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      Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

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          Abstract

          Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was −0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.

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          Data-based analysis, modelling and forecasting of the COVID-19 outbreak

          Cleo Anastassopoulou, Lucia Russo, Athanasios Tsakris … (2020)
          Since the first suspected case of coronavirus disease-2019 (COVID-19) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the publicly available epidemiological data for Hubei, China from January 11 to February 10, 2020, we provide estimates of the main epidemiological parameters. In particular, we provide an estimation of the case fatality and case recovery ratios, along with their 90% confidence intervals as the outbreak evolves. On the basis of a Susceptible-Infectious-Recovered-Dead (SIDR) model, we provide estimations of the basic reproduction number (R 0), and the per day infection mortality and recovery rates. By calibrating the parameters of the SIRD model to the reported data, we also attempt to forecast the evolution of the outbreak at the epicenter three weeks ahead, i.e. until February 29. As the number of infected individuals, especially of those with asymptomatic or mild courses, is suspected to be much higher than the official numbers, which can be considered only as a subset of the actual numbers of infected and recovered cases in the total population, we have repeated the calculations under a second scenario that considers twenty times the number of confirmed infected cases and forty times the number of recovered, leaving the number of deaths unchanged. Based on the reported data, the expected value of R 0 as computed considering the period from the 11th of January until the 18th of January, using the official counts of confirmed cases was found to be ∼4.6, while the one computed under the second scenario was found to be ∼3.2. Thus, based on the SIRD simulations, the estimated average value of R 0 was found to be ∼2.6 based on confirmed cases and ∼2 based on the second scenario. Our forecasting flashes a note of caution for the presently unfolding outbreak in China. Based on the official counts for confirmed cases, the simulations suggest that the cumulative number of infected could reach 180,000 (with a lower bound of 45,000) by February 29. Regarding the number of deaths, simulations forecast that on the basis of the up to the 10th of February reported data, the death toll might exceed 2,700 (as a lower bound) by February 29. Our analysis further reveals a significant decline of the case fatality ratio from January 26 to which various factors may have contributed, such as the severe control measures taken in Hubei, China (e.g. quarantine and hospitalization of infected individuals), but mainly because of the fact that the actual cumulative numbers of infected and recovered cases in the population most likely are much higher than the reported ones. Thus, in a scenario where we have taken twenty times the confirmed number of infected and forty times the confirmed number of recovered cases, the case fatality ratio is around ∼0.15% in the total population. Importantly, based on this scenario, simulations suggest a slow down of the outbreak in Hubei at the end of February.
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            Applications of Machine Learning and Artificial Intelligence for Covid-19 (SARS-CoV-2) pandemic: A review

            Samuel Lalmuanawma, Jamal Hussain, Lalrinfela Chhakchhuak (2020)
            Highlights • Reviewed on various information related to application of Machine Learning and Artificial Intelligent for tackling Covid-19 pandemic • Challenges on computational system with real world problem discusses suggestions conveying researchers on model design, medical experts, and policymakers for tackling the Covid-19 pandemic and ahead • Targeting on treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccination process for the Covid-19 pandemic
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              Modeling and analysis of COVID-19 epidemics with treatment in fractional derivatives using real data from Pakistan

              Parvaiz Ahmad Naik, Mehmet Yavuz, Sania Qureshi … (2020)
              Coronaviruses are a large family of viruses that cause different symptoms, from mild cold to severe respiratory distress, and they can be seen in different types of animals such as camels, cattle, cats and bats. Novel coronavirus called COVID-19 is a newly emerged virus that appeared in many countries of the world, but the actual source of the virus is not yet known. The outbreak has caused pandemic with 26,622,706 confirmed infections and 874,708 reported deaths worldwide till August 31, 2020, with 17,717,911 recovered cases. Currently, there exist no vaccines officially approved for the prevention or management of the disease, but alternative drugs meant for HIV, HBV, malaria and some other flus are used to treat this virus. In the present paper, a fractional-order epidemic model with two different operators called the classical Caputo operator and the Atangana–Baleanu–Caputo operator for the transmission of COVID-19 epidemic is proposed and analyzed. The reproduction number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\mathcal {R}}}_0$$\end{document} R 0 is obtained for the prediction and persistence of the disease. The dynamic behavior of the equilibria is studied by using fractional Routh–Hurwitz stability criterion and fractional La Salle invariant principle. Special attention is given to the global dynamics of the equilibria. Moreover, the fitting of parameters through least squares curve fitting technique is performed, and the average absolute relative error between COVID-19 actual cases and the model’s solution for the infectious class is tried to be reduced and the best fitted values of the relevant parameters are achieved. The numerical solution of the proposed COVID-19 fractional-order model under the Caputo operator is obtained by using generalized Adams–Bashforth–Moulton method, whereas for the Atangana–Baleanu–Caputo operator, we have used a new numerical scheme. Also, the treatment compartment is included in the population which determines the impact of alternative drugs applied for treating the infected individuals. Furthermore, numerical simulations of the model and their graphical presentations are performed to visualize the effectiveness of our theoretical results and to monitor the effect of arbitrary-order derivative.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Results Phys
                Journal ID (iso-abbrev): Results Phys
                Title: Results in Physics
                Publisher: Published by Elsevier B.V.
                ISSN (Electronic): 2211-3797
                Publication date PMC-release: 16 May 2022
                Publication date (Print): July 2022
                Publication date (Electronic): 16 May 2022
                Volume: 38
                Page: 105613
                Affiliations
                [a ]School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China
                [b ]Niğde Ömer Halisdemir University, Mechanical Engineering Department, Niğde, Turkey
                [c ]Department of Statistics, Quaid-i-Azam University, 45320, Islamabad 44000, Pakistan
                [d ]Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, (Jeddah-M), Riyadh-11673, Saudi Arabia
                [e ]Department of Mathematics, Faculty of Arts and Sciences, Cankaya University, 06530 Ankara, Turkey
                [f ]Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
                Author notes
                [* ]Corresponding authors.
                Article
                Publisher Item ID: S2211-3797(22)00327-8 Publisher ID: 105613
                DOI: 10.1016/j.rinp.2022.105613
                PMC ID: 9110000
                PubMed ID: 35600673
                SO-VID: f1276b4d-6a92-4502-8894-212a0c7ae86f
                Copyright © © 2022 Published by Elsevier B.V.
                License:

                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.

                History
                Date received : 8 April 2022
                Date revision received : 10 May 2022
                Date accepted : 12 May 2022
                Categories
                Subject: Article

                Keywords: reliability function,maximum likelihood estimation,artificial neural network,failure rate function
                Data availability:
                Keywords: reliability function, maximum likelihood estimation, artificial neural network, failure rate function

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