Author:
V R Manuraj V R, Dr. S. Shankar, Dr.S. UmaPublished in
Journal of Science Technology and Research( Volume , Issue )
ABSTRACT:
The Improved Mathematical Model for Continuous COVID-19 prediction provides valuable insight into monitoring and managing pandemic trends. Since its emergence in December 2019, COVID-19 has disrupted global health and economies. Accurate forecasting models have become crucial for government and healthcare planning. This study proposes an improved mathematical model based on the Generalized Inverse Weibull distribution, enhanced by iterative weighting for more accurate epidemic tracking. Combined with machine learning techniques and implemented via cloud computing, this model enables real-time, data-driven prediction of virus spread patterns. By analyzing global datasets, the model helps identify potential outbreaks and recovery phases. It also assists in anticipating second or third waves of infection, allowing timely interventions. The approach enhances prediction precision and supports proactive decision-making during health crises. The Improved Mathematical Model for Continuous forecasting presents a significant advancement in epidemic monitoring and can be adapted for future pandemics to reduce risks and improve preparedness.
INTRODUCTION:
The COVID-19 pandemic, caused by SARS-CoV-2, has significantly affected health systems and economies globally. As the virus continues to spread, accurate modeling and prediction remain vital for disease control. The Improved Mathematical Model for Continuous forecasting uses machine learning and cloud computing to analyze COVID-19 trends and predict future outbreaks. This study applies the Generalized Inverse Weibull distribution with iterative weighting to enhance prediction accuracy. Real-time analysis allows authorities to monitor infection rates, anticipate surges, and implement preventive measures accordingly. The World Health Organization declared COVID-19 a global pandemic in March 2020, and since then, countries have faced multiple infection waves. This model enables early detection of potential outbreaks, helping policymakers and healthcare professionals mitigate impacts efficiently. By offering a data-driven framework, it improves the precision of epidemic forecasting. The Improved Mathematical Model for Continuous forecasting represents a proactive strategy to address both current and future public health emergencies.