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Naqvi SAA, Sajjad M, Tariq A, Sajjad M, Waseem LA, Karuppannan S, Rehman A, Hassan M, Al-Ahmadi S, Hatamleh WA. Societal knowledge, attitude, and practices towards dengue and associated factors in epidemic-hit areas: Geoinformation assisted empirical evidence. Heliyon 2024; 10:e23151. [PMID: 38223736 PMCID: PMC10784149 DOI: 10.1016/j.heliyon.2023.e23151] [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: 06/06/2023] [Revised: 11/25/2023] [Accepted: 11/28/2023] [Indexed: 01/16/2024] Open
Abstract
Dengue is one of Pakistan's major health concerns. In this study, we aimed to advance our understanding of the levels of knowledge, attitudes, and practices (KAPs) in Pakistan's Dengue Fever (DF) hotspots. Initially, at-risk communities were systematically identified via a well-known spatial modeling technique, named, Kernel Density Estimation, which was later targeted for a household-based cross-sectional survey of KAPs. To collect data on sociodemographic and KAPs, random sampling was utilized (n = 385, 5 % margin of error). Later, the association of different demographics (characteristics), knowledge, and attitude factors-potentially related to poor preventive practices was assessed using bivariate (individual) and multivariable (model) logistic regression analyses. Most respondents (>90 %) identified fever as a sign of DF; headache (73.8 %), joint pain (64.4 %), muscular pain (50.9 %), pain behind the eyes (41.8 %), bleeding (34.3 %), and skin rash (36.1 %) were identified relatively less. Regression results showed significant associations of poor knowledge/attitude with poor preventive practices; dengue vector (odds ratio [OR] = 3.733, 95 % confidence interval [CI ] = 2.377-5.861; P < 0.001), DF symptoms (OR = 3.088, 95 % CI = 1.949-4.894; P < 0.001), dengue transmission (OR = 1.933, 95 % CI = 1.265-2.956; P = 0.002), and attitude (OR = 3.813, 95 % CI = 1.548-9.395; P = 0.004). Moreover, education level was stronger in bivariate analysis and the strongest independent factor of poor preventive practices in multivariable analysis (illiterate: adjusted OR = 6.833, 95 % CI = 2.979-15.672; P < 0.001) and primary education (adjusted OR = 4.046, 95 % CI = 1.997-8.199; P < 0.001). This situation highlights knowledge gaps within urban communities, particularly in understanding dengue transmission and signs/symptoms. The level of education in urban communities also plays a substantial role in dengue control, as observed in this study, where poor preventive practices were more prevalent among illiterate and less educated respondents.
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Affiliation(s)
- Syed Ali Asad Naqvi
- Department of Geography, Government College University Faisalabad, Faisalabad, 38000, Punjab, Pakistan
| | - Muhammad Sajjad
- Department of Geography, Government College University Faisalabad, Faisalabad, 38000, Punjab, Pakistan
| | - Aqil Tariq
- Department of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, 775 Stone Boulevard, Mississippi State, 39762-9690, MS, USA
| | - Muhammad Sajjad
- Centre for Geo-computation Studies and Department of Geography, Hong Kong Baptist University, Hong Kong Special Administrative Region, China
| | - Liaqat Ali Waseem
- Department of Geography, Government College University Faisalabad, Faisalabad, 38000, Punjab, Pakistan
| | - Shankar Karuppannan
- Department of Applied Geology, School of Applied Natural Sciences, Adama Science and Technology University, Adama P.O. Box 1888, Ethiopia
| | - Adnanul Rehman
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China
| | - Mujtaba Hassan
- Department of Space Science, Institute of Space Technology, Main Islamabad Expressway, Islamabad, Pakistan
| | - Saad Al-Ahmadi
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Saudi Arabia
| | - Wesam Atef Hatamleh
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Saudi Arabia
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Subramanian M, Shanmuga Vadivel K, Hatamleh WA, Alnuaim AA, Abdelhady M, V E S. The role of contemporary digital tools and technologies in COVID-19 crisis: An exploratory analysis. Expert Syst 2022; 39:e12834. [PMID: 34898797 PMCID: PMC8646626 DOI: 10.1111/exsy.12834] [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] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/10/2021] [Accepted: 09/09/2021] [Indexed: 05/17/2023]
Abstract
Following the COVID-19 pandemic, there has been an increase in interest in using digital resources to contain pandemics. To avoid, detect, monitor, regulate, track, and manage diseases, predict outbreaks and conduct data analysis and decision-making processes, a variety of digital technologies are used, ranging from artificial intelligence (AI)-powered machine learning (ML) or deep learning (DL) focused applications to blockchain technology and big data analytics enabled by cloud computing and the internet of things (IoT). In this paper, we look at how emerging technologies such as the IoT and sensors, AI, ML, DL, blockchain, augmented reality, virtual reality, cloud computing, big data, robots and drones, intelligent mobile apps, and 5G are advancing health care and paving the way to combat the COVID-19 pandemic. The aim of this research is to look at possible technologies, processes, and tools for addressing COVID-19 issues such as pre-screening, early detection, monitoring infected/quarantined individuals, forecasting future infection rates, and more. We also look at the research possibilities that have arisen as a result of the use of emerging technology to handle the COVID-19 crisis.
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Affiliation(s)
- Malliga Subramanian
- Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India
| | | | - Wesam Atef Hatamleh
- Department of Computer Science, College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia
| | - Abeer Ali Alnuaim
- Department of Computer Science and Engineering, College of Applied Studies and Community Services King Saud University Riyadh Saudi Arabia
| | - Mohamed Abdelhady
- Electrical and Computer Engineering Department Cleveland State University Cleveland Ohio USA
| | - Sathishkumar V E
- Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India
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Malche T, Tharewal S, Tiwari PK, Jabarulla MY, Alnuaim AA, Hatamleh WA, Ullah MA. Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring. J Healthc Eng 2022; 2022:8732213. [PMID: 35273786 PMCID: PMC8904099 DOI: 10.1155/2022/8732213] [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] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/17/2022] [Accepted: 02/09/2022] [Indexed: 11/17/2022]
Abstract
Telehealth and remote patient monitoring (RPM) have been critical components that have received substantial attention and gained hold since the pandemic's beginning. Telehealth and RPM allow easy access to patient data and help provide high-quality care to patients at a low cost. This article proposes an Intelligent Remote Patient Activity Tracking System system that can monitor patient activities and vitals during those activities based on the attached sensors. An Internet of Things- (IoT-) enabled health monitoring device is designed using machine learning models to track patient's activities such as running, sleeping, walking, and exercising, the vitals during those activities such as body temperature and heart rate, and the patient's breathing pattern during such activities. Machine learning models are used to identify different activities of the patient and analyze the patient's respiratory health during various activities. Currently, the machine learning models are used to detect cough and healthy breathing only. A web application is also designed to track the data uploaded by the proposed devices.
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Affiliation(s)
| | - Sumegh Tharewal
- School of Computer Science, Dr. Vishwanath Karad MIT World peace University, S. No.124, Paud Road, Kothrud, Pune 411038, Maharashtra, India
| | | | - Mohamed Yaseen Jabarulla
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Abeer Ali Alnuaim
- Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. BOX 22459, Riyadh 11495, Saudi Arabia
| | - Wesam Atef Hatamleh
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia
| | - Mohammad Aman Ullah
- Department of Computer Science and Engineering, International Islamic University Chittagong, Chittagong, Bangladesh
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Sathishkumar VE, Hatamleh WA, Alnuaim AA, Abdelhady M, Venkatesh B, Santhoshkumar S. Secure Dynamic Group Data Sharing in Semi-trusted Third Party Cloud Environment. Arab J Sci Eng 2021. [DOI: 10.1007/s13369-021-06155-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Narayana PL, Maurya AK, Wang XS, Harsha MR, Srikanth O, Alnuaim AA, Hatamleh WA, Hatamleh AA, Cho KK, Paturi UMR, Reddy NS. Artificial neural networks modeling for lead removal from aqueous solutions using iron oxide nanocomposites from bio-waste mass. Environ Res 2021; 199:111370. [PMID: 34043971 DOI: 10.1016/j.envres.2021.111370] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [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: 03/19/2021] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 06/12/2023]
Abstract
Heavy metal ions in aqueous solutions are taken into account as one of the most harmful environmental issues that ominously affect human health. Pb(II) is a common pollutant among heavy metals found in industrial wastewater, and various methods were developed to remove the Pb(II). The adsorption method was more efficient, cheap, and eco-friendly to remove the Pb(II) from aqueous solutions. The removal efficiency depends on the process parameters (initial concentration, the adsorbent dosage of T-Fe3O4 nanocomposites, residence time, and adsorbent pH). The relationship between the process parameters and output is non-linear and complex. The purpose of the present study is to develop an artificial neural networks (ANN) model to estimate and analyze the relationship between Pb(II) removal and adsorption process parameters. The model was trained with the backpropagation algorithm. The model was validated with the unseen datasets. The correlation coefficient adj.R2 values for total datasets is 0.991. The relationship between the parameters and Pb(II) removal was analyzed by sensitivity analysis and creating a virtual adsorption process. The study determined that the ANN modeling was a reliable tool for predicting and optimizing adsorption process parameters for maximum lead removal from aqueous solutions.
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Affiliation(s)
- P L Narayana
- School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, Republic of Korea
| | - A K Maurya
- School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, Republic of Korea
| | - Xiao-Song Wang
- School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, Republic of Korea
| | - M R Harsha
- Machine Learning and Artificial Intelligence, International Institute of Information Technology, Banglore, India
| | - O Srikanth
- Department of Mechanical Engineering, Dhanekula Institute of Engineering & Technology, Ganguru, Vijayawada, 521139, India
| | - Abeer Ali Alnuaim
- Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Wesam Atef Hatamleh
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Ashraf Atef Hatamleh
- Department of Botany and Microbiology, College of science, King Saud University, Riyadh, 11451, Saudi Arabia
| | - K K Cho
- Department of Materials Engineering and Convergence Technology & RIGET, Gyeongsang National University, Jinju, South Korea
| | | | - N S Reddy
- School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, Republic of Korea.
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