Absar Nurul, Uddin Nazim, Khandaker Mayeen Uddin, Ullah Habib
Department of Computer Science and Engineering, BGC Trust University, Bangladesh, Chittagong, 4381, Bangladesh.
Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Bandar Sunway, 47500, Selangor, Malaysia.
Infect Dis Model. 2022 Mar;7(1):170-183. doi: 10.1016/j.idm.2021.12.005. Epub 2021 Dec 28.
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 7 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.
2019年爆发的冠状病毒病引发了各种健康问题。根据世界卫生组织的数据，2020年3月7日在孟加拉国检测到首例阳性病例，但在2021年6月撰写本文时，确诊、康复和死亡病例总数分别为826922例、766266例和13118例。由于孟加拉国出现了新冠疫情，该国正面临重大的公共卫生危机。不幸的是，该国没有全面的卫生政策来应对这一问题。这使得很难预测疫情将如何影响民众。机器学习技术可以帮助我们检测疾病的传播情况。为了预测孟加拉国的疫情趋势、参数、风险并采取预防措施；这项工作采用了基于循环神经网络的深度学习方法，如长短期记忆网络。在这里，我们旨在预测孟加拉国在各种情况下一年多时间内的疫情发展情况。我们提取了2020年3月至2021年8月每日确诊、康复和死亡病例的数据。确诊、康复和死亡病例的均方根误差（RMSE）值表明，我们的结果比其他当代技术更准确。这项研究表明，长短期记忆网络模型可以有效地用于预测传染病。所获得的结果有助于解释疫情形势的严峻性，也可能有助于当局采取预防措施来控制局势。
