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  1. Article ; Online: Assessing the effect of socio-economic features of low-income communities and COVID-19 related cases: An empirical study of New York City.

    Truong, Ngoc / Asare, Andy Ohemeng

    Global public health

    2020  Volume 16, Issue 1, Page(s) 1–16

    Abstract: This study examined the effect of socio-economic features of low-income communities and COVID-19 related cases in New York City. The study developed hypotheses and conceptual framework of low-income communities and COVID-19 associated cases based on ... ...

    Abstract This study examined the effect of socio-economic features of low-income communities and COVID-19 related cases in New York City. The study developed hypotheses and conceptual framework of low-income communities and COVID-19 associated cases based on literature and theoretical review. The proposed framework was then tested using Structural Equation Model (SEM) with secondary data collected from New York Health and Mental Hygiene Department, US Census Bureau, and the Centers for Disease Control and Prevention. The findings revealed that unfavourable working conditions, underlying health conditions, and poor living conditions significantly and positively affects the number of COVID-19 confirmed cases. The study further revealed a positive and significant relationship between confirmed COVID-19 cases and COVID-19 related deaths. Theoretically, this study provides empirical results and a conceptual framework that could be used by other researchers to investigate low-income communities and COVID-19 related topics. Practically, this study called on the federal and state governments to effectively apply the health justice approach to eliminate healthcare discrimination for people living in low-income and marginalised communities as well as providing accessible, safe housing for the more vulnerable who need a place to self-quarantine due to COVID-19 exposure. Further practical and theoretical implications policies are discussed.
    MeSH term(s) COVID-19/epidemiology ; Female ; Humans ; Latent Class Analysis ; Male ; New York City/epidemiology ; Poverty Areas ; SARS-CoV-2 ; Social Determinants of Health ; Socioeconomic Factors
    Language English
    Publishing date 2020-11-21
    Publishing country England
    Document type Journal Article ; Video-Audio Media
    ZDB-ID 2234129-8
    ISSN 1744-1706 ; 1744-1692
    ISSN (online) 1744-1706
    ISSN 1744-1692
    DOI 10.1080/17441692.2020.1850830
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: The pandemic semesters: Examining public opinion regarding online learning amidst COVID-19.

    Asare, Andy Ohemeng / Yap, Robin / Truong, Ngoc / Sarpong, Eric Ohemeng

    Journal of computer assisted learning

    2021  Volume 37, Issue 6, Page(s) 1591–1605

    Abstract: The current educational disruption caused by the COVID-19 pandemic has fuelled a plethora of investments and the use of educational technologies for Emergency Remote Learning (ERL). Despite the significance of online learning for ERL across most ... ...

    Abstract The current educational disruption caused by the COVID-19 pandemic has fuelled a plethora of investments and the use of educational technologies for Emergency Remote Learning (ERL). Despite the significance of online learning for ERL across most educational institutions, there are wide mixed perceptions about online learning during this pandemic. This study, therefore, aims at examining public perception about online learning for ERL during COVID-19. The study sample included 31,009 English language Tweets extracted and cleaned using Twitter API, Python libraries and NVivo, from 10 March 2020 to 25 July 2020, using keywords: COVID-19, Corona, e-learning, online learning, distance learning. Collected tweets were analysed using word frequencies of unigrams and bigrams, sentiment analysis, topic modelling, and sentiment labeling, cluster, and trend analysis. The results identified more positive and negative sentiments within the dataset and identified topics. Further, the identified topics which are learning support, COVID-19, online learning, schools, distance learning, e-learning, students, and education were clustered among each other. The number of daily COVID-19 related cases had a weak linear relationship with the number of online learning tweets due to the low number of tweets during the vacation period from April to June 2020. The number of tweets increased during the early weeks of July 2020 as a result of the increasing number of mixed reactions to the reopening of schools. The study findings and recommendations underscore the need for educational systems, government agencies, and other stakeholders to practically implement online learning measures and strategies for ERL in the quest of reopening of schools.
    Language English
    Publishing date 2021-06-17
    Publishing country England
    Document type Journal Article
    ZDB-ID 2015214-0
    ISSN 1365-2729 ; 0266-4909
    ISSN (online) 1365-2729
    ISSN 0266-4909
    DOI 10.1111/jcal.12574
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: COVID-19 pandemic and African innovation: Finding the good from the bad using Twitter data and text mining approach.

    Asare, Andy Ohemeng / Sarpong, Eric Ohemeng / Truong Holds, Ngoc / Osei-Bonsu, Patrick / Ahado, Samuel / Mensah, William Gyasi

    International social science journal

    2022  

    Abstract: This study investigates public sentiments and the essential topics of discussion on Africa's innovation amidst COVID-19. Web scraping techniques were used to collect and parse data from Twitter platform using the keywords "Africa Innovation COVID-19". A ... ...

    Abstract This study investigates public sentiments and the essential topics of discussion on Africa's innovation amidst COVID-19. Web scraping techniques were used to collect and parse data from Twitter platform using the keywords "Africa Innovation COVID-19". A total of 54,318 cleaned English tweets were gathered and analysed using Twint Python Libraries. Our sentiment analysis findings revealed that 28,084 tweets (52 per cent) were positive, 21,037 (39 per cent), and 5197 (9 per cent) of tweets were neutral and negative, respectively, for Polarity sentiments. Notably, Healthcare, Imagination, Support, Webinar, Learning, Future, Rwanda, and Challenge were the most discussed topics on Africa's innovation during COVID-19. The topic labelling sentiments on the themes identified were positive, neutral, and negative, respectively. The study also revealed a cluster relationship between all identified topics. The relationship among these themes divulged how COVID-19 is positively shaping social and technological innovation in Africa. The study further presented practical implications to better position African leaders and policymakers to capitalise on the current innovation ecosystems and institutional capacities to transform the continent into a digital and innovation hub. The research concludes with theoretical recommendations and study limitations that will guide researchers and academicians in conducting future research in the subject area.
    Language English
    Publishing date 2022-11-30
    Publishing country England
    Document type Journal Article
    ZDB-ID 1481118-2
    ISSN 1468-2451 ; 0020-8701
    ISSN (online) 1468-2451
    ISSN 0020-8701
    DOI 10.1111/issj.12386
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: COVID-19

    Asare, Andy Ohemeng / Addo, Prince Clement / Sarpong, Eric Ohemeng / Kotei, Daniel

    Open Journal of Business and Management

    Optimizing Business Performance through Agile Business Intelligence and Data Analytics

    2020  Volume 08, Issue 05, Page(s) 2071–2080

    Keywords covid19
    Publisher Scientific Research Publishing, Inc.
    Publishing country us
    Document type Article ; Online
    ZDB-ID 2781653-9
    ISSN 2329-3292 ; 2329-3284
    ISSN (online) 2329-3292
    ISSN 2329-3284
    DOI 10.4236/ojbm.2020.85126
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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