[1] Lai C-C, Shih T-P, Ko W-C, Tang H-J, Hsueh P-R. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV–2) and coronavirus disease–2019 (COVID–19): The epidemic and the challenges. Int. J. Antimicrob. Agents [Internet]. 2020 Mar;55(3):105924. Available from: http://dx.doi.org/10.1016/j.ijantimicag.2020.105924
[2]World Health Organization, WHO Coronavirus Disease (COVID–19) Dashboard, cited on: 01 Jul 2020; Available from: https://covid19.who.int/
[3]Ministry of Health and Family Welfare, Government of India, COVID–19 India; cited on: 01 Jul 2020; Available from: https://www.mohfw.gov.in/
[4]Bhandari S, Shaktawat AS, Tak A, Patel B, Shukla J, Singhal S, et al. Logistic regression analysis to predict mortality risk in COVID–19 patients from routine hematologic parameters. Ibnosina J Med Biomed Sci [serial online] 2020 [cited 2020 Jul 1];12:123–9. Available from: http://www.ijmbs.org/text.asp?2020/12/2/123/288204
[5]Box GEP, Tiao GC. Intervention Analysis with Applications to Economic and Environmental Problems. J. Am. Stat. Assoc [Internet]. 1975 Mar;70(349):70–9. Available from: http://dx.doi.org/10.1080/01621459.1975.10480264
[6]Johns Hopkins University Center for Systems Science and Engineering, 2019. (Accessed: 25th Jun 2020). Available from: https://github.com/CSSEGISandData/COVID–19
[7]Cryer JD, Chan KS. Models for Non-stationary time series in Time series analysis: with applications in R (2nd edition) 98–99 (Springer Science & Business Media, 2008).
[8]Indrayan, Abhaya, Rajeev Kumar Malhotra Relationships: Quantitative Outcome in Medical biostatistics (4th edition) 456 (CRC Press, 2018).
[9]Metcalfe AV, Cowpertwait PS. Non-stationary models in Introductory time series with R 137–140 (Springer-Verlag New York, 2009).
[10]MATLAB Team, Statistics and Machine Learning Toolbox 10.2. version 9.0.0.341360 (R 2016a). Natick, Massachusetts: The Mathworks Inc
[11]Shinde GR, Kalamkar AB, Mahalle PN, Dey N, Chaki J, Hassanien AE. Forecasting Models for Coronavirus Disease (COVID–19): A Survey of the State-of-the-Art. SN Computer Science [Internet]. 2020 Jun 11;1(4). Available from: http://dx.doi.org/10.1007/s42979–020–00209–9
[12]National Portal of India (cited on: 27 April 2020). Available from: https://www.india.gov.in/india-glance/profile
[13]Yang C, Wang J. A mathematical model for the novel coronavirus epidemic in Wuhan, China Math Biosci Eng [Internet]. 2020;17(3):2708–24. Available from: http://dx.doi.org/10.3934/mbe.2020148
[14]Chatterjee A, Gerdes MW, Martinez SG. Statistical Explorations and Univariate Timeseries Analysis on COVID–19 Datasets to Understand the Trend of Disease Spreading and Death. Sensors [Internet]. 2020 May 29;20(11):3089. Available from: http://dx.doi.org/10.3390/s20113089
[15]Tiwari S, Kumar S, Guleria K. Outbreak Trends of Coronavirus Disease–2019 in India: A Prediction. Disaster Med Public Health Prep [Internet]. 2020 Apr 22;1–6. Available from: http://dx.doi.org/10.1017/dmp.2020.115
[16]Tomar A, Gupta N. Prediction for the spread of COVID–19 in India and effectiveness of preventive measures. Sci. Total Environ [Internet]. 2020 Aug;728:138762. Available from: http://dx.doi.org/10.1016/j.scitotenv.2020.138762
[17]Tuli S, Tuli S, Tuli R, Gill SS. Predicting the growth and trend of COVID–19 pandemic using machine learning and cloud computing. Internet of Things [Internet]. 2020 Sep;11:100222. Available from: http://dx.doi.org/10.1016/j.iot.2020.100222
[18]Giordano, Giulia, Franco Blanchini, Raffaele Bruno, Patrizio Colaneri, Alessandro Di Filippo, Angela Di Matteo, and Marta Colaneri. Modelling the COVID–19 epidemic and implementation of population-wide interventions in Italy Nat. Med. (2020): 1–6.
[19]Mandal M, Jana S, Nandi SK, Khatua A, Adak S, Kar TK. A model based study on the dynamics of COVID–19: Prediction and control. Chaos Soliton Fract [Internet]. 2020 Jul;136:109889. Available from: http://dx.doi.org/10.1016/j.chaos.2020.109889
[20]Pai C, Bhaskar A, Rawoot V. Investigating the dynamics of COVID–19 pandemic in India under lockdown. Chaos Soliton Fract [Internet]. 2020 Sep;138:109988. Available from: http://dx.doi.org/10.1016/j.chaos.2020.109988
[21]Arora P, Kumar H, Panigrahi BK. Prediction and analysis of COVID–19 positive cases using deep learning models: A descriptive case study of India. Chaos Soliton Fract [Internet]. 2020 Oct;139:110017. Available from: http://dx.doi.org/10.1016/j.chaos.2020.110017
[22]Chakraborty T, Ghosh I. Real-time forecasts and risk assessment of novel coronavirus (COVID–19) cases: A data-driven analysis. Chaos Soliton Fract [Internet]. 2020 Jun;135:109850. Available from: http://dx.doi.org/10.1016/j.chaos.2020.109850
[23]Rafiq D, Suhail SA, Bazaz MA. Evaluation and prediction of COVID–19 in India: A case study of worst hit states. Chaos Soliton Fract [Internet]. 2020 Oct;139:110014. Available from: http://dx.doi.org/10.1016/j.chaos.2020.110014
[24]Singhal A, Singh P, Lall B, Joshi SD. Modeling and prediction of COVID–19 pandemic using Gaussian mixture model. Chaos Soliton Fract [Internet]. 2020 Sep;138:110023. Available from: http://dx.doi.org/10.1016/j.chaos.2020.110023
[25]Tabish SA. The COVID–19 pandemic: Emerging perspectives and future trends. J Public Health Res [Internet]. 2020 Jun 4;9(1). Available from: http://dx.doi.org/10.4081/jphr.2020.1786
[26]Benvenuto D, Giovanetti M, Vassallo L, Angeletti S, Ciccozzi M. Application of the ARIMA model on the COVID–2019 epidemic dataset. Data Brief [Internet]. 2020 Apr;29:105340. Available from: http://dx.doi.org/10.1016/j.dib.2020.105340
[27]Bhandari S, Shaktawat AS, Tak A, Patel B, Gupta K, Gupta J, et al. A multistate ecological study comparing evolution of cumulative cases (trends) in top eight COVID–19 hit Indian states with regression modeling. Int J Acad Med [serial online] 2020 [cited 2020 Jul 1];6:91–5. Available from: http://www.ijam-web.org/text.asp?2020/6/2/91/287965
[28]Kakkar S, Bhandari S, Shaktawat A, Sharma R, Dube A, Banerjee S, et al. A preliminary clinico-epidemiological portrayal of COVID–19 pandemic at a premier medical institution of North India. Ann Thorac Med [Internet]. 2020;15(3):146. Available from: http://dx.doi.org/10.4103/atm.ATM_182_20
[29]Bhandari S, Sharma R, Singh Shaktawat A, Banerjee S, Patel B, Tak A, et al. COVID–19 related mortality profile at a tertiary care centre: a descriptive study. Scr Med 2020;51(2):69–73. DOI:10.5937/scriptamed51–27126
[30]Salgotra R, Gandomi M, Gandomi AH. Time Series Analysis and Forecast of the COVID–19 Pandemic in India using Genetic Programming Chaos Soliton Fract [Internet]. 2020 Sep;138:109945. Available from: http://dx.doi.org/10.1016/j.chaos.2020.109945
[31]Sujath R, Chatterjee JM, Hassanien AE. A machine learning forecasting model for COVID–19 pandemic in India. Stoch Env Res Risk A [Internet]. 2020 May 30;34(7):959–72. Available from: http://dx.doi.org/10.1007/s00477–020–01827–8
[32]Yadav RS. Data analysis of COVID–2019 epidemic using machine learning methods: a case study of India. International Journal of Information Technology [Internet]. 2020 May 26; Available from: http://dx.doi.org/10.1007/s41870–020–00484-y