The week's pick
Random Articles
Reseach Article
Forecasting the Number of Infections of Novel Coronavirus with Deep Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 20 |
| Year of Publication: 2020 |
| Authors: Vakada Naveen, Chunduri Aasish, Manne Kavya, Meda Vidhyalakshmi |
10.5120/ijca2020920158
|
Vakada Naveen, Chunduri Aasish, Manne Kavya, Meda Vidhyalakshmi . Forecasting the Number of Infections of Novel Coronavirus with Deep Learning. International Journal of Computer Applications. 176, 20 ( May 2020), 21-24. DOI=10.5120/ijca2020920158
Abstract
The novel coronavirus has infected thousands of people across the globe and killed hundreds of those infected. It causes an illness called COVID-19. The virus that causes this serious illness is SARS-CoV-2.There number of infections have increased gradually as more and more people are being infected. The epicentre of the outbreak has been under lockdown since the inception of the outbreak. In this paper a deep neural network has been used to forecast the number of coronavirus infections. Various deep learning techniques have been used to forecast the number of infections. The data related to the infections are collected on a daily basis since the beginning of the outbreak and fed to the deep neural network for training. Once the model is trained to achieve the desired accuracy, it is used to forecast the number of infections.
References
- Zhu, Na, Dingyu Zhang, Wenling Wang, Xingwang Li, Bo Yang, Jingdong Song, Xiang Zhao et al. "A novel coronavirus from patients with pneumonia in China, 2019." New England Journal of Medicine (2020).
- Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K.S., Lau, E.H., Wong, J.Y. and Xing, X., 2020. Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England Journal of Medicine.
- Marra, M.A., Jones, S.J., Astell, C.R., Holt, R.A., Brooks-Wilson, A., Butterfield, Y.S., Khattra, J., Asano, J.K., Barber, S.A., Chan, S.Y. and Cloutier, A., 2003. The genome sequence of the SARS-associated coronavirus. Science, 300(5624), pp.1399-1404.
- Hermanowicz SW. Forecasting the Wuhan coronavirus (2019-nCoV) epidemics using a simple (simplistic) model. medRxiv. 2020 Jan 1.
- Fu, Xinmiao, Qi Ying, Tieyong Zeng, Tao Long, and Yan Wang. "Simulating and Forecasting the Cumulative Confirmed Cases of SARS-CoV-2 in China by Boltzmann Function-based Regression Analyses." medRxiv (2020).
- Nesteruk I. Statistics based predictions of coronavirus 2019-nCoV spreading in mainland China. MedRxiv. 2020 Jan 1.
- Gamero, Javier, Juan A. Tamayo, and Juan A. Martinez-Roman. "Forecast of the evolution of the contagious disease caused by novel coronavirus (2019-nCoV) in China." arXiv preprint arXiv:2002.04739 (2020).
- Freeman, Brian S., et al. "Forecasting air quality time series using deep learning." Journal of the Air & Waste Management Association 68.8 (2018): 866-886.
- Akita, Ryo, et al. "Deep learning for stock prediction using numerical and textual information." 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE, 2016.
- Alzahrani, Ahmad, et al. "Solar irradiance forecasting using deep neural networks." Procedia Computer Science 114 (2017): 304-313.
- Anastassopoulou, Cleo, Lucia Russo, Athanasios Tsakris, and Constantinos Siettos. "Data-Based Analysis, Modelling and Forecasting of the novel Coronavirus (2019-nCoV) outbreak." medRxiv (2020).
- Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J.M., Yan, P. and Chowell, G., 2020. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infectious Disease Modelling, 5, pp.256-263.
Index Terms
Keywords