25
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          SARS-CoV-2 (COVID-19) is a new Coronavirus, with first reported human infections in late 2019. COVID-19 has been officially declared as a universal pandemic by the World Health Organization (WHO). The epidemiological characteristics of COVID-2019 have not been completely understood yet. More than 200000 persons were killed during this epidemic (till 1 May 2020). Therefore, developing forecasting models to predict the spread of that epidemic is a critical issue. In this study, statistical and artificial intelligence based approaches have been proposed to model and forecast the prevalence of this epidemic in Egypt. These approaches are autoregressive integrated moving average (ARIMA) and nonlinear autoregressive artificial neural networks (NARANN). The official data reported by The Egyptian Ministry of Health and Population of COVID-19 cases in the period between 1 March and 1 May, 2020 was used to train the models. The forecasted cases showed a good agreement with officially reported cases. The obtained results of this study may help the Egyptian decision-makers to put short-term future plans to face this epidemic.

          Related collections

          Most cited references32

          • Record: found
          • Abstract: found
          • Article: not found

          Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: A meta-analysis

          Highlights • Platelet count can discriminate between patients with severe and non-severe novel coronavirus disease 2019 (COVID-19) infections. • Patients who did not survive have a significantly lower platelet count than survivors. • Thrombocytopenia is associated with increased risk of severe disease. • A substantial decrease in platelet count should serve as clinical indicator of worsening illness in hospitalized patients with COVID-19.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Characteristics of COVID-19 infection in Beijing

            Background Since the first case of a novel coronavirus (COVID-19) infection pneumonia was detected in Wuhan, China, a series of confirmed cases of the COVID-19 were found in Beijing. We analyzed the data of 262 confirmed cases to determine the clinical and epidemiological characteristics of COVID-19 in Beijing. Methods We collected patients who were transferred by Beijing Emergency Medical Service to the designated hospitals. The information on demographic, epidemiological, clinical, laboratory test for the COVID-19 virus, diagnostic classification, cluster case and outcome were obtained. Furthermore we compared the characteristics between severe and common confirmed cases which including mild cases, no-pneumonia cases and asymptomatic cases, and we also compared the features between COVID-19 and 2003 SARS. Findings By Feb 10, 2020, 262 patients were transferred from the hospitals across Beijing to the designated hospitals for special treatment of the COVID-19 infected by Beijing emergency medical service. Among of 262 patients, 46 (17.6%) were severe cases, 216 (82.4%) were common cases, which including 192 (73.3%) mild cases, 11(4.2%) non-pneumonia cases and 13 (5.0%) asymptomatic cases respectively. The median age of patients was 47.5 years old and 48.5% were male. 192 (73.3%) patients were residents of Beijing, 50 (26.0%) of which had been to Wuhan, 116 (60.4%) had close contact with confirmed cases, 21 (10.9%) had no contact history. The most common symptoms at the onset of illness were fever (82.1%), cough (45.8%), fatigue (26.3%), dyspnea (6.9%) and headache (6.5%). The median incubation period was 6.7 days, the interval time from between illness onset and seeing a doctor was 4.5 days. As of Feb 10, 17.2% patients have discharged and 81.7% patients remain in hospital in our study, the fatality of COVID-19 infection in Beijing was 0.9%. Interpretation On the basis of this study, we provided the ratio of the COVID-19 infection on the severe cases to the mild, asymptomatic and non-pneumonia cases in Beijing. Population was generally susceptible, and with a relatively low fatality rate. The measures to prevent transmission was very successful at early stage, the next steps on the COVID-19 infection should be focused on early isolation of patients and quarantine for close contacts in families and communities in Beijing. Funding Beijing Municipal Science and Technology Commission and Ministry of Science and Technology.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A conceptual model for the outbreak of Coronavirus disease 2019 (COVID-19) in Wuhan, China with individual reaction and governmental action

              Highlights • For the ongoing novel coronavirus disease (CODID-19) outbreak in Wuhan, China, the Chinese government has implemented control measures such as city lockdown to mitigate the impact of the epidemic. • We model the outbreak in Wuhan with individual reaction and governmental action (holiday extension, city lockdown, hospitalisation and quarantine) based on some parameters of the 1918 influenza pandemic in London, United Kingdom. • We show the different effects of individual reaction and governmental action and preliminarily estimate the magnitude of these effects. • We also preliminarily estimate the time-varying reporting ratio.
                Bookmark

                Author and article information

                Contributors
                Journal
                Process Saf Environ Prot
                Process Saf Environ Prot
                Process Safety and Environmental Protection
                Institution of Chemical Engineers. Published by Elsevier B.V.
                0957-5820
                1744-3598
                20 May 2020
                20 May 2020
                Affiliations
                [a ]Department of Histology, Faculty of Medicine, Tanta University, Tanta, 31527, Egypt
                [b ]Department of Production Engineering and Mechanical Design, Faculty of Engineering, Tanta University, Tanta, 31527, Egypt
                Author notes
                Article
                S0957-5820(20)31025-9
                10.1016/j.psep.2020.05.029
                7237379
                32501368
                3ebe7fdb-7df5-4203-bb51-0ba18f9b3877
                © 2020 Institution of Chemical Engineers. Published by Elsevier B.V. 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.

                History
                : 10 April 2020
                : 5 May 2020
                : 14 May 2020
                Categories
                Article

                covid-19,forecasting,neural networks,egypt
                covid-19, forecasting, neural networks, egypt

                Comments

                Comment on this article