Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Al-qaness, Mohammed A. A.; Ewees, Ahmed A.; Fan, Hong; Elaziz, Mohamed Abd

doi:10.3390/jcm9030674

Cited by 364 publications

(318 citation statements)

References 55 publications

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“…Wu et al (2020) predicted the spread of COVID-19 for the national and global scale, to evaluate the effect of the metropolitan-wide quarantine of Wuhan and its neighbours . Al-qaness et al (2020) improved the Adaptive Neuro-Fuzzy Inference System (ANFIS) by applying an Enhanced Flower Pollination Algorithm using the Salp Swarm Algorithm to estimate the number of confirmed COVID-19 cases in China (Al-qaness et al, 2020). Anastassopoulou et al (2020) studied on the estimation of the critical epidemiological parameters as well as the modeling and predicting the spread of the COVID-19 epidemic in Hubei, China (Anastassopoulou et al, 2020).…”

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

Estimation of COVID-19 prevalence in Italy, Spain, and France

Ceylan

2020

Science of The Total Environment

495

423

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Europe has become the epicentre of the virus and hit the continent harder than China.• The apparent mortality rate of COVID-19 is approximately 13% in Italy, 11% in Spain, and 15% in France. • Time series models are significant in predicting the prevalence of the COVID-19 pandemic. • ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) were chosen as the best models for Italy, Spain, and France, respectively. were collected from the World Health Organization website. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) models with the lowest MAPE values (4.7520, 5.8486, and 5.6335) were selected as the best models for Italy, Spain, and France, respectively. This study shows that ARIMA models are suitable for predicting the prevalence of COVID-19 in the future. The results of the analysis can shed light on understanding the trends of the outbreak and give an idea of the epidemiological stage of these regions. Besides, the prediction of COVID-19 prevalence trends of Italy, Spain, and France can help take precautions and policy formulation for this epidemic in other countries.

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Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

Estimation of COVID-19 prevalence in Italy, Spain, and France

Ceylan

2020

Science of The Total Environment

495

423

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“…The estimated of number of cases was obtained by back calculation method [22]. They found that the best method for modeling and estimating the number of total cases was the FPASSA-ANFIS method [23].…”

Section: Discussionmentioning

confidence: 99%

Modelling of Covid-19 Outbreak Indicators in China Between January and April

Çelik

Ankaralı

Pasin

2020

Preprint

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Background:The aim of this study is to explain the changes of outbreak indicators for coronavirus in China with nonlinear models and time series analysis. There are lots of methods for modelling. But we want to determine the best mathematical model and the best time series method among other models. Methods:The data was obtained between January 22 and April 21, 2020 from China records. The number of total cases and the number of total deaths were used for the calculations. For modelling Weibull, Negative Exponential, Von Bertalanffy, Janoscheck, Lundqvist-Korf and Sloboda models were used and AR, MA, ARMA, Holt, Brown and Damped models were used for time series. The determination coefficient (R 2 ), Pseudo R 2 and mean square error were used for nonlinear modelling as criteria for determining the model that best describes the number of cases, the number of total deaths and BIC (Bayesian Information Criteria) was used for time series.Results: According to our results, the Sloboda model among the growth curves and ARIMA (0,2,1) model among the times series models were most suitable models for modelling of the number of total cases. In addition Lundqvist-Korf model among the growth curves and Holt linear trend exponential smoothing model among the times series models were most suitable model for modelling of the number of total deaths. Our time series models forecast that the number of total cases will 83311 on 5 May and the number of total deaths will be 5273. Conclusions:Because results of the modelling has providing information on measures to be taken and giving prior information for subsequent similar situations, it is of great importance modeling outbreak indicators for each country separately.

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“…• Given the systemic immunosuppressive state caused by malignancy and anticancer treatments, patients with advanced lung cancer may be at a higher risk of COVID-19 infection [15]. • Results of MTT assay revealed that seven synthesized compounds (i.e., 11a, 11b, 12a, 12b, 13b, 13c and 13h) particularly exhibited significant cytotoxicity against the three cancerous cell lines under investigation [16].…”

Section: Cancermentioning

confidence: 99%

CovidNLP: A Web Application for Distilling Systemic Implications of COVID-19 Pandemic with Natural Language Processing

Awasthi

Pal

Singh

et al. 2020

Preprint

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The flood of conflicting COVID-19 research has revealed that COVID-19 continues to be an enigma. Although more than 14,000 research articles on COVID-19 have been published with the disease taking a pandemic proportion, clinicians and researchers are struggling to distill knowledge for furthering clinical management and research. In this study, we address this gap for a targeted user group, i.e. clinicians, researchers, and policymakers by applying natural language processing to develop a CovidNLP dashboard in order to speed up knowledge discovery. The WHO has created a repository of about more than 5000 peer-reviewed and curated research articles on varied aspects including epidemiology, clinical features, diagnosis, treatment, social factors, and economics. We summarised all the articles in the WHO Database through an extractive summarizer followed by an exploration of the feature space using word embeddings which were then used to visualize the summarized associations of COVID-19 as found in the text. Clinicians, researchers, and policymakers will not only discover the direct effects of COVID-19 but also the systematic implications such as the anticipated rise in TB and cancer mortality due to the non-availability of drugs during the export lockdown as highlighted by our models. These demonstrate the utility of mining massive literature with natural language processing for rapid distillation and knowledge updates. This can help the users understand, synthesize, and take pre-emptive action with the available peer-reviewed evidence on COVID-19. Our models will be continuously updated with new literature and we have made our . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

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Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Cited by 364 publications

References 55 publications

Estimation of COVID-19 prevalence in Italy, Spain, and France

Estimation of COVID-19 prevalence in Italy, Spain, and France

Modelling of Covid-19 Outbreak Indicators in China Between January and April

CovidNLP: A Web Application for Distilling Systemic Implications of COVID-19 Pandemic with Natural Language Processing

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