Mathematical Modelling and Prediction Tools for the COVID-19 Pandemic: A Review (Preprint)

UNSTRUCTURED The latest threat to global health is the ongoing outbreak of the Coronavirus Disease 2019 (COVID-19). There are three main areas of modeling research, namely epidemiology, drug repurposing and vaccine design. The most important purpose of the models is to inform institutional and nationwide efforts to ensure patient safety. This study aimed to review COVID-19 modelling and prediction tools. Understanding these methods streamlines the strengths and limitations of each method. We researched the traditional model and the more current models that flourish during the pandemic. This understanding is the key to the proper use of specific models to achieve certain goals. Modeling approaches for COVID-19 can be very broadly categorized into phenomenological models and mechanistic models. Phenomenological approaches treat the modeling problem purely from an empirical perspective. From our survey, there are three major types of approaches under the phenomenological models: time-series analysis and forecasting, fractal-based models, and machine learning approaches. Mechanistic models consider the underlying mechanics of the epidemic. In this survey, compartmental models and agent-based models are categorized as mechanistic models. We studied 46 scientific articles (published between 22 February 2020 and 29 January 2021) that we think are representative of the scientific community’s approaches in modeling and prediction. We highlight the challenges and limitations of modelling approaches such as the need for high quality data, and interpretable models. Finally, we list the desired features for developing robust and reliable modelling and prediction tools.