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Kaur M, Cargill T, Hui K, Vu M, Bragazzi NL, Kong JD. A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets. JMIR Form Res 2024; 8:e46087. [PMID: 38285495 PMCID: PMC10862249 DOI: 10.2196/46087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/07/2023] [Accepted: 11/20/2023] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic has highlighted gaps in the current handling of medical resource demand surges and the need for prioritizing scarce medical resources to mitigate the risk of health care facilities becoming overwhelmed. OBJECTIVE During a health care emergency, such as the COVID-19 pandemic, the public often uses social media to express negative sentiment (eg, urgency, fear, and frustration) as a real-time response to the evolving crisis. The sentiment expressed in COVID-19 posts may provide valuable real-time information about the relative severity of medical resource demand in different regions of a country. In this study, Twitter (subsequently rebranded as X) sentiment analysis was used to investigate whether an increase in negative sentiment COVID-19 tweets corresponded to a greater demand for hospital intensive care unit (ICU) beds in specific regions of the United States, Brazil, and India. METHODS Tweets were collected from a publicly available data set containing COVID-19 tweets with sentiment labels and geolocation information posted between February 1, 2020, and March 31, 2021. Regional medical resource shortage data were gathered from publicly available data sets reporting a time series of ICU bed demand across each country. Negative sentiment tweets were analyzed using the Granger causality test and convergent cross-mapping (CCM) analysis to assess the utility of the time series of negative sentiment tweets in forecasting ICU bed shortages. RESULTS For the United States (30,742,934 negative sentiment tweets), the results of the Granger causality test (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a stochastic system) were significant (P<.05) for 14 (28%) of the 50 states that passed the augmented Dickey-Fuller test at lag 2, and the results of the CCM analysis (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a dynamic system) were significant (P<.05) for 46 (92%) of the 50 states. For Brazil (3,004,039 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (22%) of the 27 federative units, and the results of the CCM analysis were significant (P<.05) for 26 (96%) of the 27 federative units. For India (4,199,151 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (23%) of the 26 included regions (25 states and the national capital region of Delhi), and the results of the CCM analysis were significant (P<.05) for 26 (100%) of the 26 included regions. CONCLUSIONS This study provides a novel approach for identifying the regions of high hospital bed demand during a health care emergency scenario by analyzing Twitter sentiment data. Leveraging analyses that take advantage of natural language processing-driven tweet extraction systems has the potential to be an effective method for the early detection of medical resource demand surges.
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Affiliation(s)
- Mahakprit Kaur
- Department of Biology, Faculty of Science, York University, Toronto, ON, Canada
- Africa-Canada Artificial Intelligence and Data Innovation Consortium, Toronto, ON, Canada
| | - Taylor Cargill
- Department of Biology, Faculty of Science, York University, Toronto, ON, Canada
- Africa-Canada Artificial Intelligence and Data Innovation Consortium, Toronto, ON, Canada
| | - Kevin Hui
- Africa-Canada Artificial Intelligence and Data Innovation Consortium, Toronto, ON, Canada
- Department of Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada
| | - Minh Vu
- Africa-Canada Artificial Intelligence and Data Innovation Consortium, Toronto, ON, Canada
- Department of Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada
| | - Nicola Luigi Bragazzi
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, ON, Canada
| | - Jude Dzevela Kong
- Africa-Canada Artificial Intelligence and Data Innovation Consortium, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
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Javadi V, Kamfar S, Zeinali V, Rahmani K, Moghaddamemami FH. Online information-seeking behavior of Iranian web users on Google about Henoch-Schönlein purpura (HSP): an infodemiology study. BMC Health Serv Res 2023; 23:1389. [PMID: 38082454 PMCID: PMC10714479 DOI: 10.1186/s12913-023-10357-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUNDS Previous studies have indicated that users' health information-seeking behavior can serve as a reflection of current health issues within a community. This study aimed to investigate the online information-seeking behavior of Iranian web users on Google about Henoch-Schönlein purpura (HSP). METHODS Google Trends (GTr) was utilized to collect big data from the internet searches conducted by Iranian web users. A focus group discussion was employed to identify users' selected keywords when searching for HSP. Additionally, keywords related to the disease's symptoms were selected based on recent clinical studies. All keywords were queried in GTr from January 1, 2012 to October 30, 2022. The outputs were saved in an Excel format and analyzed using SPSS. RESULTS The highest and lowest search rates of HSP were recorded in winter and summer, respectively. There was a significant positive correlation between HSP search rates and the terms "joint pain" (P = 0.007), "vomiting" (P = 0.032), "hands and feet swelling" (P = 0.041) and "seizure" (P < 0.001). CONCLUSION The findings were in accordance with clinical facts about HSP, such as its seasonal pattern and accompanying symptoms. It appears that the information-seeking behavior of Iranian users regarding HSP can provide valuable insights into the outbreak of this disease in Iran.
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Grants
- 18441 Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 18441 Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 18441 Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 18441 Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 18441 Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- Pediatric Pathology Research Center, Research Institute for Children’s Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Affiliation(s)
- Vadood Javadi
- Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sharareh Kamfar
- Pediatric Congenital Hematologic Disorders Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Vahide Zeinali
- Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Khosro Rahmani
- Department of pediatric rheumatology, Shahid Beheshti University of Medical Sciences, Mofid children's Hospital, Tehran, Iran
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3
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Hermosilla M, Ni J, Wang H, Zhang J. Leveraging the E-commerce footprint for the surveillance of healthcare utilization. Health Care Manag Sci 2023; 26:604-625. [PMID: 37642859 DOI: 10.1007/s10729-023-09645-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 05/11/2023] [Indexed: 08/31/2023]
Abstract
The utilization of healthcare services serves as a barometer for current and future health outcomes. Even in countries with modern healthcare IT infrastructure, however, fragmentation and interoperability issues hinder the (short-term) monitoring of utilization, forcing policymakers to rely on secondary data sources, such as surveys. This deficiency may be particularly problematic during public health crises, when ensuring proper and timely access to healthcare acquires special importance. We show that, in specific contexts, online pharmacies' digital footprint data may contain a strong signal of healthcare utilization. As such, online pharmacy data may enable utilization surveillance, i.e., the monitoring of short-term changes in utilization levels in the population. Our analysis takes advantage of the scenario created by the first wave of the Covid-19 pandemic in Mainland China, where the virus' spread lead to pervasive and deep reductions of healthcare service utilization. Relying on a large sample of online pharmacy transactions with full national coverage, we first detect variation that is strongly consistent with utilization reductions across geographies and over time. We then validate our claims by contrasting online pharmacy variation against credit-card transactions for medical services. Using machine learning methods, we show that incorporating online pharmacy data into the models significantly improves the accuracy of utilization surveillance estimates.
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Affiliation(s)
- Manuel Hermosilla
- Carey Business School, Johns Hopkins University, Baltimore, Maryland, USA.
| | - Jian Ni
- Pamplin College of Business, Virginia Tech, Blacksburg, Virginia, USA
| | - Haizhong Wang
- School of Management, Sun Yat-sen University, Guangzhou, China
| | - Jin Zhang
- School of Management, Jinan University, Guangzhou, China
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Ndejjo R, Kabwama SN, Namale A, Tusubira AK, Wanyana I, Kizito S, Kiwanuka SN, Wanyenze RK. Harnessing digital technology for COVID-19 response in Uganda: lessons and implications for future public health emergencies. BMJ Glob Health 2023; 8:e013288. [PMID: 37793838 PMCID: PMC10551983 DOI: 10.1136/bmjgh-2023-013288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/14/2023] [Indexed: 10/06/2023] Open
Abstract
COVID-19 was one of the greatest disruptors of the 21st century, causing significant morbidity and mortality globally. Countries around the world adopted digital technologies and innovations to support the containment of the pandemic. This study explored the use of digital technology and barriers to its utilisation in responding to COVID-19 and sustaining essential health services in Uganda to inform response to future public health emergencies in low-resource settings. We reviewed published and grey literature on the use of digital technology in Uganda's response from March 2020 to April 2021 and conducted interviews with key informants. We thematically synthesised and summarised information on digital technology use as well as related challenges. During the COVID-19 response, digital technology was used in testing, contact tracing and surveillance, risk communication, supportive supervision and training, and maintenance of essential health services. The challenges with technology use were the disparate digital tools and health information systems leading to duplication of effort; limited access and coverage of digital tools, poor data quality; inaccessibility of data and an inability to support data manipulation, analysis and visualisation. Moreover, the inherent inadequate technology support systems such as poor internet and electricity infrastructure in some areas posed challenges of inequity. The harnessing of technology was key in supporting the COVID-19 response in Uganda. However, gaps existed in access, adoption, harmonisation, evaluation, sustainability and scale up of technology options. These issues should be addressed in preparedness efforts to foster technology adoption and application in public health emergencies with a focus on equity.
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Affiliation(s)
- Rawlance Ndejjo
- Department of Disease Control and Environmental Health, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Steven Ndugwa Kabwama
- Department of Community Health and Behavioural Sciences, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Alice Namale
- Department of Disease Control and Environmental Health, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Andrew K Tusubira
- Department of Community Health and Behavioural Sciences, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Irene Wanyana
- Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Susan Kizito
- Department of Disease Control and Environmental Health, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Suzanne N Kiwanuka
- Department of Health Policy, Planning and Management, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Rhoda K Wanyenze
- Department of Disease Control and Environmental Health, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda
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5
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Dolatabadi E, Moyano D, Bales M, Spasojevic S, Bhambhoria R, Bhatti J, Debnath S, Hoell N, Li X, Leng C, Nanda S, Saab J, Sahak E, Sie F, Uppal S, Vadlamudi NK, Vladimirova A, Yakimovich A, Yang X, Kocak SA, Cheung AM. Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach. J Med Internet Res 2023; 25:e45767. [PMID: 37725432 PMCID: PMC10510753 DOI: 10.2196/45767] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 05/18/2023] [Accepted: 06/05/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND While scientific knowledge of post-COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians. OBJECTIVE In this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline's potential as a surveillance tool. METHODS We used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several countries. RESULTS UmlsBERT-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuropsychiatric (such as anxiety and brain fog), and respiratory (such as shortness of breath). In addition, we also found novel symptom and condition terms that had not been categorized in previous studies, such as infection and pain. Regarding the co-occurring symptoms, the pair of fatigue and headaches was among the most co-occurring term pairs across both platforms. Based on the temporal analysis, the neuropsychiatric terms were the most prevalent, followed by the systemic category, on both social media platforms. Our spatial analysis concluded that 42% (10,938/26,247) of the analyzed terms included location information, with the majority coming from the United States, United Kingdom, and Canada. CONCLUSIONS The outcome of our social media-derived pipeline is comparable with the results of peer-reviewed articles relevant to PCC symptoms. Overall, this study provides unique insights into patient-reported health outcomes of PCC and valuable information about the patient's journey that can help health care providers anticipate future needs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1101/2022.12.14.22283419.
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Affiliation(s)
- Elham Dolatabadi
- Faculty of Health, School of Health Policy and Management, York University, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
- Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | | | | | - Rohan Bhambhoria
- Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada
| | | | | | | | - Xin Li
- Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | | | - Jad Saab
- TELUS Health, Montreal, QC, Canada
| | - Esmat Sahak
- Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Fanny Sie
- Hoffmann-La Roche Ltd, Toronto, ON, Canada
| | | | - Nirma Khatri Vadlamudi
- Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | | | | | | | | | - Angela M Cheung
- Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
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6
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Ruan Y, Huang T, Zhou W, Zhu J, Liang Q, Zhong L, Tang X, Liu L, Chen S, Xie Y. The lead time and geographical variations of Baidu Search Index in the early warning of COVID-19. Sci Rep 2023; 13:14705. [PMID: 37679512 PMCID: PMC10484897 DOI: 10.1038/s41598-023-41939-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 09/04/2023] [Indexed: 09/09/2023] Open
Abstract
Internet search data was a useful tool in the pre-warning of COVID-19. However, the lead time and indicators may change over time and space with the new variants appear and massive nucleic acid testing. Since Omicron appeared in late 2021, we collected the daily number of cases and Baidu Search Index (BSI) of seven search terms from 1 January to 30 April, 2022 in 12 provinces/prefectures to explore the variation in China. Two search peaks of "COVID-19 epidemic", "Novel Coronavirus" and "COVID-19" can be observed. One in January, which showed 3 days lead time in Henan and Tianjin. Another on early March, which occurred 0-28 days ahead of the local epidemic but the lead time had spatial variation. It was 4 weeks in Shanghai, 2 weeks in Henan and 5-8 days in Jilin Province, Jilin and Changchun Prefecture. But it was only 1-3 days in Tianjin, Quanzhou Prefecture, Fujian Province and 0 day in Shenzhen, Shandong Province, Qingdao and Yanbian Prefecture. The BSI was high correlated (rs:0.70-0.93) to the number of cases with consistent epidemiological change trend. The lead time of BSI had spatial and temporal variation and was close related to the strength of nucleic acid testing. The case detection ability should be strengthened when perceiving BSI increase.
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Affiliation(s)
- Yuhua Ruan
- State Key Laboratory of Infectious Disease Prevention and Control (SKLID), National Center for AIDS/STD Control and Prevention (NCAIDS), Chinese Center for Disease Control and Prevention (China CDC), Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Beijing, China
| | - Tengda Huang
- Department of Epidemiology and Biostatistics, Guangxi Medical University, Nanning, China
| | - Wanwan Zhou
- Department of Epidemiology and Biostatistics, Guangxi Medical University, Nanning, China
| | - Jinhui Zhu
- Guangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, China
| | - Qiuyu Liang
- Department of Health Management, The People's Hospital of Guangxi Zhuang Autonomous Region & Research Center of Health Management, Guangxi Academy of Medical Sciences, Nanning, China
| | - Lixian Zhong
- Department of Epidemiology and Biostatistics, Guangxi Medical University, Nanning, China
| | - Xiaofen Tang
- Department of Epidemiology and Biostatistics, Guangxi Medical University, Nanning, China
| | - Lu Liu
- Department of Epidemiology and Biostatistics, Guangxi Medical University, Nanning, China
| | - Shiwen Chen
- Department of Epidemiology and Biostatistics, Guangxi Medical University, Nanning, China
| | - Yihong Xie
- Department of Epidemiology and Biostatistics, Guangxi Medical University, Nanning, China.
- Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Diseases, Guangxi Medical University, Nanning, China.
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Ansell L, Dalla Valle L. A new data integration framework for Covid-19 social media information. Sci Rep 2023; 13:6170. [PMID: 37061597 PMCID: PMC10105535 DOI: 10.1038/s41598-023-33141-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/07/2023] [Indexed: 04/17/2023] Open
Abstract
The Covid-19 pandemic presents a serious threat to people's health, resulting in over 250 million confirmed cases and over 5 million deaths globally. To reduce the burden on national health care systems and to mitigate the effects of the outbreak, accurate modelling and forecasting methods for short- and long-term health demand are needed to inform government interventions aiming at curbing the pandemic. Current research on Covid-19 is typically based on a single source of information, specifically on structured historical pandemic data. Other studies are exclusively focused on unstructured online retrieved insights, such as data available from social media. However, the combined use of structured and unstructured information is still uncharted. This paper aims at filling this gap, by leveraging historical and social media information with a novel data integration methodology. The proposed approach is based on vine copulas, which allow us to exploit the dependencies between different sources of information. We apply the methodology to combine structured datasets retrieved from official sources and a big unstructured dataset of information collected from social media. The results show that the combined use of official and online generated information contributes to yield a more accurate assessment of the evolution of the Covid-19 pandemic, compared to the sole use of official data.
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Affiliation(s)
- Lauren Ansell
- School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, PL48AA, UK
| | - Luciana Dalla Valle
- School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, PL48AA, UK.
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8
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Adulyasak Y, Benomar O, Chaouachi A, Cohen MC, Khern-am-nuai W. Using AI to detect panic buying and improve products distribution amid pandemic. AI & SOCIETY 2023:1-30. [PMID: 37358947 PMCID: PMC10105357 DOI: 10.1007/s00146-023-01654-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 03/29/2023] [Indexed: 06/28/2023]
Abstract
The COVID-19 pandemic has triggered panic-buying behavior around the globe. As a result, many essential supplies were consistently out-of-stock at common point-of-sale locations. Even though most retailers were aware of this problem, they were caught off guard and are still lacking the technical capabilities to address this issue. The primary objective of this paper is to develop a framework that can systematically alleviate this issue by leveraging AI models and techniques. We exploit both internal and external data sources and show that using external data enhances the predictability and interpretability of our model. Our data-driven framework can help retailers detect demand anomalies as they occur, allowing them to react strategically. We collaborate with a large retailer and apply our models to three categories of products using a dataset with more than 15 million observations. We first show that our proposed anomaly detection model can successfully detect anomalies related to panic buying. We then present a prescriptive analytics simulation tool that can help retailers improve essential product distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help retailers increase access to essential products by 56.74%.
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Affiliation(s)
- Yossiri Adulyasak
- HEC Montreal, 3000, Chemin de La Cote-Sainte-Catherine, Montreal, QC H3T 2A7 Canada
| | - Omar Benomar
- IVADO Labs, 6795 Rue Marconi #200, Montreal, QC H2S 3J9 Canada
| | - Ahmed Chaouachi
- IVADO Labs, 6795 Rue Marconi #200, Montreal, QC H2S 3J9 Canada
| | - Maxime C. Cohen
- IVADO Labs, 6795 Rue Marconi #200, Montreal, QC H2S 3J9 Canada
| | - Warut Khern-am-nuai
- Desautels Faculty of Management, McGill University, 1001 Rue Sherbrooke O., Montreal, QC H3A 1G5 Canada
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Ye Q, Ozbay K, Zuo F, Chen X. Impact of Social Media on Travel Behaviors during the COVID-19 Pandemic: Evidence from New York City. TRANSPORTATION RESEARCH RECORD 2023; 2677:219-238. [PMID: 37153201 PMCID: PMC10149522 DOI: 10.1177/03611981211033857] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
During the outbreak of COVID-19, people's reliance on social media for pandemic-related information exchange, daily communications, and online professional interactions increased because of self-isolation and lockdown implementation. Most of the published research addresses the performance of nonpharmaceutical interventions (NPIs) and measures on the issues impacted by COVID-19, such as health, education, and public safety; however, not much is known about the interplay between social media use and travel behaviors. This study aims to determine the effect of social media on human mobility before and after the COVID-19 outbreak, and its impact on personal vehicle and public transit use in New York City (NYC). Apple mobility trends and Twitter data are used as two data sources. The results indicate that Twitter volume and mobility trend correlations are negative for both driving and transit categories in general, especially at the beginning of the COVID-19 outbreak in NYC. A significant time lag (13 days) between the online communication rise and mobility drop can be observed, thereby providing evidence of social networks taking quicker reactions to the pandemic than the transportation system. In addition, social media and government policies had different impacts on vehicular traffic and public transit ridership during the pandemic with varied performance. This study provides insights on the complex influence of both anti-pandemic measures and user-generated content, namely social media, on people's travel decisions during pandemics. The empirical evidence can help decision-makers formulate timely emergency responses, prepare targeted traffic intervention policies, and conduct risk management in similar outbreaks in the future.
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Affiliation(s)
- Qian Ye
- Key Laboratory of Road and Traffic
Engineering of the Ministry of Education, College of Transportation Engineering,
Tongji University, Shanghai, China
- Transport Planning and Research
Institute of Ministry of Transport P.R. China, Beijing, China
| | - Kaan Ozbay
- C2SMART Center, Department of Civil and
Urban Engineering, Tandon School of Engineering, New York University, Brooklyn,
NY
- Center for Urban Science and Progress
(CUSP), Tandon School of Engineering, New York University, Brooklyn, NY
| | - Fan Zuo
- C2SMART Center, Department of Civil and
Urban Engineering, Tandon School of Engineering, New York University, Brooklyn,
NY
| | - Xiaohong Chen
- Key Laboratory of Road and Traffic
Engineering of the Ministry of Education, College of Transportation Engineering,
Tongji University, Shanghai, China
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10
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Dai S, Han L. Influenza surveillance with Baidu index and attention-based long short-term memory model. PLoS One 2023; 18:e0280834. [PMID: 36689543 PMCID: PMC9870163 DOI: 10.1371/journal.pone.0280834] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 01/10/2023] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND The prediction and prevention of influenza is a public health issue of great concern, and the study of timely acquisition of influenza transmission trend has become an important research topic. For achieving more quicker and accurate detection and prediction, the data recorded on the Internet, especially on the search engine from Google or Baidu are widely introduced into this field. Moreover, with the development of intelligent technology and machine learning algorithm, many updated and advanced trend tracking and forecasting methods are also being used in this research problem. METHODS In this paper, a new recurrent neural network architecture, attention-based long short-term memory model is proposed for influenza surveillance. This is a kind of deep learning model which is trained by processing from Baidu Index series so as to fit the real influenza survey time series. Previous studies on influenza surveillance by Baidu Index mostly used traditional autoregressive moving average model or classical machine learning models such as logarithmic linear regression, support vector regression or multi-layer perception model to fit influenza like illness data, which less considered the deep learning structure. Meanwhile, some new model that considered the deep learning structure did not take into account the application of Baidu index data. This study considers introducing the recurrent neural network with long short-term memory combined with attention mechanism into the influenza surveillance research model, which not only fits the research problems well in model structure, but also provides research methods based on Baidu index. RESULTS The actual survey data and Baidu Index data are used to train and test the proposed attention-based long short-term memory model and the other comparison models, so as to iterate the value of the model parameters, and to describe and predict the influenza epidemic situation. The experimental results show that our proposed model has better performance in the mean absolute error, mean absolute percentage error, index of agreement and other indicators than the other comparison models. CONCLUSION Our proposed attention-based long short-term memory model vividly verifies the ability of this attention-based long short-term memory structure for better surveillance and prediction the trend of influenza. In comparison with some of the latest models and methods in this research field, the model we proposed is also excellent in effect, even more lightweight and robust. Future research direction can consider fusing multimodal data based on this model and developing more application scenarios.
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Affiliation(s)
- Shangfang Dai
- School of Economics and Management, Tsinghua University, Beijing, China
| | - Litao Han
- School of Mathematics, Renmin University of China, Beijing, China
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Khademi Habibabadi S, Palmer C, Dimaguila GL, Javed M, Clothier HJ, Buttery J. Australasian Institute of Digital Health Summit 2022-Automated Social Media Surveillance for Detection of Vaccine Safety Signals: A Validation Study. Appl Clin Inform 2023; 14:1-10. [PMID: 36351547 PMCID: PMC9812583 DOI: 10.1055/a-1975-4061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 10/25/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Social media platforms have emerged as a valuable data source for public health research and surveillance. Monitoring of social media and user-generated data on the Web enables timely and inexpensive collection of information, overcoming time lag and cost of traditional health reporting systems. OBJECTIVES This article identifies personally experienced coronavirus disease 2019 (COVID-19) vaccine reactions expressed on Twitter and validate the findings against an established vaccine reactions reporting system. METHODS We collected around 3 million tweets from 1.4 million users between February 1, 2021, to January 31, 2022, using COVID-19 vaccines and vaccine reactions keyword lists. We performed topic modeling on a sample of the data and applied a modified F1 scoring technique to identify a topic that best differentiated vaccine-related personal health mentions. We then manually annotated 4,000 of the records from this topic, which were used to train a transformer-based classifier to identify likely personally experienced vaccine reactions. Applying the trained classifier to the entire data set allowed us to select records we could use to quantify potential vaccine side effects. Adverse events following immunization (AEFI) referred to in these records were compared with those reported to the state of Victoria's spontaneous vaccine safety surveillance system, SAEFVIC (Surveillance of Adverse Events Following Vaccination In the Community). RESULTS The most frequently mentioned potential vaccine reactions generally aligned with SAEFVIC data. Notable exceptions were increased Twitter reporting of bleeding-related AEFI and allergic reactions, and more frequent SAEFVIC reporting of cardiac AEFI. CONCLUSION Social media conversations are a potentially valuable supplementary data source for detecting vaccine adverse event mentions. Monitoring of online observations about new vaccine-related personal health experiences has the capacity to provide early warnings about emerging vaccine safety issues.
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Affiliation(s)
- Sedigheh Khademi Habibabadi
- Department of Paediatrics, Centre for Health Analytics, Murdoch Children's Research Institute Melbourne, Australia
- Department of Paediatrics, Centre for Health Analytics, Health Informatics Group, Murdoch Children's Research Institute, Melbourne, Australia
- Department of General Practice, University of Melbourne, Melbourne, Australia
| | - Christopher Palmer
- Department of Paediatrics, Centre for Health Analytics, Health Informatics Group, Murdoch Children's Research Institute, Melbourne, Australia
| | - Gerardo L. Dimaguila
- Department of Paediatrics, Centre for Health Analytics, Murdoch Children's Research Institute Melbourne, Australia
| | - Muhammad Javed
- Department of Paediatrics, Centre for Health Analytics, Health Informatics Group, Murdoch Children's Research Institute, Melbourne, Australia
| | - Hazel J. Clothier
- Department of Paediatrics, Centre for Health Analytics, Murdoch Children's Research Institute Melbourne, Australia
- Department of Paediatrics, Infectious Diseases Group, SAEFVIC, Murdoch Children's Research Institute, Melbourne, Australia
- Faculty of Medicine, Dentistry, and Health Sciences, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Jim Buttery
- Department of Paediatrics, Centre for Health Analytics, Murdoch Children's Research Institute Melbourne, Australia
- Department of Paediatrics, Infectious Diseases Group, SAEFVIC, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
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12
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Basch C, Eysenbach G, Nakayama Y, Suda T, Uno T, Hashimoto T, Toyoda M, Yoshinaga N, Kitsuregawa M, Rocha LEC. Evolution of Public Opinion on COVID-19 Vaccination in Japan: Large-Scale Twitter Data Analysis. J Med Internet Res 2022; 24:e41928. [PMID: 36343186 PMCID: PMC9856430 DOI: 10.2196/41928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/09/2022] [Accepted: 10/24/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Vaccines are promising tools to control the spread of COVID-19. An effective vaccination campaign requires government policies and community engagement, sharing experiences for social support, and voicing concerns about vaccine safety and efficiency. The increasing use of online social platforms allows us to trace large-scale communication and infer public opinion in real time. OBJECTIVE This study aimed to identify the main themes in COVID-19 vaccine-related discussions on Twitter in Japan and track how the popularity of the tweeted themes evolved during the vaccination campaign. Furthermore, we aimed to understand the impact of critical social events on the popularity of the themes. METHODS We collected more than 100 million vaccine-related tweets written in Japanese and posted by 8 million users (approximately 6.4% of the Japanese population) from January 1 to October 31, 2021. We used Latent Dirichlet Allocation to perform automated topic modeling of tweet text during the vaccination campaign. In addition, we performed an interrupted time series regression analysis to evaluate the impact of 4 critical social events on public opinion. RESULTS We identified 15 topics grouped into the following 4 themes: (1) personal issue, (2) breaking news, (3) politics, and (4) conspiracy and humor. The evolution of the popularity of themes revealed a shift in public opinion, with initial sharing of attention over personal issues (individual aspect), collecting information from news (knowledge acquisition), and government criticism to focusing on personal issues. Our analysis showed that the Tokyo Olympic Games affected public opinion more than other critical events but not the course of vaccination. Public opinion about politics was significantly affected by various social events, positively shifting attention in the early stages of the vaccination campaign and negatively shifting attention later. CONCLUSIONS This study showed a striking shift in public interest in Japan, with users splitting their attention over various themes early in the vaccination campaign and then focusing only on personal issues, as trust in vaccines and policies increased. An interrupted time series regression analysis showed that the vaccination rollout to the general population (under 65 years) increased the popularity of tweets about practical advice and personal vaccination experience, and the Tokyo Olympic Games disrupted public opinion but not the course of the vaccination campaign. The methodology developed here allowed us to monitor the evolution of public opinion and evaluate the impact of social events on public opinion, using large-scale Twitter data.
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Affiliation(s)
| | | | - Yuri Nakayama
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Towa Suda
- Principles of Informatics Research Division, National Institute of Informatics, Tokyo, Japan
| | - Takeaki Uno
- Principles of Informatics Research Division, National Institute of Informatics, Tokyo, Japan
| | - Takako Hashimoto
- Faculty of Commerce and Economics, Chiba University of Commerce, Ichikawa, Japan
| | - Masashi Toyoda
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Naoki Yoshinaga
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Masaru Kitsuregawa
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.,National Institute of Informatics, Tokyo, Japan
| | - Luis E C Rocha
- Department of Economics, Ghent University, Ghent, Belgium.,Department of Physics and Astronomy, Ghent University, Ghent, Belgium
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13
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Twitter conversations predict the daily confirmed COVID-19 cases. Appl Soft Comput 2022; 129:109603. [PMID: 36092470 PMCID: PMC9444159 DOI: 10.1016/j.asoc.2022.109603] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 08/03/2022] [Accepted: 08/22/2022] [Indexed: 12/19/2022]
Abstract
As of writing this paper, COVID-19 (Coronavirus disease 2019) has spread to more than 220 countries and territories. Following the outbreak, the pandemic’s seriousness has made people more active on social media, especially on the microblogging platforms such as Twitter and Weibo. The pandemic-specific discourse has remained on-trend on these platforms for months now. Previous studies have confirmed the contributions of such socially generated conversations towards situational awareness of crisis events. The early forecasts of cases are essential to authorities to estimate the requirements of resources needed to cope with the outgrowths of the virus. Therefore, this study attempts to incorporate the public discourse in the design of forecasting models particularly targeted for the steep-hill region of an ongoing wave. We propose a sentiment-involved topic-based latent variables search methodology for designing forecasting models from publicly available Twitter conversations. As a use case, we implement the proposed methodology on Australian COVID-19 daily cases and Twitter conversations generated within the country. Experimental results: (i) show the presence of latent social media variables that Granger-cause the daily COVID-19 confirmed cases, and (ii) confirm that those variables offer additional prediction capability to forecasting models. Further, the results show that the inclusion of social media variables introduces 48.83%–51.38% improvements on RMSE over the baseline models. We also release the large-scale COVID-19 specific geotagged global tweets dataset, MegaGeoCOV, to the public anticipating that the geotagged data of this scale would aid in understanding the conversational dynamics of the pandemic through other spatial and temporal contexts.
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14
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THE COVID-19 PANDEMIC AND ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN HEALTH: HOW MUCH ARE WE INTERESTED IN? JOURNAL OF BASIC AND CLINICAL HEALTH SCIENCES 2022. [DOI: 10.30621/jbachs.984596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Objective New viruses have emerged, causing global damage and mass deaths that can spread to international borders, the latest of which is the new coronavirus (COVID-19). After the Second International Congress on Artificial Intelligence in Health, themed "Artificial Intelligence in Health During COVID-19 Pandemic Process" organized online by İzmir Bakırçay University and İzmir Provincial Health Directorate with the contributions of the International Association of Artificial Intelligence in Health, a questionnaire was conducted to evaluate the knowledge of the participants about artificial intelligence applications.
Materials and Methods: This study aimed to evaluate the interest of the congress participants in this field with the questions which form the questionnaire such as the duration of the interest of the participants in the field of artificial intelligence in health, their publication status, the development of studies on artificial intelligence with the COVID-19 pandemic, demographic structures such as age and gender, and educational level. 130 participants answered the questionnaire consisting of 23 questions. Questionnaire responses were analyzed in a statistical setting.
Results: We found that 130 people filled out the questionnaire and the majority of the participants were female, with participation from many organizations, but university staff showed more interest. We have seen that the 30-39 age group is more interested in artificial intelligence than the other age groups, but the majority of the participants do not have academic studies in this field. We found that the technical terms related to artificial intelligence were not well known by the participants, and that the number of participants who tended to this field, especially in the recent year, was high. Another important point was that people working in this field stated that they would definitely follow up if scientific activities continued.
Conclusion: We know how important congresses, symposiums, courses and other meetings are, especially for scientist candidates, which will be held to raise awareness about the usage areas of artificial intelligence-based health technologies, to develop new communication and work networks by bringing together different disciplines, to create an agenda and to lay the groundwork for new studies, and we think that there is a need for many repetitive activities in this field and that these activities should be continued.
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15
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Huang X, Wang S, Zhang M, Hu T, Hohl A, She B, Gong X, Li J, Liu X, Gruebner O, Liu R, Li X, Liu Z, Ye X, Li Z. Social media mining under the COVID-19 context: Progress, challenges, and opportunities. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION : ITC JOURNAL 2022; 113:102967. [PMID: 36035895 PMCID: PMC9391053 DOI: 10.1016/j.jag.2022.102967] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 06/17/2022] [Accepted: 08/05/2022] [Indexed: 05/21/2023]
Abstract
Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and reproducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies.
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Affiliation(s)
- Xiao Huang
- Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Siqin Wang
- School of Earth Environmental Sciences, University of Queensland, Brisbane, Queensland 4076, Australia
| | - Mengxi Zhang
- Department of Nutrition and Health Science, Ball State University, Muncie, IN 47304, USA
| | - Tao Hu
- Department of Geography, Oklahoma State University, Stillwater, OK 74078, USA
| | - Alexander Hohl
- Department of Geography, The University of Utah, Salt Lake City, UT 84112, USA
| | - Bing She
- Institute for social research, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xi Gong
- Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA
| | - Jianxin Li
- School of Information Technology, Deakin University, Geelong, Victoria 3220, Australia
| | - Xiao Liu
- School of Information Technology, Deakin University, Geelong, Victoria 3220, Australia
| | - Oliver Gruebner
- Department of Geography, University of Zurich, Zürich CH-8006, Switzerland
| | - Regina Liu
- Department of Biology, Mercer University, Macon, GA 31207, USA
| | - Xiao Li
- Texas A&M Transportation Institute, Bryan, TX 77807, USA
| | - Zhewei Liu
- Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Xinyue Ye
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77840, USA
| | - Zhenlong Li
- Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC 29208, USA
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16
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Ethical Considerations in the Application of Artificial Intelligence to Monitor Social Media for COVID-19 Data. Minds Mach (Dordr) 2022; 32:759-768. [PMID: 36042870 PMCID: PMC9406274 DOI: 10.1007/s11023-022-09610-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 08/04/2022] [Indexed: 10/27/2022]
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17
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A novel approach for COVID-19 Infection forecasting based on multi-source deep transfer learning. Comput Biol Med 2022; 149:105915. [PMID: 36063688 PMCID: PMC9354391 DOI: 10.1016/j.compbiomed.2022.105915] [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: 01/30/2022] [Revised: 07/10/2022] [Accepted: 07/23/2022] [Indexed: 11/28/2022]
Abstract
COVID-19 is a contagious disease; so, predicting its future infections in a provincial region requires the consideration of the related data (i.e., rates of infection, mortality and recovery, etc.) over a period of time. Clearly, the COVID-19 data of a particular provincial region can be easily modelled as a time-series. However, predicting the future COVID-19 infections in a particular region is quite challenging when the availability of COVID-19 dataset of the province is of little quantity. Accordingly, ML models when deployed for such tasks usually results in low infection prediction accuracy. To overcome such issues of low variance and high bias in a model due to data scarcity, multi-source transfer learning (MSTL) along with deep learning may be quite useful and effective. Therefore, this paper proposes a novel technique based on multi-source deep transfer learning (MSDTL) to efficiently forecast the future COVID-19 infections in the provinces with insufficient COVID-19 data. The proposed approach is a novel contribution as it considers the fact that future COVID-19 transmission in a region also depends on its population density and economic conditions (GDP) for accurate forecasting of the infections to tackle the pandemic efficiently. The importance of this feature selection is experimentally proved in this paper. Our proposed approach employs the well-known recurrent neural network architecture, the Long-short term memory (LSTM), a popular deep-learning model for history-dependent tasks. A comparative analysis has been performed with existing state-of-art algorithms to portray the efficiency of LSTM. Thus, formation of MSDTL approach enhances the predictive precision capability of the LSTM. We evaluate the proposed methodology over the COVID-19 dataset from sixty-two provinces belonging to different nations. We then empirically evaluate the performance of the proposed approach using two different evaluation metrics, viz. The mean absolute percentage error and the coefficient of determination. We show that our proposed MSDTL based approach is better in terms of the accuracy of the future infection prediction, and produces improvements up to 96% over its without-TL counterpart.
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18
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Dasgupta A, Bakshi A, Mukherjee S, Das K, Talukdar S, Chatterjee P, Mondal S, Das P, Ghosh S, Som A, Roy P, Kundu R, Sarkar A, Biswas A, Paul K, Basak S, Manna K, Saha C, Mukhopadhyay S, Bhattacharyya NP, De RK. Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2022; 12:e1462. [PMID: 35942397 PMCID: PMC9350133 DOI: 10.1002/widm.1462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 03/28/2022] [Accepted: 04/28/2022] [Indexed: 05/02/2023]
Abstract
World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug-protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. This article is categorized under:Application Areas > Health CareAlgorithmic Development > Biological Data MiningTechnologies > Machine Learning.
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Affiliation(s)
- Abhijit Dasgupta
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Abhisek Bakshi
- Department of Information TechnologyBengal Institute of TechnologyKolkataWest BengalIndia
| | - Srijani Mukherjee
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Kuntal Das
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Soumyajeet Talukdar
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Pratyayee Chatterjee
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Sagnik Mondal
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Puspita Das
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Subhrojit Ghosh
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Archisman Som
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Pritha Roy
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Rima Kundu
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Akash Sarkar
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Arnab Biswas
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Karnelia Paul
- Department of BiotechnologyUniversity of CalcuttaKolkataWest BengalIndia
| | - Sujit Basak
- Department of Physiology and BiophysicsStony Brook UniversityStony BrookNew YorkUSA
| | - Krishnendu Manna
- Department of Food and NutritionUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Chinmay Saha
- Department of Genome Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Satinath Mukhopadhyay
- Department of Endocrinology and MetabolismInstitute of Post Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial HospitalKolkataWest BengalIndia
| | - Nitai P. Bhattacharyya
- Department of Endocrinology and MetabolismInstitute of Post Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial HospitalKolkataWest BengalIndia
| | - Rajat K. De
- Machine Intelligence UnitIndian Statistical InstituteKolkataWest BengalIndia
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The Built Environment Assessment of Residential Areas in Wuhan during the Coronavirus Disease (COVID-19) Outbreak. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19137814. [PMID: 35805475 PMCID: PMC9266129 DOI: 10.3390/ijerph19137814] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 02/04/2023]
Abstract
The COVID-19 epidemic has emerged as one of the biggest challenges, and the world is focused on preventing and controlling COVID-19. Although there is still insufficient understanding of how environmental conditions may impact the COVID-19 pandemic, airborne transmission is regarded as an important environmental factor that influences the spread of COVID-19. The natural ventilation potential (NVP) is critical for airborne infection control in the micro-built environment, where infectious and susceptible people share air spaces. Taking Wuhan as the research area, we evaluated the NVP in residential areas to combat COVID-19 during the outbreak. We determined four fundamental residential area layouts (point layout, parallel layout, center-around layout, and mixed layout) based on the semantic similarity model for point of interest (POI) picking. Our analyses indicated that the center-around and point layout had a higher NVP, while the mixed and parallel layouts had a lower NVP in winter and spring. Further analysis showed that the proportion of the worst NVP has been rising, while the proportion of the poor NVP remains very high in Wuhan. This study suggested the need to efficiently improve the residential area layout in Wuhan for better urban ventilation to combat COVID-19 without losing other benefits.
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Exploring the Relationship among Human Activities, COVID-19 Morbidity, and At-Risk Areas Using Location-Based Social Media Data: Knowledge about the Early Pandemic Stage in Wuhan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19116523. [PMID: 35682104 PMCID: PMC9180261 DOI: 10.3390/ijerph19116523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/23/2022] [Accepted: 05/25/2022] [Indexed: 12/12/2022]
Abstract
It is significant to explore the morbidity patterns and at-risk areas of the COVID-19 outbreak in megacities. In this paper, we studied the relationship among human activities, morbidity patterns, and at-risk areas in Wuhan City. First, we excavated the activity patterns from Sina Weibo check-in data during the early COVID-19 pandemic stage (December 2019~January 2020) in Wuhan. We considered human-activity patterns and related demographic information as the COVID-19 influencing determinants, and we used spatial regression models to evaluate the relationships between COVID-19 morbidity and the related factors. Furthermore, we traced Weibo users’ check-in trajectories to characterize the spatial interaction between high-morbidity residential areas and activity venues with POI (point of interest) sites, and we located a series of potential at-risk places in Wuhan. The results provide statistical evidence regarding the utility of human activity and demographic factors for the determination of COVID-19 morbidity patterns in the early pandemic stage in Wuhan. The spatial interaction revealed a general transmission pattern in Wuhan and determined the high-risk areas of COVID-19 transmission. This article explores the human-activity characteristics from social media check-in data and studies how human activities played a role in COVID-19 transmission in Wuhan. From that, we provide new insights for scientific prevention and control of COVID-19.
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Rizaldi AA, Xie S, Hubbard RA, Himes BE. Neighborhood Characteristics and COVID-19 Incidence and Mortality in Southeastern Pennsylvania. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2022:422-431. [PMID: 35854746 PMCID: PMC9285166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The COVID-19 pandemic has differentially impacted people according to their race/ethnicity, socioeconomic status, and preexisting conditions. Public health surveillance efforts, especially those occurring early in the pandemic, did not gather nor report adequate individual-level demographic information to identify these differences, and thus, neighborhood-level characteristics were used to note striking disparities in the US. We sought to determine whether risk factors associated with COVID-19 incidence and mortality in five Southeastern Pennsylvania counties could be better understood by using neighborhood-level demographic data augmented with health, socioeconomic, and environmental characteristics derived from publicly available sources. Although we found that education level and age of residents were the most salient predictors of COVID-19 incidence and mortality, respectively, neighborhoods exhibited a high degree of segregation with multiple correlated factors, which limits the ability of neighborhood-level analysis to identify actionable factors underlying COVID-19 disparities.
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22
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Li J, Huang W, Sia CL, Chen Z, Wu T, Wang Q. Enhancing COVID-19 Epidemics Forecasting Accuracy by Combining Real-time and Historical Data from Multiple Internet-based Sources: Analysis of Social Media Data, Online News Articles, and Search Queries. JMIR Public Health Surveill 2022; 8:e35266. [PMID: 35507921 PMCID: PMC9205424 DOI: 10.2196/35266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/12/2022] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is the key to sustaining interventions and policies and efficient resources allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored. OBJECTIVE The main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources. METHODS We first used core terms and symptoms-related keywords-based methods to extract COVID-19 related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating the real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used the lagged Pearson correlations for the COVID-19 forecasting timeliness analysis. RESULTS Our proposed model achieved the highest accuracy in all the five accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In the mainland China except for Hubei, the COVID-19 epidemics forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t=-8.722, P<.001; model 2, t=-5.000, P<.001, model 3, t=-1.882, P =0.063, model 4, t=-4.644, P<.001; model 5, t=-4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical COVID-19 new confirmed case counts only (model 1, t=-1.732, P=0.086). Our results also showed that Internet-based sources could provide a 2-6 days earlier warning for COVID-19 outbreaks. CONCLUSIONS Our approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for COVID-19 epidemics and its variants, which may help improve public health agencies' interventions and resources allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics. CLINICALTRIAL
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Affiliation(s)
- Jingwei Li
- School of Management, Xi'an Jiaotong University, Xi'an, CN.,Department of Information Systems, City University of Hong Kong, Hong Kong, HK
| | - Wei Huang
- College of Business, Southern University of Science and Technology, No. 1088, Xueyuan Avenue, Nanshan District, Shenzhen, CN.,School of Management, Xi'an Jiaotong University, Xi'an, CN
| | - Choon Ling Sia
- Department of Information Systems, City University of Hong Kong, Hong Kong, HK
| | - Zhuo Chen
- College of Public Health, University of Georgia, Athens, US.,School of Economics, University of Nottingham Ningbo China, Ningbo, CN
| | - Tailai Wu
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, CN
| | - Qingnan Wang
- School of Management, Xi'an Jiaotong University, Xi'an, CN
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Ding W, Wang QG, Zhang JX. Analysis and prediction of COVID-19 epidemic in South Africa. ISA TRANSACTIONS 2022; 124:182-190. [PMID: 33551132 PMCID: PMC7842146 DOI: 10.1016/j.isatra.2021.01.050] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 12/01/2020] [Accepted: 01/25/2021] [Indexed: 06/12/2023]
Abstract
The coronavirus disease-2019 (COVID-19) has been spreading rapidly in South Africa (SA) since its first case on 5 March 2020. In total, 674,339 confirmed cases and 16,734 mortality cases were reported by 30 September 2020, and this pandemic has made severe impacts on economy and life. In this paper, analysis and long-term prediction of the epidemic dynamics of SA are made, which could assist the government and public in assessing the past Infection Prevention and Control Measures and designing the future ones to contain the epidemic more effectively. A Susceptible-Infectious-Recovered model is adopted to analyse epidemic dynamics. The model parameters are estimated over different phases with the SA data. They indicate variations in the transmissibility of COVID-19 under different phases and thus reveal weakness of the past Infection Prevention and Control Measures in SA. The model also shows that transient behaviours of the daily growth rate and the cumulative removal rate exhibit periodic oscillations. Such dynamics indicates that the underlying signals are not stationary and conventional linear and nonlinear models would fail for long-term prediction. Therefore, a large class of mappings with rich functions and operations is chosen as the model class and the evolutionary algorithm is utilized to obtain the optimal model for long term prediction. The resulting models on the daily growth rate, the cumulative removal rate and the cumulative mortality rate predict that the peak and inflection point will occur on November 4, 2020 and October 15, 2020, respectively; the virus shall cease spreading on April 28, 2021; and the ultimate numbers of the COVID-19 cases and mortality cases will be 785,529 and 17,072, respectively. The approach is also benchmarked against other methods and shows better accuracy of long-term prediction.
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Affiliation(s)
- Wei Ding
- Faculty of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu, 215500, PR China; Institute for Intelligent Systems, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, 2006, South Africa
| | - Qing-Guo Wang
- Institute of Artificial Intelligence and Future Networks, Beijing Normal University at Zhuhai; BNU-HKBU United International College, Zhuhai, 519000, PR China.
| | - Jin-Xi Zhang
- State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, PR China
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24
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Awan TM, Aziz M, Sharif A, Ch TR, Jasam T, Alvi Y. Fake news during the pandemic times: A Systematic Literature Review using PRISMA. OPEN INFORMATION SCIENCE 2022. [DOI: 10.1515/opis-2022-0131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
The purpose of this systematic literature review is to review the major studies about misinformation and fake news during COVID-19 on social media. A total of 144 articles studies were retrieved from ScienceDirect, Scopus, and Web of Science databases and 20 relevant articles were selected using the PRISMA technique. It was found that altruism, instant news sharing, self-promotion, and socialization are predictors of fake news sharing. Furthermore, the human mind plays a significant role in spreading misinformation while the role of critical thinking of individuals is very much important in controlling the flow of misinformation.
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Affiliation(s)
| | - Mahroz Aziz
- COMSATS University Islamabad , Islamabad , Federal Pakistan
| | - Aruba Sharif
- COMSATS University Islamabad , Islamabad , Federal Pakistan
| | | | - Taha Jasam
- COMSATS University Islamabad , Islamabad , Federal Pakistan
| | - Yusra Alvi
- COMSATS University Islamabad , Islamabad , Federal Pakistan
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25
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Nagpal S, Pal R, Ashima, Tyagi A, Tripathi S, Nagori A, Ahmad S, Mishra HP, Malhotra R, Kutum R, Sethi T. Genomic Surveillance of COVID-19 Variants With Language Models and Machine Learning. Front Genet 2022; 13:858252. [PMID: 35464852 PMCID: PMC9024110 DOI: 10.3389/fgene.2022.858252] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/14/2022] [Indexed: 12/23/2022] Open
Abstract
The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present Strainflow, a supervised and causally predictive model using unsupervised latent space features of SARS-CoV-2 genome sequences. Strainflow was trained and validated on 0.9 million sequences for the period December, 2019 to June, 2021 and the frozen model was prospectively validated from July, 2021 to December, 2021. Strainflow captured the rise in cases 2 months ahead of the Delta and Omicron surges in most countries including the prediction of a surge in India as early as beginning of November, 2021. Entropy analysis of Strainflow unsupervised embeddings clearly reveals the explore-exploit cycles in genomic feature-space, thus adding interpretability to the deep learning based model. We also conducted codon-level analysis of our model for interpretability and biological validity of our unsupervised features. Strainflow application is openly available as an interactive web-application for prospective genomic surveillance of COVID-19 across the globe.
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26
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A Suggestion on the LDA-Based Topic Modeling Technique Based on ElasticSearch for Indexing Academic Research Results. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12063118] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Most academic researchers use the academic information system when they want to write a reference, such as a related research for a paper. Specific classification rules are applied based on vast amounts of data and the latest references to classify and search keywords. Meta information is designed for specific classification rules and search results are restructured. The search results can be classified and rearranged to suit academic research paper keywords by applying the restructured classification system and the LDA-based topic modeling technique. To implement this, the ElasticSearch classification method and topic-based LDA model were applied to extract the characteristics of academic papers in this study. Stable topics that could detect topic estimation and keyword search results within the minimum time were extracted to classify the paper search results. In addition, by analyzing the distribution of document weight among topics, the system performance was proven to be excellent.
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27
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Tsao SF, MacLean A, Chen H, Li L, Yang Y, Butt ZA. Public Attitudes During the Second Lockdown: Sentiment and Topic Analyses Using Tweets From Ontario, Canada. Int J Public Health 2022; 67:1604658. [PMID: 35264920 PMCID: PMC8900133 DOI: 10.3389/ijph.2022.1604658] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 02/03/2022] [Indexed: 12/23/2022] Open
Abstract
Objective: This study aimed to explore topics and sentiments using tweets from Ontario, Canada, during the second wave of the COVID-19 pandemic. Methods: Tweets were collected from December 5, 2020, to March 6, 2021, excluding non-individual accounts. Dates of vaccine-related events and policy changes were collected from public health units in Ontario. The daily number of COVID-19 cases was retrieved from the Ontario provincial government’s public health database. Latent Dirichlet Allocation was used for unsupervised topic modelling. VADER was used to calculate daily and average sentiment compound scores for topics identified. Results: Vaccine, pandemic, business, lockdown, mask, and Ontario were six topics identified from the unsupervised topic modelling. The average sentiment compound score for each topic appeared to be slightly positive, yet the daily sentiment compound scores varied greatly between positive and negative emotions for each topic. Conclusion: Our study results have shown a slightly positive sentiment on average during the second wave of the COVID-19 pandemic in Ontario, along with six topics. Our research has also demonstrated a social listening approach to identify what the public sentiments and opinions are in a timely manner.
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Affiliation(s)
- Shu-Feng Tsao
- School of Public Health SciencesUniversity of Waterloo, Waterloo, ON, Canada
| | - Alexander MacLean
- Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Helen Chen
- School of Public Health SciencesUniversity of Waterloo, Waterloo, ON, Canada
| | - Lianghua Li
- Faculty of Science, University of Waterloo, Waterloo, ON, Canada
| | - Yang Yang
- School of Public Health SciencesUniversity of Waterloo, Waterloo, ON, Canada
| | - Zahid Ahmad Butt
- School of Public Health SciencesUniversity of Waterloo, Waterloo, ON, Canada
- *Correspondence: Zahid Ahmad Butt,
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Hasan A, Levene M, Weston D, Fromson R, Koslover N, Levene T. Monitoring Covid-19 on social media using a novel triage and diagnosis approach. J Med Internet Res 2022; 24:e30397. [PMID: 35142636 PMCID: PMC8887561 DOI: 10.2196/30397] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/09/2021] [Accepted: 02/05/2022] [Indexed: 12/23/2022] Open
Abstract
Background The COVID-19 pandemic has created a pressing need for integrating information from disparate sources in order to assist decision makers. Social media is important in this respect; however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. Here, we adopt a triage and diagnosis approach to analyzing social media posts using machine learning techniques for the purpose of disease detection and surveillance. We thus obtain useful prevalence and incidence statistics to identify disease symptoms and their severities, motivated by public health concerns. Objective This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts in order to provide researchers and public health practitioners with additional information on the symptoms, severity, and prevalence of the disease rather than to provide an actionable decision at the individual level. Methods The text processing pipeline first extracted COVID-19 symptoms and related concepts, such as severity, duration, negations, and body parts, from patients’ posts using conditional random fields. An unsupervised rule-based algorithm was then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations were subsequently used to construct 2 different vector representations of each post. These vectors were separately applied to build support vector machine learning models to triage patients into 3 categories and diagnose them for COVID-19. Results We reported macro- and microaveraged F1 scores in the range of 71%-96% and 61%-87%, respectively, for the triage and diagnosis of COVID-19 when the models were trained on human-labeled data. Our experimental results indicated that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. In addition, we highlighted important features uncovered by our diagnostic machine learning models and compared them with the most frequent symptoms revealed in another COVID-19 data set. In particular, we found that the most important features are not always the most frequent ones. Conclusions Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from social media natural language narratives, using a machine learning pipeline in order to provide information on the severity and prevalence of the disease for use within health surveillance systems.
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Affiliation(s)
- Abul Hasan
- Birkbeck, University of London, Malet street, bloomsbury, London, GB
| | - Mark Levene
- Birkbeck, University of London, Malet street, bloomsbury, London, GB
| | - David Weston
- Birkbeck, University of London, Malet street, bloomsbury, London, GB
| | - Renate Fromson
- Barnet General Hospital, Wellhouse Lane, London EN5 3DJ, United Kingdom, London, GB
| | - Nicolas Koslover
- Barnet General Hospital, Wellhouse Lane, London EN5 3DJ, United Kingdom, London, GB
| | - Tamara Levene
- Barnet General Hospital, Wellhouse Lane, London EN5 3DJ, United Kingdom, London, GB
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29
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Gunasekeran D, Chew AMK, Chandrasekar E, Rajendram P, Kandarpa V, Rajendram M, Chia A, Smith H, Leong CK. The impact and applications of social media platforms for public health responses before and during COVID-19. J Med Internet Res 2022; 24:e33680. [PMID: 35129456 PMCID: PMC9004624 DOI: 10.2196/33680] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 01/27/2022] [Accepted: 02/04/2022] [Indexed: 12/21/2022] Open
Abstract
Background Social media platforms have numerous potential benefits and drawbacks on public health, which have been described in the literature. The COVID-19 pandemic has exposed our limited knowledge regarding the potential health impact of these platforms, which have been detrimental to public health responses in many regions. Objective This review aims to highlight a brief history of social media in health care and report its potential negative and positive public health impacts, which have been characterized in the literature. Methods We searched electronic bibliographic databases including PubMed, including Medline and Institute of Electrical and Electronics Engineers Xplore, from December 10, 2015, to December 10, 2020. We screened the title and abstracts and selected relevant reports for review of full text and reference lists. These were analyzed thematically and consolidated into applications of social media platforms for public health. Results The positive and negative impact of social media platforms on public health are catalogued on the basis of recent research in this report. These findings are discussed in the context of improving future public health responses and incorporating other emerging digital technology domains such as artificial intelligence. However, there is a need for more research with pragmatic methodology that evaluates the impact of specific digital interventions to inform future health policy. Conclusions Recent research has highlighted the potential negative impact of social media platforms on population health, as well as potentially useful applications for public health communication, monitoring, and predictions. More research is needed to objectively investigate measures to mitigate against its negative impact while harnessing effective applications for the benefit of public health.
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Affiliation(s)
| | | | | | | | | | - Mallika Rajendram
- National University of Singapore (NUS), 10 Medical Drive, Singapore, SG
| | - Audrey Chia
- National University of Singapore (NUS), 10 Medical Drive, Singapore, SG
| | - Helen Smith
- Lee Kong Chian School of Medicine (LKCMedicine), Singapore, SG
| | - Choon Kit Leong
- National University of Singapore (NUS), 10 Medical Drive, Singapore, SG.,Mission Medical Clinic, Singapore, SG
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30
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Design and development of an IoT based intelligent multi parameter screening system. MATERIALS TODAY: PROCEEDINGS 2022; 58:7-12. [PMID: 34931166 PMCID: PMC8675049 DOI: 10.1016/j.matpr.2021.12.071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The SARS-CoV-2 or shortly COVID-19, is a viral disease which causes serious lung fever and hugely impacts different body parts from mild to critical depending on tolerant immune system. As the virus multiplies through human-to-human contact, it has affected our lives in a devastating way, including the vigorous pressure on the public health system, the world economy, education sector, workplaces, and shopping malls. Viral spreading of this virus can only be prevented by early detection of positive cases and to treat infected patients as quickly as possible. As many businesses, banks, gymnasiums, and stores etc., are using temperature screening as the primary step to assess for possible COVID-19 infection. Moreover, the proper hand sanitization is the very effective method to limit the outspread of this virus. This paper proposes the design and development of a fully automated low-cost portable electronic system in the form of a robot named CovBot that can be installed in the above-mentioned places by incorporating the mechanisms to automatically detect the body temperature, store the details directly to cloud so as to get the data latter by the authorities, to control/restrict the entry, a hand sanitization dispenser unit, auto alert to refill the sanitizer, a mobile display unit etc. Whole system can be managed by a mobile application. The system is controlled using an Arduino-Uno development board. The mobile and the microcontroller system is wirelessly communicated and that to cloud is done by IoT facility. Once this system is implemented, the primary concern and the initial screening associated to COVID-19 can be fully resolved. Comparing to other systems CovBot is cost effective and can be easily installed and operated.
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31
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Liang G, Zhao J, Lau HYP, Leung CWK. Using Social Media to Analyze Public Concerns and Policy Responses to COVID-19 in Hong Kong. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2021. [DOI: 10.1145/3460124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The outbreak of COVID-19 has caused huge economic and societal disruptions. To fight against the coronavirus, it is critical for policymakers to take swift and effective actions. In this article, we take Hong Kong as a case study, aiming to leverage social media data to support policymakers’ policy-making activities in different phases. First, in the agenda setting phase, we facilitate policymakers to identify key issues to be addressed during COVID-19. In particular, we design a novel epidemic awareness index to continuously monitor public discussion hotness of COVID-19 based on large-scale data collected from social media platforms. Then we identify the key issues by analyzing the posts and comments of the extensively discussed topics. Second, in the policy evaluation phase, we enable policymakers to conduct real-time evaluation of anti-epidemic policies. Specifically, we develop an accurate Cantonese sentiment classification model to measure the public satisfaction with anti-epidemic policies and propose a keyphrase extraction technique to further extract public opinions. To the best of our knowledge, this is the first work which conducts a large-scale social media analysis of COVID-19 in Hong Kong. The analytical results reveal some interesting findings: (1) there is a very low correlation between the number of confirmed cases and the public discussion hotness of COVID-19. The major public concern in the early stage is the shortage of anti-epidemic items. (2) The top-3 anti-epidemic measures with the greatest public satisfaction are daily press conference on COVID-19 updates, border closure, and social distancing rules.
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Affiliation(s)
- Guanqing Liang
- Wisers AI Lab, Wisers Information Limited, Wan Chai, Hong Kong, China
| | - Jingxin Zhao
- Wisers AI Lab, Wisers Information Limited, Wan Chai, Hong Kong, China
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32
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Yu Z, Zheng X, Yang Z, Lu B, Li X, Fu M. Interaction-Temporal GCN: A Hybrid Deep Framework For Covid-19 Pandemic Analysis. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2021; 2:97-103. [PMID: 34812421 PMCID: PMC8545025 DOI: 10.1109/ojemb.2021.3063890] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 01/10/2021] [Accepted: 03/01/2021] [Indexed: 12/23/2022] Open
Abstract
The Covid-19 pandemic is still spreading around the world and seriously imperils humankind's health. This swift spread has caused the public to panic and look to scientists for answers. Fortunately, these scientists already have a wealth of data—the Covid-19 reports that each country releases, reports with valuable spatial-temporal properties. These data point toward some key actions that humans can take in their fight against Covid-19. Technically, the Covid-19 records can be described as sequences, which represent spatial-temporal linkages among the data elements with graph structure. Therefore, we propose a novel framework, the Interaction-Temporal Graph Convolution Network (IT-GCN), to analyze pandemic data. Specifically, IT-GCN introduces ARIMA into GCN to model the data which originate on nodes in a graph, indicating the severity of the pandemic in different cities. Instead of regular spatial topology, we construct the graph nodes with the vectors via ARIMA parameterization to find out the interaction topology underlying in the pandemic data. Experimental results show that IT-GCN is able to capture the comprehensive interaction-temporal topology and achieve well-performed short-term prediction of the Covid-19 daily infected cases in the United States. Our framework outperforms state-of-art baselines in terms of MAE, RMSE and MAPE. We believe that IT-GCN is a valid and reasonable method to forecast the Covid-19 daily infected cases and other related time-series. Moreover, the prediction can assist in improving containment policies.
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Affiliation(s)
- Zehua Yu
- College of EngineeringShantou University Shantou Guangdong 515063 China
| | - Xianwei Zheng
- School of Mathematics and Big DataFoshan University Foshan Guangdong 528000 China
| | - Zhulun Yang
- College of EngineeringShantou University Shantou Guangdong 515063 China
| | - Bowen Lu
- College of EngineeringShantou University Shantou Guangdong 515063 China
| | - Xutao Li
- College of EngineeringShantou University Shantou Guangdong 515063 China
| | - Maxian Fu
- The Second Affiliated Hospital of Shantou University Medical CollegeShantou University Shantou Guangdong 515063 China
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33
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Deng Y, Xing S, Zhu M, Lei J. Impact of insufficient detection in COVID-19 outbreaks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:9727-9742. [PMID: 34814365 DOI: 10.3934/mbe.2021476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The COVID-19 (novel coronavirus disease 2019) pandemic has tremendously impacted global health and economics. Early detection of COVID-19 infections is important for patient treatment and for controlling the epidemic. However, many countries/regions suffer from a shortage of nucleic acid testing (NAT) due to either resource limitations or epidemic control measures. The exact number of infective cases is mostly unknown in counties/regions with insufficient NAT, which has been a major issue in predicting and controlling the epidemic. In this paper, we propose a mathematical model to quantitatively identify the influences of insufficient detection on the COVID-19 epidemic. We extend the classical SEIR (susceptible-exposed-infections-recovered) model to include random detections which are described by Poisson processes. We apply the model to the epidemic in Guam, Texas, the Virgin Islands, and Wyoming in the United States and determine the detection probabilities by fitting model simulations with the reported number of infected, recovered, and dead cases. We further study the effects of varying the detection probabilities and show that low level-detection probabilities significantly affect the epidemic; increasing the detection probability of asymptomatic infections can effectively reduce the the scale of the epidemic. This study suggests that early detection is important for the control of the COVID-19 epidemic.
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Affiliation(s)
- Yue Deng
- School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China
| | - Siming Xing
- School of Mathematical Sciences, Tiangong University, Tianjin, 300387, China
| | - Meixia Zhu
- School of Software, Tiangong University, Tianjin, 300387, China
| | - Jinzhi Lei
- School of Mathematical Sciences, Tiangong University, Tianjin, 300387, China
- Center for Applied Mathematics, Tiangong University, Tianjin, 300387, China
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34
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Kostkova P, Saigí-Rubió F, Eguia H, Borbolla D, Verschuuren M, Hamilton C, Azzopardi-Muscat N, Novillo-Ortiz D. Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic. Front Digit Health 2021; 3:707902. [PMID: 34713179 PMCID: PMC8522016 DOI: 10.3389/fdgth.2021.707902] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 06/30/2021] [Indexed: 12/23/2022] Open
Abstract
Background: In order to prevent spread and improve control of infectious diseases, public health experts need to closely monitor human and animal populations. Infectious disease surveillance is an established, routine data collection process essential for early warning, rapid response, and disease control. The quantity of data potentially useful for early warning and surveillance has increased exponentially due to social media and other big data streams. Digital epidemiology is a novel discipline that includes harvesting, analysing, and interpreting data that were not initially collected for healthcare needs to enhance traditional surveillance. During the current COVID-19 pandemic, the importance of digital epidemiology complementing traditional public health approaches has been highlighted. Objective: The aim of this paper is to provide a comprehensive overview for the application of data and digital solutions to support surveillance strategies and draw implications for surveillance in the context of the COVID-19 pandemic and beyond. Methods: A search was conducted in PubMed databases. Articles published between January 2005 and May 2020 on the use of digital solutions to support surveillance strategies in pandemic settings and health emergencies were evaluated. Results: In this paper, we provide a comprehensive overview of digital epidemiology, available data sources, and components of 21st-century digital surveillance, early warning and response, outbreak management and control, and digital interventions. Conclusions: Our main purpose was to highlight the plausible use of new surveillance strategies, with implications for the COVID-19 pandemic strategies and then to identify opportunities and challenges for the successful development and implementation of digital solutions during non-emergency times of routine surveillance, with readiness for early-warning and response for future pandemics. The enhancement of traditional surveillance systems with novel digital surveillance methods opens a direction for the most effective framework for preparedness and response to future pandemics.
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Affiliation(s)
- Patty Kostkova
- UCL Centre for Digital Public Health in Emergencies (dPHE), Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
| | - Francesc Saigí-Rubió
- Faculty of Health Sciences, Universitat Oberta de Catalunya, Barcelona, Spain.,Interdisciplinary Research Group on ICTs, Barcelona, Spain
| | - Hans Eguia
- Faculty of Health Sciences, Universitat Oberta de Catalunya, Barcelona, Spain.,SEMERGEN New Technologies Working Group, Madrid, Spain
| | - Damian Borbolla
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
| | - Marieke Verschuuren
- Division of Country Health Policies and Systems, Regional Office for Europe, World Health Organization, Copenhagen, Denmark
| | - Clayton Hamilton
- Division of Country Health Policies and Systems, Regional Office for Europe, World Health Organization, Copenhagen, Denmark
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, Regional Office for Europe, World Health Organization, Copenhagen, Denmark
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, Regional Office for Europe, World Health Organization, Copenhagen, Denmark
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Lopez CE, Gallemore C. An augmented multilingual Twitter dataset for studying the COVID-19 infodemic. SOCIAL NETWORK ANALYSIS AND MINING 2021; 11:102. [PMID: 34697560 PMCID: PMC8528187 DOI: 10.1007/s13278-021-00825-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 09/26/2021] [Accepted: 09/28/2021] [Indexed: 12/23/2022]
Abstract
This work presents an openly available dataset to facilitate researchers' exploration and hypothesis testing about the social discourse of the COVID-19 pandemic. The dataset currently consists of over 2.2 billions tweets (count as of September, 2021), from all over the world, in multiple languages. Tweets start from January 22, 2020, when the total cases of reported COVID-19 were below 600 worldwide. The dataset was collected using the Twitter API and by rehydrating tweets from other available datasets, data collection is ongoing as of the time of writing. To facilitate hypothesis testing and exploration of social discourse, the English and Spanish tweets have been augmented with state-of-the-art Twitter Sentiment and Named Entity Recognition algorithms. The dataset and the summary files provided allow researchers to avoid some computationally intensive analyses, facilitating more widespread use of social media data to gain insights on issues such as (mis)information diffusion, semantic networks, sentiments, and the evolution of COVID-19 discussions. In addition, the dataset provides an archive for researchers in the social sciences wishing to have access to a dataset covering the entire duration of the pandemic.
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Affiliation(s)
- Christian E. Lopez
- Department of Computer Science and Mechanical Engineering Department, Lafayette College, Easton, Pennsylvania USA
| | - Caleb Gallemore
- International Affairs Program, Lafayette College, Easton, PA USA
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Rismanbaf A. Social Media as a Double-Edged Sword: Lessons from COVID-19 Outbreak. Int J Prev Med 2021; 12:87. [PMID: 34584653 PMCID: PMC8428315 DOI: 10.4103/ijpvm.ijpvm_173_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 02/10/2021] [Indexed: 11/04/2022] Open
Affiliation(s)
- Ali Rismanbaf
- Department of Clinical Pharmacy and Pharmacy Practice, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
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Yao Y, Geara TG, Shi W. Impact of COVID-19 on city-scale transportation and safety: An early experience from Detroit. SMART HEALTH (AMSTERDAM, NETHERLANDS) 2021; 22:100218. [PMID: 34541278 PMCID: PMC8438802 DOI: 10.1016/j.smhl.2021.100218] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 05/12/2021] [Accepted: 05/12/2021] [Indexed: 12/23/2022]
Abstract
The COVID-19 pandemic brought unprecedented levels of disruption to the local and regional transportation networks throughout the United States, especially the Motor City---Detroit. That was mainly a result of swift restrictive measures such as statewide quarantine and lock-down orders to confine the spread of the virus and the rising number of COVID-19 confirmed cases and deaths. This work is driven by analyzing five types of real-world data sets from Detroit related to traffic volume, daily cases, weather, social distancing index, and crashes from January 2019 to June 2020. The primary goals of this work are: i) figuring out the impacts of COVID-19 on the transportation network usage (traffic volume) and safety (crashes) for the City of Detroit, ii) determining whether each type of data (e.g. traffic volume data) could be a useful factor in the confirmed-cases prediction, and iii) providing an early future prediction method for COVID-19 rates, which can be a vital contributor to life-saving advanced preventative and preparatory responses. In addressing these problems, the prediction results of six feature groups are presented and analyzed to quantify the prediction effectiveness of each type of data. Then, a deep learning model was developed using long short-term memory networks to predict the number of confirmed cases within the next week. The model demonstrated a promising prediction result with a coefficient of determination (R2) of up to approximately 0.91. Furthermore, six essential observations with supporting evidence are presented, which will be helpful for decision-makers to take specific measures that aid in preventing the spread of COVID-19 and protecting public health and safety. The proposed approaches could be applied, customized, adjusted, and replicated for analysis of the impact of COVID-19 on a transportation network and prediction of the anticipated COVID-19 cases using a similar data set obtained for other large cities in the USA or from around the world.
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Affiliation(s)
- Yongtao Yao
- Department of Computer Science, Wayne State University, Detroit, Ml, 48202, USA
| | - Tony G Geara
- Department of Public Works, City of Detroit, Detroit, Ml, 48216, USA
| | - Weisong Shi
- Department of Computer Science, Wayne State University, Detroit, Ml, 48202, USA
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Al-Khalifa KS, Bakhurji E, Halawany HS, Alabdurubalnabi EM, Nasser WW, Shetty AC, Sadaf S. Pattern of dental needs and advice on Twitter during the COVID-19 pandemic in Saudi Arabia. BMC Oral Health 2021; 21:456. [PMID: 34535114 PMCID: PMC8448172 DOI: 10.1186/s12903-021-01825-4] [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: 06/12/2021] [Accepted: 09/13/2021] [Indexed: 01/15/2023] Open
Abstract
Aim To compare and evaluate the influence of the COVID-19 outbreak on tweets related to dental treatment needs and advice of Saudi Twitter users in 2020 by comparing them to the same time-period in 2019. Methods Eight independent searches based on dentistry related keywords: “teeth, mouth and gingiva” were carried out within the timeframe between the 23rd of March and the 21st of June for the years 2020 and 2019. Extracted tweets were analyzed by two calibrated examiners as tweets containing expressed dental needs and tweets for dental advice, while spam tweets were excluded. Descriptive analysis was performed to present the overview of the findings using SPSS. Bivariate analysis was performed with Pearson’s Chi Square, Fisher’s Exact test and Mann–Whitney U test. Statistical significance was set at p ≤ 0.05. Results A total of 595 tweets from the year 2019 and 714 tweets from the year 2020 were obtained. Overall, combined dental needs and advice tweets, retweets, likes, and replies were higher in 2020 compared to 2019. Dental needs tweets were higher in 2020 compared to 2019, while dental advice tweets were lower in 2020 compared to 2019. Statistically significant differences were found between 2020 and 2019 with regards to dental needs well as with dental advice (p < 0.05). In addition, statistically significant differences were found between 2019 and 2020 with presence of pain, urgency of the dental need and type of advisor (p < 0.05). Conclusion An obvious impact of the pandemic can be seen in the form of increased self-reported dental needs, pain and urgency among the public in Saudi Arabia. This study highlights the importance of social media, specifically Twitter, in expressing the public needs and utilizing it as a platform for education and advice.
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Affiliation(s)
- Khalifa S Al-Khalifa
- Department of Preventive Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
| | - Eman Bakhurji
- Department of Preventive Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Hassan S Halawany
- Department of Periodontics and Community Dentistry, College of Dentistry, King Saud University, Riyadh, Saudi Arabia
| | - Esraa M Alabdurubalnabi
- Dental Internship Program, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Wejdan W Nasser
- Dental Internship Program, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Ashwin C Shetty
- Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Shazia Sadaf
- Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
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Geva GA, Ketko I, Nitecki M, Simon S, Inbar B, Toledo I, Shapiro M, Vaturi B, Votta Y, Filler D, Yosef R, Shpitzer SA, Hir N, Peri Markovich M, Shapira S, Fink N, Glasberg E, Furer A. Data Empowerment of Decision-Makers in an Era of a Pandemic: Intersection of "Classic" and Artificial Intelligence in the Service of Medicine. J Med Internet Res 2021; 23:e24295. [PMID: 34313589 PMCID: PMC8437401 DOI: 10.2196/24295] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 12/14/2020] [Accepted: 04/10/2021] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND The COVID-19 outbreak required prompt action by health authorities around the world in response to a novel threat. With enormous amounts of information originating in sources with uncertain degree of validation and accuracy, it is essential to provide executive-level decision-makers with the most actionable, pertinent, and updated data analysis to enable them to adapt their strategy swiftly and competently. OBJECTIVE We report here the origination of a COVID-19 dedicated response in the Israel Defense Forces with the assembly of an operational Data Center for the Campaign against Coronavirus. METHODS Spearheaded by directors with clinical, operational, and data analytics orientation, a multidisciplinary team utilized existing and newly developed platforms to collect and analyze large amounts of information on an individual level in the context of SARS-CoV-2 contraction and infection. RESULTS Nearly 300,000 responses to daily questionnaires were recorded and were merged with other data sets to form a unified data lake. By using basic as well as advanced analytic tools ranging from simple aggregation and display of trends to data science application, we provided commanders and clinicians with access to trusted, accurate, and personalized information and tools that were designed to foster operational changes and mitigate the propagation of the pandemic. The developed tools aided in the in the identification of high-risk individuals for severe disease and resulted in a 30% decline in their attendance to their units. Moreover, the queue for laboratory examination for COVID-19 was optimized using a predictive model and resulted in a high true-positive rate of 20%, which is more than twice as high as the baseline rate (2.28%, 95% CI 1.63%-3.19%). CONCLUSIONS In times of ambiguity and uncertainty, along with an unprecedented flux of information, health organizations may find multidisciplinary teams working to provide intelligence from diverse and rich data a key factor in providing executives relevant and actionable support for decision-making.
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Affiliation(s)
- Gil A Geva
- Medical Corps, Israel Defense Force, Ramat Gan, Israel
| | - Itay Ketko
- Medical Corps, Israel Defense Force, Ramat Gan, Israel
- Heller Institute of Medical Research, Sheba Medical Center, Tel-HaShomer, Ramat Gan, Israel
| | - Maya Nitecki
- Medical Corps, Israel Defense Force, Ramat Gan, Israel
- Department of Military Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Shoham Simon
- Planning Directorate, Israel Defense Force, Tel Aviv, Israel
| | - Barr Inbar
- Computer and IT Directorate, Israel Defense Force, Tel Aviv, Israel
| | - Itay Toledo
- Computer and IT Directorate, Israel Defense Force, Tel Aviv, Israel
| | | | - Barak Vaturi
- Computer and IT Directorate, Israel Defense Force, Tel Aviv, Israel
| | - Yoni Votta
- Computer and IT Directorate, Israel Defense Force, Tel Aviv, Israel
| | - Daniel Filler
- Computer and IT Directorate, Israel Defense Force, Tel Aviv, Israel
| | - Roey Yosef
- Computer and IT Directorate, Israel Defense Force, Tel Aviv, Israel
| | | | - Nabil Hir
- Medical Corps, Israel Defense Force, Ramat Gan, Israel
| | - Michal Peri Markovich
- Medical Corps, Israel Defense Force, Ramat Gan, Israel
- Israel Veterinary Services, Ministry of Agriculture and Rural Development, Ramat Gan, Israel
| | - Shachar Shapira
- Medical Corps, Israel Defense Force, Ramat Gan, Israel
- Department of Military Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
- Institute for Research in Military Medicine, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Noam Fink
- Medical Corps, Israel Defense Force, Ramat Gan, Israel
| | - Elon Glasberg
- Medical Corps, Israel Defense Force, Ramat Gan, Israel
- The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Ariel Furer
- Medical Corps, Israel Defense Force, Ramat Gan, Israel
- Department of Military Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
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Fang M, Hu W, Liu B. Characterization of bat coronaviruses: a latent global threat. J Vet Sci 2021; 22:e72. [PMID: 34553517 PMCID: PMC8460465 DOI: 10.4142/jvs.2021.22.e72] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/27/2021] [Accepted: 08/03/2021] [Indexed: 12/23/2022] Open
Abstract
It has been speculated that bats serve as reservoirs of a huge variety of emerging coronaviruses (CoVs) that have been responsible for severe havoc in human health systems as well as negatively affecting human economic and social systems. A prime example is the currently active severe acute respiratory syndrome (SARS)-CoV2, which presumably originated from bats, demonstrating that the risk of a new outbreak of bat coronavirus is always latent. Therefore, an in-depth investigation to better comprehend bat CoVs has become an important issue within the international community, a group that aims to attenuate the consequences of future outbreaks. In this review, we present a concise introduction to CoVs found in bats and discuss their distribution in Southeast Asia. We also discuss the unique adaptation features in bats that confer the ability to be a potential coronavirus reservoir. In addition, we review the bat coronavirus-linked diseases that have emerged in the last two decades. Finally, we propose key factors helpful in the prediction of a novel coronavirus outbreak and present the most recent methods used to forecast an evolving outbreak.
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Affiliation(s)
- Manxin Fang
- College of Life Science and Resources and Environment, Yichun University, Yichun 336000, Jiangxi, China
| | - Wei Hu
- College of Life Science and Resources and Environment, Yichun University, Yichun 336000, Jiangxi, China
| | - Ben Liu
- College of Life Science and Resources and Environment, Yichun University, Yichun 336000, Jiangxi, China
- Jiangxi Lvke Agriculture and Animal Husbandry Technology Co., Ltd, Yichun 336000, Jiangxi, China
- Engineering Technology Research Center of Jiangxi Universities and Colleges for Selenium Agriculture, Yichun University, Yichun 336000, Jiangxi, China.
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Guo S, Fang F, Zhou T, Zhang W, Guo Q, Zeng R, Chen X, Liu J, Lu X. Improving Google Flu Trends for COVID-19 estimates using Weibo posts. DATA SCIENCE AND MANAGEMENT 2021. [PMCID: PMC8280378 DOI: 10.1016/j.dsm.2021.07.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
While incomplete non-medical data has been integrated into prediction models for epidemics, the accuracy and the generalizability of the data are difficult to guarantee. To comprehensively evaluate the ability and applicability of using social media data to predict the development of COVID-19, a new confirmed case prediction algorithm improving the Google Flu Trends algorithm is established, called Weibo COVID-19 Trends (WCT), based on the post dataset generated by all users in Wuhan on Sina Weibo. A genetic algorithm is designed to select the keyword set for filtering COVID-19 related posts. WCT can constantly outperform the highest average test score in the training set between daily new confirmed case counts and the prediction results. It remains to produce the best prediction results among other algorithms when the number of forecast days increases from one to eight days with the highest correlation score from 0.98 (P < 0.01) to 0.86 (P < 0.01) during all analysis period. Additionally, WCT effectively improves the Google Flu Trends algorithm's shortcoming of overestimating the epidemic peak value. This study offers a highly adaptive approach for feature engineering of third-party data in epidemic prediction, providing useful insights for the prediction of newly emerging infectious diseases at an early stage.
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Big Data Technology Applications and the Right to Health in China during the COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18147325. [PMID: 34299776 PMCID: PMC8307229 DOI: 10.3390/ijerph18147325] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 07/04/2021] [Accepted: 07/06/2021] [Indexed: 01/08/2023]
Abstract
Individuals have the right to health according to the Constitution and other laws in China. Significant barriers have prevented the full realisation of the right to health in the COVID-19 era. Big data technology, which is a vital tool for COVID-19 containment, has been a central topic of discussion, as it has been used to protect the right to health through public health surveillance, contact tracing, real-time epidemic outbreak monitoring, trend forecasting, online consultations, and the allocation of medical and health resources in China. Big data technology has enabled precise and efficient epidemic prevention and control and has improved the efficiency and accuracy of the diagnosis and treatment of this new form of coronavirus pneumonia due to Chinese institutional factors. Although big data technology has successfully supported the containment of the virus and protected the right to health in the COVID-19 era, it also risks infringing on individual privacy rights. Chinese policymakers should understand the positive and negative impacts of big data technology and should prioritise the Personal Information Protection Law and other laws that are meant to protect and strengthen the right to privacy.
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Shen YT, Chen L, Yue WW, Xu HX. Digital Technology-Based Telemedicine for the COVID-19 Pandemic. Front Med (Lausanne) 2021; 8:646506. [PMID: 34295908 PMCID: PMC8289897 DOI: 10.3389/fmed.2021.646506] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 05/31/2021] [Indexed: 12/23/2022] Open
Abstract
In the year 2020, the coronavirus disease 2019 (COVID-19) crisis intersected with the development and maturation of several digital technologies including the internet of things (IoT) with next-generation 5G networks, artificial intelligence (AI) that uses deep learning, big data analytics, and blockchain and robotic technology, which has resulted in an unprecedented opportunity for the progress of telemedicine. Digital technology-based telemedicine platform has currently been established in many countries, incorporated into clinical workflow with four modes, including "many to one" mode, "one to many" mode, "consultation" mode, and "practical operation" mode, and has shown to be feasible, effective, and efficient in sharing epidemiological data, enabling direct interactions among healthcare providers or patients across distance, minimizing the risk of disease infection, improving the quality of patient care, and preserving healthcare resources. In this state-of-the-art review, we gain insight into the potential benefits of demonstrating telemedicine in the context of a huge health crisis by summarizing the literature related to the use of digital technologies in telemedicine applications. We also outline several new strategies for supporting the use of telemedicine at scale.
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Affiliation(s)
- Yu-Ting Shen
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Tongji University School of Medicine, Shanghai, China
| | - Liang Chen
- Department of Gastroenterology, Shanghai Tenth People's Hospital, Shanghai, China
| | - Wen-Wen Yue
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Tongji University School of Medicine, Shanghai, China
| | - Hui-Xiong Xu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Tongji University School of Medicine, Shanghai, China
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Alamo T, G Reina D, Millán Gata P, Preciado VM, Giordano G. Data-driven methods for present and future pandemics: Monitoring, modelling and managing. ANNUAL REVIEWS IN CONTROL 2021; 52:448-464. [PMID: 34220287 PMCID: PMC8238691 DOI: 10.1016/j.arcontrol.2021.05.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 05/29/2023]
Abstract
This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.
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Affiliation(s)
- Teodoro Alamo
- Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Escuela Superior de Ingenieros, Sevilla, Spain
| | - Daniel G Reina
- Departamento de Ingeniería Electrónica, Universidad de Sevilla, Escuela Superior de Ingenieros, Sevilla, Spain
| | - Pablo Millán Gata
- Departamento de Ingeniería, Universidad Loyola Andalucía, Seville, Spain
| | - Victor M Preciado
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA
| | - Giulia Giordano
- Department of Industrial Engineering, University of Trento, Trento, Italy
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Elsotouhy M, Jain G, Shrivastava A. Disaster Management during Pandemic: A Big Data-Centric Approach. INTERNATIONAL JOURNAL OF INNOVATION AND TECHNOLOGY MANAGEMENT 2021. [DOI: 10.1142/s0219877021400034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The concept of big data (BD) has been coupled with disaster management to improve the crisis response during pandemic and epidemic. BD has transformed every aspect and approach of handling the unorganized set of data files and converting the same into a piece of more structured information. The constant inflow of unstructured data shows the research lacuna, especially during a pandemic. This study is an effort to develop a pandemic disaster management approach based on BD. BD text analytics potential is immense in effective pandemic disaster management via visualization, explanation, and data analysis. To seize the understanding of using BD toward disaster management, we have taken a comprehensive approach in place of fragmented view by using BD text analytics approach to comprehend the various relationships about disaster management theory. The study’s findings indicate that it is essential to understand all the pandemic disaster management performed in the past and improve the future crisis response using BD. Though worldwide, all the communities face big chaos and have little help reaching a potential solution.
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Ding W, Nayak J, Swapnarekha H, Abraham A, Naik B, Pelusi D. Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data. Neurocomputing 2021; 457:40-66. [PMID: 34149184 PMCID: PMC8206574 DOI: 10.1016/j.neucom.2021.06.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 06/02/2021] [Accepted: 06/11/2021] [Indexed: 12/11/2022]
Abstract
The unprecedented surge of a novel coronavirus in the month of December 2019, named as COVID-19 by the World Health organization has caused a serious impact on the health and socioeconomic activities of the public all over the world. Since its origin, the number of infected and deceased cases has been growing exponentially in almost all the affected countries of the world. The rapid spread of the novel coronavirus across the world results in the scarcity of medical resources and overburdened hospitals. As a result, the researchers and technocrats are continuously working across the world for the inculcation of efficient strategies which may assist the government and healthcare system in controlling and managing the spread of the COVID-19 pandemic. Therefore, this study provides an extensive review of the ongoing strategies such as diagnosis, prediction, drug and vaccine development and preventive measures used in combating the COVID-19 along with technologies used and limitations. Moreover, this review also provides a comparative analysis of the distinct type of data, emerging technologies, approaches used in diagnosis and prediction of COVID-19, statistics of contact tracing apps, vaccine production platforms used in the COVID-19 pandemic. Finally, the study highlights some challenges and pitfalls observed in the systematic review which may assist the researchers to develop more efficient strategies used in controlling and managing the spread of COVID-19.
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Affiliation(s)
- Weiping Ding
- School of Information Science and Technology, Nantong University, China
| | - Janmenjoy Nayak
- Aditya Institute of Technology and Management (AITAM), India
| | - H Swapnarekha
- Aditya Institute of Technology and Management (AITAM), India
- Veer Surendra Sai University of Technology, India
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Guo JW, Sisler SM, Wang CY, Wallace AS. Exploring experiences of COVID-19-positive individuals from social media posts. Int J Nurs Pract 2021; 27:e12986. [PMID: 34128296 PMCID: PMC8420411 DOI: 10.1111/ijn.12986] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 05/16/2021] [Accepted: 05/27/2021] [Indexed: 12/18/2022]
Abstract
Aims This study aimed to explore the experience of individuals who claimed to be COVID‐19 positive via their Twitter feeds. Background Public social media data are valuable to understanding people's experiences of public health phenomena. To improve care to those with COVID‐19, this study explored themes from Twitter feeds, generated by individuals who self‐identified as COVID‐19 positive. Design This study utilized a descriptive design for text analysis for social media data. Methods This study analysed social media text retrieved by tweets of individuals in the United States who self‐reported being COVID‐19 positive and posted on Twitter in English between April 2, 2020, and April 24, 2020. In extracting embedded topics from tweets, we applied topic modelling approach based on latent Dirichlet allocation and visualized the results via LDAvis, a related web‐based interactive visualization tool. Results Three themes were mined from 721 eligible tweets: (i) recognizing the seriousness of the condition in COVID‐19 pandemic; (ii) having symptoms of being COVID‐19 positive; and (iii) sharing the journey of being COVID‐19 positive. Conclusion Leveraging the knowledge and context of study themes, we present experiences that may better reflect patient needs while experiencing COVID‐19. The findings offer more descriptive support for public health nursing and other translational public health efforts during a global pandemic. What is already known about this topic? Social media data can be used to predict potential outbreak areas, which has been beneficial to informing decision makers. Public opinions about the pandemic can be extracted from social media data, which helps understand patients and the public's experiences and potential needs. In the early stage of the novel COVID‐19 pandemic in the United States, little was known about the experiences and needs of those with COVID‐19.
What this paper adds? Individuals who claimed to be COVID‐19 positive used the social media platform (e.g., Twitter) as a broader communication strategy to their social networks during the social distancing phase at the early stage of the COVID‐19 pandemic. Key messages delivered through the social media included warning their family, friends and network take the pandemic seriously, expressing their symptom profiles related to COVID‐19 and sharing their journey of being a COVID‐19 patient. Although latent Dirichlet allocation may be underutilized in nursing research, valuable information regarding mental health issues can be discovered and amplified from the social media data using this methodology.
The implications of this paper: Substantial evidence encourages the public health community to engage in more detailed mental health screening of patients with COVID‐19. By leveraging the knowledge and context of the social media threads (e.g., patient perspective), there is ample opportunity for more focal and descriptive public health interventions to mitigate the stress and uncertainty of a pandemic.
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Affiliation(s)
- Jia-Wen Guo
- College of Nursing, University of Utah, Salt Lake City, Utah, USA
| | - Shawna M Sisler
- College of Nursing, University of Utah, Salt Lake City, Utah, USA
| | - Ching-Yu Wang
- College of Nursing, University of Utah, Salt Lake City, Utah, USA
| | - Andrea S Wallace
- College of Nursing, University of Utah, Salt Lake City, Utah, USA
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Choli M, Kuss DJ. Perceptions of blame on social media during the coronavirus pandemic. COMPUTERS IN HUMAN BEHAVIOR 2021; 124:106895. [PMID: 34103785 PMCID: PMC8175992 DOI: 10.1016/j.chb.2021.106895] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 01/26/2021] [Accepted: 05/08/2021] [Indexed: 12/23/2022]
Abstract
The outbreak of the coronavirus (COVID-19) disease is overwhelming resources, economies and countries around the world. Millions of people have been infected and hundreds of thousands have succumbed to the virus. Research regarding the coronavirus pandemic is published every day. However, there is limited discourse regarding societal perception. Thus, this paper examines blame attribution concerning the origin and propagation of the coronavirus crisis according to public perception. Specifically, data were extracted from the social media platform Twitter concerning the coronavirus during the early stages of the outbreak and further investigated using thematic analysis. The findings revealed the public predominantly blames national governments for the coronavirus pandemic. In addition, the results documented the explosion of conspiracy theories among social media users regarding the virus' origin. In the early stages of the pandemic, the blame tendency was most frequent to conspiracy theories and restriction of information from the government, whilst in the later months, responsibility had shifted to political leaders and the media. The findings indicate an emerging government mistrust that may result in disregard of preventive health behaviours and the amplification of conspiracy theories, and an evolving dynamic of blame. This study argues for a transparent, continuing dialogue between governments and the public to stop the spread of the coronavirus.
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Affiliation(s)
- Marilena Choli
- Cyberpsychology Research Group, International Gaming Research Unit, Psychology Department, School of Social Sciences, Nottingham Trent University, UK
| | - Daria J Kuss
- Cyberpsychology Research Group, International Gaming Research Unit, Psychology Department, School of Social Sciences, Nottingham Trent University, UK
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49
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Mallah SI, Ghorab OK, Al-Salmi S, Abdellatif OS, Tharmaratnam T, Iskandar MA, Sefen JAN, Sidhu P, Atallah B, El-Lababidi R, Al-Qahtani M. COVID-19: breaking down a global health crisis. Ann Clin Microbiol Antimicrob 2021; 20:35. [PMID: 34006330 PMCID: PMC8129964 DOI: 10.1186/s12941-021-00438-7] [Citation(s) in RCA: 141] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 04/26/2021] [Indexed: 02/06/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is the second pandemic of the twenty-first century, with over one-hundred million infections and over two million deaths to date. It is a novel strain from the Coronaviridae family, named Severe Acute Respiratory Distress Syndrome Coronavirus-2 (SARS-CoV-2); the 7th known member of the coronavirus family to cause disease in humans, notably following the Middle East Respiratory syndrome (MERS), and Severe Acute Respiratory Distress Syndrome (SARS). The most characteristic feature of this single-stranded RNA molecule includes the spike glycoprotein on its surface. Most patients with COVID-19, of which the elderly and immunocompromised are most at risk, complain of flu-like symptoms, including dry cough and headache. The most common complications include pneumonia, acute respiratory distress syndrome, septic shock, and cardiovascular manifestations. Transmission of SARS-CoV-2 is mainly via respiratory droplets, either directly from the air when an infected patient coughs or sneezes, or in the form of fomites on surfaces. Maintaining hand-hygiene, social distancing, and personal protective equipment (i.e., masks) remain the most effective precautions. Patient management includes supportive care and anticoagulative measures, with a focus on maintaining respiratory function. Therapy with dexamethasone, remdesivir, and tocilizumab appear to be most promising to date, with hydroxychloroquine, lopinavir, ritonavir, and interferons falling out of favour. Additionally, accelerated vaccination efforts have taken place internationally, with several promising vaccinations being mass deployed. In response to the COVID-19 pandemic, countries and stakeholders have taken varying precautions to combat and contain the spread of the virus and dampen its collateral economic damage. This review paper aims to synthesize the impact of the virus on a global, micro to macro scale.
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Affiliation(s)
- Saad I Mallah
- School of Medicine, Royal College of Surgeons in Ireland, Bahrain, Kingdom of Bahrain.
- The National Taskforce for Combating the Coronavirus (COVID-19), Bahrain, Kingdom of Bahrain.
| | - Omar K Ghorab
- School of Medicine, Royal College of Surgeons in Ireland, Bahrain, Kingdom of Bahrain
| | - Sabrina Al-Salmi
- School of Medicine, Royal College of Surgeons in Ireland, Bahrain, Kingdom of Bahrain
| | - Omar S Abdellatif
- Department of Political Science, Faculty of Arts and Science, University of Toronto, Toronto, Canada
- G7 and G20 Research Groups, Munk School of Global Affairs and Public Policy, University of Toronto, Toronto, Canada
| | - Tharmegan Tharmaratnam
- School of Medicine, Royal College of Surgeons in Ireland, Bahrain, Kingdom of Bahrain
- School of Medicine, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Mina Amin Iskandar
- School of Medicine, Royal College of Surgeons in Ireland, Bahrain, Kingdom of Bahrain
| | | | - Pardeep Sidhu
- School of Medicine, Royal College of Surgeons in Ireland, Bahrain, Kingdom of Bahrain
| | - Bassam Atallah
- Department of Pharmacy Services, Cleveland Clinic Abu Dhabi, Al Maryah Island, Abu Dhabi, United Arab Emirates
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Rania El-Lababidi
- Department of Pharmacy Services, Cleveland Clinic Abu Dhabi, Al Maryah Island, Abu Dhabi, United Arab Emirates
| | - Manaf Al-Qahtani
- The National Taskforce for Combating the Coronavirus (COVID-19), Bahrain, Kingdom of Bahrain.
- Department of Medicine, Royal College of Surgeons in Ireland, Bahrain, Kingdom of Bahrain.
- Department of Infectious Diseases, Royal Medical Services, Bahrain Defence Force Hospital, Riffa, Kingdom of Bahrain.
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50
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Using data mining techniques to fight and control epidemics: A scoping review. HEALTH AND TECHNOLOGY 2021; 11:759-771. [PMID: 33977022 PMCID: PMC8102070 DOI: 10.1007/s12553-021-00553-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/20/2021] [Indexed: 12/14/2022]
Abstract
The main objective of this survey is to study the published articles to determine the most favorite data mining methods and gap of knowledge. Since the threat of pandemics has raised concerns for public health, data mining techniques were applied by researchers to reveal the hidden knowledge. Web of Science, Scopus, and PubMed databases were selected for systematic searches. Then, all of the retrieved articles were screened in the stepwise process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist to select appropriate articles. All of the results were analyzed and summarized based on some classifications. Out of 335 citations were retrieved, 50 articles were determined as eligible articles through a scoping review. The review results showed that the most favorite DM belonged to Natural language processing (22%) and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies show a predominance of applying supervised learning techniques (90%). Concerning healthcare scopes, we found that infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline. The most common software used in the studies was SPSS (22%) and R (20%). The results revealed that some valuable researches conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. But most researches will need in terms of treatment and disease control.
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