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Forecasting the spread of COVID-19 using LSTM network
Shiu Kumar1, Ronesh Sharma2, Tatsuhiko Tsunoda3,4,5
1School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji. shiu748@gmail.com.
BMC Bioinformatics
|June 11, 2021
Summary
This study developed a long short-term memory (LSTM) network model to forecast COVID-19 containment dates. The model accurately predicted New Zealand's containment and offers insights for other nations to manage the pandemic effectively.
Area of Science:
- Epidemiology
- Data Science
- Public Health
Background:
- The COVID-19 pandemic caused global disruption, necessitating effective containment strategies.
- Economic impacts of lockdowns and travel restrictions underscore the need for predictive tools.
- Informed decision-making for governments and businesses requires foresight into pandemic containment timelines.
Purpose of the Study:
- To develop and validate a predictive model for forecasting COVID-19 containment dates.
- To provide a tool for social and administrative decision-making regarding pandemic response.
- To assist policymakers and entrepreneurs in understanding potential pandemic trajectories.
Main Methods:
- Utilized a long short-term memory (LSTM) network for time-series forecasting.
- Trained and validated the model using COVID-19 data from New Zealand.
- Applied the validated model to forecast containment dates for other countries.
Main Results:
- The LSTM-based model accurately predicted New Zealand's COVID-19 containment timeline.
- The model successfully forecasted the date when daily new cases reached zero in New Zealand.
- Forecasting for other nations indicates potential containment timelines.
Conclusions:
- Forecasted dates serve as predictions to guide practical decision-making and influence future outcomes.
- The model suggests that most countries may require over three months for containment.
- Implementing stringent actions and restrictions is indicated as necessary for effective pandemic control.
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