Visualizing and Forecasting Trends of Covid-19 for Large Scale Epidemic Prevention

Samatha Juluri*, Madhavi Gudavalli **
* Department of Computer Science and Engineering, Matrusri Engineering College, Hyderabad, Telangana, India.
** Department of Computer Science and Engineering, JNTUK University College of Engineering, Narasaraopet, Andhra Pradesh, India.
Periodicity:December - February'2020
DOI : https://doi.org/10.26634/jit.9.1.17342

Abstract

The pandemic of Corona Virus Disease (COVID-19) has shaken the globe with its wide spread, resulting in human deaths due to limited understanding of medical community and scarcity of medical resources. The rapid rise in the number of COVID-19 incidents has promoted the need for public awareness and effective preventive measures to control the disastrous effects of this epidemic. In the current context, it is necessary to predict the trend of COVID-19 to support the public health sector to effectively prevent and control this epidemic in order to save mankind. Traditional pandemic models consume lot of time to predict the number of infections as these models consider all individuals with corona virus have the same infection rate. These prediction results can only provide general trends which are not useful for epidemic control and prevention at the right time. Therefore, we proposed a machine learning approach for scenario analysis and forecasting time series data of COVID-19 to visualize its impact globally and accelerate the containment of the virus. The results analyzed through this forecasting model will help the people better understand the potential implications of corona virus and predict the possible future cases of COVID-19.

Keywords

COVID-19, Data Wrangling, Epidemic, Machine Learning, Forecasting, Prediction.

How to Cite this Article?

Juluri, S., and Gudavalli, M. (2020). Visualizing and Forecasting Trends of Covid-19 for Large Scale Epidemic Prevention. i-manager's Journal on Information Technology, 9(1), 14-21. https://doi.org/10.26634/jit.9.1.17342

References

[1]. Baldwin, R., & Tomiura, E. (2020). Thinking ahead about the trade impact of COVID-19. Economics in the Time of COVID-19.
[2]. Bullock, J., Pham, K. H., Lam, C. S. N., & Luengo-Oroz, M. (2020). Mapping the landscape of artificial intelligence applications against COVID-19. arXiv preprint.
[3]. Chamola, V., Hassija, V., Gupta, V., & Guizani, M. (2020). A comprehensive review of the COVID-19 Pandemic and the role of IoT, Drones, AI, Blockchain, and 5G in managing its impact. IEEE Access, 8, 90225-90265. https://doi.org/10.1109/ACCESS.2020.2992341
[4]. Chen, H., Guo, J., Wang, C., Luo, F., Yu, X., Zhang, W., ..., & Liao, J. (2020). Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: A retrospective review of medical records. The Lancet, 395(10226), 809-815. https://doi.org/10.1016/S0140-6736(20)30360-3
[5]. Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., ..., & Yu, T. (2020). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. The Lancet, 395(10223), 507-513. https://doi.org/10.1016/ S0140-6736(20)30211-7
[6]. Ding, X. R., Clifton, D., Nan, J. I., Lovell, N. H., Bonato, P., Chen, W., ..., & Xu, K. (2020). Wearable sensing and telehealth technology with potential applications in the coronavirus pandemic. IEEE Reviews in Biomedical Engineering. https://doi.org/10.1109/RBME.2020.299 2838
[7]. Fang, L., Karakiulakis, G., & Roth, M. (2020). Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection?. The Lancet: Respiratory Medicine, 8(4), e21. https://doi.org/10.10 16%2FS2213-2600(20)30116-8
[8]. Gaál, G., Maga, B., & Lukács, A. (2020). Attention unet based adversarial architectures for chest x-ray lung segmentation. arXiv preprint.
[9]. Kurama, (2020). Fighting COVID-19 with data and AI: A Review of active research groups and datasets. Paper Space Blog. https://blog.paperspace.com/fightingcovid- 19-using-artificial-intelligence-and-data/
[10]. Lavreniuk, M., & Novikov, A. (2018). Overview of machine learning to classify large volumes of satellite data. System Research & Information Technologies, 1, 52-71.
[11]. Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., ..., & Shi, Y. (2020). Lung Infection Quantification of Covid-19 in CT Images with Deep Learning. arXiv preprint.
[12]. Surveillances, V. (2020). The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19)-China, 2020. China CDC Weekly, 2(8), 113-122.
[13]. Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., ..., & Zhao, Y. (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus – infected pneumonia in Wuhan, China. Jama, 323(11), 1061-1069.
[14]. WHO Situation Report 79 (2020). Coronavirus Disease 2019 (COVID-19). https://www.who.int/docs/ default-source/coronaviruse/situation-reports/2020 0408-sitrep-79-covid-19.pdf?sfvrsn=4796b143_6
[15]. WHO Situation Report 87 (2020). Coronavirus Disease 2019 (COVID-19). https://www.who.int/docs/ default-source/coronaviruse/situation-reports/20200416- sitrep-87-covid-19.pdf?sfvrsn=9523115a_2
[16]. WHO (2020). Coronavirus Disease (COVID-19) Pandemic. https://www.who.int/emergencies/diseases/ novel-coronavirus-2019
[17]. WHO (n.d.). Province/State, Country/Region, Lat, Long, Date, Confirme, Deaths, Recovered, Active, WHO Region. https://raw.githubusercontent.com/umang kejriwal1122/Machine-Learning/master/Data%20Sets/ covid_19_clean_complete.csv
[18]. Wong, S. H., Lui, R. N., & Sung, J. J. (2020). Covid‐19 and the digestive system. Journal of Gastroenterology and Hepatology, 35(5), 744-748. https://doi.org/10.1111/ jgh.15047
[19]. Zheng, N., Du, S., Wang, J., Zhang, H., Cui, W., Kang, Z., ... & Ma, M. (2020). Predicting COVID-19 in china using hybrid AI model. IEEE Transactions on Cybernetics, 50(7), 2891 - 2904. https://doi.org/10.1109/TCYB.2020.2990162
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