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Price BS, Khodaverdi M, Hendricks B, Smith GS, Kimble W, Halasz A, Guthrie S, Fraustino JD, Hodder SL. Enhanced SARS-CoV-2 case prediction using public health data and machine learning models. JAMIA Open 2024; 7:ooae014. [PMID: 38444986 PMCID: PMC10913390 DOI: 10.1093/jamiaopen/ooae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/29/2024] [Accepted: 02/08/2024] [Indexed: 03/07/2024] Open
Abstract
Objectives The goal of this study is to propose and test a scalable framework for machine learning (ML) algorithms to predict near-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases by incorporating and evaluating the impact of real-time dynamic public health data. Materials and Methods Data used in this study include patient-level results, procurement, and location information of all SARS-CoV-2 tests reported in West Virginia as part of their mandatory reporting system from January 2021 to March 2022. We propose a method for incorporating and comparing widely available public health metrics inside of a ML framework, specifically a long-short-term memory network, to forecast SARS-CoV-2 cases across various feature sets. Results Our approach provides better prediction of localized case counts and indicates the impact of the dynamic elements of the pandemic on predictions, such as the influence of the mixture of viral variants in the population and variable testing and vaccination rates during various eras of the pandemic. Discussion Utilizing real-time public health metrics, including estimated Rt from multiple SARS-CoV-2 variants, vaccination rates, and testing information, provided a significant increase in the accuracy of the model during the Omicron and Delta period, thus providing more precise forecasting of daily case counts at the county level. This work provides insights on the influence of various features on predictive performance in rural and non-rural areas. Conclusion Our proposed framework incorporates available public health metrics with operational data on the impact of testing, vaccination, and current viral variant mixtures in the population to provide a foundation for combining dynamic public health metrics and ML models to deliver forecasting and insights in healthcare domains. It also shows the importance of developing and deploying ML frameworks in rural settings.
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Affiliation(s)
- Bradley S Price
- Department of Management Information Systems, West Virginia University, Morgantown, WV 26505, United States
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
| | - Maryam Khodaverdi
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
| | - Brian Hendricks
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
- Department of Epidemiology and Biostatistics, West Virginia University, Morgantown, WV 26505, United States
| | - Gordon S Smith
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
- Department of Epidemiology and Biostatistics, West Virginia University, Morgantown, WV 26505, United States
| | - Wes Kimble
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
| | - Adam Halasz
- School of Mathematics and Data Science, West Virginia University, Morgantown, WV 26506, United States
| | - Sara Guthrie
- Department of Sociology and Anthropology, West Virginia University, Morgantown, WV 26505, United States
| | - Julia D Fraustino
- Department of Strategic Communication, Reed College of Media, West Virginia University, Morgantown, WV 26505, United States
| | - Sally L Hodder
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
- Department of Medicine, West Virginia University, Morgantown, WV 26506, United States
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2
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Wang L, Khan SU, Khan FU, A AlQahtani S, M Alamri A. Advancing COVID-19 stochastic modeling: a comprehensive examination integrating vaccination classes through higher-order spectral scheme analysis. Comput Methods Biomech Biomed Engin 2024:1-15. [PMID: 38396364 DOI: 10.1080/10255842.2024.2319276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 01/23/2024] [Indexed: 02/25/2024]
Abstract
This research article presents a comprehensive analysis aimed at enhancing the stochastic modeling of COVID-19 dynamics by incorporating vaccination classes through a higher-order spectral scheme. The ongoing COVID-19 pandemic has underscored the critical need for accurate and adaptable modeling techniques to inform public health interventions. In this study, we introduce a novel approach that integrates various vaccination classes into a stochastic model to provide a more nuanced understanding of disease transmission dynamics. We employ a higher-order spectral scheme to capture complex interactions between different population groups, vaccination statuses, and disease parameters. Our analysis not only enhances the predictive accuracy of COVID-19 modeling but also facilitates the exploration of various vaccination strategies and their impact on disease control. The findings of this study hold significant implications for optimizing vaccination campaigns and guiding policy decisions in the ongoing battle against the COVID-19 pandemic.
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Affiliation(s)
- Laiquan Wang
- Department of Basic Courses, Changji Vocational and Technical College, Changji, China
| | - Sami Ullah Khan
- Department of Mathematics, City University of Science and Information Technology Peshawar, Peshawar, Pakistan
| | - Farman U Khan
- Department of Mathematics, HITEC University, Rawalpindi, Pakistan
| | - Salman A AlQahtani
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Atif M Alamri
- Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
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3
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Davila-Payan CS, Hill A, Kayembe L, Alexander JP, Lynch M, Pallas SW. Analysis of the yearly transition function in measles disease modeling. Stat Med 2024; 43:435-451. [PMID: 38100282 DOI: 10.1002/sim.9951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/03/2023] [Accepted: 10/16/2023] [Indexed: 12/17/2023]
Abstract
Globally, there were an estimated 9.8 million measles cases and 207 500 measles deaths in 2019. As the effort to eliminate measles around the world continues, modeling remains a valuable tool for public health decision-makers and program implementers. This study presents a novel approach to the use of a yearly transition function that formulates mathematically the vaccine schedules for different age groups while accounting for the effects of the age of vaccination, the timing of vaccination, and disease seasonality on the yearly number of measles cases in a country. The methodology presented adds to an existing modeling framework and expands its analysis, making its utilization more adjustable for the user and contributing to its conceptual clarity. This article also adjusts for the temporal interaction between vaccination and exposure to disease, applying adjustments to estimated yearly counts of cases and the number of vaccines administered that increase population immunity. These new model features provide the ability to forecast and compare the effects of different vaccination timing scenarios and seasonality of transmission on the expected disease incidence. Although the work presented is applied to the example of measles, it has potential relevance to modeling other vaccine-preventable diseases.
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Affiliation(s)
- C S Davila-Payan
- Global Immunization Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - A Hill
- Global Immunization Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - L Kayembe
- Global Immunization Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - J P Alexander
- Global Immunization Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - M Lynch
- Global Immunization Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - S W Pallas
- Global Immunization Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
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4
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Bai J, Wang J. Modeling long COVID dynamics: Impact of underlying health conditions. J Theor Biol 2024; 576:111669. [PMID: 37977479 PMCID: PMC10754059 DOI: 10.1016/j.jtbi.2023.111669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/03/2023] [Accepted: 11/13/2023] [Indexed: 11/19/2023]
Abstract
We propose a new mathematical model to investigate the population dynamics of long COVID, with a focus on the impact of chronic health conditions. Our model connects long COVID with the transmission of COVID-19 so as to accurately predict the prevalence of long COVID from the progression of the infection in the host population. The model additionally incorporates the effects of COVID-19 vaccination. We implement the model with data from both the US and the UK to demonstrate the real-world applications of this modeling framework.
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Affiliation(s)
- Jie Bai
- School of Mathematics and Statistics, Liaoning University, Shenyang 110036, China.
| | - Jin Wang
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga TN 37403, USA.
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5
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Bürger R, Chowell G, Kröker I, Lara-Díaz LY. A computational approach to identifiability analysis for a model of the propagation and control of COVID-19 in Chile. J Biol Dyn 2023; 17:2256774. [PMID: 37708159 PMCID: PMC10620014 DOI: 10.1080/17513758.2023.2256774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 08/30/2023] [Indexed: 09/16/2023]
Abstract
A computational approach is adapted to analyze the parameter identifiability of a compartmental model. The model is intended to describe the progression of the COVID-19 pandemic in Chile during the initial phase in early 2020 when government declared quarantine measures. The computational approach to analyze the structural and practical identifiability is applied in two parts, one for synthetic data and another for some Chilean regional data. The first part defines the identifiable parameter sets when these recover the true parameters used to create the synthetic data. The second part compares the results derived from synthetic data, estimating the identifiable parameter sets from regional Chilean epidemic data. Experiments provide evidence of the loss of identifiability if some initial conditions are estimated, the period of time used to fit is before the peak, and if a significant proportion of the population is involved in quarantine periods.
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Affiliation(s)
- Raimund Bürger
- CI[Formula: see text]MA and Departamento de Ingeniería Matemática, Facultad de Ciencias Físicas y Matemáticas, Universidad de Concepción, Concepción, Chile
| | - Gerardo Chowell
- School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Ilja Kröker
- Stochastic Simulation & Safety Research for Hydrosystems (LS3), Institute for Modelling Hydraulic and Environmental Systems (IWS), Universität Stuttgart, Stuttgart, Germany
| | - Leidy Yissedt Lara-Díaz
- Departamento de Matemática, Física y Estadística, Facultad de Ciencias Básicas, Universidad Católica del Maule, Talca, Chile
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Derrick J, Patterson B, Bai J, Wang J. A Mechanistic Model for Long COVID Dynamics. Mathematics (Basel) 2023; 11:4541. [PMID: 38111916 PMCID: PMC10727852 DOI: 10.3390/math11214541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Long COVID, a long-lasting disorder following an acute infection of COVID-19, represents a significant public health burden at present. In this paper, we propose a new mechanistic model based on differential equations to investigate the population dynamics of long COVID. By connecting long COVID with acute infection at the population level, our modeling framework emphasizes the interplay between COVID-19 transmission, vaccination, and long COVID dynamics. We conducted a detailed mathematical analysis of the model. We also validated the model using numerical simulation with real data from the US state of Tennessee and the UK.
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Affiliation(s)
- Jacob Derrick
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA
| | - Ben Patterson
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA
| | - Jie Bai
- School of Mathematics and Statistics, Liaoning University, Shenyang 110036, China
| | - Jin Wang
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA
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Zissette S, Kiti MC, Bennett BW, Liu CY, Nelson KN, Zelaya A, Kellogg JT, Johnson Ii TM, Clayton P, Fridkin SK, Omer SB, Lopman BA, Adams C. Social contact patterns among employees in U.S. long-term care facilities during the COVID-19 pandemic, December 2020 to June 2021. BMC Res Notes 2023; 16:294. [PMID: 37884967 PMCID: PMC10604856 DOI: 10.1186/s13104-023-06563-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 10/10/2023] [Indexed: 10/28/2023] Open
Abstract
OBJECTIVE We measured contact patterns using social contact diaries for 157 U.S. long-term care facility employees from December 2020 - June 2021. These data are crucial for analyzing mathematical transmission models and for informing healthcare setting infection control policy. RESULTS The median number of daily contacts was 10 (IQR 8-11). Household contacts were more likely partially masked than fully masked, more likely to involve physical contact, and longer in duration compared to facility contacts.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Pam Clayton
- Georgia Health Care Association, Atlanta, GA, USA
| | | | - Saad B Omer
- Peter O'Donnell Jr. School of Public Health at UT Southwestern, Dallas, TX, USA
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8
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Cheng Z, Lai Y, Jin K, Zhang M, Wang J. Modeling the XBB strain of SARS-CoV-2: Competition between variants and impact of reinfection. J Theor Biol 2023; 574:111611. [PMID: 37640233 PMCID: PMC10592017 DOI: 10.1016/j.jtbi.2023.111611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/16/2023] [Accepted: 08/22/2023] [Indexed: 08/31/2023]
Abstract
XBB, an Omicron subvariant of SARS-CoV-2 that began to circulate in late 2022, has been dominant in the US since early 2023. To quantify the impact of XBB on the progression of COVID-19, we propose a new mathematical model which describes the interplay between XBB and other SARS-CoV-2 variants at the population level and which incorporates the effects of reinfection. We apply the model to COVID-19 data in the US that include surveillance data on the cases and variant proportions from the New York City, the State of New York, and the State of Washington. Our fitting and simulation results show that the transmission rate of XBB is significantly higher than that of other variants and the reinfection from XBB may play an important role in shaping the pandemic/epidemic pattern in the US.
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Affiliation(s)
- Ziqiang Cheng
- School of Mathematics, Hefei University of Technology, Hefei, Anhui 230009, China
| | - Yinglei Lai
- School of Mathematical Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Kui Jin
- Department of Emergency Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China
| | - Mengping Zhang
- School of Mathematical Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China.
| | - Jin Wang
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA.
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9
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Cheng Z, Wang J. A two-phase fluid model for epidemic flow. Infect Dis Model 2023; 8:920-938. [PMID: 37547262 PMCID: PMC10403727 DOI: 10.1016/j.idm.2023.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 07/03/2023] [Indexed: 08/08/2023] Open
Abstract
We propose a new mathematical and computational modeling framework that incorporates fluid dynamics to study the spatial spread of infectious diseases. We model the susceptible and infected populations as two inviscid fluids which interact with each other. Their motion at the macroscopic level characterizes the progression and spread of the epidemic. To implement the two-phase flow model, we employ high-order numerical methods from computational fluid dynamics. We apply this model to simulate the COVID-19 outbreaks in the city of Wuhan in China and the state of Tennessee in the US. Our modeling and simulation framework allows us to conduct a detailed investigation into the complex spatiotemporal dynamics related to the transmission and spread of COVID-19.
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Affiliation(s)
- Ziqiang Cheng
- School of Mathematics, Hefei University of Technology, Hefei, Anhui, 230009, China
| | - Jin Wang
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN, 37403, USA
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10
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Gao S, Binod P, Chukwu CW, Kwofie T, Safdar S, Newman L, Choe S, Datta BK, Attipoe WK, Zhang W, van den Driessche P. A mathematical model to assess the impact of testing and isolation compliance on the transmission of COVID-19. Infect Dis Model 2023; 8:427-444. [PMID: 37113557 PMCID: PMC10116127 DOI: 10.1016/j.idm.2023.04.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 04/07/2023] [Accepted: 04/10/2023] [Indexed: 04/29/2023] Open
Abstract
The COVID-19 pandemic has ravaged global health and national economies worldwide. Testing and isolation are effective control strategies to mitigate the transmission of COVID-19, especially in the early stage of the disease outbreak. In this paper, we develop a deterministic model to investigate the impact of testing and compliance with isolation on the transmission of COVID-19. We derive the control reproduction number R C , which gives the threshold for disease elimination or prevalence. Using data from New York State in the early stage of the disease outbreak, we estimate R C = 7.989 . Both elasticity and sensitivity analyses show that testing and compliance with isolation are significant in reducing R C and disease prevalence. Simulation reveals that only high testing volume combined with a large proportion of individuals complying with isolation have great impact on mitigating the transmission. The testing starting date is also crucial: the earlier testing is implemented, the more impact it has on reducing the infection. The results obtained here would also be helpful in developing guidelines of early control strategies for pandemics similar to COVID-19.
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Affiliation(s)
- Shasha Gao
- School of Mathematics and Statistics, Jiangxi Normal University, Nanchang, 330000, Jiangxi, China
- Department of Mathematics, University of Florida, Gainesville, 32611, FL, USA
| | - Pant Binod
- Department of Mathematics, University of Maryland, College Park, 20742, MD, USA
| | | | - Theophilus Kwofie
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, 85287, AZ, USA
| | - Salman Safdar
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, 85287, AZ, USA
| | - Lora Newman
- Department of Mathematical Sciences, University of Cincinnati, Cincinnati, 45221, OH, USA
| | - Seoyun Choe
- Department of Mathematics, University of Central Florida, Orlando, 32816, FL, USA
| | - Bimal Kumar Datta
- Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, 33431, FL, USA
| | | | - Wenjing Zhang
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, 79409, TX, USA
| | - P van den Driessche
- Department of Mathematics and Statistics, University of Victoria, Victoria, V8W 2Y2, B.C, Canada
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Siewe N, Yakubu AA. Hybrid discrete-time-continuous-time models and a SARS CoV-2 mystery: Sub-Saharan Africa's low SARS CoV-2 disease burden. J Math Biol 2023; 86:91. [PMID: 37149541 PMCID: PMC10163930 DOI: 10.1007/s00285-023-01923-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 01/18/2023] [Accepted: 04/18/2023] [Indexed: 05/08/2023]
Abstract
Worldwide, the recent SARS-CoV-2 virus has infected more than 670 million people and killed nearly 67.0 million. In Africa, the number of confirmed COVID-19 cases was approximately 12.7 million as of January 11, 2023, that is about 2% of the infections around the world. Many theories and modeling techniques have been used to explain this lower-than-expected number of reported COVID-19 cases in Africa relative to the high disease burden in most developed countries. We noted that most epidemiological mathematical models are formulated in continuous-time interval, and taking Cameroon in Sub-Saharan Africa, and New York State in the USA as case studies, in this paper we developed parameterized hybrid discrete-time-continuous-time models of COVID-19 in Cameroon and New York State. We used these hybrid models to study the lower-than-expected COVID-19 infections in developing countries. We then used error analysis to show that a time scale for a data-driven mathematical model should match that of the actual data reporting.
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Affiliation(s)
- Nourridine Siewe
- School of Mathematical Sciences, College of Science, Rochester Institute of Technology, Rochester, NY, USA.
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Chen K, Jiang X, Li Y, Zhou R. A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility. Nonlinear Dyn 2023; 111:1-17. [PMID: 37361002 PMCID: PMC10148626 DOI: 10.1007/s11071-023-08489-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 03/20/2023] [Indexed: 06/28/2023]
Abstract
The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. A major challenge in forecasting the transmission of COVID-19 is the accurate assessment of the multiscale human mobility and how it impacts infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of geographical places, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behavior and individual health conditions on the disease outbreak and the probability of zero-COVID in the population. Specifically, individuals perform power law-type local movements within a container and global transport between different-level containers. It is revealed that frequent long-distance movements inside a small-level container (e.g., a road or a county) and a small population size reduce both the local crowdedness and disease transmission. It takes only half of the time to induce global disease outbreaks when the population increases from 150 to 500 (normalized unit). When the exponent c 1 of the long-tail distribution of distance k moved in the same-level container, p ( k ) ∼ k - c 1 · level , increases, the outbreak time decreases rapidly from 75 to 25 (normalized unit). In contrast, travel between large-level containers (e.g., cities and nations) facilitates global spread of the disease and outbreak. When the mean traveling distance across containers 1 d increases from 0.5 to 1 (normalized unit), the outbreak occurs almost twice as fast. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or to a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing population size and restricting global travel help achieve zero-COVID-19. Specifically, when c 1 is smaller than 0.2, the ratio of people with low levels of mobility is larger than 80% and the population size is smaller than 400, zero-COVID can be achieved within fewer than 1000 time steps. In summary, the Mob-Cov model considers more realistic human mobility at a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to apply when investigating pandemic dynamics and when planning actions against disease. Supplementary Information The online version contains supplementary material available at 10.1007/s11071-023-08489-5.
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Affiliation(s)
- Kejie Chen
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
| | - Xiaomo Jiang
- Provincial Key Lab of Digital Twin for Industrial Equipment, Dalian, 116024 China
- School of Energy and Power Engineering, Dalian, 116024 China
| | - Yanqing Li
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
| | - Rongxin Zhou
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
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13
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Yagin FH, Cicek İB, Alkhateeb A, Yagin B, Colak C, Azzeh M, Akbulut S. Explainable artificial intelligence model for identifying COVID-19 gene biomarkers. Comput Biol Med 2023; 154:106619. [PMID: 36738712 PMCID: PMC9889119 DOI: 10.1016/j.compbiomed.2023.106619] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/11/2023] [Accepted: 01/28/2023] [Indexed: 02/04/2023]
Abstract
AIM COVID-19 has revealed the need for fast and reliable methods to assist clinicians in diagnosing the disease. This article presents a model that applies explainable artificial intelligence (XAI) methods based on machine learning techniques on COVID-19 metagenomic next-generation sequencing (mNGS) samples. METHODS In the data set used in the study, there are 15,979 gene expressions of 234 patients with COVID-19 negative 141 (60.3%) and COVID-19 positive 93 (39.7%). The least absolute shrinkage and selection operator (LASSO) method was applied to select genes associated with COVID-19. Support Vector Machine - Synthetic Minority Oversampling Technique (SVM-SMOTE) method was used to handle the class imbalance problem. Logistics regression (LR), SVM, random forest (RF), and extreme gradient boosting (XGBoost) methods were constructed to predict COVID-19. An explainable approach based on local interpretable model-agnostic explanations (LIME) and SHAPley Additive exPlanations (SHAP) methods was applied to determine COVID-19- associated biomarker candidate genes and improve the final model's interpretability. RESULTS For the diagnosis of COVID-19, the XGBoost (accuracy: 0.930) model outperformed the RF (accuracy: 0.912), SVM (accuracy: 0.877), and LR (accuracy: 0.912) models. As a result of the SHAP, the three most important genes associated with COVID-19 were IFI27, LGR6, and FAM83A. The results of LIME showed that especially the high level of IFI27 gene expression contributed to increasing the probability of positive class. CONCLUSIONS The proposed model (XGBoost) was able to predict COVID-19 successfully. The results show that machine learning combined with LIME and SHAP can explain the biomarker prediction for COVID-19 and provide clinicians with an intuitive understanding and interpretability of the impact of risk factors in the model.
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Affiliation(s)
- Fatma Hilal Yagin
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey.
| | - İpek Balikci Cicek
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey.
| | - Abedalrhman Alkhateeb
- Software Engineering Department, King Hussein School for Computing Sciences, Amman, Jordan.
| | - Burak Yagin
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey.
| | - Cemil Colak
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey.
| | - Mohammad Azzeh
- Data Science Department, King Hussein School for Computing Sciences, Amman, Jordan.
| | - Sami Akbulut
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey; Inonu University, Faculty of Medicine, Department of Surgery, 44280, Malatya, Turkey; Inonu University, Faculty of Medicine, Department of Public Health, 44280, Malatya, Turkey.
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Koutou O, Diabaté AB, Sangaré B. Mathematical analysis of the impact of the media coverage in mitigating the outbreak of COVID-19. Math Comput Simul 2023; 205:600-618. [PMID: 36312512 PMCID: PMC9596178 DOI: 10.1016/j.matcom.2022.10.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 08/25/2022] [Accepted: 10/15/2022] [Indexed: 05/25/2023]
Abstract
In this paper, a mathematical model with a standard incidence rate is proposed to assess the role of media such as facebook, television, radio and tweeter in the mitigation of the outbreak of COVID-19. The basic reproduction number R 0 which is the threshold dynamics parameter between the disappearance and the persistence of the disease has been calculated. And, it is obvious to see that it varies directly to the number of hospitalized people, asymptomatic, symptomatic carriers and the impact of media coverage. The local and the global stabilities of the model have also been investigated by using the Routh-Hurwitz criterion and the Lyapunov's functional technique, respectively. Furthermore, we have performed a local sensitivity analysis to assess the impact of any variation in each one of the model parameter on the threshold R 0 and the course of the disease accordingly. We have also computed the approximative rate at which herd immunity will occur when any control measure is implemented. To finish, we have presented some numerical simulation results by using some available data from the literature to corroborate our theoretical findings.
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Affiliation(s)
- Ousmane Koutou
- CUP-Kaya/Université Joseph KI-ZERBO, 01 BP 7021 Ouagadougou 01, Burkina Faso, Burkina Faso
| | - Abou Bakari Diabaté
- Département de mathématiques/Université Nazi BONI, 01 BP 1091 Bobo-Dioulasso 01, Burkina Faso
| | - Boureima Sangaré
- Département de mathématiques/Université Nazi BONI, 01 BP 1091 Bobo-Dioulasso 01, Burkina Faso
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15
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Liu W, Guo Z, Abudunaibi B, Ouyang X, Wang D, Yang T, Deng B, Huang J, Zhao B, Su Y, Su C, Chen T. Model-Based Evaluation of Transmissibility and Intervention Measures for a COVID-19 Outbreak in Xiamen City, China. Front Public Health 2022; 10:887146. [PMID: 35910883 PMCID: PMC9326243 DOI: 10.3389/fpubh.2022.887146] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Background In September 2021, there was an outbreak of coronavirus disease 2019 (COVID-19) in Xiamen, China. Various non-pharmacological interventions (NPIs) and pharmacological interventions (PIs) have been implemented to prevent and control the spread of the disease. This study aimed to evaluate the effectiveness of various interventions and to identify priorities for the implementation of prevention and control measures. Methods The data of patients with COVID-19 were collected from 8 to 30 September 2021. A Susceptible-Exposed-Infectious-Recovered (SEIR) dynamics model was developed to fit the data and simulate the effectiveness of interventions (medical treatment, isolation, social distancing, masking, and vaccination) under different scenarios. The effective reproductive number (Reff) was used to assess the transmissibility and transmission risk. Results A total of 236 cases of COVID-19 were reported in Xiamen. The epidemic curve was divided into three phases (Reff = 6.8, 1.5, and 0). Notably, the cumulative number of cases was reduced by 99.67% due to the preventive and control measures implemented by the local government. In the effective containment stage, the number of cases could be reduced to 115 by intensifying the implementation of interventions. The total number of cases (TN) could be reduced by 29.66–95.34% when patients voluntarily visit fever clinics. When only two or three of these measures are implemented, the simulated TN may be greater than the actual number. As four measures were taken simultaneously, the TN may be <100, which is 57.63% less than the actual number. The simultaneous implementation of five interventions could rapidly control the transmission and reduce the number of cases to fewer than 25. Conclusion With the joint efforts of the government and the public, the outbreak was controlled quickly and effectively. Authorities could promptly cut the transmission chain and control the spread of the disease when patients with fever voluntarily went to the hospital. The ultimate effect of controlling the outbreak through only one intervention was not obvious. The combined community control and mask wearing, along with other interventions, could lead to rapid control of the outbreak and ultimately lower the total number of cases. More importantly, this would mitigate the impact of the outbreak on society and socioeconomics.
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Affiliation(s)
- Weikang Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Zhinan Guo
- Xiamen Center for Disease Control and Prevention, Xiamen, China
| | - Buasiyamu Abudunaibi
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Xue Ouyang
- Xiamen Center for Disease Control and Prevention, Xiamen, China
| | - Demeng Wang
- Xiamen Center for Disease Control and Prevention, Xiamen, China
| | - Tianlong Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Bin Deng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Jiefeng Huang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Benhua Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Yanhua Su
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Chenghao Su
- Zhongshan Hospital, Fudan University (Xiamen Branch), Xiamen, China
- Chenghao Su
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- *Correspondence: Tianmu Chen
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16
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Cheng Z, Wang J. Modeling epidemic flow with fluid dynamics. Math Biosci Eng 2022; 19:8334-8360. [PMID: 35801468 DOI: 10.3934/mbe.2022388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In this paper, a new mathematical model based on partial differential equations is proposed to study the spatial spread of infectious diseases. The model incorporates fluid dynamics theory and represents the epidemic spread as a fluid motion generated through the interaction between the susceptible and infected hosts. At the macroscopic level, the spread of the infection is modeled as an inviscid flow described by the Euler equation. Nontrivial numerical methods from computational fluid dynamics (CFD) are applied to investigate the model. In particular, a fifth-order weighted essentially non-oscillatory (WENO) scheme is employed for the spatial discretization. As an application, this mathematical and computational framework is used in a simulation study for the COVID-19 outbreak in Wuhan, China. The simulation results match the reported data for the cumulative cases with high accuracy and generate new insight into the complex spatial dynamics of COVID-19.
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Affiliation(s)
- Ziqiang Cheng
- School of Mathematics, Hefei University of Technology, Hefei, Anhui 230009, China
| | - Jin Wang
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA
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17
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Sanchez F, Calvo JG, Mery G, García YE, Vásquez P, Barboza LA, Pérez MD, Rivas T. A multilayer network model of Covid-19: Implications in public health policy in Costa Rica. Epidemics 2022; 39:100577. [PMID: 35636309 PMCID: PMC9116977 DOI: 10.1016/j.epidem.2022.100577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 11/21/2021] [Accepted: 05/05/2022] [Indexed: 01/19/2023] Open
Abstract
Successful partnerships between researchers, experts, and public health authorities have been critical to navigate the challenges of the Covid-19 pandemic worldwide. In this collaboration, mathematical models have played a decisive role in informing public policy, with findings effectively translated into public health measures that have shaped the pandemic in Costa Rica. As a result of interdisciplinary and cross-institutional collaboration, we constructed a multilayer network model that incorporates a diverse contact structure for each individual. In July 2020, we used this model to test the effect of lifting restrictions on population mobility after a so-called "epidemiological fence" imposed to contain the country's first big wave of cases. Later, in August 2020, we used it to predict the effects of an open and close strategy (the Hammer and Dance). Scenarios constructed in July 2020 showed that lifting restrictions on population mobility after less than three weeks of epidemiological fence would produce a sharp increase in cases. Results from scenarios in August 2020 indicated that the Hammer and Dance strategy would only work with 50% of the population adhering to mobility restrictions. The development, evolution, and applications of a multilayer network model of Covid-19 in Costa Rica has guided decision-makers to anticipate implementing sanitary measures and contributed to gain valuable time to increase hospital capacity.
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Affiliation(s)
- Fabio Sanchez
- Universidad de Costa Rica, Centro de Investigación en Matemática Pura y Aplicada - Escuela de Matemática, San José, Costa Rica,Corresponding author
| | - Juan G. Calvo
- Universidad de Costa Rica, Centro de Investigación en Matemática Pura y Aplicada - Escuela de Matemática, San José, Costa Rica
| | - Gustavo Mery
- Pan American Health Organization, World Health Organization, San José 10102, Costa Rica
| | - Yury E. García
- Universidad de Costa Rica, Centro de Investigación en Matemática Pura y Aplicada, San José, Costa Rica,Department of Public Health Sciences, University of California Davis, CA, USA
| | - Paola Vásquez
- Universidad de Costa Rica, Centro de Investigación en Matemática Pura y Aplicada, San José, Costa Rica
| | - Luis A. Barboza
- Universidad de Costa Rica, Centro de Investigación en Matemática Pura y Aplicada - Escuela de Matemática, San José, Costa Rica
| | - María Dolores Pérez
- Pan American Health Organization, World Health Organization, San José 10102, Costa Rica
| | - Tania Rivas
- Ministry of Health, San José 10102, Costa Rica
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18
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Chen H, Shen Z, Zhang Y, Zheng Z, Li L, Tian X, Peng J, Peng X, Zhou Y. Statistical Analysis of IABP-Surgery Data with the Co-use of Anticoagulants, Pulse of Dorsalis Pedis Artery, D-Dimer Data, and Coagulation Function. Front Surg 2022; 9:919009. [PMID: 35651683 PMCID: PMC9149272 DOI: 10.3389/fsurg.2022.919009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
Data analysis was performed on IABP (intra-aortic balloon pump) patients for the use of anticoagulants, pulse of dorsalis pedis artery, D-dimer data, and coagulation function. According to the differential diagnosis of 52 patients admitted to hospital, data on the use of anticoagulants, dorsalis pedis artery pulsation, D-dimer data, and coagulation function were collected. These data were analyzed by using a nonparametric test, linear regression analysis, adjustment effect analysis, and chi-square test. Some findings of the analysis included: (1) There were differences in the dorsalis pedis artery pulsation of samples from different sexes, all of which were significant. (2) Coagulation function has a significant positive relationship with D-dimer. (3) When the D-dimer affects the prognosis, the regulatory variable (dorsalis pedis artery pulse) is at different levels, and the influence amplitude has significant differences. (4) Samples taken with different anticoagulants all showed significant differences in the dorsalis pedis artery pulsation.
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Affiliation(s)
- Huali Chen
- Quality Control Office of Nursing Department, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
- Clinical Medicine Research Center of Heart Failure of Hunan Province, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Zhoumin Shen
- Quality Control Office of Nursing Department, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Yan Zhang
- Clinical Medicine Research Center of Heart Failure of Hunan Province, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
- Department of Cardiology, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
- Correspondence: Yan Zhang
Yi Zhou
| | - Zhaofen Zheng
- Clinical Medicine Research Center of Heart Failure of Hunan Province, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
- Department of Cardiology, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Lihua Li
- Clinical Medicine Research Center of Heart Failure of Hunan Province, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
- Department of Cardiology, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Xinyi Tian
- Emergency Department, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Jianqiang Peng
- Clinical Medicine Research Center of Heart Failure of Hunan Province, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
- Department of Cardiology, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Xi Peng
- Clinical Medicine Research Center of Heart Failure of Hunan Province, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
- Department of Cardiology, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Yi Zhou
- Department of Orthopedics (Department of Osteoarticular and Sports Medicine), Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
- Correspondence: Yan Zhang
Yi Zhou
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19
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Elías LL, Elías SL, Del Rey AM. An analysis of contact tracing protocol in an over-dispersed SEIQR Covid-like disease. Physica A 2022; 590:126754. [PMID: 34924687 PMCID: PMC8670085 DOI: 10.1016/j.physa.2021.126754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 09/26/2021] [Indexed: 05/09/2023]
Abstract
The aim of this work is to study an over-dispersed SEIQR infectious disease and obtain optimal methods of contact tracing. A prototypical example of such a disease is that of the current SARS-CoV-2 pandemic. In consequence, this study is immediately applicable to the current health crisis. In this paper, we introduce both a discrete and continuous model for various modes of contact tracing. From the continuous model, we derive a basic reproductive number and study the stability of the equilibrium points. We also implement the continuous and discrete models numerically and further analyze the effectiveness of different types of contact tracing and their cost on society. Additionally, through these simulations, we also study the effect that various parameters of the disease have on its evolution.
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Affiliation(s)
| | | | - A Martín Del Rey
- University of Salamanca, Institute of Fundamental Physics and Mathematics, Department of Applied Mathematics, Salamanca, Spain
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20
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González-parra G, Cogollo MR, Arenas AJ. Mathematical Modeling to Study Optimal Allocation of Vaccines against COVID-19 Using an Age-Structured Population. Axioms 2022; 11:109. [DOI: 10.3390/axioms11030109] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Vaccination against the coronavirus disease 2019 (COVID-19) started in early December of 2020 in the USA. The efficacy of the vaccines vary depending on the SARS-CoV-2 variant. Some countries have been able to deploy strong vaccination programs, and large proportions of their populations have been fully vaccinated. In other countries, low proportions of their populations have been vaccinated, due to different factors. For instance, countries such as Afghanistan, Cameroon, Ghana, Haiti and Syria have less than 10% of their populations fully vaccinated at this time. Implementing an optimal vaccination program is a very complex process due to a variety of variables that affect the programs. Besides, science, policy and ethics are all involved in the determination of the main objectives of the vaccination program. We present two nonlinear mathematical models that allow us to gain insight into the optimal vaccination strategy under different situations, taking into account the case fatality rate and age-structure of the population. We study scenarios with different availabilities and efficacies of the vaccines. The results of this study show that for most scenarios, the optimal allocation of vaccines is to first give the doses to people in the 55+ age group. However, in some situations the optimal strategy is to first allocate vaccines to the 15–54 age group. This situation occurs whenever the SARS-CoV-2 transmission rate is relatively high and the people in the 55+ age group have a transmission rate 50% or less that of those in the 15–54 age group. This study and similar ones can provide scientific recommendations for countries where the proportion of vaccinated individuals is relatively small or for future pandemics.
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21
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Valiati NC, Villela DA. Modelling policy combinations of vaccination and transmission suppression of SARS-CoV-2 in Rio de Janeiro, Brazil. Infect Dis Model 2022; 7:231-242. [PMID: 35005325 PMCID: PMC8719375 DOI: 10.1016/j.idm.2021.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 11/29/2022] Open
Abstract
COVID-19 vaccination in Brazil required a phased program, with priorities for age groups, health workers, and vulnerable people. Social distancing and isolation interventions have been essential to mitigate the advance of the pandemic in several countries. We developed a mathematical model capable of capturing the dynamics of the SARS-CoV-2 dissemination aligned with social distancing, isolation measures, and vaccination. Surveillance data from the city of Rio de Janeiro provided a case study to analyze possible scenarios, including non-pharmaceutical interventions and vaccination in the epidemic scenario. Our results demonstrate that the combination of vaccination and policies of transmission suppression potentially lowered the number of hospitalized cases by 380+ and 66+ thousand cases, respectively, compared to an absence of such policies. On top of transmission suppression-only policies, vaccination impacted more than 230+ thousand averted hospitalized cases and 43+ thousand averted deaths. Therefore, health surveillance activities should be maintained along with vaccination planning in scheduled groups until a large vaccinated coverage is reached. Furthermore, this analytical framework enables evaluation of such scenarios.
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Affiliation(s)
| | - Daniel A.M. Villela
- Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brazil
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22
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Khamees A, Bani-Issa J, Zoubi MSA, Qasem T, AbuAlArjah MI, Alawadin SA, Al-Shami K, Hussein FE, Hussein E, Bashayreh IH, Tambuwala MM, Al-Saghir M, Cornelison CT. SARS-CoV-2 and Coronavirus Disease Mitigation: Treatment Options, Vaccinations and Variants. Pathogens 2022; 11:275. [PMID: 35215217 PMCID: PMC8876838 DOI: 10.3390/pathogens11020275] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/07/2022] [Accepted: 02/10/2022] [Indexed: 01/27/2023] Open
Abstract
COVID-19 is caused by a novel coronavirus (2019-nCoV), which was declared as a pandemic after it emerged in China 2019. A vast international effort has been conducted to prevent and treat COVID-19 due to its high transmissibility and severe morbidity and mortality rates, particularly in individuals with chronic co-morbidities. In addition, polymorphic variants increased the need for proper vaccination to overcome the infectivity of new variants that are emerging across the globe. Many treatment options have been proposed and more than 25 vaccines are in various stages of development; however, the infection peaks are oscillating periodically, which raises a significant question about the effectiveness of the prevention measures and the persistence of this pandemic disease. In this review, we are exploring the most recent knowledge and advances in the treatment and vaccination options as well as the new emerging variants of 2019-nCoV and the possible mitigation of one of the most aggressive pandemics in the last centuries.
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Affiliation(s)
- Almu’atasim Khamees
- Department of Clinical Sciences, Faculty of Medicine, Yarmouk University, Irbid 211-63, Jordan; (A.K.); (J.B.-I.); (K.A.-S.); (F.E.H.)
| | - Jamal Bani-Issa
- Department of Clinical Sciences, Faculty of Medicine, Yarmouk University, Irbid 211-63, Jordan; (A.K.); (J.B.-I.); (K.A.-S.); (F.E.H.)
| | - Mazhar Salim Al Zoubi
- Department of Basic Medical Sciences, Faculty of Medicine, Yarmouk University, Irbid 211-63, Jordan; (M.S.A.Z.); (T.Q.); (M.I.A.)
| | - Taqwa Qasem
- Department of Basic Medical Sciences, Faculty of Medicine, Yarmouk University, Irbid 211-63, Jordan; (M.S.A.Z.); (T.Q.); (M.I.A.)
| | - Manal Issam AbuAlArjah
- Department of Basic Medical Sciences, Faculty of Medicine, Yarmouk University, Irbid 211-63, Jordan; (M.S.A.Z.); (T.Q.); (M.I.A.)
| | | | - Khayry Al-Shami
- Department of Clinical Sciences, Faculty of Medicine, Yarmouk University, Irbid 211-63, Jordan; (A.K.); (J.B.-I.); (K.A.-S.); (F.E.H.)
| | - Farah E. Hussein
- Department of Clinical Sciences, Faculty of Medicine, Yarmouk University, Irbid 211-63, Jordan; (A.K.); (J.B.-I.); (K.A.-S.); (F.E.H.)
| | - Emad Hussein
- Department of Food Science and Human Nutrition, A’Sharqiyah University, P.O. Box 42, Ibra 400, Oman;
- Department of Biological Sciences, Faculty of Sciences, Yarmouk University, Irbid 211-63, Jordan
| | - Ibrahim H. Bashayreh
- Nursing Department, Fatima College of Health Sciences, Al-Ain Campus, P.O. Box 24162, Abu-Dhabi 31201, United Arab Emirates;
| | - Murtaza M. Tambuwala
- School of Pharmacy and Pharmaceutical Science, Ulster University, Coleraine BT52 1SA, UK;
| | - Mohannad Al-Saghir
- Department of Biological Sciences, Ohio University, Zanesville, OH 43701, USA;
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23
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Liu K, Lou Y. Optimizing COVID-19 vaccination programs during vaccine shortages: A review of mathematical models. Infect Dis Model 2022; 7:286-298. [PMID: 35233475 PMCID: PMC8872681 DOI: 10.1016/j.idm.2022.02.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/10/2022] [Accepted: 02/10/2022] [Indexed: 12/12/2022] Open
Affiliation(s)
- Kaihui Liu
- Institute of Applied System Analysis, Jiangsu University, Zhenjiang, Jiangsu, 212013, PR China
| | - Yijun Lou
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
- Corresponding author.
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24
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Guan J, Zhao Y, Wei Y, Shen S, You D, Zhang R, Lange T, Chen F. Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges. Med Rev (Berl) 2022; 2:89-109. [PMID: 35658113 PMCID: PMC9047651 DOI: 10.1515/mr-2021-0022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 01/03/2022] [Indexed: 12/20/2022]
Abstract
Since late 2019, the beginning of coronavirus disease 2019 (COVID-19) pandemic, transmission dynamics models have achieved great development and were widely used in predicting and policy making. Here, we provided an introduction to the history of disease transmission, summarized transmission dynamics models into three main types: compartment extension, parameter extension and population-stratified extension models, highlight the key contribution of transmission dynamics models in COVID-19 pandemic: estimating epidemiological parameters, predicting the future trend, evaluating the effectiveness of control measures and exploring different possibilities/scenarios. Finally, we pointed out the limitations and challenges lie ahead of transmission dynamics models.
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Affiliation(s)
- Jinxing Guan
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yang Zhao
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.,China International Cooperation Center for Environment and Human Health, Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, China.,Center of Biomedical BigData, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yongyue Wei
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.,China International Cooperation Center for Environment and Human Health, Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Sipeng Shen
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Dongfang You
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ruyang Zhang
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Theis Lange
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Feng Chen
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.,China International Cooperation Center for Environment and Human Health, Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, China
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25
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Badfar E, Zaferani EJ, Nikoofard A. Design a robust sliding mode controller based on the state and parameter estimation for the nonlinear epidemiological model of Covid-19. Nonlinear Dyn 2022; 109:5-18. [PMID: 34776637 PMCID: PMC8572654 DOI: 10.1007/s11071-021-07036-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 10/27/2021] [Indexed: 05/17/2023]
Abstract
In this research, the challenging problem of Covid-19 mitigation is looked at from an engineering point of view. At first, the behavior of coronavirus in the Iranian and Russian societies is expressed by a set of ordinary differential equations. In the proposed model, the control input signals are vaccination, social distance and facial masks, and medical treatment. The unknown parameters of the system are estimated by long short-term memory (LSTM) algorithm. In the LSTM algorithm, the problem of long-term dependency is prevented. The uncertainty and measurement noises are inherent characteristics of epidemiological models. For this reason, an extended Kalman filter (EKF) is developed to estimate the state variables of the proposed model. In continuation, a robust sliding mode controller is designed to control the spread of coronavirus under vaccination, social distance and facial masks, and medical treatment. The stability of the closed-loop system is guaranteed by the Lyapunov theorems. The official confirmed data provided by the Iranian and Russian ministries of health are employed to simulate the proposed algorithms. It is understood from simulation results that global vaccination has the potential to create herd immunity in long term. Under the proposed controller, daily Covid-19 infections and deaths become less than 500 and 10 people, respectively.
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Affiliation(s)
- Ehsan Badfar
- Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | | | - Amirhossein Nikoofard
- Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
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Pathan SA, Moinudheen J, Simon K, Thomas SH. COVID-19 cases presenting to the Emergency Department predict Qatar National COVID-19 trends and numbers. Qatar Med J 2021; 2021:56. [PMID: 34733709 PMCID: PMC8542101 DOI: 10.5339/qmj.2021.56] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 09/27/2021] [Indexed: 11/03/2022] Open
Abstract
In this short communication, we summarized the analyses, models, and interpretations of the corporate department of emergency medicine's (CDEM) COVID-19 numbers and their relationship to predict the national COVID-19 trends and numbers in Qatar. Data included in this analysis were obtained between March 1, 2020 and July 31, 2021. It included the number of COVID-19 cases that presented to four major EDs under the Hamad Medical Corporation CDEM umbrella and published data from the Qatar Ministry of public health (MoPH). On plotting weighted scatterplot smoothing (lowess) trend lines, there were striking similarities between CDEM and national COVID-19 n curves for overall trends and peaks. In conclusion, CDEM COVID-19 spike may be useful to predict national COVID-19 spike in 2-3 weeks.
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Affiliation(s)
| | - Jibin Moinudheen
- Corporate Department of Emergency Medicine, Hamad Medical Corporation, Qatar E-mail:
| | - Katie Simon
- Corporate Department of Emergency Medicine, Hamad Medical Corporation, Qatar E-mail:
| | - Stephen H Thomas
- Corporate Department of Emergency Medicine, Hamad Medical Corporation, Qatar E-mail: .,Blizard Institute, Barts and The London School of Medicine, Queen Mary Univ. of London, UK
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Jithesh PK. A model based on cellular automata for investigating the impact of lockdown, migration and vaccination on COVID-19 dynamics. Comput Methods Programs Biomed 2021; 211:106402. [PMID: 34530391 PMCID: PMC8423711 DOI: 10.1016/j.cmpb.2021.106402] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 09/01/2021] [Indexed: 05/14/2023]
Abstract
BACKGROUND AND OBJECTIVE COVID-19 pandemic continues unabated due to the rapid spread of new mutant strains of the virus. Decentralized cluster containment is an efficient approach to manage the pandemic in the long term, without straining the healthcare system and economy. In this study, the objective is to forecast the peak and duration of COVID-19 spread in a cluster under different conditions, using a probabilistic cellular automata configuration designed to include the observed characteristics of the pandemic with appropriate neighbourhood schemes and transition rules. METHODS The cellular automata, initially configured to have only susceptible and exposed states, enlarges and evolves in discrete time steps to different infection states of the COVID-19 pandemic. The transition rules take into account the probability and proximity of contact between infected hosts and susceptible individuals. A transmittable and transition neighbourhoods are defined to identify the most probable individuals infected from a single host in a time step. RESULTS The model with novel neighbourhood schemes and transition rules reproduce the macroscopic behaviour of infection and recovery observed in pandemics. The temporal evolution of the pandemic trajectory is sensitive to lattice size, range, latent and recovery periods but has constraints in capturing the changes in the infectious period. A study of lockdown and migration scenarios shows strict social isolation is crucial in controlling the pandemic. The simulations also indicate that earlier vaccination with a higher capacity and rate is essential to mitigate the pandemic. A comparison of simulated and actual data shows a good match. CONCLUSIONS The study concludes that social isolation during movement and interaction of people can limit the spread of new infections. Vaccinating a large proportion of the population reduces new cases in subsequent waves of the pandemic. The model and algorithm with real-world data as input can quickly forecast the trajectory of the pandemic, for effective response in cluster containment.
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Affiliation(s)
- P K Jithesh
- Assistant Professor in Mechanical Engineering, Government Engineering College Kozhikode, Westhill Post, Kozhikode, Kerala 673005, India
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