Analysis and Prediction of COVID-19 Timeline and Infection Rates

IEEE/ACM International Conference Advances in in Social Social Networks Analysis andand Mining (ASONAM) 20202020 IEEE/ACM International Conference ononAdvances Networks Analysis Mining (ASONAM)  2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) | 978-1-7281-1056-1/20/$31.00 ©2020 IEEE | DOI: 10.1109/ASONAM49781.2020.9381338 $QDO\VLVDQG3UHGLFWLRQRI&29,'7LPHOLQH DQG,QIHFWLRQ5DWHV  +LVKDP$O0XEDLG 'HSWRI&RPSWXHU6FLHQFH Univ. of Houston , Clear Lake +RXVWRQ7;86$ KLVKDP#XKFOHGX  Abstract—The number of new cases of infection of the Coronavirus disease, COVID-19, is alarming in many places in the world. In several world countries, including USA, the infection rates and daily cases numbers are fairly high; and there are even some spike increases in some USA states. Since the USA is experiencing the highest number of daily new cases in the world from May through July, the most important question is when we will witness an effective decline in the number of daily new cases? This paper follows a data-driven approach to induce the disease decline values from two country groups in the world where the disease declined already to less than 25% of its peak daily new cases. We apply these country groups' models to predict the decline to 25% of the US's peak. We compiled, examined, and analyzed pandemic data and statistics of two countries: g1: 42 countries, and g2: 14 countries. We utilize their data in the prediction of the decline timeline of the US. Group g2 consists of 14 countries having a similar number of cases per one million population. The majority of the models predict that the decline to 25% of the US's peak will be around the end of November to the first week of October. The results are significant and impressive as it is highly demanded to have clues and methods for the timeline prediction of this pandemic in the USA. Keywords—COVID-19 pandemic, Coronavirus disease, timeline prediction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© IEEE 2021. 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