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      Forecasting of COVID-19 cases using Deep learning models: Is it reliable and practically significant?

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          Abstract

          The ongoing outbreak of the COVID-19 pandemic prevails as an ultimatum to the global economic growth and henceforth, all of society since neither a curing drug nor a preventing vaccine is discovered. The spread of COVID-19 is increasing day by day, imposing human lives and economy at risk. Due to the increased enormity of the number of COVID-19 cases, the role of Artificial Intelligence (AI) is imperative in the current scenario. AI would be a powerful tool to fight against this pandemic outbreak by predicting the number of cases in advance. Deep learning-based time series techniques are considered to predict world-wide COVID-19 cases in advance for short-term and medium-term dependencies with adaptive learning. Initially, the data pre-processing and feature extraction is made with the real world COVID-19 dataset. Subsequently, the prediction of cumulative confirmed, death and recovered global cases are modelled with Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (SLSTM) and Prophet approaches. For long-term forecasting of COVID 19 cases, multivariate LSTM models employed. The performance metrics are computed for all the models and the prediction results are subjected to comparative analysis to identify the most reliable model. From the results, it is evident that the Stacked LSTM algorithm yields higher accuracy with an error of less than 2% compared to the other considered algorithms for the studied performance metrics. Country-specific analysis of India and city-specific analysis of Chennai COVID-19 cases are predicted and analyzed in detail. Also, statistical hypothesis analysis and correlation analysis are done on the COVID 19 datasets by including the features like temperature, rainfall, population, total infected cases, area and population density during the months of May, June, July and August to find out the best suitable model. Further, Practical significance of predicting COVID-19 cases is elucidated in terms of assessing pandemic characteristics, scenario planning, optimization of models and supporting Sustainable Development Goals (SDGs).

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          Most cited references 48

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          Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges

          Highlights • Emergence of 2019 novel coronavirus (2019-nCoV) in China has caused a large global outbreak and major public health issue. • At 9 February 2020, data from the WHO has shown >37 000 confirmed cases in 28 countries (>99% of cases detected in China). • 2019-nCoV is spread by human-to-human transmission via droplets or direct contact. • Infection estimated to have an incubation period of 2–14 days and a basic reproduction number of 2.24–3.58. • Controlling infection to prevent spread of the 2019-nCoV is the primary intervention being used.
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            Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

            Summary Background Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19. Methods We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R 0), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort. Findings Simulated outbreaks starting with five initial cases, an R 0 of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R 0 was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R 0 of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R 0 of 2·5 more than 70% of contacts had to be traced, and for an R 0 of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R 0 was 1·5. For R 0 values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset. Interpretation In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts. Funding Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.
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              COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses

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                Author and article information

                Journal
                Results Phys
                Results Phys
                Results in Physics
                The Author(s). Published by Elsevier B.V.
                2211-3797
                14 January 2021
                14 January 2021
                Affiliations
                [a ]Department of Information Technology, Sri Venkateswara College of Engineering, Chennai 602117, India
                [b ]Clean and Resilient Energy Systems Laboratory, Texas A&M University, Galveston TX 77553, USA
                [c ]Department of Mechanical Engineering, Sri Venkateswara College of Engineering, Chennai 602117, India
                [d ]Discipline of Engineering and Energy, Murdoch University, 90 South St, Murdoch, WA 6150, Australia
                [e ]Department of Electrical Engineering and Renewable Energy, Oregon Renewable Energy Center (OREC), Oregon Institute of Technology, Klamath Falls, OR 97601, USA
                Author notes
                [* ]Corresponding author.
                Article
                S2211-3797(21)00004-8 103817
                10.1016/j.rinp.2021.103817
                7806459
                © 2021 The Author(s)

                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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