{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:47:37Z","timestamp":1770338857945,"version":"3.49.0"},"reference-count":28,"publisher":"Wiley","issue":"26","license":[{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2022,11,30]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>How will the newly discovered coronavirus (COVID\u201019) affect the world and what will be its global impact? For answering this question, we will require a prediction of overall recoveries and fatalities, as well as a reliable prognosis of coronavirus cases. Predicting, however, requires an ample total of past data related to it. On any particular day, the prediction is unclear since events in the future rarely repeat themselves the way that they did in the past. Furthermore, forecasts and predictions are determined by the absolute interests, accuracy of the data, and prophesied variables. In addition, psychological factors play an enormous role in how people perceive and react to the danger from the disease and therefore the fear that it is going to affect them personally. This research paper advances an unbiased method for predicting the increase of the COVID\u201019 employing a simple, but powerful method to do so. Assumed that the data are accurate and reliable which the longer term will still follow an equivalent disease pattern, our projections intimate with a large association. Within the COVID\u201019 cases were documented, in contingency, there is a steady increase. The hazards are far away from symmetric, as underestimating a pandemic's spread and failing to do enough to prevent it is far a lot worse than overspending and being too cautious when it will not be needed. This paper illustrates the timeline of a live forecasting study with huge implied implications for devising and decision\u2010making and gives unbiased predictions on COVID\u201019 confirmed cases, recovered cases, deaths, and ongoing cases are shown on a continental map using data science and machine learning (ML) approaches. Utilizing these ML\u2010based techniques, the proposed system predicts the accurate COVID\u201019 cases and gives better performance.<\/jats:p>","DOI":"10.1002\/cpe.7286","type":"journal-article","created":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T23:11:30Z","timestamp":1661814690000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Prediction and forecasting of worldwide corona virus (<scp>COVID<\/scp>\u201019) outbreak using time series and machine learning"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7988-1338","authenticated-orcid":false,"given":"Priyank","family":"Jain","sequence":"first","affiliation":[{"name":"Indian Institute of Information Technology  Bhopal Madhya Pradesh India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shriya","family":"Sahu","sequence":"additional","affiliation":[{"name":"Atal Bihari Vajpayee University  Bilaspur Chhattisgarh India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,8,29]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/09720529.2020.1784535"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","unstructured":"DirekogluC SahM.Worldwide and regional forecasting of coronavirus (Covid\u201019) spread using a deep learning model.bioRxiv;2020. doi:10.1101\/2020.05.23.20111039","DOI":"10.1101\/2020.05.23.20111039"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/app10113880"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12714"},{"issue":"5","key":"e_1_2_10_6_1","article-title":"Novel coronavirus forecasting model using nonlinear autoregressive artificial neural network","volume":"29","author":"Ghazaly NM","year":"2020","journal-title":"Int J Adv Sci Technol"},{"key":"e_1_2_10_7_1","unstructured":"RatreS SharmaR KevadiaM BathinaV.COVID 19 prediction using regression;2021."},{"key":"e_1_2_10_8_1","doi-asserted-by":"crossref","unstructured":"KumarP KalitaH PatairiyaS et al.Forecasting the dynamics of COVID\u201019 pandemic in top 15 countries in April 2020: ARIMA model with machine learning approach.bioRxiv;2020. doi:10.1101\/2020.03.30.20046227","DOI":"10.1101\/2020.03.30.20046227"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2020.100222"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2021.04.051"},{"key":"e_1_2_10_11_1","unstructured":"SaxenK VohraA RanaA.Covid\u201019 Outbreak Prediction Through Prophet Base Model."},{"key":"e_1_2_10_12_1","first-page":"1","article-title":"Time series forecasting of COVID\u201019 transmission in Asia Pacific countries using deep neural networks","author":"Rauf HT","year":"2021","journal-title":"Pers Ubiquit Comput"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110137"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11356\u2010021\u201013824\u20107"},{"key":"e_1_2_10_15_1","doi-asserted-by":"crossref","unstructured":"ArdabiliS MosaviA BandSS Varkonyi\u2010KoczyAR.Coronavirus disease (COVID\u201019) global prediction using hybrid artificial intelligence method of ANN trained with grey wolf optimizer. 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