Evaluating mathematical models for predicting the transmission of COVID-19 and its variants towards sustainable health and well-being
Amarpreet Kaur Sabherwal, Anju Sood, Mohd Asif Shah
Abstract
Abstract This 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.