Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm

Zelenkov, Yuri; Reshettsov, Ivan

doi:10.1016/j.eswa.2023.120034

Cited by 12 publications

(3 citation statements)

References 48 publications

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“…To determine these rates accurately, we compare the predicted data using the GA-SEIR model with the actual data using the standard SEIR model. We propose using a GA to optimize the three probability values, aiming to minimize the error between the predicted and actual daily infection numbers 27 , 28 .…”

Section: Methodsmentioning

confidence: 99%

Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

Wang,

Wu,

Luo

et al. 2024

Sci Rep

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The global spread of COVID-19 has profoundly affected health and economies, highlighting the need for precise epidemic trend predictions for effective interventions. In this study, we used infectious disease models to simulate and predict the trajectory of COVID-19. An SEIR (susceptible, exposed, infected, removed) model was established using Wuhan data to reflect the pandemic. We then trained a genetic algorithm-based SEIR (GA-SEIR) model using data from a specific U.S. region and focused on individual susceptibility and infection dynamics. By integrating socio-psychological factors, we achieved a significant enhancement to the GA-SEIR model, leading to the development of an optimized version. This refined GA-SEIR model significantly improved our ability to simulate the spread and control of the epidemic and to effectively track trends. Remarkably, it successfully predicted the resurgence of COVID-19 in mainland China in April 2023, demonstrating its robustness and reliability. The refined GA-SEIR model provides crucial insights for public health authorities, enabling them to design and implement proactive strategies for outbreak containment and mitigation. Its substantial contributions to epidemic modelling and public health planning are invaluable, particularly in managing and controlling respiratory infectious diseases such as COVID-19.

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Section: Methodsmentioning

confidence: 99%

Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

Wang,

Wu,

Luo

et al. 2024

Sci Rep

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“…In addition to fitting the complex process of outbreak transmission through the fine division of compartments, much of the research has centered around the parameters in the model. Reconstructing the real distribution of transition rate for each compartment based on genetic algorithms yields time-varying parameters that allow the model to be used to characterize the peaks of epidemics in different regions (Zelenkov & Reshettsov, 2023).…”

Section: Introductionmentioning

confidence: 99%

COVID-19 dynamical system and cellular automaton based on an extended compartment model of contact network and vaccination impact

Zhou,

Shi,

Cao

et al. 2024

Preprint

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In this paper, we study a new round of novel coronavirus disease 2019 (COVID-19) infection scenarios due to a mutated strain in the middle of the epidemic. By considering the characteristics of the presence or absence of symptoms, the severity of symptoms, and vaccination, we develop an extended compartmental model containing 11 compartments and describe its compartmental transformation process from both discrete and continuous perspectives by probabilistic cellular automata and a system of ordinary differential equations. Further, we analyze the relevance of the endemic equilibrium points as well as the trajectory types corresponding to the ordinary differential equations. Based on the model, we simulate the reoccurrence of an outbreak in the United States in the winter of 2021, and the results fit well with the actual data and show the consistency between the two forms of Probabilistic Cellular automata and the system of Ordinary Differential Equations. The effects of contact network and vaccination are investigated by applying the model to different scenarios. The results show that premature relaxation of social isolation campaigns may lead to subsequent waves of infection and that widespread vaccination is effective in reducing the number of severe illnesses and deaths. The significance of this paper is to provide a model of infectious diseases for mid-epidemic and to assist in epidemic prevention policy-making through Probabilistic Cellular Automata.

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“…Inspired by natural evolution, these algorithms efficiently explore vast and unknown search spaces [20]. Their ability to solve complex and dynamic projects makes them valuable in diverse fields, including medicine [21][22][23], epidemic dynamical systems [24,25], geotechnics [26], market forecasts [27], and industry [28], among others. A particularly successful application in the Deep Learning era is the optimization of neural networks, which are huge computational models in which genetic algorithms help to find optimal combinations of hyperparameters [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Cosmological Parameter Estimation with Genetic Algorithms

Medel-Esquivel,

Gómez-Vargas,

Morales Sánchez

et al. 2023

Universe

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Genetic algorithms are a powerful tool in optimization for single and multimodal functions. This paper provides an overview of their fundamentals with some analytical examples. In addition, we explore how they can be used as a parameter estimation tool in cosmological models to maximize the likelihood function, complementing the analysis with the traditional Markov chain Monte Carlo methods. We analyze that genetic algorithms provide fast estimates by focusing on maximizing the likelihood function, although they cannot provide confidence regions with the same statistical meaning as Bayesian approaches. Moreover, we show that implementing sharing and niching techniques ensures an effective exploration of the parameter space, even in the presence of local optima, always helping to find the global optima. This approach is invaluable in the cosmological context, where an exhaustive space exploration of parameters is essential. We use dark energy models to exemplify the use of genetic algorithms in cosmological parameter estimation, including a multimodal problem, and we also show how to use the output of a genetic algorithm to obtain derived cosmological functions. This paper concludes that genetic algorithms are a handy tool within cosmological data analysis, without replacing the traditional Bayesian methods but providing different advantages.

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Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm

Cited by 12 publications

References 48 publications

Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

COVID-19 dynamical system and cellular automaton based on an extended compartment model of contact network and vaccination impact

Cosmological Parameter Estimation with Genetic Algorithms

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