COVID-19 Case Predictions: Anticipating Future Outbreaks Through Data

Volume 19 No 7 (2021)

Vijay Kumar Reddy Voddi, Komali Reddy Konda

DOI: 10.48047/nq.2021.19.7.NQ21136

Abstract

The COVID-19 pandemic has highlighted the critical need for accurate forecasting models to predict disease outbreaks and guide public health interventions. This study evaluates various data-driven predictive models for forecasting COVID-19 cases, utilizing epidemiological data from 2020 to 2023. By comparing statistical models, machine learning algorithms, and hybrid approaches, we identify the most effective methods for anticipating future outbreaks. The results demonstrate that incorporating real-time data and ensemble modeling techniques significantly improves prediction accuracy, aiding in timely decision-making for pandemic response efforts.

Keywords

COVID-19, Case Predictions, Machine Learning, Time Series Forecasting, Pandemic Response, Epidemiological Modeling

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