  The emergence and spread of deadly pandemics has repeatedly occurred
throughout history, causing widespread infections and loss of life. The rapid
spread of pandemics have made governments across the world adopt a range of
actions, including non-pharmaceutical measures to contain its impact. However,
the dynamic nature of pandemics makes selecting intervention strategies
challenging. Hence, the development of suitable monitoring and forecasting
tools for tracking infected cases is crucial for designing and implementing
effective measures. Motivated by this, we present a hybrid pandemic infection
forecasting methodology that integrates compartmental model and learning-based
approaches. In particular, we develop a compartmental model that includes
time-varying infection rates, which are the key parameters that determine the
pandemic's evolution. To identify the time-dependent infection rates, we
establish a hybrid methodology that combines the developed compartmental model
and tools from optimization and neural networks. Specifically, the proposed
methodology estimates the infection rates by fitting the model to available
data, regarding the COVID-19 pandemic in Cyprus, and then predicting their
future values through either a) extrapolation, or b) feeding them to neural
networks. The developed approach exhibits strong accuracy in predicting
infections seven days in advance, achieving low average percentage errors both
using the extrapolation (9.90%) and neural network (5.04%) approaches.
