymptomatic infected persons have a strong interaction with epidemic transmission under different social restriction levels. We draw the same conclusion with a group of research who extended the SEIR model to study the epidemic in Mexico 17 . Once the patients show symptoms, both the patients themselves and the society will be vigilant 18 .…”

Extended SEIQR type model for COVID-19 epidemic and data analysis

“…MATLAB based nonlinear least-square solver fmincon and patternsearch are used to fit simulated and observed daywise cumulative number of infected cases for three different time intervals. Detailed description of this method and its implementation can be found in [32,[38][39][40].…”

“…In reality the rate of infection is not a constant throughout the epidemic rather it changes time to time due to variable social behavior. Although it is difficult to obtain actual pattern of variation [21,25,26,[30][31][32].…”

“…Several data-driven modeling experiments have been performed in various regions [15]. In [20], a compartmental mathematical model to try to understand the outbreak of COVID-19 in Mexico. This data driven analysis would let us compare how different the outbreak will be in the two studied regions.…”

A novel demographic-based model shows that intensive testing and social distancing are concurrently required to extinguish COVID-19 progression in densely populated urban areas

“…While Madrid and NYC have transitioned through the pandemic peak and successfully extinguished the pandemic, Mexico City is still in the middle of it. Several mathematical models have been used to forecast pandemic scenarios for Mexico City ( 33, 34 ). However, the predictions of the progression of COVID-19 and the occurrence of the pandemic peak in the capital of Mexico have not been accurate.…”