Abstract

In this chapter, we aim to provide a biological, statistical, and mathematical framework to understand and formulate sensible models to study the spreading dynamics of COVID-19. First, we discuss the epidemiological and clinical features that make COVID-19 challenging-to-control in different scales and ways. We then describe the different error sources present in raw COVID-19 epidemiological data and the logistic limitations associated with non-pharmaceutical interventions (NPIs), like test-trace-and-isolate (TTI). By studying compartmental SIR and SIR-like mathematical models and their underlying hypotheses, we demonstrate the derivation of significant parameters for evaluating this pandemic's progression, as the reproduction number {\$}{\$}R{\_}t{\$}{\$}Rt. Then, we provide the statistical basis for the correction of delay-induced errors in raw data through the ``nowcasting'' of infections and describe the Machine-Learning-based approaches to tackle significant challenges in modeling COVID-19. We end our chapter with several case studies, where we describe the modeling aspects as carefully as their results, providing the reader with fresh multi-disciplinary insights to inspire their own models