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- W3122123251 abstract "As it is said history repeats itself, in 2020, the world witnessed another epidemic with the name of novel corona virus (2019-nCoV). The dataset has been prepared through the information from John Hopkins University, WHO source, along with the data of Indian Government. In this paper, we will take a deeper view through the epidemiological data on corona virus affected patients. We have assessed various trends and discovered patterns through the data. We have studied the outbreak progression of the 2019-nCoV in India through exponential growth model. Various patterns are analyzed like gender affected, age group of people being affected the most and percentage people survived based on assessing delays between symptom onset and initialization of treatment by the professionals. Through modeling of maximum likelihood function, we have drawn an approximate conclusion on number of unreported cases of 2019-nCoV patients (initially as per data). Serial interval taken for other known corona-viruses (nCoV), Severe Acute Respiratory Syndrome (SARS), and Middle East Respiratory Syndrome (MERS) are used as an approximate measure to predict the number of unreported cases. The counts of unreported cases are more vital than its counterpart as it can cause severe outbreak if not checked. Along with that, we have proposed a LSTM-based architecture to predict the number of cases in India." @default.
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- W3122123251 date "2021-01-01" @default.
- W3122123251 modified "2023-09-27" @default.
- W3122123251 title "An Analytics Overview & LSTM-Based Predictive Modeling of Covid-19: A Hardheaded Look Across India" @default.
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- W3122123251 doi "https://doi.org/10.1007/978-981-15-9516-5_25" @default.
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