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Threshold dynamics and epidemic-informed machine learning for forecasting of mpox: A U.S. case study
S Nivetha1, Parthasakha Das2, Mini Ghosh1
1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai Campus, Chennai 600127, Tamil Nadu, India.
Chaos (Woodbury, N.Y.)
|November 13, 2025
Summary
This study models mpox (monkeypox) transmission dynamics, finding that timely treatment, hygiene, and vaccination are crucial for mitigating spread. Our integrated framework aids in predicting outbreaks and informing public health strategies.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- Mpox (monkeypox) is a re-emerging viral disease with sustained human transmission globally.
- Understanding mpox transmission dynamics is critical for effective public health interventions.
Purpose of the Study:
- To develop and validate a compartmental epidemic model for mpox transmission.
- To assess the impact of medical interventions on mpox disease progression and recovery.
- To enhance real-time forecasting of mpox outbreaks using machine learning.
Main Methods:
- Developed a compartmental epidemic model incorporating treatment functions.
- Calibrated the model using U.S. mpox case data and Trust Region Reflective optimization.
- Performed global sensitivity analysis and examined bifurcation structures using the basic reproduction number (RM0).
- Integrated mechanistic insights into Autoregressive Neural Network (ARNN), ARIMA, and LSTM models for forecasting.
Main Results:
- The model accurately captures mpox transmission trends and identifies key influencing parameters.
- Bifurcation analysis revealed complex endemic dynamics and threshold behaviors.
- Epidemic-informed machine learning models demonstrated improved predictive accuracy for real-time forecasting.
- Sensitivity analysis highlighted the significance of parameters related to treatment and transmission.
Conclusions:
- Timely medical treatment, enhanced hygiene practices, and vaccination are essential for controlling mpox.
- The developed integrated modeling and forecasting framework offers valuable tools for public health decision-making.
- This research provides a robust approach for anticipating mpox outbreaks and guiding strategic responses.
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