Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron...

Area of Science:

• Epidemiology
• Data Science
• Public Health

Background:

• The COVID-19 pandemic presents a global healthcare crisis, with rapidly evolving data on cases, hospitalizations, and mortality.
• The emergence of new variants, such as Omicron, necessitates advanced methods for early detection and forecasting to manage outbreaks effectively.

Purpose of the Study:

• To develop and evaluate a predictive framework for forecasting COVID-19 related deaths using weekly mobility data.
• To analyze the current global COVID-19 situation and predict future trends to inform public health strategies and economic recovery.

Main Methods:

• Utilized weekly mobility data for statistical analysis and forecasting of COVID-19 deaths.
• Employed a multi-layer perceptron neural network (MLPNN), a deep learning model, to create a predictive framework.
• Assessed forecasting performance using metrics including Case Fatality Ratio (CFR) and Cronbach's alpha.

Main Results:

• The MLPNN demonstrated superior performance in forecasting statistics for infected patients and deaths in selected regions.
• The methodology provides a robust approach for analyzing current trends and predicting future COVID-19 outbreaks.
• Analysis included emerging variants, challenges, and issues critical for preventing future pandemics.

Conclusions:

• Deep learning models, specifically MLPNN, are effective tools for accurate COVID-19 forecasting.
• Mobility data combined with advanced analytics can enhance pandemic response and management strategies.
• Continued research into variants and public health challenges is crucial for future outbreak prevention.