{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:32:27Z","timestamp":1754155947929,"version":"3.41.2"},"reference-count":21,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:00:00Z","timestamp":1620086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["LHT"],"published-print":{"date-parts":[[2021,9,13]]},"abstract":"Purpose<\/jats:title>The emergence of a coronavirus disease 2019 (COVID-19) epidemic has had a tremendous impact on the world, and the characteristics of its evolution need to be better understood.<\/jats:p><\/jats:sec>Design\/methodology\/approach<\/jats:title>To explore the changes of cases and control them effectively, this paper analyzes and models the fluctuation and dynamic characteristics of the daily growth rate based on the data of newly confirmed cases around the world. Based on the data, the authors identify the inflection points and analyze the causes of the new daily confirmed cases and deaths worldwide.<\/jats:p><\/jats:sec>Findings<\/jats:title>The study found that the growth sequence of the number of new confirmed COVID-19 cases per day has a significant cluster of fluctuations. The impact of previous fluctuations in the future is gradually attenuated and shows a relatively gentle long-term downward trend. There are four inflection points in the global time series of new confirmed cases and the number of deaths per day. And these inflection points show the state of an accelerated rise, a slowdown in the rate of decline, a slowdown in the rate of growth and an accelerated decline in turn.<\/jats:p><\/jats:sec>Originality\/value<\/jats:title>This paper has a certain guiding and innovative significance for the dynamic research of COVID-19 cases in the world.<\/jats:p><\/jats:sec>","DOI":"10.1108\/lht-10-2020-0263","type":"journal-article","created":{"date-parts":[[2021,5,2]],"date-time":"2021-05-02T02:20:41Z","timestamp":1619922041000},"page":"888-902","source":"Crossref","is-referenced-by-count":6,"title":["On fluctuating characteristics of global COVID-19 cases and identification of inflection points"],"prefix":"10.1108","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0140-8003","authenticated-orcid":false,"given":"Xin","family":"Feng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3677-7332","authenticated-orcid":false,"given":"Hanshui","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1789-0209","authenticated-orcid":false,"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6211-3594","authenticated-orcid":false,"given":"Liming","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8720-6022","authenticated-orcid":false,"given":"Jiapei","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9038-2900","authenticated-orcid":false,"given":"Ye","family":"Wu","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2021,5,4]]},"reference":[{"volume-title":"A Poisson Autoregressive Model to Understand COVID-19 Contagion dynamics","year":"2020","key":"key2021091107363471300_ref001"},{"issue":"05","key":"key2021091107363471300_ref002","first-page":"543","article-title":"Application of autoregressive integrated moving average (ARIMA) model in global prediction of COVID-19 incidence","volume":"24","year":"2020","journal-title":"Chinese Journal of Disease Control and Prevention"},{"issue":"9","key":"key2021091107363471300_ref003","first-page":"40","article-title":"Statistical analysis and autoregressive modeling of confirmed coronavirus disease 2019 epidemic cases","volume":"69","year":"2020","journal-title":"Acta Physica Sinica"},{"key":"key2021091107363471300_ref004","doi-asserted-by":"crossref","first-page":"580327","DOI":"10.3389\/fpubh.2020.580327","article-title":"Global forecasting confirmed and fatal cases of COVID-19 outbreak using autoregressive integrated moving average model","volume":"8","year":"2020","journal-title":"Frontiers in Public Health"},{"issue":"5","key":"key2021091107363471300_ref005","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/S1473-3099(20)30120-1","article-title":"An interactive web-based dashboard to track COVID-19 in real time","volume":"20","year":"2020","journal-title":"The Lancet Infectious Diseases"},{"key":"key2021091107363471300_ref006","doi-asserted-by":"crossref","first-page":"104340","DOI":"10.1016\/j.ijmedinf.2020.104340","article-title":"Whether the weather will help us weather the COVID-19 pandemic: using machine learning to measure twitter users' perceptions","volume":"145","year":"2021","journal-title":"International Journal of Medical Informatics"},{"issue":"1","key":"key2021091107363471300_ref007","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jnlssr.2020.06.007","article-title":"ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India","volume":"1","year":"2020","journal-title":"Journal of Safety Science and Resilience"},{"issue":"3","key":"key2021091107363471300_ref008","first-page":"349","article-title":"Analysis of spread dynamics of coronavirus SARS-CoV-2,sars-CoV and MERS-CoV","volume":"49","year":"2020","journal-title":"Journal of University of Electronic Science and Technology of China"},{"issue":"06","key":"key2021091107363471300_ref009","first-page":"1059","article-title":"Research on the temporal and spatial characteristics of the COVID-19 in Hubei province with the use of crystal ball and GIS","volume":"54","year":"2020","journal-title":"Journal of Central China Normal University (Nat. 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