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Predicting Infectious Diseases: A Bibliometric Review on Africa

Author

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  • Paulina Phoobane

    (Department of Information Technology, Central University of Technology, Free State, Private Bag X200539, Bloemfontein 9300, South Africa)

  • Muthoni Masinde

    (Department of Information Technology, Central University of Technology, Free State, Private Bag X200539, Bloemfontein 9300, South Africa)

  • Tafadzwanashe Mabhaudhi

    (Department of Information Technology, Central University of Technology, Free State, Private Bag X200539, Bloemfontein 9300, South Africa
    Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Pietermaritzburg 3201, South Africa
    International Water Management Institute (IWMI-GH), West Africa Office, PMB CT 112 Cantonments, Accra GA015, Ghana)

Abstract

Africa has a long history of novel and re-emerging infectious disease outbreaks. This reality has attracted the attention of researchers interested in the general research theme of predicting infectious diseases. However, a knowledge mapping analysis of literature to reveal the research trends, gaps, and hotspots in predicting Africa’s infectious diseases using bibliometric tools has not been conducted. A bibliometric analysis of 247 published papers on predicting infectious diseases in Africa, published in the Web of Science core collection databases, is presented in this study. The results indicate that the severe outbreaks of infectious diseases in Africa have increased scientific publications during the past decade. The results also reveal that African researchers are highly underrepresented in these publications and that the United States of America (USA) is the most productive and collaborative country. The relevant hotspots in this research field include malaria, models, classification, associations, COVID-19, and cost-effectiveness. Furthermore, weather-based prediction using meteorological factors is an emerging theme, and very few studies have used the fourth industrial revolution (4IR) technologies. Therefore, there is a need to explore 4IR predicting tools such as machine learning and consider integrated approaches that are pivotal to developing robust prediction systems for infectious diseases, especially in Africa. This review paper provides a useful resource for researchers, practitioners, and research funding agencies interested in the research theme—the prediction of infectious diseases in Africa—by capturing the current research hotspots and trends.

Suggested Citation

  • Paulina Phoobane & Muthoni Masinde & Tafadzwanashe Mabhaudhi, 2022. "Predicting Infectious Diseases: A Bibliometric Review on Africa," IJERPH, MDPI, vol. 19(3), pages 1-20, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1893-:d:744385
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    References listed on IDEAS

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    1. M. C. Thomson & F. J. Doblas-Reyes & S. J. Mason & R. Hagedorn & S. J. Connor & T. Phindela & A. P. Morse & T. N. Palmer, 2006. "Malaria early warnings based on seasonal climate forecasts from multi-model ensembles," Nature, Nature, vol. 439(7076), pages 576-579, February.
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    1. Wanida Mala & Polrat Wilairatana & Apichai Wattanapisit & Kwuntida Uthaisar Kotepui & Manas Kotepui, 2022. "Malaria Publications before and during COVID-19 Pandemic: A Bibliometric Analysis," Publications, MDPI, vol. 10(3), pages 1-14, August.
    2. Hasan Ejaz & Hafiz Muhammad Zeeshan & Fahad Ahmad & Syed Nasir Abbas Bukhari & Naeem Anwar & Awadh Alanazi & Ashina Sadiq & Kashaf Junaid & Muhammad Atif & Khalid Omer Abdalla Abosalif & Abid Iqbal & , 2022. "Bibliometric Analysis of Publications on the Omicron Variant from 2020 to 2022 in the Scopus Database Using R and VOSviewer," IJERPH, MDPI, vol. 19(19), pages 1-25, September.

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