SIRSi-vaccine dynamical model for the Covid-19 pandemic

Batistela, Cristiane M.; Correa, Diego Paolo Ferruzzo; Bueno, Átila Madureira; Piqueira, José Roberto Castilho

doi:10.1016/j.isatra.2023.05.008

Cited by 4 publications

(3 citation statements)

References 70 publications

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“…Presently, we opted to take a different bias, now in the populational domain, in order to show the broadness of the method analyzing vaccination effort as a control parameter of a contagious disease. The model we use is the common SIR (Susceptible, Infected, Recovered) model plus the vaccinated V state, or the SIRV model (e.g., [41,42]). The present analysis is not intended to bring changes in the current understanding of this model, but to show an alternative way of interpreting the results.…”

Section: A Working Examplementioning

confidence: 99%

Entropy Generation and Control: Criteria to Calculate Flow Optimization in Biological Systems

Bicudo,

Nogueira-de-Sá,

Chaui-Berlinck

2023

Foundations

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Living beings are composite thermodynamic systems in non-equilibrium conditions. Within this context, there are a number of thermodynamic potential differences (forces) between them and the surroundings, as well as internally. These forces lead to flows, which, ultimately, are essential to life itself, but, at the same time, are associated with entropy generation, i.e., a loss of useful work. The maintenance of homeostatic conditions, the tenet of physiology, demands the regulation of these flows by control of variables. However, due to the very nature of these systems, the regulation of flows and control of variables become entangled in closed loops. Here, we show how to combine entropy generation with respect to a process, and control of parameters (in such a process) in order to create a criterium of optimal ways to regulate changes in flows, the coefficient of flow-entropy (CJσ). We demonstrate the restricted possibility to obtain an increase in flow along with a decrease in entropy generation, and the more general situation of increases in flow along with increases in entropy generation of the process. In this scenario, the CJσ aims to identify the best way to combine the gain in flow and the associated loss of useful work. As an example, we analyze the impact of vaccination effort in the spreading of a contagious disease in a population, showing that the higher the vaccination effort the higher the control over the spreading and the lower the loss of useful work by the society.

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Section: A Working Examplementioning

confidence: 99%

Entropy Generation and Control: Criteria to Calculate Flow Optimization in Biological Systems

Bicudo,

Nogueira-de-Sá,

Chaui-Berlinck

2023

Foundations

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“…A more detailed study considering validation with real data of the control strategy based on vaccination was carried out in [51]. The model considers the possibility of reinfection and allows checking information on unreported infected people.…”

Section: Effects Of Changes In Vaccination Campaign Prioritizationmentioning

confidence: 99%

“…Due to the urgency of COVID-19, researchers worldwide accepted the challenge of outlining mathematical models for this new epidemic. Several mathematical models have already been formulated for the population dynamics of COVID-19 in several countries [10,[44][45][46][47][48][49][50][51], and pioneer methods are structured upon machine learning and statistical models, such as decision trees and linear regression [52] or even more powerful ones like artificial neural networks [53,54], showing promising results. Machine learning has also been used to assist in early detection [55] and prediction of severity [56] of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Controlling COVID-19 Spreading: A Three-Level Algorithm

Dieguez,

Batistela,

Piqueira

2023

Mathematics

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As the main methods of the coronavirus disease (COVID-19) transmission are air and physical contact, actions to mitigate and suppress its spread must be developed in order to change population dynamics and provide efficient control strategies. Here, these actions are described as a simple heuristic framework to establish public policies. Two control systems were studied: the first organized in the form of an algorithm stratified into three levels and the second as a minimization problem similar to optimal control strategies, applied to both social distancing and vaccination. The possible effects of these actions are modeled and applied to an extension of the Susceptible - Infected - Removed (SIR) compartmental model. The control system is developed, which is organized in the form of an algorithm stratified into three levels. These levels intend to represent social distancing strategies implemented by sanitary authorities around the globe, representing stronger or weaker grades of isolation intensity according to the ability of the healthcare system to cope with symptomatic individuals. The algorithm control is applied in a simulation, and the results give evidence of the effectiveness of the procedures adopted against the coronavirus. The model dynamics are analyzed and validated with simulations considering parameters obtained from epidemiological data from Brazil and Uruguay and in a more detailed way for three Brazilian states: São Paulo, Minas Gerais and Rio de Janeiro. The model was validated using cumulative data on cases and deaths. For cases of death, the results were satisfactory, while for case data, the response was reasonable, considering the possibility of adding delays or variations in parameters in the model. In addition, the effective reproduction number was proposed for the cities studied in Brazil, the result being relevant because it has a qualitative behavior similar to that published by official centers. This paper also discusses the implementation and optimization of social distancing and vaccination control strategies, considering different parameters and their effects on reducing the number of cases and deaths. Model simulations present promising results for developing strategies to attack COVID-19 dissemination.

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Game-theoretical perspectives on COVID-19 pandemic

Nagkoulis

2024

Discov Public Health

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SIRSi-vaccine dynamical model for the Covid-19 pandemic

Cited by 4 publications

References 70 publications

Entropy Generation and Control: Criteria to Calculate Flow Optimization in Biological Systems

Entropy Generation and Control: Criteria to Calculate Flow Optimization in Biological Systems

Controlling COVID-19 Spreading: A Three-Level Algorithm

Game-theoretical perspectives on COVID-19 pandemic

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