SEIR-FMi: A coronavirus disease epidemiological model based on intra-city movement, inter-city movement and medical resource investment

Zhang, Wen; Xie, Rui; Dong, Xuefan; Li, Jian; Peng, Peng; Gonzalez, Ernesto DR Santibanez

doi:10.1016/j.compbiomed.2022.106046

Cited by 11 publications

(5 citation statements)

References 49 publications

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“…The performance of the proposed model was tested for the US COVID-19 data obtained from the world meter. Similarly, Zhang et al [44] proposed a model to study the effect of intra-city, and inter-city population movements as well as medical investments on the spread of COVID-19 in three cities of Hubei Province, China. Reproduction numbers of the proposed model were derived theoretically using the next-generation matrix method and the effect of selected parameters on the spread of COVID-19 was simulated.…”

Section: Literature Surveymentioning

confidence: 99%

Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach

et al. 2023

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In 2020, coronavirus (COVID-19) was declared a global pandemic and it remains prevalent today. A necessity to model the transmission of the virus has emerged as a result of COVID-19’s exceedingly contagious characteristics and its rapid propagation throughout the world. Assessing the incidence of infection could enable policymakers to identify measures to halt the pandemic and gauge the required capacity of healthcare centers. Therefore, modeling the susceptibility, exposure, infection, and recovery in relation to the COVID-19 pandemic is crucial for the adoption of interventions by regulatory authorities. Fundamental factors, such as the infection rate, mortality rate, and recovery rate, must be considered in order to accurately represent the behavior of the pandemic using mathematical models. The difficulty in creating a mathematical model is in identifying the real model variables. Parameters might vary significantly across models, which can result in variations in the simulation results because projections primarily rely on a particular dataset. The purpose of this work was to establish a susceptible–exposed–infected–recovered (SEIR) model describing the propagation of the COVID-19 outbreak throughout the Kingdom of Saudi Arabia (KSA). The goal of this study was to derive the essential COVID-19 epidemiological factors from actual data. System dynamics modeling and design of experiment approaches were used to determine the most appropriate combination of epidemiological parameters and the influence of COVID-19. This study investigates how epidemiological variables such as seasonal amplitude, social awareness impact, and waning time can be adapted to correctly estimate COVID-19 scenarios such as the number of infected persons on a daily basis in KSA. This model can also be utilized to ascertain how stress (or hospital capacity) affects the percentage of hospitalizations and the number of deaths. Additionally, the results of this study can be used to establish policies or strategies for monitoring or restricting COVID-19 in Saudi Arabia.

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Section: Literature Surveymentioning

confidence: 99%

Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach

et al. 2023

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“…These tools can inform public health strategies by testing control methods and identifying effective interventions 1 . During the SARS-CoV-2 pandemic, unprecedented data availability enabled application of mathematical models in many locations and at different geographical scales worldwide [2][3][4][5] . Metapopulation modeling approaches provide an efficient framework for simulating and evaluating the spatiotemporal progression of infectious disease over large geographic areas.…”

Section: Introductionmentioning

confidence: 99%

Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter

Perini,

Yamana,

Galanti

et al. 2024

Preprint

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SummaryBackgroundMetapopulation models provide platforms for understanding infectious disease dynamics and predicting clinical outcomes across interconnected populations, particularly for large epidemics and pandemics like COVID-19.MethodsWe developed a novel metapopulation model for simulating respiratory virus transmission in the North America region, specifically for the 96 states, provinces, and territories of Canada, Mexico and the United States. The model is informed by COVID-19 case data, which are assimilated using the Ensemble Adjustment Kalman filter (EAKF), a Bayesian inference algorithm, and commuting and mobility data, which are used to build and adjust the network and movement across locations on a daily basis.FindingsThis model-inference system provides estimates of transmission dynamics, infection rates, and ascertainment rates for each of the 96 locations from January 2020 to March 2021. The results highlight differences in disease dynamics and ascertainment among the three countries.InterpretationThe metapopulation structure enables rapid simulation at large scale, and the data assimilation method makes the system responsive to changes in system dynamics. This model can serve as a versatile platform for modeling other infectious diseases across the North American region.FundingUS Centers for Disease Control and Prevention Contract 75D30122C14289; US NIH Grant AI163023.

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“…Then, the individuals flow between these compartments according to a set of parameters. For instance, in Zhang et al (2022) the authors proposed a SEIR-FMi (Susceptible - Exposed - Infectious - Recovery with Flow and Medical investments) method in order to extend traditional epidemiological models including intra- and inter-city population movement and medical investment resources. Haghrah, Ghaemi, and Badamchizadeh (2022) presented another approach, using data from seven countries to build a fuzzy SIDR (Susceptible - Infectious - Recovered - Dead) model to solve the lack of parameter adaptability of conventional SIDR models.…”

Section: Introductionmentioning

confidence: 99%

Cluster analysis and forecasting of viruses incidence growth curves: Application to SARS-CoV-2

Díaz-Lozano¹,

Guijo-Rubio²,

Gutiérrez³

et al. 2023

Expert Systems with Applications

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SEIR-FMi: A coronavirus disease epidemiological model based on intra-city movement, inter-city movement and medical resource investment

Cited by 11 publications

References 49 publications

Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach

Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach

Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter

Cluster analysis and forecasting of viruses incidence growth curves: Application to SARS-CoV-2

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