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Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases
Muhammad Shoaib1, Hamza Salahudin1, Muhammad Hammad2
1Agricultural Engineering Department, Bahauddin Zakariya University, Multan, Pakistan.
SN Computer Science
|July 14, 2021
Summary
Forecasting COVID-19 daily new cases using advanced models like Long Short-Term Memory (LSTM) neural networks accurately predicts future trends. This helps in developing effective strategies to manage the pandemic
Area of Science:
- Epidemiology and Public Health
- Data Science and Machine Learning
- Infectious Disease Modeling
Background:
- The COVID-19 pandemic presents significant global challenges, necessitating accurate disease transmission forecasting.
- Despite extensive research, no definitive cure for COVID-19 has been established, highlighting the need for predictive modeling.
- Understanding disease dynamics and projecting future trends are crucial for effective pandemic management.
Purpose of the Study:
- To predict daily new COVID-19 cases in Pakistan, USA, India, and Brazil using various forecasting techniques.
- To evaluate and compare the performance of Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Artificial Neural Network (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) models.
- To identify the most accurate model for forecasting COVID-19 cases to aid in future pandemic preparedness.
Main Methods:
- Utilized time-series data of daily confirmed COVID-19 cases from the initial registration date to November 30, 2020, for four countries.
- Applied five distinct forecasting models: ARIMA, ETS, ANN, GEP, and LSTM.
- Assessed model performance using statistical parameters including R-squared (R²), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE).
Main Results:
- The Long Short-Term Memory (LSTM) neural network model demonstrated superior performance in forecasting COVID-19 cases across all four selected countries.
- Model accuracy, measured by R², ranged from 0.85 in Brazil to 0.96 in Pakistan.
- High Nash-Sutcliffe Efficiency (NSE) values were achieved: 0.99 in India, 0.98 in the USA and Pakistan, and 0.97 in Brazil.
Conclusions:
- The LSTM model provides a highly accurate method for forecasting future COVID-19 case numbers.
- Accurate case projections enable the anticipation of potential pandemic peaks, facilitating proactive public health interventions.
- This forecasting capability is vital for developing robust strategies to mitigate the impact of future pandemic waves.
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