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      The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges

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          Abstract

          Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.

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          Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions

          Zifeng Yang, Zhiqi Zeng, Ke Wang … (2020)
          Background The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. Methods We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. Results We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. Conclusions Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.
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            COVID-19, SARS and MERS: are they closely related?

            N Petrosillo, G Viceconte, O Ergonul … (2020)
            Background The 2019 novel coronavirus (SARS-CoV-2) is a new human coronavirus which is spreading with epidemic features in China and other Asian countries; cases have also been reported worldwide. This novel coronavirus disease (COVID-19) is associated with a respiratory illness that may lead to severe pneumonia and acute respiratory distress syndrome (ARDS). Although related to the severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS), COVID-19 shows some peculiar pathogenetic, epidemiological and clinical features which to date are not completely understood. Aims To provide a review of the differences in pathogenesis, epidemiology and clinical features of COVID-19, SARS and MERS. Sources The most recent literature in the English language regarding COVID-19 has been reviewed, and extracted data have been compared with the current scientific evidence about SARS and MERS epidemics. Content COVID-19 seems not to be very different from SARS regarding its clinical features. However, it has a fatality rate of 2.3%, lower than that of SARS (9.5%) and much lower than that of MERS (34.4%). The possibility cannot be excluded that because of the less severe clinical picture of COVID-19 it can spread in the community more easily than MERS and SARS. The actual basic reproductive number (R0) of COVID-19 (2.0–2.5) is still controversial. It is probably slightly higher than the R0 of SARS (1.7–1.9) and higher than that of MERS (<1). A gastrointestinal route of transmission for SARS-CoV-2, which has been assumed for SARS-CoV and MERS-CoV, cannot be ruled out and needs further investigation. Implications There is still much more to know about COVID-19, especially as concerns mortality and its capacity to spread on a pandemic level. Nonetheless, all of the lessons we learned in the past from the SARS and MERS epidemics are the best cultural weapons with which to face this new global threat.
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              Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China

              Benjamin F. Maier, Dirk Brockmann (2020)
              The recent outbreak of COVID-19 in Mainland China was characterized by a distinctive subexponential increase of confirmed cases during the early phase of the epidemic, contrasting an initial exponential growth expected for an unconstrained outbreak. We show that this effect can be explained as a direct consequence of containment policies that effectively deplete the susceptible population. To this end, we introduce a parsimonious model that captures both, quarantine of symptomatic infected individuals as well as population-wide isolation practices in response to containment policies or behavioral changes and show that the model captures the observed growth behavior accurately. The insights provided here may aid the careful implementation of containment strategies for ongoing secondary outbreaks of COVID-19 or similar future outbreaks of other emergent infectious diseases.
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                Author and article information

                Contributors
                Amir Ahmad: amirahmad@uaeu.ac.ae
                Journal
                Journal ID (nlm-ta): Arch Comput Methods Eng
                Journal ID (iso-abbrev): Arch Comput Methods Eng
                Title: Archives of Computational Methods in Engineering
                Publisher: Springer Netherlands (Dordrecht )
                ISSN (Print): 1134-3060
                ISSN (Electronic): 1886-1784
                Publication date (Electronic): 4 August 2020
                Pages: 1-9
                Affiliations
                [1 ]GRID grid.43519.3a, ISNI 0000 0001 2193 6666, College of Information Technology, , United Arab Emirates University, ; Al Ain, UAE
                [2 ]GRID grid.412436.6, ISNI 0000 0004 0500 6866, Department of Computer Science and Engineering, , Thapar University, ; Patiala, India
                [3 ]GRID grid.456391.c, ISNI 0000 0004 1762 9825, Department of Information Technology, , Khawarizmi International College, ; Al Ain, UAE
                [4 ]GRID grid.417972.e, ISNI 0000 0001 1887 8311, Department of Physics, , Indian Institute of Technology Guwahati, ; Guwahati, Assam 781039 India
                [5 ]GRID grid.412125.1, ISNI 0000 0001 0619 1117, Faculty of Computing and Information Technology, , King Abdulaziz University, ; P.O. Box 411, Rabigh, Jeddah 21911 Saudi Arabia
                Article
                Publisher ID: 9472
                DOI: 10.1007/s11831-020-09472-8
                PMC ID: 7399353
                PubMed ID: 32837183
                SO-VID: cf0925d9-c1a1-4005-974d-71848cdf4e7f
                Copyright © © CIMNE, Barcelona, Spain 2020
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Date received : 2 July 2020
                Date accepted : 23 July 2020
                Categories
                Subject: Original Paper

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