Abstract
Forecasting the trend of the COVID-19 pandemic has been crucial for controlling the spread and making related disease control policies. Various forecasting techniques can be served thereby assisting in strengthening the healthcare system to fight the pandemic. With the development of big data and machine learning techniques, prediction models become more accurate in yielding preparations against risks and threats. In this chapter, three types of forecasting methods, machine learning models, time series forecasting techniques, and deep learning algorithms, are categorized and introduced, mathematically and empirically. To justify the outcomes from each model, this chapter has presented case studies of three pandemic scenarios, including the early stage, the second wave, and the real-time prediction, with real data for the United States. Model comparisons and evaluations have been also illustrated to forecast the number of possible causes. Various existing studies about pandemic predictions are included in the current research by big data analytics.