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Zareie A, Sakellariou R. Mitigating virus spread through dynamic control of community-based social interactions for infection rate and cost. SOCIAL NETWORK ANALYSIS AND MINING 2022; 12:132. [PMID: 36105921 PMCID: PMC9461421 DOI: 10.1007/s13278-022-00953-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/06/2022] [Accepted: 07/29/2022] [Indexed: 11/28/2022]
Abstract
The emergence of a new virus in a community may cause significant overload on health services and may spread out to other communities quickly. Social distancing may help reduce the infection rate within a community and prevent the spread of the virus to other communities. However, social distancing comes at a cost; how to strike a good balance between reduction in infection rate and cost of social distancing may be a challenging problem. In this paper, this problem is formulated as a bi-objective optimization problem. Assuming that in a community-based society interaction links have different capacities, the problem is how to determine link capacity to achieve a good trade-off between infection rate and the costs of social distancing restrictions. A standard epidemic model, Susceptible-Infected-Recovered, is extended to model the spread of a virus in the communities. Two methods are proposed to determine dynamically the extent of contact restriction during a virus outbreak. These methods are evaluated using two synthetic networks; the experimental results demonstrate the effectiveness of the methods in decreasing both infection rate and social distancing cost compared to naive methods.
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Affiliation(s)
- Ahmad Zareie
- Department of Computer Science, University of Manchester, Manchester, UK
| | - Rizos Sakellariou
- Department of Computer Science, University of Manchester, Manchester, UK
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2
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Saldaña F, Velasco-Hernández JX. Modeling the COVID-19 pandemic: a primer and overview of mathematical epidemiology. SEMA JOURNAL 2022. [PMCID: PMC8318333 DOI: 10.1007/s40324-021-00260-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Since the start of the still ongoing COVID-19 pandemic, there have been many modeling efforts to assess several issues of importance to public health. In this work, we review the theory behind some important mathematical models that have been used to answer questions raised by the development of the pandemic. We start revisiting the basic properties of simple Kermack-McKendrick type models. Then, we discuss extensions of such models and important epidemiological quantities applied to investigate the role of heterogeneity in disease transmission e.g. mixing functions and superspreading events, the impact of non-pharmaceutical interventions in the control of the pandemic, vaccine deployment, herd-immunity, viral evolution and the possibility of vaccine escape. From the perspective of mathematical epidemiology, we highlight the important properties, findings, and, of course, deficiencies, that all these models have.
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Affiliation(s)
- Fernando Saldaña
- Instituto de Matemáticas, Universidad Nacional Autónoma de México, Campus Juriquilla, 76230, Quéretaro, Mexico
| | - Jorge X. Velasco-Hernández
- Instituto de Matemáticas, Universidad Nacional Autónoma de México, Campus Juriquilla, 76230, Quéretaro, Mexico
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3
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Alam KN, Khan MS, Dhruba AR, Khan MM, Al-Amri JF, Masud M, Rawashdeh M. Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:4321131. [PMID: 34899965 PMCID: PMC8660217 DOI: 10.1155/2021/4321131] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/12/2021] [Indexed: 11/17/2022]
Abstract
The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people's feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people's minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public's opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.
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Affiliation(s)
- Kazi Nabiul Alam
- Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Md Shakib Khan
- Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Abdur Rab Dhruba
- Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Mohammad Monirujjaman Khan
- Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Jehad F. Al-Amri
- Department of Information Technology, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia
| | - Mehedi Masud
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
| | - Majdi Rawashdeh
- Department of Business Information Technology, Princess Sumaya University for Technology, P. O. Box 1438, Amman 11941, Jordan
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Leite GS, Albuquerque AB, Pinheiro PR. Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases-A Systematic Literature Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:10765. [PMID: 34682511 PMCID: PMC8535524 DOI: 10.3390/ijerph182010765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/09/2021] [Accepted: 10/11/2021] [Indexed: 12/23/2022]
Abstract
With the growing concern about the spread of new respiratory infectious diseases, several studies involving the application of technology in the prevention of these diseases have been carried out. Among these studies, it is worth highlighting the importance of those focused on the primary forms of prevention, such as social distancing, mask usage, quarantine, among others. This importance arises because, from the emergence of a new disease to the production of immunizers, preventive actions must be taken to reduce contamination and fatalities rates. Despite the considerable number of studies, no records of works aimed at the identification, registration, selection, and rigorous analysis and synthesis of the literature were found. For this purpose, this paper presents a systematic review of the literature on the application of technological solutions in the primary ways of respiratory infectious diseases transmission prevention. From the 1139 initially retrieved, 219 papers were selected for data extraction, analysis, and synthesis according to predefined inclusion and exclusion criteria. Results enabled the identification of a general categorization of application domains, as well as mapping of the adopted support mechanisms. Findings showed a greater trend in studies related to pandemic planning and, among the support mechanisms adopted, data and mathematical application-related solutions received greater attention. Topics for further research and improvement were also identified such as the need for a better description of data analysis and evidence.
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Affiliation(s)
- Gleidson Sobreira Leite
- UNIFOR, Department of Computer Science, University of Fortaleza, Fortaleza 60811-905, Ceará, Brazil; (A.B.A.); (P.R.P.)
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Jordan E, Shin DE, Leekha S, Azarm S. Optimization in the Context of COVID-19 Prediction and Control: A Literature Review. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:130072-130093. [PMID: 35781925 PMCID: PMC8768956 DOI: 10.1109/access.2021.3113812] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 09/10/2021] [Indexed: 05/08/2023]
Abstract
This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.
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Affiliation(s)
- Elizabeth Jordan
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
| | - Delia E. Shin
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
| | - Surbhi Leekha
- Department of Epidemiology and Public HealthUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Shapour Azarm
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
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Munir MS, Kim DH, Bairagi AK, Hong CS. When CVaR Meets With Bluetooth PAN: A Physical Distancing System for COVID-19 Proactive Safety. IEEE SENSORS JOURNAL 2021; 21:13858-13869. [PMID: 35790090 PMCID: PMC8768991 DOI: 10.1109/jsen.2021.3068782] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 03/19/2021] [Indexed: 06/15/2023]
Abstract
In this work, we propose a risk-aware physical distancing system to assure a private safety distance from others for reducing the chance of being affected by the COVID-19 or such kind of pandemic. In particular, we have formulated a physical distancing problem by capturing Conditional Value-at-Risk (CVaR) of a Bluetooth-enabled personal area network (PAN). To solve the formulated risk-aware physical distancing problem, we propose two stages solution approach by imposing control flow, linear model, and curve-fitting schemes. Notably, in the first stage, we determine a PAN creator's safe movement distance by proposing a probabilistic linear model. This scheme can effectively cope with a tail-risk from the probability distribution by satisfying the CVaR constraint for estimating safe movement distance. In the second stage, we design a Levenberg-Marquardt (LM)-based curve fitting algorithm upon the recommended safety distance and current distances between the PAN creator and others to find an optimal high-risk trajectory plan for the PAN creator. Finally, we have performed an extensive performance analysis using state-of-the-art Bluetooth data to establish the proposed risk-aware physical distancing system's effectiveness. Our experimental results show that the proposed solution approach can effectively reduce the risk of recommending safety distance towards ensuring private safety. In particular, for a 95% CVaR confidence, we can successfully deal with 45.11% of the risk for measuring the PAN creator's safe movement distance.
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Affiliation(s)
- Md. Shirajum Munir
- Department of Computer Science and EngineeringKyung Hee UniversityYongin17104Republic of Korea
| | - Do Hyeon Kim
- Department of Computer Science and EngineeringKyung Hee UniversityYongin17104Republic of Korea
| | - Anupam Kumar Bairagi
- Department of Computer Science and EngineeringKyung Hee UniversityYongin17104Republic of Korea
| | - Choong Seon Hong
- Department of Computer Science and EngineeringKyung Hee UniversityYongin17104Republic of Korea
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Biswas S, Sharif K, Li F, Bairagi AK, Latif Z, Mohanty SP. GlobeChain: An Interoperable Blockchain for Global Sharing of Healthcare Data—A COVID-19 Perspective. IEEE CONSUMER ELECTRONICS MAGAZINE 2021; 10:64-69. [PMCID: PMC9128831 DOI: 10.1109/mce.2021.3074688] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Accepted: 04/17/2021] [Indexed: 06/13/2023]
Abstract
Contagious diseases are prevalent even in the current era of advanced technology. A uniformed initiative is required to build a reliable interactive information exchange service targeting vaccination data management and other medical services. The conventional data exchange mechanism is centralized, creating many vulnerable issues such as a single point failure, data leakage, access control, etc. This article introduces a Blockchain-based medical data-sharing framework (called GlobeChain) to overcome the technical challenges to handle the outbreak records. The challenges that might arise due to the proposed Blockchain-based framework are also presented as a future direction that grabs the proposal's effectiveness.
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Affiliation(s)
| | | | - Fan Li
- Beijing Institute of TechnologyChina
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Eksin C, Ndeffo-Mbah M, Weitz JS. Reacting to outbreaks at neighboring localities. J Theor Biol 2021; 520:110632. [PMID: 33639138 PMCID: PMC7904447 DOI: 10.1016/j.jtbi.2021.110632] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 01/28/2021] [Accepted: 02/05/2021] [Indexed: 01/12/2023]
Abstract
We study the dynamics of epidemics in a networked metapopulation model. In each subpopulation, representing a locality, the disease propagates according to a modified susceptible-exposed-infected-recovered (SEIR) dynamics. In the modified SEIR dynamics, individuals reduce their number of contacts as a function of the weighted sum of cumulative number of cases within the locality and in neighboring localities. We consider a scenario with two localities where disease originates in one locality and is exported to the neighboring locality via travel of exposed (latently infected) individuals. We establish a lower bound on the outbreak size at the origin as a function of the speed of spread. Using the lower bound on the outbreak size at the origin, we establish an upper bound on the outbreak size at the importing locality as a function of the speed of spread and the level of preparedness for the low mobility regime. We evaluate the critical levels of preparedness that stop the disease from spreading at the importing locality. Finally, we show how the benefit of preparedness diminishes under high mobility rates. Our results highlight the importance of preparedness at localities where cases are beginning to rise such that localities can help stop local outbreaks when they respond to the severity of outbreaks in neighboring localities.
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Affiliation(s)
- Ceyhun Eksin
- Industrial and Systems Engineering Department, Texas A&M University, College Station, TX, USA; Electrical and Computer Engineering Department, Texas A&M University, College Station, TX, USA.
| | - Martial Ndeffo-Mbah
- College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Joshua S Weitz
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA; School of Physics, Georgia Institute of Technology, Atlanta, GA, USA
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Awal MA, Masud M, Hossain MS, Bulbul AAM, Mahmud SMH, Bairagi AK. A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:10263-10281. [PMID: 34786301 PMCID: PMC8545233 DOI: 10.1109/access.2021.3050852] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 01/05/2021] [Indexed: 05/04/2023]
Abstract
The whole world faces a pandemic situation due to the deadly virus, namely COVID-19. It takes considerable time to get the virus well-matured to be traced, and during this time, it may be transmitted among other people. To get rid of this unexpected situation, quick identification of COVID-19 patients is required. We have designed and optimized a machine learning-based framework using inpatient's facility data that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic. The proposed framework uses Bayesian optimization to optimize the hyperparameters of the classifier and ADAptive SYNthetic (ADASYN) algorithm to balance the COVID and non-COVID classes of the dataset. Although the proposed technique has been applied to nine state-of-the-art classifiers to show the efficacy, it can be used to many classifiers and classification problems. It is evident from this study that eXtreme Gradient Boosting (XGB) provides the highest Kappa index of 97.00%. Compared to without ADASYN, our proposed approach yields an improvement in the kappa index of 96.94%. Besides, Bayesian optimization has been compared to grid search, random search to show efficiency. Furthermore, the most dominating features have been identified using SHapely Adaptive exPlanations (SHAP) analysis. A comparison has also been made among other related works. The proposed method is capable enough of tracing COVID patients spending less time than that of the conventional techniques. Finally, two potential applications, namely, clinically operable decision tree and decision support system, have been demonstrated to support clinical staff and build a recommender system.
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Affiliation(s)
- Md. Abdul Awal
- Electronics and Communication Engineering DisciplineKhulna UniversityKhulna9208Bangladesh
| | - Mehedi Masud
- Department of Computer ScienceCollege of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia
| | - Md. Shahadat Hossain
- Department of Quantitative SciencesInternational University of Business Agriculture and TechnologyDhaka1230Bangladesh
| | | | - S. M. Hasan Mahmud
- School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Anupam Kumar Bairagi
- Computer Science and Engineering DisciplineKhulna UniversityKhulna9208Bangladesh
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