Forecast of COVID-19 Confirmed Cases across Region in Korea Using Long Short-term Memory
1
조회
0
다운로드
3
인용
0
참고문헌
키워드
초록
The spread of COVID-19 prediction is one of the very important tasks in determining quarantine policies in terms of a pandemic. With the recent development of artificial intelligence, the use of various prediction methods has been proposed. In addition, various data on the spread of COVID-19 are provided by each country. However, despite abundant data availability because of different prevention policies and situations in each country, the use of data available through machine learning to predict COVID-19 situations in certain countries is still limited. To leverage sufficient data for the prediction using deep learning, we attempted to make predictions via regional spread data in a country for weekly predictions of COVID-19 growth over time using long short-term memory (LSTM) methods. In addition, the numbers of confirmed cases, recoveries, and deaths, and the variable population were noted to track the correlation with the accuracy of prediction. The accuracy assessment of the presented model was based on root mean square error, mean absolute percentage error, and graph visualization. We believe that achieving it accurate predictions using biased data, such as a sharp increase in the numbers of confirmed cases because of highly contagious variants, including omicron variants, is not easy. However, LSTM can predict patterns similar to that of actual data using various variables and regional data.
Abstract
The spread of COVID-19 prediction is one of the very important tasks in determining quarantine policies in terms of a pandemic. With the recent development of artificial intelligence, the use of various prediction methods has been proposed. In addition, various data on the spread of COVID-19 are provided by each country. However, despite abundant data availability because of different prevention policies and situations in each country, the use of data available through machine learning to predict COVID-19 situations in certain countries is still limited. To leverage sufficient data for the prediction using deep learning, we attempted to make predictions via regional spread data in a country for weekly predictions of COVID-19 growth over time using long short-term memory (LSTM) methods. In addition, the numbers of confirmed cases, recoveries, and deaths, and the variable population were noted to track the correlation with the accuracy of prediction. The accuracy assessment of the presented model was based on root mean square error, mean absolute percentage error, and graph visualization. We believe that achieving it accurate predictions using biased data, such as a sharp increase in the numbers of confirmed cases because of highly contagious variants, including omicron variants, is not easy. However, LSTM can predict patterns similar to that of actual data using various variables and regional data.
참고문헌 (18)
- Lewnard JA, "Clinical outcomes among patients infected with Omicron (B.1.1.529) SARS-CoV-2 variant in southern California", med Rxiv2022.01.11.22269045, ~
- Cooper I, "A SIR model assumption for the spread of Covid-19 in different communities", Chaos Soliton Fract, 139, pp.110057~ (2020)
- Kim YJ, "Estimating a breakpoint in the pattern of spread of COVID-19 in South Korea", Int J Infect Dis, 97, pp.360~364 (2020)
- Goo T, "Forecasting of the COVID-19 pandemic situation of Korea", Genomics Inform, 19, pp.e11~ (2021)
- Yang Z, "Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions", J Thoracic Dis, 12, pp.165~174 (2020)
- Moein S, "Inefficiency of SIR models in forecasting COVID-19 epidemic : a case study of Isfahan", Sci Rep, 11, pp.4725~ (2021)
- Zhang G, "Forecasting with artificial neural networks : The state of the art", Int J Forecasting, 14(1), pp.35~62 (1998)[DOI]
- Hochreiter S, "Long short-term Memory", Neural Comput, 9, pp.1735~1780 (1997)
- Längkvist M, "A review of unsupervised feature learning and deep learning for time-series modeling", Pattern Recogn Lett, 42, pp.11~24 (2014)
- 이동원, "KOSPI200 Prediction through Low-Pass Filtered Long Short-Term Memory Algorithm", Quantitative Bio-Science, 39(1), pp.25~31 (2020), 자연과학연구소[DOI]
- 양준혁, "Forecasting the KOSPI 200 Stock Index Based on LSTM Autoencoder", Quantitative Bio-Science, 39(2), pp.101~109 (2020), 자연과학연구소[DOI]
- Saba AI, "Forecasting the prevalence of COVID19 outbreak in egypt using non-linear autoregressive artificial neural networks", Process Saf Environ, 141, pp.1~8 (2020)
- Yousaf M, "Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan", Chaos Soliton Fract, 138, pp.109926~ (2020)
- Velásquez R, "Forecast and evaluation of COVID-19spreading in USA with reduced-space Gaussian process regression", Chaos Soliton Fract, 136, pp.109924~ (2020)
- Chimmula VKR, "Time series forecasting of COVID19 transmission in Canada using LSTM networks", Chaos Soliton Fract, 135, pp.109864~ (2020)
- Yang Z, "Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions", J Thorac Dis, 12, pp.165~174 (2020)
- "2005", ~
- "National Institute of Mathematical Sciences"[URL]