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Uncertainty Quantification for Epidemic Risk Management: Case of SARS-CoV-2 in Morocco.
Lamia Hammadi1,2, Hajar Raillani1,2, Babacar Mbaye Ndiaye3
1Laboratory of Engineering Sciences for Energy, National School of Applied Sciences ENSAJ, UCD, El Jadida 24000, Morocco.
Summary
This study introduces a novel uncertainty quantification (UQ) method for precise epidemic risk modeling and prediction. The approach accurately forecasts key indicators like new cases and deaths, aiding disaster management.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Data Science
Background:
- Accurate epidemic risk modeling is crucial for public health preparedness.
- Existing methods often struggle with quantifying uncertainty in predictions.
- Uncertainty quantification (UQ) offers a framework to address these limitations.
Purpose of the Study:
- To develop and validate a new UQ-based method for epidemic risk modeling and prediction.
- To apply the proposed method to SARS-CoV-2 data from Morocco.
- To establish a foundation for quantitative disaster management tools.
Main Methods:
- State variables represented in finite-dimensional Hilbert subspaces.
- Utilized collocation (COL) and moment matching (MM) approaches for coefficient determination.
- Applied UQ to estimate probability distributions of epidemic risk variables.
Main Results:
- Achieved high precision in estimating epidemic risk indicators, including detections, deaths, and new cases.
- Demonstrated very low root mean square errors (RMSE) between predicted and observed values.
- Validated the model's effectiveness using SARS-CoV-2 data from Morocco.
Conclusions:
- The proposed UQ method provides accurate epidemic risk predictions.
- The approach facilitates the development of decision-making tools for epidemic risk management.
- This work contributes to a quantitative disaster management framework for humanitarian supply chains.
Keywords:
Hilbert expansionSARS-CoV-2collocationepidemic riskmoment matchingrisk managementuncertainty quantificationMore Related Videos
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