Analysis of SEIR-type models used at the beginning of COVID-19 pandemic reported in high-impact journals

Abstract

Introduction The Susceptible-Exposed-Infected-Recovered (SEIR) mathematical-epidemiological model has been exhaustively used since de beggining of the COVID-19 pandemic. These models intended to predict hospital burden and evaluate health measures to contain its spread. In this sense, flaws have been evidenced in the predictions of the first published models. It is considered necessary to evaluate the differences in the approach and verification of the models. Objectives We carried out a systematic review of the articles published in journals indexed in the Web of Science, of the first quartile and with an impact factor greater than two, that met the selection and inclusion criteria following the PRISMA-ScR standards. We included a total of 32 articles, which were evaluated according to demographic characteristics such as the month of receipt and publication, the country of origin of the information, the subject matter of the journal, and the characteristics of the modeling such as the presence of additional compartments, graphical analysis, conceptual model approach, interpretation of the basic reproductive number, and estimation of parameters. Methods Articles published in medical and health journals were predominant from February to July 2020. These articles most frequently used data from China and mostly focused on SEIR or full quarantine compartment models. The articles published in journals in mathematics were predominant from August to December 2020. Models used data from different world regions, considering a greater diversity of compartments such as asymptomatic patients or partial or complete quarantine. Results The articles analyzed mostly use SEIR-type models expanded with additional compartments. There are discrepancies in the breadth and methodological quality of the articles published according to the journal’s subject matter. The unification of quality criteria for describing the models in any journal is recommended.

An updated estimation approach for SEIR models with stochastic perturbations: Application to COVID-19 data in Bogotá

“…Actually, [53] suggests that the climatology parameters could potentially affect the spread of the COVID-19. On the other hand, in [16] is highlight that the deterministic models "do not involve the variability of the sources of the information nor the possible errors and biases", therefore, we model also the infection rate randomly. Some studies as [54][55][56] use the Brownian motion to model spatial-temporally as the temperature and weather variations affect the pollen dynamic, and the infection rate on epidemic; using equations similarly to 29 to some model parameters.…”

“…Statistically, it should be used all the types of populations under an epidemic and a methodology to estimate the parameters of the model. Actually [16] is highlighted that "it is difficult to consider all possible interactions between interventions in the same model and find parameters close to reality through simulations". In this way, we must search for a model which includes all the populations under the epidemic and estimates the parameters based on as much information as possible.…”

Population infection estimation from wastewater surveillance for SARS-CoV-2 in Nagpur, India during the second pandemic wave

“…We selected our sampling period during the second wave of COVID-19 in India in 2021. We next developed a modified version of the SEIR compartment mathematical model that has been frequently used to model COVID-19 dynamics in different populations [ 18 , 19 , 22 ], herein termed the “SEIPR model” to predict the number of infected individuals within specific Nagpur district partitioned zones and the total urban population under study. After predicting the number of infected individuals, the estimates were used to perform Monte-Carlo simulations to model the variations in the concentration of SARS-CoV-2 RNA in wastewater over time.…”

“…The SEIR model and MC simulation have established themselves as valuable tools in epidemiological research because of their ability to provide insights into the complex systems involved in infection transmission, population dynamics, and uncertainty analysis. The SEIR compartment model forms the foundation for understanding disease transmission dynamics [18][19][20][21][22]. The SEIR model categorizes individuals into different compartments based on their disease status, encompassing susceptible, exposed, infectious, and recovered individuals.…”

A mathematical model and simulation scenarios for T and B cells immune response to severe acute respiratory syndrome-coronavirus-2

“…Viral infectious diseases are a global health concern that has led to socio-economic, health, and public order problems, leading society to be in states of hysteria and sometimes to take measures that can threaten the physical and mental integrity of the human being [1,2]. Mathematical modeling of viral infections uses different mathematical approaches to solve biological problems, however, most of the solutions are evidenced from ordinary differential equations [3][4][5][6]. A model that considers the viral dynamics of diseases, must assume its possible alterations, for example, the presence of susceptible cells and viruses in the host, which leads to having cells infected by the latter, among others [7][8][9][10][11][12].…”