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      Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables

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          Highlights

          • Hybrid and single models are employed to forecast COVID-19 cases in the Brazilian and USA context.

          • Models for multi-step-ahead forecasting coupled with climatic variables are evaluated.

          • Out-of-sample forecasting errors lower than 3.08% are achieved by best models.

          • Temperature and Precipitation variables play a key role in the forecasting model.

          • VMD-based models are the most suitable tools to forecast COVID-19 cases six-days-ahead.

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          Abstract

          The novel coronavirus disease (COVID-19) is a public health problem once according to the World Health Organization up to June 24th, 2020, more than 9.1 million people were infected, and more than 470 thousand have died worldwide. In the current scenario, the Brazil and the United States of America present a high daily incidence of new cases and deaths. Therefore, it is important to forecast the number of new cases in a time window of one week, once this can help the public health system developing strategic planning to deals with the COVID-19. The application of the forecasting artificial intelligence (AI) models has the potential of deal with dynamical behavior of time-series like of COVID-19. In this paper, Bayesian regression neural network, cubist regression, k-nearest neighbors, quantile random forest, and support vector regression, are used stand-alone, and coupled with the recent pre-processing variational mode decomposition (VMD) employed to decompose the time series into several intrinsic mode functions. All AI techniques are evaluated in the task of time-series forecasting with one, three, and six-days-ahead the cumulative COVID-19 cases in five Brazilian and American states, with a high number of cases up to April 28th, 2020. Previous cumulative COVID-19 cases and exogenous variables as daily temperature and precipitation were employed as inputs for all forecasting models. The models’ effectiveness are evaluated based on the performance criteria. In general, the hybridization of VMD outperformed single forecasting models regarding the accuracy, specifically when the horizon is six-days-ahead, the hybrid VMD–single models achieved better accuracy in 70% of the cases. Regarding the exogenous variables, the importance ranking as predictor variables is, from the upper to the lower, past cases, temperature, and precipitation. Therefore, due to the efficiency of evaluated models to forecasting cumulative COVID-19 cases up to six-days-ahead, the adopted models can be recommended as a promising models for forecasting and be used to assist in the development of public policies to mitigate the effects of COVID-19 outbreak.

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          Most cited references37

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          Clinical Characteristics of Coronavirus Disease 2019 in China

          Abstract Background Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. Methods We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. Results The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. Conclusions During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.)
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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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              Renal histopathological analysis of 26 postmortem findings of patients with COVID-19 in China

              Although the respiratory and immune systems are the major targets of Coronavirus Disease 2019 (COVID-19), acute kidney injury and proteinuria have also been observed. Currently, detailed pathologic examination of kidney damage in critically ill patients with COVID-19 has been lacking. To help define this we analyzed kidney abnormalities in 26 autopsies of patients with COVID-19 by light microscopy, ultrastructural observation and immunostaining. Patients were on average 69 years (19 male and 7 female) with respiratory failure associated with multiple organ dysfunction syndrome as the cause of death. Nine of the 26 showed clinical signs of kidney injury that included increased serum creatinine and/or new-onset proteinuria. By light microscopy, diffuse proximal tubule injury with the loss of brush border, non-isometric vacuolar degeneration, and even frank necrosis was observed. Occasional hemosiderin granules and pigmented casts were identified. There were prominent erythrocyte aggregates obstructing the lumen of capillaries without platelet or fibrinoid material. Evidence of vasculitis, interstitial inflammation or hemorrhage was absent. Electron microscopic examination showed clusters of coronavirus particles with distinctive spikes in the tubular epithelium and podocytes. Furthermore, the receptor of SARS-CoV-2, ACE2 was found to be upregulated in patients with COVID-19, and immunostaining with SARS-CoV nucleoprotein antibody was positive in tubules. In addition to the direct virulence of SARS-CoV-2, factors contributing to acute kidney injury included systemic hypoxia, abnormal coagulation, and possible drug or hyperventilation-relevant rhabdomyolysis. Thus, our studies provide direct evidence of the invasion of SARSCoV-2 into kidney tissue. These findings will greatly add to the current understanding of SARS-CoV-2 infection.
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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
                30 June 2020
                October 2020
                30 June 2020
                : 139
                : 110027
                Affiliations
                [a ]Industrial & Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Parana (PUCPR), 1155, Rua Imaculada Conceicao, Curitiba, PR, Brazil, 80215-901
                [b ]Department of Mathematics, Federal Technological University of Parana (UTFPR), Via do Conhecimento, KM 01 - Fraron, Pato Branco, PR, Brazil, 85503–390
                [c ]Mechanical Engineering Graduate Program (PPGEM), Pontifical Catholic University of Parana (PUCPR), 1155, Rua Imaculada Conceicao, Curitiba, PR, Brazil, 80215-901
                [d ]Department of Electrical Engineering, Federal University of Parana (UFPR), 100, Avenida Coronel Francisco Heraclito dos Santos, Curitiba, PR, Brazil, 81530-000
                Author notes
                [* ]Corresponding author. gomes.ramon@ 123456pucpr.edu.br
                Article
                S0960-0779(20)30425-2 110027
                10.1016/j.chaos.2020.110027
                7324930
                32834591
                a9cca00e-8f84-4f67-bb8a-1873a49318fe
                © 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.

                History
                : 16 May 2020
                : 10 June 2020
                : 15 June 2020
                Categories
                Article

                artificial intelligence,covid-19,exogenous variables,forecasting,variational mode decomposition,machine learning

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