Infectious Disease Prediction Model: A Systematic Review

DOI: https://doi.org/10.62517/jmhs.202605212

Author(s)

Chenrui Sun

Affiliation(s)

Anhui University, Hefei, Anhui, China

Abstract

Predictive modeling for infectious diseases plays a vital role in guiding global public health strategies, supporting epidemic containment, assessing pathogen severity, and optimizing healthcare resource allocation. The COVID-19 pandemic has spurred extensive research in epidemic forecasting, offering valuable insights for public health decision-making. This article systematically reviews studies up to 2025, focusing on dynamic models, time series models, machine learning models, and hybrid models. It examines their theoretical foundations, applications, and evolution in COVID-19 prediction, and critically compares their performance, limitations, and suitability across different scenarios-such as short-term case forecasting, long-term trend simulation, and medical resource demand warning. The review aims to outline future research directions and provide guidance for researchers in model selection and integration.

Keywords

Infectious Disease Prediction; Dynamic Models; Systematic Review; COVID-19

References

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