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  1. Article: Mathematical modelling of the second wave of COVID-19 infections using deterministic and stochastic SIDR models.

    Lobato, Fran Sérgio / Libotte, Gustavo Barbosa / Platt, Gustavo Mendes

    Nonlinear dynamics

    2021  Volume 106, Issue 2, Page(s) 1359–1373

    Abstract: Recently, various countries from across the globe have been facing the second wave of COVID-19 infections. In order to understand the dynamics of the spread of the disease, much effort has been made in terms of mathematical modeling. In this scenario, ... ...

    Abstract Recently, various countries from across the globe have been facing the second wave of COVID-19 infections. In order to understand the dynamics of the spread of the disease, much effort has been made in terms of mathematical modeling. In this scenario, compartmental models are widely used to simulate epidemics under various conditions. In general, there are uncertainties associated with the reported data, which must be considered when estimating the parameters of the model. In this work, we propose an effective methodology for estimating parameters of compartmental models in multiple wave scenarios by means of a dynamic data segmentation approach. This robust technique allows the description of the dynamics of the disease without arbitrary choices for the end of the first wave and the start of the second. Furthermore, we adopt a time-dependent function to describe the probability of transmission by contact for each wave. We also assess the uncertainties of the parameters and their influence on the simulations using a stochastic strategy. In order to obtain realistic results in terms of the basic reproduction number, a constraint is incorporated into the problem. We adopt data from Germany and Italy, two of the first countries to experience the second wave of infections. Using the proposed methodology, the end of the first wave (and also the start of the second wave) occurred on 166 and 187 days from the beginning of the epidemic, for Germany and Italy, respectively. The estimated effective reproduction number for the first wave is close to that obtained by other approaches, for both countries. The results demonstrate that the proposed methodology is able to find good estimates for all parameters. In relation to uncertainties, we show that slight variations in the design variables can give rise to significant changes in the value of the effective reproduction number. The results provide information on the characteristics of the epidemic for each country, as well as elements for decision-making in the economic and governmental spheres.
    Language English
    Publishing date 2021-07-07
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2012600-1
    ISSN 1573-269X ; 0924-090X
    ISSN (online) 1573-269X
    ISSN 0924-090X
    DOI 10.1007/s11071-021-06680-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: A Novel Reliability-Based Robust Design Multiobjective Optimization Formulation Applied in Chemical Engineering

    Libotte, Gustavo Barbosa / Lobato, Fran Sérgio / Moura Neto, Francisco Duarte / Platt, Gustavo Mendes

    Industrial & engineering chemistry process design and development. 2022 Feb. 22, v. 61, no. 9

    2022  

    Abstract: Engineering systems are often subject to variations and uncertainties associated with external factors, environmental changes, equipment inaccuracies, and other aspects. Our fundamental objective is to formulate a multiobjective optimization problem that ...

    Abstract Engineering systems are often subject to variations and uncertainties associated with external factors, environmental changes, equipment inaccuracies, and other aspects. Our fundamental objective is to formulate a multiobjective optimization problem that is capable of handling robust and reliability-based optimizations, to obtain solutions that satisfy prescribed reliability levels and are least sensitive to external noise. In project optimization, these models play a fundamental role, allowing us to obtain parameters and attributes capable of enhancing product performance, reducing costs, and operating time. To accomplish this task, we consider two different approaches capable of quantifying uncertainties during the optimization of mathematical models. In the first, robust optimization, the sensitivity of decision variables in relation to deviations caused by external factors is evaluated. The second approach, reliability-based optimization, measures the probability of system failure and obtains model parameters that ensure an established level of reliability. We tested our novel approach on benchmark and chemical engineering problems, usually treated as deterministic problems. The proposed methodology provides a systematic way to evaluate uncertainties, in order to achieve more realistic results, considering external factors.
    Keywords chemistry ; equipment ; process design ; system optimization
    Language English
    Dates of publication 2022-0222
    Size p. 3483-3501.
    Publishing place American Chemical Society
    Document type Article
    ZDB-ID 1484436-9
    ISSN 1520-5045 ; 0888-5885
    ISSN (online) 1520-5045
    ISSN 0888-5885
    DOI 10.1021/acs.iecr.1c04635
    Database NAL-Catalogue (AGRICOLA)

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  3. Article ; Online: Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic Using Robust Multiobjective Optimization and Stochastic Fractal Search.

    Lobato, Fran Sérgio / Libotte, Gustavo Barbosa / Platt, Gustavo Mendes

    Computational and mathematical methods in medicine

    2020  Volume 2020, Page(s) 9214159

    Abstract: Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not ... ...

    Abstract Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function. In this work, the SIDR (Susceptible, Infected, Dead, and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables. For this purpose, a robust multiobjective optimization problem is formulated, considering the minimization of uncertainties associated with the estimation process and the maximization of the robustness parameter. To solve this problem, the Multiobjective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness. The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results.
    MeSH term(s) Algorithms ; Betacoronavirus ; COVID-19 ; China/epidemiology ; Computational Biology ; Computer Simulation ; Coronavirus Infections/epidemiology ; Fractals ; Humans ; Models, Statistical ; Pandemics/statistics & numerical data ; Pneumonia, Viral/epidemiology ; SARS-CoV-2 ; Stochastic Processes ; Uncertainty
    Keywords covid19
    Language English
    Publishing date 2020-10-15
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2252430-7
    ISSN 1748-6718 ; 1748-670X ; 1027-3662
    ISSN (online) 1748-6718
    ISSN 1748-670X ; 1027-3662
    DOI 10.1155/2020/9214159
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic Using Robust Multiobjective Optimization and Stochastic Fractal Search

    Fran Sérgio Lobato / Gustavo Barbosa Libotte / Gustavo Mendes Platt

    Computational and Mathematical Methods in Medicine, Vol

    2020  Volume 2020

    Abstract: Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not ... ...

    Abstract Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function. In this work, the SIDR (Susceptible, Infected, Dead, and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables. For this purpose, a robust multiobjective optimization problem is formulated, considering the minimization of uncertainties associated with the estimation process and the maximization of the robustness parameter. To solve this problem, the Multiobjective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness. The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results.
    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 510
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher Hindawi Limited
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Impacts of a delayed and slow-paced vaccination on cases and deaths during the COVID-19 pandemic: a modelling study.

    Barbosa Libotte, Gustavo / Dos Anjos, Lucas / Célia Cerqueira de Almeida, Regina / Mara Cardoso Malta, Sandra / de Andrade Medronho, Roberto

    Journal of the Royal Society, Interface

    2022  Volume 19, Issue 190, Page(s) 20220275

    Abstract: In Brazil, vaccination has always cut across party political and ideological lines, which has delayed its start and brought the whole process into disrepute. Such divergences put the immunization of the population in the background and create additional ... ...

    Abstract In Brazil, vaccination has always cut across party political and ideological lines, which has delayed its start and brought the whole process into disrepute. Such divergences put the immunization of the population in the background and create additional hurdles beyond the pandemic, mistrust and scepticism over vaccines. We conduct a mathematical modelling study to analyse the impacts of late vaccination along with slowly increasing coverage, as well as how harmful it would be if part of the population refused to get vaccinated or missed the second dose. We analyse data from confirmed cases, deaths and vaccination in the state of Rio de Janeiro in the period between 10 March 2020 and 27 October 2021. We estimate that if the start of vaccination had been 30 days earlier, combined with efforts to drive vaccination rates up, about 31 657 deaths could have been avoided. In addition, the slow pace of vaccination and the low demand for the second dose could cause a resurgence of cases as early as 2022. Even when reaching the expected vaccination coverage for the first dose, it is still challenging to increase adherence to the second dose and maintain a high vaccination rate to avoid new outbreaks.
    MeSH term(s) Brazil/epidemiology ; COVID-19/epidemiology ; COVID-19/prevention & control ; Humans ; Pandemics/prevention & control ; Vaccination ; Vaccines
    Chemical Substances Vaccines
    Language English
    Publishing date 2022-05-25
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2156283-0
    ISSN 1742-5662 ; 1742-5689
    ISSN (online) 1742-5662
    ISSN 1742-5689
    DOI 10.1098/rsif.2022.0275
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic using Robust Multi-objective Optimization and Stochastic Fractal Search

    Libotte, Gustavo Barbosa / Lobato, Fran S'ergio / Platt, Gustavo Mendes

    Abstract: Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not ... ...

    Abstract Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function. In this work, the SIDR (Susceptible, Infected, Dead and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables. For this purpose, a robust multi-objective optimization problem is formulated, considering the minimization of uncertainties associated to the estimation process and the maximization of the robustness parameter. To solve this problem, the Multi-objective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness. The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results.
    Keywords covid19
    Publisher ArXiv
    Document type Article
    Database COVID19

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  7. Book ; Online: Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic using Robust Multi-objective Optimization and Stochastic Fractal Search

    Libotte, Gustavo Barbosa / Lobato, Fran Sérgio / Platt, Gustavo Mendes

    2020  

    Abstract: Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not ... ...

    Abstract Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function. In this work, the SIDR (Susceptible, Infected, Dead and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables. For this purpose, a robust multi-objective optimization problem is formulated, considering the minimization of uncertainties associated to the estimation process and the maximization of the robustness parameter. To solve this problem, the Multi-objective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness. The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results.
    Keywords Quantitative Biology - Populations and Evolution ; Mathematics - Optimization and Control ; covid19
    Subject code 510
    Publishing date 2020-05-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article: Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic Using Robust Multiobjective Optimization and Stochastic Fractal Search

    Lobato, Fran Sérgio Libotte / Gustavo Barbosa, Platt / Mendes, Gustavo

    Computational & Mathematical Methods in Medicine

    Abstract: Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties This aspect simplifies the estimation process, but does not ... ...

    Abstract Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function In this work, the SIDR (Susceptible, Infected, Dead, and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables For this purpose, a robust multiobjective optimization problem is formulated, considering the minimization of uncertainties associated with the estimation process and the maximization of the robustness parameter To solve this problem, the Multiobjective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results [ABSTRACT FROM AUTHOR] Copyright of Computational & Mathematical Methods in Medicine is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #879822
    Database COVID19

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  9. Article ; Online: Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment.

    Libotte, Gustavo Barbosa / Lobato, Fran Sérgio / Platt, Gustavo Mendes / Silva Neto, Antônio J

    Computer methods and programs in biomedicine

    2020  Volume 196, Page(s) 105664

    Abstract: Background and objective: For decades, mathematical models have been used to predict the behavior of physical and biological systems, as well as to define strategies aiming at the minimization of the effects regarding different types of diseases. In the ...

    Abstract Background and objective: For decades, mathematical models have been used to predict the behavior of physical and biological systems, as well as to define strategies aiming at the minimization of the effects regarding different types of diseases. In the present days, the development of mathematical models to simulate the dynamic behavior of the novel coronavirus disease (COVID-19) is considered an important theme due to the quantity of infected people worldwide. In this work, the objective is to determine an optimal control strategy for vaccine administration in COVID-19 pandemic treatment considering real data from China. Two optimal control problems (mono- and multi-objective) to determine a strategy for vaccine administration in COVID-19 pandemic treatment are proposed. The first consists of minimizing the quantity of infected individuals during the treatment. The second considers minimizing together the quantity of infected individuals and the prescribed vaccine concentration during the treatment.
    Methods: An inverse problem is formulated and solved in order to determine the parameters of the compartmental Susceptible-Infectious-Removed model. The solutions for both optimal control problems proposed are obtained by using Differential Evolution and Multi-objective Optimization Differential Evolution algorithms.
    Results: A comparative analysis on the influence related to the inclusion of a control strategy in the population subject to the epidemic is carried out, in terms of the compartmental model and its control parameters. The results regarding the proposed optimal control problems provide information from which an optimal strategy for vaccine administration can be defined.
    Conclusions: The solution of the optimal control problem can provide information about the effect of vaccination of a population in the face of an epidemic, as well as essential elements for decision making in the economic and governmental spheres.
    MeSH term(s) Algorithms ; Betacoronavirus ; COVID-19 ; COVID-19 Vaccines ; China/epidemiology ; Communicable Disease Control/methods ; Computer Simulation ; Coronavirus Infections/epidemiology ; Coronavirus Infections/prevention & control ; Decision Making ; Health Services Accessibility ; Humans ; Immunization Programs/organization & administration ; Models, Theoretical ; Pandemics/prevention & control ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/prevention & control ; Public Health ; SARS-CoV-2 ; Viral Vaccines/therapeutic use
    Chemical Substances COVID-19 Vaccines ; Viral Vaccines
    Keywords covid19
    Language English
    Publishing date 2020-07-19
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 632564-6
    ISSN 1872-7565 ; 0169-2607
    ISSN (online) 1872-7565
    ISSN 0169-2607
    DOI 10.1016/j.cmpb.2020.105664
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment

    Libotte, Gustavo Barbosa / Lobato, Fran Sérgio / Platt, Gustavo Mendes / Silva Neto, Antônio J.

    Computer Methods and Programs in Biomedicine

    2020  Volume 196, Page(s) 105664

    Keywords Software ; Health Informatics ; Computer Science Applications ; covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 632564-6
    ISSN 1872-7565 ; 0169-2607
    ISSN (online) 1872-7565
    ISSN 0169-2607
    DOI 10.1016/j.cmpb.2020.105664
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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