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Learning where to look for COVID-19 growth: Multivariate analysis of COVID-19 cases over time using explainable
Novanto Yudistira1, Sutiman Bambang Sumitro2, Alberth Nahas3,4
1Faculty of Computer Science, University of Brawijaya, Indonesia.
Applied Soft Computing
|May 17, 2021
Summary
Multivariate analysis, including environmental factors like UV radiation, helps predict COVID-19 spread. An explainable Convolution-LSTM model identifies key drivers for better pandemic decision-making.
Area of Science:
- Epidemiology
- Data Science
- Public Health
Background:
- Predicting COVID-19 dynamics requires understanding complex, multivariate factors.
- Environmental, educational, governmental, health, economic, and behavioral elements influence epidemic spread.
Purpose of the Study:
- To develop and validate a model for identifying key drivers of daily COVID-19 cases.
- To provide explainable insights into pandemic dynamics for stakeholders.
Main Methods:
- Utilized multivariate analysis and descriptive statistics.
- Developed an explainable Convolution-LSTM model with 1D CNN and LSTM layers.
- Employed gradient-based visual attribution for generating saliency maps.
Main Results:
- The Convolution-LSTM model achieved lower prediction errors than existing models.
- Identified specific variables and time intervals crucial for COVID-19 case growth.
- Visual attribution highlighted the contribution of factors like UV radiation.
Conclusions:
- Explainable AI models are crucial for understanding and managing pandemics.
- The model provides actionable insights for public health decision-making during and after pandemics.
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