Numerical Simulation and Design of COVID-19 Forecasting Framework Using Efficient Data Analytics Methodologies

Main Article Content

Puneet Pathak
Chetan Kumar

Abstract

The COVID-19 pandemic hit globally in December 2019 when a certain virus strain from Wuhan, China started proliferating throughout the world. By the end of March 2020, lockdowns and curfews were imposed all over the world halting trade, commerce, education, and various other essential activities. It has been nearly a year since the WHO declared a pandemic but there is still a consistent rise of the cases even with the administration of various types of vaccines and preventive measure. One of the main struggles that the healthcare workers face is to find out the how the virus is spreading amongst a community. The knowledge of this can be used to stop the spread of virus. This is a very important step towards getting things back into momentum to restore activities globally. Many attempts have been made under epidemiology to study the spread of COVID and many mathematical models have emerged as a result that can help with this. A popular model that is used for estimating the effective reproduction number (Rt) has the shortcoming that it cannot simultaneously forecast the future number of cases. This work explores an extension of another model, the SIR-model, in which the model parameters are fitted to recorded data. This makes the model adaptive, opening up the possibilities for estimating the Rt daily and making predictions of future number of confirmed cases. The paper use this adaptive SIR-model (aSIR) to estimate the Rt and create forecasts of new cases in India. The paper purpose is to determine how precise aSIR-models are at estimating the Rt (when compared with FHM’s model). It will also analyze how accurate aSIR-models are at simultaneously forecasting the future spread of Covid-19 in India. The coronavirus spread can be mathematically modelled using factors such as the number of susceptible people, exposed people, infected people, asymptotic people and the number of recovered people. The Khan-Atangana system is an integer-order coronavirus model that uses the above-mentioned factors. Since the coronavirus model depends on the initial conditions, the Khan-Atangana model uses the Atangana-Baleanu operate as it has a non-variant and non-local kernel. Instead, we replace the equations with fractional-order derivatives using the Grünwald-Letnikov derivative. The fractional order derivatives need to be fed with initial conditions and are useful to determine the spread due to their non-local nature. This project proposes to solve these fractional-order derivatives using numerical methods and analyse the stability of this epidemiological model.

Article Details

How to Cite
Pathak, P. ., & Kumar, C. . (2022). Numerical Simulation and Design of COVID-19 Forecasting Framework Using Efficient Data Analytics Methodologies. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(4), 45–53. Retrieved from https://www.ijfrcsce.org/index.php/ijfrcsce/article/view/2116
Section
Articles

References

M. Waqas, M. Farooq, R. Ahmad, A. Ahmad, Analysis and Prediction of COVID-19 Pandemic in Pakistan using Time-dependent SIR Model, arXiv: Populations and Evolution, 2020. URL: https://arxiv.org/pdf/2005.02353.pdf

M. N. Alenezi, F. S. Al-Anzi, H. Alabdulrazzaq, Building a sensible SIR estimation model for COVID-19 outspread in Kuwait, Alexandria Engineering Journal 60(3) (2021) 3161–3175. doi: 10.1016/j.aej.2021.01.025.

J. P. Hespanha, R. Chinchilla, R. R. Costa, M. K. Erdal, G. Yang, Forecasting COVID-19 cases based on a parameter-varying stochastic SIR model, Annual Reviews in Control 51 (2021) 460– 476. doi: 10.1016/j.arcontrol.2021.03.008.

A. B. Lawson, J. Kim, Space-time covid-19 Bayesian SIR modeling in South Carolina, PLoS ONE 16(3) (2021). doi: 10.1371/journal.pone.0242777.

I. Rahimi, A. H. Gandomi, P. G. Asteris, F. Chen, Analysis and Prediction of COVID-19 Using SIR, SEIQR and Machine Learning Models: Australia, Italy and UK Cases, Information 12(3) (2021). doi: 10.3390/info12030109.

P. Pandey, V. Katoch, P. Kumar, Analysis and forecast of COVID-19 spreading in India using Nonlinear curve fitting model with machine learning, Research Square (2000). doi: 10.21203/rs.3.rs-87147/v1.

F. A. Binti Hamzah, C. Lau, H. Nazri, D. V. Ligot, G. Lee, C. L. Tan, CoronaTracker: Worldwide COVID-19 Outbreak Data Analysis and Prediction, Bull World Health Organisation (2020). doi: 10.2471/BLT.20.255695.

D. Fanelli, F. Piazza, Analysis and forecast of COVID-19 spreading in China, Italy and France, Chaos, Solitons & Fractals 134 (2020). doi: 10.1016/j.chaos.2020.109761.

S. Lalwani, G. Sahni, B. Mewara, R. Kumar, Predicting optimal lockdown period with parametric approach using three-phase maturation SIRD model for COVID-19 pandemic, Chaos, Solitons & Fractals 138 (2020). doi: 10.1016/j.chaos.2020.109939.

C. Anastassopoulou, L. Russo, A. Tsakris, C. Siettos, Data-based analysis, modelling and forecasting of the COVID-19 outbreak, PLoS ONE 15(3) (2020). doi: 10.1371/journal.pone.0230405.

A. Hasan, E. R. M. Putri, H. Susanto, N. Nuraini, Data-driven modeling and forecasting of COVID-19 outbreak for public policy making, ISA Transactions (2021). doi: 10.1016/j.isatra.2021.01.028

L.-P. Chen, Analysis and Prediction of COVID-19 Data in Taiwan, 2020. URL: https://ssrn.com/abstract=3611761. doi: 10.2139/ssrn.3611761.

A. J. Q. Sarnaglia, B. Zamprogno, F. A. F. Molinares, L. G. de Godoi, N. A. Jiménez Monroy, Correcting notification delay and forecasting of COVID-19 data, Journal of Mathematical Analysis and Applications (2021). doi: 10.1016/j.jmaa.2021.125202.

M. Niazkar, G. E. Türkkan, H. R. Niazkar, Y. A. Türkkan, Assessment of Three Mathematical Prediction Models for Forecasting the COVID-19 Outbreak in Iran and Turkey, Computational and Mathematical Methods in Medicine (2020). doi: 10.1155/2020/7056285.

M. Triacca, U. Triacca, Forecasting the number of confirmed new cases of COVID-19 in Italy for the period from 19 May to 2 June 2020, Infectious Disease Modelling 6 (2021) 362–369, doi: 10.1016/j.idm.2021.01.003.

G. L. Watson, D. Xiong, L. Zhang, J. A. Zoller, J. Shamshoian, P. Sundin, Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model. PLoS Comput Biol 17(3) (2021). doi: 10.1371/journal.pcbi.1008837.

K. N. Nabi, M. T. Tahmid, A. Rafi, M. E. Kader, Md. A. Haider, Forecasting COVID-19 cases: A comparative analysis between recurrent and convolutional neural networks, Results in Physics 24 (2021). doi: 10.1016/j.rinp.2021.104137.

A. Zeroual, F. Harrou, A. Dairi, Y. Sun, Deep learning methods for forecasting COVID-19 time- Series data: A Comparative study, Chaos, Solitons & Fractals 140 (2020). doi: 10.1016/j.chaos.2020.110121.

Y. Gautam, Transfer Learning for COVID-19 cases and deaths forecast using LSTM network, ISA Transactions (2021). To appear. doi: 10.1016/j.isatra.2020.12.057.

V. K. Gupta, A. Gupta, D. Kumar, A. Sardana, Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model, Big Data Mining and Analytics 4(2) (2021) 116– 123. doi: 10.26599/BDMA.2020.9020016.

K. T. Ly, A COVID-19 forecasting system using adaptive neuro-fuzzy inference, Finance Research Letters 41 (2021). doi: 10.1016/j.frl.2020.101844.

M. A. A. Al-qaness, A. I. Saba, A. H. Elsheikh, M. Abd Elaziz, R. Ali Ibrahim, S. Lu, A. A. Hemedan, S. Shanmugan, A. A. Ewees, Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil, Process Safety and Environmental Protection 149 (2021) 399–409. doi: 10.1016/j.psep.2020.11.007.

N. Talkhi, N. A. Fatemi, Z. Ataei, M. J. Nooghabi, Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods, Biomedical Signal Processing and Control 66 (2021). doi: 10.1016/j.bspc.2021.102494.

N. Kumar, S. Susan, COVID-19 Pandemic Prediction using Time Series Forecasting Models, in: Proceedings of the 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2020, pp. 1–7. doi: 10.1109/ICCCNT49239.2020.9225319.

M. Yousaf, S. Zahir, M. Riaz, S. M. Hussain, K. Shah, Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan, Chaos, Solitons & Fractals 138 (2020). doi: 10.1016/j.chaos.2020.109926.

T. Kufel, ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries, Equilibrium. Quarterly Journal of Economics and Economic Policy 15(2) (2020) 181–204. doi: 10.24136/eq.2020.009.

M. H. Dal Molin Ribeiro, R. G. da Silva, V. C. Mariani, L. dos Santos Coelho, Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil, Chaos, Solitons & Fractals 135 (2020). doi: 10.1016/j.chaos.2020.109853.

E. Gecili, A. Ziady, R. D. Szczesniak, Forecasting COVID-19 confirmed cases, deaths and recoveries: Revisiting established time series modeling through novel applications for the USA and Italy, PLoS ONE 16(1) (2021). doi: 10.1371/journal.pone.0244173.

M. Kalantari, Forecasting COVID-19 pandemic using optimal singular spectrum analysis, Chaos, Solitons & Fractals 142 (2021). doi: 10.1016/j.chaos.2020.110547.

A. Ahmadi, Y. Fadaei, M. Shirani, F. Rahmani, Modeling and forecasting trend of COVID-19 epidemic in Iran until May 13, 2020, Med J Islam Repub Iran (2020). doi: 10.34171/mjiri.34.27.

R. K. Mojjada, A. Yadav, A. V. Prabhu, Y. Natarajan, Machine learning models for covid-19 future forecasting, Materials Today: Proceedings (2020). doi: 10.1016/j.matpr.2020.10.962.

A. R. Appadu, A. S. Kelil, Y. O. Tijani, Comparison of some forecasting methods for COVID-19, Alexandria Engineering Journal 60(1) (2021) 1565–1589. doi: 10.1016/j.aej.2020.11.011.

H. M. Paiva, R. J. M. Afonso, F. M. S. de Lima Alvarenga Caldeira, Ester de Andrade Velasquez, A computational tool for trend analysis and forecast of the COVID-19 pandemic, Applied Soft Computing 105 (2021). doi: 10.1016/j.asoc.2021.107289.

S. Balli, Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods, Chaos, Solitons & Fractals 142 (2021). doi: 10.1016/j.chaos.2020.110512.

F. Petropoulos, S. Makridakis, N. Stylianou, COVID-19: Forecasting confirmed cases and deaths with a simple time series model, International Journal of Forecasting (2020). doi: 10.1016/j.ijforecast.2020.11.010.

D. Guleryuz, Forecasting outbreak of COVID-19 in Turkey; Comparison of Box–Jenkins, Brown’s exponential smoothing and long short-term memory models, Process Safety and Environmental Protection 149 (2021) 927–935. doi: 10.1016/j.psep.2021.03.032.

P. H. Borghi, O. Zakordonets, J. P. Teixeira, A COVID-19 time series forecasting model based on MLP ANN, Procedia Computer Science 181 (2021) 940–947, doi: 10.1016/j.procs.2021.01.250.

K. E. ArunKumar, D. V. Kalaga, Ch. Mohan Sai Kumar, G. Chilkoor, M. Kawaji, T. M. Brenza, Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA), Applied Soft Computing 103 (2021). doi: 10.1016/j.asoc.2021.107161.

M. Ali, D. M. Khan, M. Aamir, U. Khalil, Z. Khan, Forecasting COVID-19 in Pakistan, PLoS ONE 15(11) (2020). doi: 10.1371/journal.pone.0242762.

K. E. ArunKumar, D. V. Kalaga, Ch. Mohan Sai Kumar, M. Kawaji, T. M. Brenza, Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells, Chaos, Solitons & Fractals 146 (2021). doi: 10.1016/j.chaos.2021.110861.

Jayanthi Devaraj, Rajvikram Madurai Elavarasan, Rishi Pugazhendhi, G.M. Shafiullah, Sumathi Ganesan, Ajay Kaarthic Jeysree, Irfan Ahmad Khan, Eklas Hossain, Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?, Results in Physics, Volume 21, 2021, 103817, doi: 10.1016/j.rinp.2021.103817.

A. H. Elsheikh, A. I. Saba, M. Abd Elaziz, S. Lu, S. Shanmugan, T. Muthuramalingam, R. Kumar,O. Mosleh, F. A. Essa, T. A. Shehabeldeen, Deep learning-based forecasting model for COVID- 19 outbreak in Saudi Arabia, Process Safety and Environmental Protection 149 (2021) 223–233. doi: 10.1016/j.psep.2020.10.048.

T. Chakraborty, I. Ghosh, Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis, Chaos, Solitons & Fractals 135 (2020). doi: 10.1016/j.chaos.2020.109850.

R. G. da Silva, M. H. Dal Molin Ribeiro, V. C. Mariani, L. dos Santos Coelho, Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables, Chaos, Solitons & Fractals 139 (2020). doi: 10.1016/j.chaos.2020.110027.

S. Moein, N. Nickaeen, A. Roointan, Inefficiency of SIR models in forecasting COVID-19 epidemic: a case study of Isfahan, Scientific Reports 11 (2021). doi: 10.1038/s41598-021-84055- 6.

Coronavirus Resource Center of Johns Hopkins University & Medicine. URL: https://coronavirus.jhu.edu/

C. Shorten, T. M. Khoshgoftaar, B. Furht, Deep Learning applications for COVID-19, Journal of Big Data 8 (2021). doi: 10.1186/s40537-020-00392-9.

Alaria, S. K. "A.. Raj, V. Sharma, and V. Kumar.“Simulation and Analysis of Hand Gesture Recognition for Indian Sign Language Using CNN”." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 4 (2022): 10-14.

Ashish Raj, Vijay Kumar, S. K. A. V. S. Design Simulation and Assessment of Prediction of Mortality in Intensive Care Unit Using Intelligent Algorithms. MSEA 2022, 71, 355-367.

Alaria, S. K. . Analysis of WAF and Its Contribution to Improve Security of Various Web Applications: Benefits, Challenges. ijfrcsce 2019, 5, 01-03.