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      Time Series Forecasting of Covid-19 using Deep Learning Models: India-USA Comparative Case Study

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          Highlights

          • Deep Learning based time series forecasting and comparative case study of Covid-19 confirmed and death cases in India and USA.

          • Recurrent neural network (RNN) based variants of long short term memory (LSTM) are being used to design proposed models.

          • Convolutional LSTM based model outperform other models with high accuracy and very less error.

          • One of the unique studies providing state-of-the-art results to help both countries to recede Covid-19 impact.

          Abstract

          Covid-19 is a highly contagious virus which almost freezes the world along with its economy. Its ability of human-to-human and surface-to-human transmission turns the world into catastrophic phase. In this study, our aim is to predict the future conditions of novel Coronavirus to recede its impact. We have proposed deep learning based comparative analysis of Covid-19 cases in India and USA. The datasets of confirmed and death cases of Covid-19 are taken into consideration. The recurrent neural network (RNN) based variants of long short term memory (LSTM) such as Stacked LSTM, Bi-directional LSTM and Convolutional LSTM are used to design the proposed methodology and forecast the Covid-19 cases for one month ahead. Convolution LSTM outperformed the other two models and predicts the Covid-19 cases with high accuracy and very less error for all four datasets of both countries. Upward/downward trend of forecasted Covid-19 cases are also visualized graphically, which would be helpful for researchers and policy makers to mitigate the mortality and morbidity rate by streaming the Covid-19 into right direction.

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          Most cited references 17

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          Framewise phoneme classification with bidirectional LSTM and other neural network architectures.

          In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a modified, full gradient version of the LSTM learning algorithm. We evaluate Bidirectional LSTM (BLSTM) and several other network architectures on the benchmark task of framewise phoneme classification, using the TIMIT database. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short Term Memory (LSTM) is much faster and also more accurate than both standard Recurrent Neural Nets (RNNs) and time-windowed Multilayer Perceptrons (MLPs). Our results support the view that contextual information is crucial to speech processing, and suggest that BLSTM is an effective architecture with which to exploit it.
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            Is Open Access

            Megadrought and Megadeath in 16th Century Mexico

            The native population collapse in 16th century Mexico was a demographic catastrophe with one of the highest death rates in history. Recently developed tree-ring evidence has allowed the levels of precipitation to be reconstructed for north central Mexico, adding to the growing body of epidemiologic evidence and indicating that the 1545 and 1576 epidemics of cocoliztli (Nahuatl for "pest”) were indigenous hemorrhagic fevers transmitted by rodent hosts and aggravated by extreme drought conditions.
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              Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art

              COVID-19 is a pandemic that has affected over 170 countries around the world. The number of infected and deceased patients has been increasing at an alarming rate in almost all the affected nations. Forecasting techniques can be inculcated thereby assisting in designing better strategies and in taking productive decisions. These techniques assess the situations of the past thereby enabling better predictions about the situation to occur in the future. These predictions might help to prepare against possible threats and consequences. Forecasting techniques play a very important role in yielding accurate predictions. This study categorizes forecasting techniques into two types, namely, stochastic theory mathematical models and data science/machine learning techniques. Data collected from various platforms also play a vital role in forecasting. In this study, two categories of datasets have been discussed, i.e., big data accessed from World Health Organization/National databases and data from a social media communication. Forecasting of a pandemic can be done based on various parameters such as the impact of environmental factors, incubation period, the impact of quarantine, age, gender and many more. These techniques and parameters used for forecasting are extensively studied in this work. However, forecasting techniques come with their own set of challenges (technical and generic). This study discusses these challenges and also provides a set of recommendations for the people who are currently fighting the global COVID-19 pandemic.
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                Author and article information

                Contributors
                Journal
                Chaos Solitons Fractals
                Chaos Solitons Fractals
                Chaos, Solitons, and Fractals
                Elsevier Ltd.
                0960-0779
                0960-0779
                20 August 2020
                20 August 2020
                Affiliations
                Department of Computer Science & IT, University of Jammu, Jammu & Kashmir, India
                Author notes
                [* ]Corresponding author. Department of Computer Science & IT, University of Jammu, Jammu & Kashmir, India sourabhshastri@ 123456gmail.com
                [#]

                Mentor.

                Article
                S0960-0779(20)30623-8 110227
                10.1016/j.chaos.2020.110227
                7440083
                © 2020 Elsevier Ltd. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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