Abstract
Corona virus disease (COVID-19) is a novel disease that originated from the SARS-CoV-2 virus. COVID-19 is a worldwide pandemic that has affected a multitude of people all over the world. It affects humans in multiple ways such as difficulty in breathing (trouble in respiratory syndrome), fever, cold and dry cough. This has affected numerous people throughout the world. In India, there are numerous COVID-19 cases and the number is rapidly increasing. There is no exact vaccination and medical treatment for those presently affected. According to the current government report, all states in India are involved in the stepping up phase. Especially now a day’s, in rural areas in states much more people are affected because of lack of knowledge of how to handle the pandemic. This chapter proposes machine learning (ML) algorithms to help predict the current perspective of the COVID-19 in Virudhunagar district. The algorithms classify the COVID-19 affected regions into various zones such as: danger, moderate, and safe zone respectively. In addition, we propose a system to predict COVID-19 affected zones using the covid-19 dataset of Virudhunagar district from the period of March to July 2020. The deep learning algorithm efficiently predicts the zones with an accuracy of 98.06%, less error rate of 1.94% when compared with the C5.0 algorithm accuracy of 95.92% and error rate of 4.08%. By using the proposed system, health departments can predict the danger zones swiftly and act promptly to prevent infection in various states.
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References
El Wahid, A. A., Patel, P., Heidenreich, D., Hubert, F. T., & Weidmann, M. (2013). Reverse transcription recombinase polymerase amplification assay for the detection of Middle East respiratory syndrome coronavirus. PLoSCurr
Lessler, J., et al. (2009). Incubation periods of acute respiratory viral infections: A systematic review. The Lancet Infectious Diseases, 9(5), 291–300.
Anwar, S., Nasrullah, M., & Hosen, M. J. (2020). COVID-19 and Bangladesh: Challenges and how to address them. Frontiers in Public Health, 8(154), 1–8.
Sehgal, D. (2020). Analysis of vaccines to tackle Covid-19 with patent. The Pharma Innovation Journal, 9(7), 498–513.
Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID 19 epidemic analysis using machine learning and deep learning algorithms. medRxiv.
Shinde, G. R., Kalamkar, A. B., Mahalle, P. N., Dey, N., Chaki, J., & Hassanien, A. E. (2020). Forecasting models for coronavirus disease (COVID-19): A survey of the state-of-the-art. SN Computer Science, 1(4), 1–15.
Sujath, R., Chatterjee, J. M., & Hassanien, A. E. (2020). A machine learning forecasting model for COVID-19 pandemic in India. Stochastic Environmental Research and Risk Assessment, 34(7), 959–972.
Al-Qaness, M. A. A., Ewees, A. A., Fan, H., & Aziz, M. A. E. (2020). Optimization method for forecasting confirmed cases of COVID-19 in China. Applied Sciences, 9(3), 1–9.
Elmousalami, H. H., & Hassanien, A. E. (2020). Day level forecasting for coronavirus disease (COVID-19) spread: Analysis, modeling and recommendations. Quantitative Biology. http://arxiv.org/abs/2003.07778
Yang, Z., Zeng, Z., Wang, K., Wong, S. S., Liang, W., Zanin, M., et al. (2020). Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease, 12(3), 165–174.
Yudistira, N. (2020). COVID-19 growth prediction using multivariate long short term memory. Quantitative Biology, 14(8), 1–8.
Tomar, A., & Gupta, N. (2020). Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Science of the Total Environment.
Prabira, K. S., & Santi, K. B. (2020). Detection of coronavirus disease (COVID-19) based on deep features, 1–9. https://doi.org/10.20944/preprints/202003.0300.v1
Li, Y., Guo, F., Cao, Y., Li, L., & Guo, Y. (2020). Insight into COVID-2019 for pediatricians. Pediatric Pulmonology. https://doi.org/10.1002/ppul.24734
Ardabili, S. F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., Reuter, U., Rabczuk, T., & Atkinson, P. M. Covid-19 outbreak prediction with machine learning.
Xiong, P., Xu, K., & Xiao, G. (2020). How to deal with 2019 novel coronavirus (COVID-19): Public health practices from the centers for disease control and prevention in Zhanggong district, Ganzhou city, China. Infection Control & Hospital Epidemiology, 14(8), 976–977. https://www.cambridge.org/core/journals/infection-control-and-hospital-epidemiology/issue/174788131C200159B9E88F0310CAAEE6
Pinter, G., Felde, I., Mosavi, A., Ghamisi, P., & Gloaguen, R. (2020). COVID-19 pandemic prediction for Hungary; a hybrid machine learning approach. Mathematics, 8(6), 890.
Salau, A. O., & Jain, S. (2021). Adaptive diagnostic machine learning technique for classification of cell decisions for AKT protein. Informatics in Medicine Unlocked, 23(1), 1–9. https://doi.org/10.1016/j.imu.2021.100511
Prakash, K. B., Imambi, S. S., Ismail, M., Kumar, T. P., & Pawan, Y. N. (2020). Analysis, prediction and evaluation of covid-19 datasets using machine learning algorithms. International Journal, 8(5).
Revathy, R., Balamurali, S., & Lawrance, R. (2019). Classifying agricultural crop pest data using Hadoop mapreduce based c5.0 algorithm. Journal of Cyber Security and Mobility, 393–408.
Chen, E., Lerman, K., & Ferrara, E. (2020). Tracking social media discourse about the covid-19 pandemic: Development of a public coronavirus twitter data set. JMIR Public Health and Surveillance, 6(2), e19273.
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Acknowledgements
The authors would like to show gratitude to the management of Kalasalingam Academy of Research and Education for providing the fellowship to carry out this research work. We are also thankful to our alumni student Arun Pandian, who provided the real-time covid-19 dataset for Virudhunagar district.
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Indumathi, N., Shanmuga Eswari, M., Salau, A.O., Ramalakshmi, R., Revathy, R. (2022). Prediction of COVID-19 Outbreak with Current Substantiation Using Machine Learning Algorithms. In: Tyagi, A.K., Abraham, A., Kaklauskas, A. (eds) Intelligent Interactive Multimedia Systems for e-Healthcare Applications. Springer, Singapore. https://doi.org/10.1007/978-981-16-6542-4_10
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