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The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases
Nurul Absar1, Nazim Uddin1, Mayeen Uddin Khandaker2,3
1Department of Computer Science and Engineering, BGC Trust University, Bangladesh, Chittagong, 4381, Bangladesh.
Insights
This study used Long Short-Term Memory (LSTM) deep learning to forecast COVID-19 trends in Bangladesh. The accurate predictions can aid authorities in implementing preventive measures to control the pandemic
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- The COVID-19 pandemic presents a significant public health crisis in Bangladesh, exacerbated by the lack of a comprehensive health policy.
- Predicting the pandemic's trajectory is crucial for effective population health management.
- Machine learning offers potential solutions for disease spread detection and trend prediction.
Purpose of the Study:
- To predict the progression of the COVID-19 epidemic in Bangladesh for over a year.
- To utilize deep learning methodologies for forecasting epidemic parameters, risks, and trends.
- To inform preventive strategies and policy-making through accurate epidemic modeling.
Main Methods:
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) deep learning models were employed.
- Daily confirmed, recovered, and death case data from March 2020 to August 2021 were utilized.
- Model accuracy was validated using Root Mean Square Error (RMSE) values.
Main Results:
- The LSTM model demonstrated high accuracy in predicting confirmed, recovered, and death cases.
- Achieved lower Root Mean Square Error (RMSE) values compared to contemporary techniques.
- The model successfully predicted epidemic progression under various scenarios.
Conclusions:
- The Long Short-Term Memory (LSTM) model is effective for predicting contagious disease outbreaks.
- The findings provide valuable insights into the pandemic's severity in Bangladesh.
- Results can assist authorities in developing timely and effective precautionary measures.
Abstract:
The coronavirus disease that outbreak in 2019 has caused various health issues. According to the WHO, the first positive case was detected in Bangladesh on 7th March 2020, but while writing this paper in June 2021, the total confirmed, recovered, and death cases were 826922, 766266 and 13118, respectively. Due to the emergence of COVID-19 in Bangladesh, the country is facing a major public health crisis. Unfortunately, the country does not have a comprehensive health policy to address this issue. This makes it hard to predict how the pandemic will affect the population. Machine learning techniques can help us detect the disease's spread. To predict the trend, parameters, risks, and to take preventive measure in Bangladesh; this work utilized the Recurrent Neural Networks based Deep Learning methodologies like LongShort-Term Memory. Here, we aim to predict the epidemic's progression for a period of more than a year under various scenarios in Bangladesh. We extracted the data for daily confirmed, recovered, and death cases from March 2020 to August 2021. The obtained Root Mean Square Error (RMSE) values of confirmed, recovered, and death cases indicates that our result is more accurate than other contemporary techniques. This study indicates that the LSTM model could be used effectively in predicting contagious diseases. The obtained results could help in explaining the seriousness of the situation, also mayhelp the authorities to take precautionary steps to control the situation.
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