Forecast and prediction of Covid-19 impacts on society through ML

The COVID-19 pandemic has profoundly impacted global health, economies, and societies. As the disease continues to evolve, there is an increasing need for accurate and timely predictions to aid in formulating effective public health strategies and resource allocation. This abstract presents a study on applying machine learning techniques to predict and understand the dynamics of COVID-19. Machine learning models, specifically supervised learning algorithms, were trained on a comprehensive dataset encompassing various epidemiological factors,demographic information, and socio-economic indicators [2]. Feature engineering techniques were employed to extract relevant features from the dataset, such as daily cases, testing rates, vaccination coverage, and mobility patterns. Different machine learning algorithms, including random forests, support vector machines, and gradient boosting, were evaluated and optimized to achieve the best predictive performance. Cross-validation techniques were applied to ensure the robustness and generalizability of the models. Hyper parameter tuning and model selection processes were carried out to enhance the accuracy and interpretability of the predictions. The trained models demonstrated promising results in predicting COVID-19 outcomes, such as the number of cases, hospitalizations, and mortality rates. Furthermore, feature importance analysis provided valuable insights into the factors driving the spread and severity of the disease, enabling public health officials to make informed decisions. This study contributes to the growing body of machine learning research to understand and forecast the COVID-19 pandemic [1]. By leveraging the power of data-driven models, policymakers and healthcare professionals can better anticipate the trajectory of the disease, allocate resources efficiently, and implement targeted interventions to mitigate its impact.