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Infectious Disease Modeling: From Traditional to Evolutionary Algorithms

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Abstract

The recent worldwide catastrophe named COVID-19 has motivated experts from various fields to contribute to tackling the situation, such as by forecasting the spread of infectious disease, which is the need of the hour. Screening, contact tracing, forecasting, and medication development have all seen tremendous advancements as a result of the technical and medical industry’s evolution. Models utilized in previously prevailed infectious diseases across the globe gave them a base to study and implement in the current scenario. This work aims to provide a comprehensive analysis of the Compartmental, Time-Series, and Machine Learning (ML), including the subset Deep Learning (DL) models illustrating the spread of infectious diseases. Reliable predictions can help in the choice and application of measures to scale back the resulting morbidity and mortality. This paper highlights the studies from traditional to evolutionary algorithms carried out in the field of mathematics, statistics, ML, and DL to model the spread of infectious diseases, with special focus on COVID-19. It also addresses the scope of improvement in the research work done by utilizing such algorithms. The implemented models have shown the need to include additional factors and characteristics to enhance accuracy.

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    Isha Malhotra & Nidhi Goel

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Malhotra, I., Goel, N. Infectious Disease Modeling: From Traditional to Evolutionary Algorithms. Arch Computat Methods Eng 31, 663–699 (2024). https://doi.org/10.1007/s11831-023-09997-8

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