A Deep Learning Method to Forecast COVID-19 Outbreak

Author:

Dash Satyabrata,Chakravarty Sujata,Mohanty Sachi NandanORCID,Pattanaik Chinmaya Ranjan,Jain Sarika

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Reference28 articles.

1. WHO: Coronavirus disease 2019 (COVID19). Situation report 24. February 13, 2020. World Health Organization, Geneva (2020)

2. Li, Y., Wang, B., Peng, R., Zhou, C., Zhan, Y., Liu, Z., Jiang, X., Zhao, B.: Mathematical modeling and epidemic prediction of COVID19 and its significance to epidemic prevention and control measures. Ann. Infect. Dis. Epidemiol. 5(1), 10521 (2020)

3. nCoV-2019 Data Working Group.: Epidemiological data from the nCoV-2019 outbreak: early descriptions from publicly available data (2020). http://virological.org/t/epidemiological-data-from-the-ncov2019-outbreak-early-descriptions-from-publicly-available-data/337. Accessed 13 Feb 2020

4. Camacho, A., Kucharski, A., Aki-Sawyerr, Y., White, M.A., Flasche, S., Baguelin, M., Tiffany, A.: Temporal changes in Ebola transmission in Sierra Leone and implications for control requirements: a real-time modelling study. PLoS Curr. 7 (2015). https://doi.org/10.1371/currents.outbreaks.406ae55e83ec0b5193e30856b9235ed2

5. Funk, S., Ciglenecki, I., Tiffany, A., Gignoux, E., Camacho, A., Eggo, R.M., Clement, {: The impact of control strategies and behavioural changes on the elimination of Ebola from Lofa County, Liberia. Philos. Trans. R. Soc. B Biol. Sci. 372(1721), 20160302 (2017)

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