A statistical and deep learning-based daily infected count prediction system for the coronavirus pandemic
Tóm tắt
We present new data analytics-based predictions results that can help governments to plan their future actions and also help medical services to be better prepared for the future. Our system can predict new corona cases with 99.82% accuracy using susceptible infected recovered (SIR) model. We have predicted the results of new COVID cases per day for dense and highly populated country i.e. India. We found that traditional statistical methods will not work efficiently as they do not consider the limited population in a particular country. Using the data analytics-based curve we predicted four most likely possibilities for the number of new cases in India. Hence, we expect that the results mentioned in the manuscript help people to better understand the progress of this disease.
Tài liệu tham khảo
Kane MJ, Price N, Scotch M, Rabinowitz P (2014) Comparison of arima and random forest time series models for prediction of avian influenza h5n1 outbreaks. BMC Bioinform 15(1):276
Ji D, Zhang D, Xu J, Chen Z, Yang T, Zhao P, Chen G et al (2020) Prediction for progression risk in patients withCOVID-19 pneumonia: the CALL score. Clin Infect Dis 71(6):1393–1399
Qasim M, Ahmad W, Yoshida M, Gould M, Yasir M (2020) Analysis of the worldwide corona virus (covid-19) pandemic trend: a modelling study to predict its spread, medRxiv
Arti M, Bhatnagar K Modeling and predictions for covid 19 spread in India, ResearchGate. https://doi.org/10.13140/RG.2.2.11427.81444
Dhanwant, JN, Ramanathan V (2020) Forecasting covid 19 growth in India using susceptible-infected-recovered (sir) model, arXiv preprint arXiv:2004.00696
Tandon H, Ranjan P, Chakraborty T, Suhag V (2020) Coronavirus (covid-19): arima based time-series analysis to forecast near future. arXiv preprint arXiv:2004.07859
Chen YC, Lu PE, Chang CS, Liu TH (2020) A time-dependent sir model for covid-19 with undetectable infected persons. arXiv preprint arXiv:2003.00122
Zhang J, Man K (1998) In: SMC’98 conference proceedings. 1998 IEEE international conference on systems, man, and cybernetics (Cat. No. 98CH36218), vol. 2, pp. 1868–1873
Chatfield C, Yar M (1988) Holt-winters forecasting: some practical issues. J Royal Stat Soc: Series D (The Statistician) 37(2):129–140
Roser Max, Ritchie Hannah, Ortiz-Ospina Esteban, Hasell Joe (2020) Coronavirus pandemic (COVID-19), our world in data. https://ourworldindata.org/coronavirus
Kalekar PS (2004) Time series forecasting using holt-winters exponential smoothing. Kanwal Rekhi School Inform Technol 4329008(13)
Taylor JW (2003) Exponential smoothing with a damped multiplicative trend. Int J Forecast 19(4):715
Jain G, Mallick B (2017) A study of time series models arima and ets. Available at SSRN 2898968
Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the arma, arima, and the autoregressive artificial neural network models in forecasting the monthly inflow of dez dam reservoir. J Hydrol 476:433
Harvey AC (1984) A unified view of statistical forecasting procedures. J Forecast 3(3):245
Wang D, Zhao X (2003) Empirical analysis and forecasting for SARS epidemic situation. Beijing da xue xue bao Yi xue ban= J Peking Univ Health Sci 35:72–74
Vermaak J, Botha E (1998) Recurrent neural networks for short-term load forecasting. IEEE Trans Power Sys 13(1):126
Wong JB (2020) Pandemic surge models in the time of severe acute respiratory syndrome coronavirus-2: Wrong or useful? Ann Int Med 173(5):396–398
Salgotra R, Gandomi M, Gandomi AH (2020) Time series analysis and forecast of the covid-19 pandemic in india using genetic programming. Chaos, Solitons & Fractals p, p 109945
Roosa K, Lee Y, Luo R, Kirpich A, Rothenberg R, Hyman J, Yan P, Chowell G (2020) Real-time forecasts of the covid-19 epidemic in china from February 5th to February 24th, 2020. Infect Dis Modell 5:256
Kolozsvari LR, Berczes T, Hajdu A, Gesztelyi R, TIba A, Varga I, Szollosi GJ, Harsanyi S, Garboczy S, Zsuga J (2020) Predicting the epidemic curve of the coronavirus (sars-cov-2) disease (covid-19) using artificial intelligence, medRxiv
Fuller J (2020) Models V. Evidence, Boston Review. Serial on the Internet
Utsunomiya YT, Utsunomiya ATH, Torrecilha RBP, Paulan SDC, Milanesi M, Garcia JF (2020) Growth rate and acceleration analysis of the covid-19 pandemic reveals the effect of public health measures in real time. Front Med 7:247
Tian L, Li X, Qi F, Tang QY, Tang V, Liu J, Cheng X, Li X, Shi Y, Liu H, et al., (2020) Quantifying the infected population for calibrated intervention and containment of the covid-19 pandemic. https://arxiv.org/pdf/2003.07353v3.pdf
Schüttler J, Schlickeiser R, Schlickeiser F, Kröger M (2020) Covid-19 predictions using a gauss model, based on data from April 2. Physics 2(2):197
Roda WC, Varughese MB, Han D, Li MY (2020) Why is it difficult to accurately predict the covid-19 epidemic? Infect Dis Modell