Simon James FongLobo Marques, Joao AlexandreJoao AlexandreLobo MarquesG. LiN. DeyRubén G. CrespoE. Herrera-ViedmaF. Nauber Bernardo GoisJosé Xavier Neto2026-02-062026-02-06202297830309528089783030952815https://dspace.usj.edu.mo/handle/123456789/700610.1007/978-3-030-95281-5_3The COVID-19 pandemic spread generated an urgent need for computational systems to model its behavior and support governments and healthcare teams to make proper decisions. There are not many cases of global pandemics in history, and the most recent one has unique characteristics, which are tightly connected to the current society’s lifestyle and beliefs, creating an environment of uncertainty. Because of that, the development of mathematical/computational models to forecast the pandemic behavior since its beginning, i.e., with a restricted amount of data collected, is necessary. This chapter focuses on the analysis of different data mining techniques to allow the pandemic prediction with a small amount of data. A case study is presented considering the data from Wuhan, the Chinese city where the virus was first detected, and the place where the major outbreak occurred. The PNN + CF method (Polynomial Neural Network with Corrective Feedback) is presented as the technique with the best prediction performance. This is a promising method that might be considered in future eventual waves of the current pandemic or event to have a suitable model for future epidemic outbreaks around the world.enbook-chapter