Exploring the Factors behind COVID-19 Surge: Predictive Modeling and Analysis

The COVID-19 pandemic has had a significant and unprecedented worldwide influence across the globe, increasing the number of cases and fatalities in various nations. To effectively mitigate and prevent its spread, it is crucial to comprehend the various factors contributing to its transmission. This research paper aims to address this issue by exploring the data collected from official sources in India, which includes variables such as temperature, humidity, air quality index, demographic characteristics, medical history, and lifestyle choices. We aim to use predictive modelling approaches to explore the connection between numerous parameters and the number of COVID-19 cases in the Delhi-NCR region. The dataset's characteristics were identified through exploratory data analysis, and subsequent modelling processes included feature selection, model training, and evaluation to ensure accurate predictions. A Light Gradient Boosted Machine (LightGBM) model is employed for the analysis. In addition, the influence of demographic characteristics, medical and surgical history, smoking and alcohol habits, and an athletic lifestyle on COVID-19 patients is also investigated. The findings of this study contribute to understanding the various dynamics of COVID-19 transmission and may be used to design specific treatments and public health initiatives for effective control and prevention.