Introduction to Group-Structured-Epidemic Model

Dias, Samaherni; Queiroz, Kurios; Araujo, Aldayr D.

doi:10.1007/s40313-021-00841-7

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…Many papers create sub-groups or sub-compartments of the Exposed compartment, such as in age stratified models, or models with multiple strains of transmissible agents, or models with temporarily asymptomatic and permanently asymptomatic persons. Although referred to as sub-compartments of the Exposed compartment, such groups are actually sub-compartments of the already infected (Latent) compartment ( Abernethy & Glass, 2022 ; Berger, & HerkenhoffHuangMongey, 2022 ; Dias, & QuelrozArujo, 2022 ; Lyra et al., 2020 ; Pinky & Dobrovolny, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Origins of the problematic E in SEIR epidemic models

Burke

2024

Infectious Disease Modelling

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Section: Discussionmentioning

confidence: 99%

Origins of the problematic E in SEIR epidemic models

Burke

2024

Infectious Disease Modelling

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A Multi-Agent-Based Simulation Model for the Spreading of Diseases Through Social Interactions During Pandemics

Salem

Moreno

2022

J Control Autom Electr Syst

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Epidemiological models have a vital and consolidated role in aiding decision-making during crises such as the Coronavirus Disease 2019 (COVID-19) pandemic. However, the influence of social interactions in the spreading of communicable diseases is left aside from the main models in the literature. The main contribution of this work is the introduction of a probabilistic simulation model based on a multi-agent approach that is capable of predicting the spreading of diseases. Our proposal has a simple model for the main source of infections in pandemics of respiratory viruses: social interactions. This simplicity is key for incorporating complex networks topology into the model, which is a more accurate representation for real-world interactions. This flexibility in network structure allows the evaluation of specific phenomena, such as the presence of super-spreaders. We provide the modeling for the dynamical network topology in two different simulation scenarios. Another contribution is the generic microscopic model for infection evolution that enables the evaluation of impact from more specific behaviors and interventions on the overall spreading of the disease. It also enables a more intuitive process for going from data to model parameters. This ease of changing the infection evolution model is key for performing more complete analyses than would be possible in other models from the literature. Further, we give specific parameters for a controlled scenario with quick testing and tracing. We present computational results that illustrate the model utilization for predicting the spreading of COVID-19 in a city. Also, we show the results of applying the model for assessing the risk of resuming on-site activities at a collective use facility.

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