Abstract
AbstractThis review thoroughly explores numerous key areas contributing to sustainable health and well-being. It encompasses precision medicine, eco-friendly healthcare practices, digital health technologies, holistic well-being approaches, community health promotion, global health protection, and data-driven public health techniques, providing a roadmap for a greater resilient healthcare future. The study evaluates the effectiveness of mathematical modelling in predicting COVID-19 transmission patterns and variants. It starts by providing an overview of COVID-19 and its variants, which include their origins and modes of transmission, then delves into prediction techniques and mathematical modelling, focusing especially on the use of differential equations-based modelling to understand disease progression. The objective is to enhance scientific information of COVID-19 variants and their effect on public health by providing insights, situation analyses, and policy recommendations derived from mathematical modelling. This comprehensive review focuses on serving as a useful resource for researchers, policymakers, and healthcare experts in addressing the pandemic and its evolving variants.
Publisher
Springer Science and Business Media LLC
Reference94 articles.
1. Segel L, Edelstein-Keshet L. A Primer on Mathematical Models in Biology (Vol.129). Society for Industrial and Applied Mathematics; 2013.
2. Jumper TJK, Alpha Fold HPD. Computational predictions of protein structures associated with COVID-19; 2020. Retrieved from https://www.deepmind.com/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19.
3. Zhavoronkov A, Aladinskiy V, Zhebrak A, Zagribelnyy B, Terentiev V, Bezrukov DS, et al. otential COVID-19 3C-like protease inhibitors designed using generative deep learning approaches. Chem Rxiv. Preprint; 2020. https://doi.org/10.26434/chemrxiv, 11829102, v2.
4. CDC. (2023) SARS-CoV-2 Variant Classifications and Definitions. Centers for Disease Control and Prevention. Retrieved from https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/variant-surveillance/variant-info.html
5. WHO (2023). Tracking SARS-CoV-2 variants. World Health Organization. Retrieved from https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/