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Data-driven mathematical modeling approaches for COVID-19: A survey
Jacques Demongeot1, Pierre Magal2
1Université Grenoble Alpes, AGEIS EA7407, La Tronche, F-38700, France.
Physics of Life Reviews
|August 14, 2024
Summary
This review details phenomenological modeling methods for COVID-19 epidemic waves, including single and multiple waves. It analyzes reported and unreported cases, aiding in understanding disease dynamics and forecasting future trends.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- The COVID-19 pandemic necessitated robust methods for tracking and predicting disease spread.
- Understanding epidemic dynamics is crucial for effective public health interventions.
Purpose of the Study:
- To review and present phenomenological modeling approaches for COVID-19 case evolution.
- To analyze single and multiple epidemic waves, including endemic periods.
- To compare phenomenological and mechanistic modeling strategies.
Main Methods:
- Systematic literature review of 260 articles across 11 sections.
- Phenomenological modeling of reported and unreported COVID-19 cases.
- Development and application of multi-compartmental models (with/without age structure).
Main Results:
- Detailed presentation of modeling methods for exponential growth, complete waves, and successive waves.
- Simulations of COVID-19 case evolution for 10 diverse geographical regions.
- Emphasis on the utility of phenomenological over mechanistic approaches for this data.
Conclusions:
- Phenomenological modeling provides a valuable framework for understanding and forecasting COVID-19 dynamics.
- The review synthesizes a broad range of modeling techniques relevant to infectious disease outbreaks.
- The findings support the application of these models for public health surveillance and planning.
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