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Espinosa O, Mora L, Sanabria C, Ramos A, Rincón D, Bejarano V, Rodríguez J, Barrera N, Álvarez-Moreno C, Cortés J, Saavedra C, Robayo A, Franco OH. Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review. Syst Rev 2024; 13:30. [PMID: 38229123 PMCID: PMC10790449 DOI: 10.1186/s13643-023-02411-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 12/04/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). METHODOLOGY PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. FINDINGS In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. INTERPRETATION The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.
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Affiliation(s)
- Oscar Espinosa
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia.
| | - Laura Mora
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Cristian Sanabria
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Antonio Ramos
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Duván Rincón
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Valeria Bejarano
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Jhonathan Rodríguez
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Nicolás Barrera
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | | | - Jorge Cortés
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Carlos Saavedra
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Adriana Robayo
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Oscar H Franco
- University Medical Center Utrecht, Utrecht University & Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, USA
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Rafiei H, Salehi A, Baghbani F, Parsa P, Akbarzadeh-T MR. Interval type-2 Fuzzy control and stochastic modeling of COVID-19 spread based on vaccination and social distancing rates. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 232:107443. [PMID: 36889249 PMCID: PMC9951621 DOI: 10.1016/j.cmpb.2023.107443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Besides efforts on vaccine discovery, robust and intuitive government policies could also significantly influence the pandemic state. However, such policies require realistic virus spread models, and the major works on COVID-19 to date have been only case-specific and use deterministic models. Additionally, when a disease affects large portions of the population, countries develop extensive infrastructures to contain the condition that should adapt continuously and extend the healthcare system's capabilities. An accurate mathematical model that reasonably addresses these complex treatment/population dynamics and their corresponding environmental uncertainties is necessary for making appropriate and robust strategic decisions. METHODS Here, we propose an interval type-2 fuzzy stochastic modeling and control strategy to deal with the realistic uncertainties of pandemics and manage the size of the infected population. For this purpose, we first modify a previously established COVID-19 model with definite parameters to a Stochastic SEIAR (S2EIAR) approach with uncertain parameters and variables. Next, we propose to use normalized inputs, rather than the usual parameter settings in the previous case-specific studies, hence offering a more generalized control structure. Furthermore, we examine the proposed genetic algorithm-optimized fuzzy system in two scenarios. The first scenario aims to keep infected cases below a certain threshold, while the second addresses the changing healthcare capacities. Finally, we examine the proposed controller on stochasticity and disturbance in parameters, population sizes, social distance, and vaccination rate. RESULTS The results show the robustness and efficiency of the proposed method in the presence of up to 1% noise and 50% disturbance in tracking the desired size of the infected population. The proposed method is compared to Proportional Derivative (PD), Proportional Integral Derivative (PID), and type-1 fuzzy controllers. In the first scenario, both fuzzy controllers perform more smoothly despite PD and PID controllers reaching a lower mean squared error (MSE). Meanwhile, the proposed controller outperforms PD, PID, and the type-1 fuzzy controller for the MSE and decision policies for the second scenario. CONCLUSIONS The proposed approach explains how we should decide on social distancing and vaccination rate policies during pandemics against the prevalent uncertainties in disease detection and reporting.
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Affiliation(s)
- H Rafiei
- Departments of Electrical and Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran
| | - A Salehi
- Departments of Electrical and Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran
| | - F Baghbani
- Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran
| | - P Parsa
- Departments of Electrical and Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran
| | - M-R Akbarzadeh-T
- Departments of Electrical and Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran.
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Saha P, Biswas SK, Biswas MHA, Ghosh U. An SEQAIHR model to study COVID-19 transmission and optimal control strategies in Hong Kong, 2022. NONLINEAR DYNAMICS 2023; 111:6873-6893. [PMID: 36644569 PMCID: PMC9825089 DOI: 10.1007/s11071-022-08181-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
During the COVID-19 pandemic, one of the major concerns was a medical emergency in human society. Therefore it was necessary to control or restrict the disease spreading among populations in any fruitful way at that time. To frame out a proper policy for controlling COVID-19 spreading with limited medical facilities, here we propose an SEQAIHR model having saturated treatment. We check biological feasibility of model solutions and compute the basic reproduction number ( R 0 ). Moreover, the model exhibits transcritical, backward bifurcation and forward bifurcation with hysteresis with respect to different parameters under some restrictions. Further to validate the model, we fit it with real COVID-19 infected data of Hong Kong from 19th December, 2021 to 3rd April, 2022 and estimate model parameters. Applying sensitivity analysis, we find out the most sensitive parameters that have an effect on R 0 . We estimate R 0 using actual initial growth data of COVID-19 and calculate effective reproduction number for same period. Finally, an optimal control problem has been proposed considering effective vaccination and saturated treatment for hospitalized class to decrease density of the infected class and to minimize implemented cost.
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Affiliation(s)
- Pritam Saha
- Department of Applied Mathematics, University of Calcutta, Kolkata, 700009 India
| | | | | | - Uttam Ghosh
- Department of Applied Mathematics, University of Calcutta, Kolkata, 700009 India
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Abbasi Z, Shafieirad M, Amiri Mehra AH, Zamani I. Vaccination and isolation based control design of the COVID-19 pandemic based on adaptive neuro fuzzy inference system optimized with the genetic algorithm. EVOLVING SYSTEMS 2022; 14:413-435. [PMID: 37193369 PMCID: PMC9476442 DOI: 10.1007/s12530-022-09459-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 08/18/2022] [Indexed: 11/24/2022]
Abstract
The study of the COVID-19 pandemic is of pivotal importance due to its tremendous global impacts. This paper aims to control this disease using an optimal strategy comprising two methods: isolation and vaccination. In this regard, an optimized Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed using the Genetic Algorithm (GA) to control the dynamic model of the COVID-19 termed SIDARTHE (Susceptible, Infected, Diagnosed, Ailing, Recognized, Threatened, Healed, and Extinct). The number of diagnosed and recognized people is reduced by isolation, and the number of susceptible people is reduced by vaccination. The GA generates optimal control efforts related to the random initial number of each chosen group as the input data for ANFIS to train Takagi-Sugeno (T-S) fuzzy structure coefficients. Also, three theorems are presented to indicate the positivity, boundedness, and existence of the solutions in the presence of the controller. The performance of the proposed system is evaluated through the mean squared error (MSE) and the root-mean-square error (RMSE). The simulation results show a significant decrease in the number of diagnosed, recognized, and susceptible individuals by employing the proposed controller, even with a 70% increase in transmissibility caused by various variants.
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Affiliation(s)
- Zohreh Abbasi
- Department of Electrical and Computer Engineering, University of Kashan, Kashan, Iran
| | - Mohsen Shafieirad
- Department of Electrical and Computer Engineering, University of Kashan, Kashan, Iran
| | | | - Iman Zamani
- Electrical and Electronic Engineering Department, Shahed University, Tehran, Iran
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Almatroud AO, Djenina N, Ouannas A, Grassi G, Al-Sawalha MM. A novel discrete-time COVID-19 epidemic model including the compartment of vaccinated individuals. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:12387-12404. [PMID: 36654003 DOI: 10.3934/mbe.2022578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Referring tothe study of epidemic mathematical models, this manuscript presents a noveldiscrete-time COVID-19 model that includes the number of vaccinated individuals as an additional state variable in the system equations. The paper shows that the proposed compartment model, described by difference equations, has two fixed points, i.e., a disease-free fixed point and an epidemic fixed point. By considering both the forward difference system and the backward difference system, some stability analyses of the disease-free fixed point are carried out.In particular, for the backward difference system a novel theorem is proved, which gives a condition for the disappearance of the pandemic when an inequality involving some epidemic parameters is satisfied. Finally, simulation results of the conceived discrete model are carried out, along with comparisons regarding the performances of both the forward difference system and the backward difference system.
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Affiliation(s)
- A Othman Almatroud
- Department of Mathematics, Faculty of Science, University of Ha'il, Ha'il 81451, Saudi Arabia
| | - Noureddine Djenina
- Laboratory of Dynamical Systems and Control, University of Larbi Ben M'hidi, Oum El-Bouaghi, Algeria
| | - Adel Ouannas
- Laboratory of Dynamical Systems and Control, University of Larbi Ben M'hidi, Oum El-Bouaghi, Algeria
| | - Giuseppe Grassi
- Dipartimento Ingegneria Innovazione, Universita Del Salento, Lecce 73100, Italy
| | - M Mossa Al-Sawalha
- Department of Mathematics, Faculty of Science, University of Ha'il, Ha'il 81451, Saudi Arabia
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Lacarbonara W, Ma J, Nataraj C. Preface to the special issue "Complex dynamics of COVID-19: modeling, prediction and control (part II)". NONLINEAR DYNAMICS 2022; 109:1-3. [PMID: 35698477 PMCID: PMC9178931 DOI: 10.1007/s11071-022-07588-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Affiliation(s)
| | - Jun Ma
- Lanzhou University of Technology, Lanzhou, China
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