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Prophylactic and therapeutic measures for emerging and re-emerging viruses: artificial intelligence and machine learning - the key to a promising future

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Abstract

Purpose

Emerging and re-emerging viral infections are accountable for fatal outbreaks across the globe. In the light of the COVID-19 catastrophe and mpox exigency, the gaps in prophylactic measures have been envisaged. Emerging and re-emerging infections like poxviruses, Zika, Marburg, Ebola, Hanta, Nipah viruses have further challenged the healthcare sector by putting additional burden on therapeutic and diagnostic limitations. In the present review we also highlighted potential implications of artificial intelligence for long term solutions.

Methods

Artificial Intelligence (AI) and Machine Learning (ML)-based models have shown promise in accelerating the discovery of new antivirals or potential vaccine candidates. Deep learning (DL) based algorithms can integrate prodigious global data comprising epidemiology, genomics, pathology and molecular behaviour. Subsequently, support vector machine/ random forest/ neural network guided interpretations can comprehensively compile the available datasets for therapeutic and diagnostic predictions.

Results

The present review compiled the various AI-based algorithms and servers which were used for modelling studies as well as prophylactic measures during recent viral outbreaks. The impact of AI on surveillance, outcome prediction, patient monitoring, genomic tracking, clinical assistance, therapeutic screening, drug/ vaccine design and other experimental studies were emphasized.

Conclusions

The present review not only highlighted public-health management models but also provide leads in potential therapeutic targets as well as vaccine/ antiviral candidates. To support the context, the issues with existing therapeutic strategies are also overviewed and the prospects were identified. This review discusses a wide range of applications of AI and ML pertaining to the clinical domain.

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Data availability

The data associated with the manuscript are included in the manuscript.

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Acknowledgements

The authors would like to acknowledge the management of VIT, Vellore, for providing the necessary facilities to carry out the research. RGS would like to thank the Indian Council of Medical Research, New Delhi, for the Research Associateship [IRIS ID: 2021–8220]. The authors gratefully acknowledge ICMR for the research grant (Project number: AMR/Adhoc/290/2022-ECD-II).

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Author notes

  1. RC Theijeswini, Soumya Basu and Rayapadi G. Swetha contributed equally to this work.

Authors and Affiliations

  1. Medical and Biological Computing Laboratory, School of Biosciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, 632014, India

    RC Theijeswini, Rayapadi G. Swetha & Anand Anbarasu

  2. Department of Biotechnology, SBST, VIT, Vellore, 632014, India

    RC Theijeswini, Soumya Basu & Anand Anbarasu

  3. Department of Medical Microbiology and Immunology, Wisconsin Institute for Discovery, University of Wisconsin- Madison, 330N Orchard Road Madison, Madison, 53715, USA

    Jayaraman Tharmalingam

  4. Department of Biosciences, SBST, VIT, Vellore, 632014, India

    Sudha Ramaiah

  5. Department of Languages, School of Social Sciences and Languages (SSL), VIT, Vellore, 632014, India

    R Calaivanane

  6. Cardiff School of Management, Cardiff Metropolitan University, Cardiff, CF5 2YB, UK

    V. Raja Sreedharan

  7. Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, CF5 2YB, UK

    Paul Livingstone

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  1. RC Theijeswini
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Contributions

Conceptualization: Rayapadi G. Swetha, Sudha Ramaiah, and Anand Anbarasu; Data curation: RC Theijeswini, Rayapadi G. Swetha, and Soumya Basu; Writing-original draft preparation: RC Theijeswini, Rayapadi G. Swetha, and Soumya Basu; Validation: Rayapadi G. Swetha, Paul Livingstone, and Jayaraman Tharmalingam; Writing-review and editing: Raja Sreedharan, Paul Livingstone, Jayaraman Tharmalingam, Sudha Ramaiah, RC Theijeswini, and Anand Anbarasu; Supervision: Paul Livingstone, and Anand Anbarasu. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Paul Livingstone or Anand Anbarasu.

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Theijeswini, R., Basu, S., Swetha, R.G. et al. Prophylactic and therapeutic measures for emerging and re-emerging viruses: artificial intelligence and machine learning - the key to a promising future. Health Technol. 14, 251–261 (2024). https://doi.org/10.1007/s12553-024-00816-z

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