Abstract
The coronavirus disease 2019 (Covid-19) epidemic has caused a worldwide health catastrophe that has had a profound influence on how we see our planet and our daily lives. In this pandemic circumstance, machine learning (ML) based prediction models demonstrate their value in predicting perioperative outcomes to enhance decision-making on future course of action. Ensemble learning is used in the majority of ML based forecasting approaches. The ML models anticipate the number of patients who will be affected by Covid-19, and use this information to forecast the end of the pandemic is to be leveraged. Three types of predictions are made: the number of newly infected cases, the number of deaths, and the number of recoveries in the next ‘x’ number of days. By combining one of the forecasting models with classifiers, we can predict the end of the pandemic. The proposed idea combines the SIRF model from epidemiology and a forecasting machine learning model named Prophet and a Naïve Bayes Classifier to predict the end of the pandemic. Using the theoretical equations of the SIRF model, we developed a formula for infectious growth rate. The classifier uses this infectious growth rate to check if the infection is fading. With confirmed, recovered and fatalities data, the infectious growth rate is calculated. Naïve Bayes classifier is used to check if the pandemic is about to end or not. If not then forecast the data for ‘x’ number of days and do the calculations again. The process continues until we get a time frame where the pandemic may reach its end. The results are discussed for 2 countries India and Israel. The forecasts done for Israel were very accurate to the actual data, whilst for India it was less comparatively as India was hit by 2 waves of Covid-19 pandemic. By leveraging the forecasting and classification capabilities of machine learning models like FBProphet, Naïve Bayes Classifier, and the mathematical equations of the SIRF model from epidemiology, the life span of the pandemic is determined.
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References
Suggala RK, Anurag (2018) A survey on prediction and detection of epidemic diseases outbreaks. Int J Manage Technol Eng, DECEMBER/2018. ISSN NO: 2249-7455
Zeb A, Alzahrani E, Erturk VS, Zaman G (2020) Mathematical model for coronavirus disease 2019 (COVID-19) containing ısolation class. In: Research article biomed research ınternational, vol 2020, Article ID 3452402
Andreas A, Mavromoustakis CX, Mastorakis G (2020) Modified machine learning technique for curve fitting on regression models for COVID-19 projections. IEEE transactions © IEEE 2020
Rustam F, Rishi AA, Mehmood A, Ullah S, On B-W, Aslam W, Choi GS (2020) COVID-19 future forecasting using supervised machine learning models. IEEE. https://doi.org/10.1109/ACCESS.2020.2997311
Arun SS, NeelakantaIyer G (2020) On the analysis of COVID19—novel corona viral disease pandemic spread data using machine learning techniques. In: International conference on ıntelligent computing and control systems (ICICCS 2020) IEEE Xplore ISBN: 978-1-7281-4876-2
Kumar N, Susan S (2020) COVID-19 pandemic prediction using time series forecasting models © IEEE 2020, IEEE—49239
Mustafa HI, Fareed NY (2020) COVID-19 CASES in Iraq; forecasting ıncidents using box—Jenkins ARIMA model. In: 2nd Al-Noor ınternational conference for science and technology (2NICST2020) Baghdad , Iraq © IEEE 2020
Marmarelis VZ (2020) Predictive modeling of Covid-19 data in the US: adaptive phase-space approach. IEEE Open J Eng Med Biol. https://doi.org/10.1109/OJEMB.2020.3008313
Chen Y-C, Lu PE, Chang CS, Liu T-H (2020) A time-dependent SIR model for COVID-19 with undetectable ınfected persons. arXiv:2003.00122v6 [q-bio.PE] Apr 28th, 2020
Zheng N, Du S, Wang J (2020) Predicting COVID-19 in China using hybrid AI model. IEEE Trans Cybern 50:2168–2267 c 2020 IEEE
Bakar AA, Keifli Z, Abdullah S, Sahani M (2019) Predictive models for dengue outbreak using multiple rule-tbase classifiers. In: 2019 ınternational conference on electrical engineering and ınformatics 17–19 July 2011, Bandung, Indonesia. 978-1-4577-0752-0/11/$26.00 ©2019 IEEE
Singh S, Raj P, Kumar R, Chaujar R (2020) Prediction and forecast for COVID-19 outbreak in India based on enhanced epidemiological models. In: Second ınternational conference on ınventive research in computing applications (ICIRCA-2020) IEEE Xplore ISBN: 978-1-7281-5374-2
Almansouri HT, Masmoudi Y (2019) Hadoop distributed file system for big data analysis. 978-1-7281-1232-9/19/$31.00 ©2019 IEEE
Seref B, Bostanci E (2019) Performance comparison of Naïve Bayes and complement Naïve Bayes algorithms. In: 2019 6th ınternational conference on electrical and electronics engineering (ICEEE), 978-1-7281-3910-4/19/$31.00 ©2019 IEEE. https://doi.org/10.1109/ICEEE2019.2019.00033
Alakus TB, Turkoglu I (2020) Comparison of deep learning approaches to predict COVID-19 infection. Chaos Solitons Fractals 140:110120
Acknowledgements
This study is financially supported by Karnataka State Council for Science and Technology with Project Proposal Reference No. 44S_MTECH_040. The authors are thankful to M. S. Ramaiah Institute of Technology, Bangalore-560054, and Visvesvaraya Technological University, Jnana Sangama, Belagavi-590018, for their support..
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Shwetha, S., Sunagar, P., Rajarajeswari, S., Kanavalli, A. (2022). Ensemble Model to Forecast the End of the Covid-19 Pandemic. In: Bindhu, V., Tavares, J.M.R.S., Du, KL. (eds) Proceedings of Third International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 844. Springer, Singapore. https://doi.org/10.1007/978-981-16-8862-1_53
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DOI: https://doi.org/10.1007/978-981-16-8862-1_53
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