{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T04:41:25Z","timestamp":1784176885259,"version":"3.55.0"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"S6","license":[{"start":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"JST CREST","award":["JPMJCR 1412"],"award-info":[{"award-number":["JPMJCR 1412"]}]},{"name":"JSPS KAKENHI","award":["JP17H06307, JP17H06299, and JP20H03240"],"award-info":[{"award-number":["JP17H06307, JP17H06299, and JP20H03240"]}]},{"DOI":"10.13039\/501100001700","name":"Ministry of Education, Culture, Sports, Science and Technology of Japan","doi-asserted-by":"crossref","award":["JP16H06299"],"award-info":[{"award-number":["JP16H06299"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The novel coronavirus (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, and within a few months, it has become a global pandemic. This forced many affected countries to take stringent measures such as complete lockdown, shutting down businesses and trade, as well as travel restrictions, which has had a tremendous economic impact. Therefore, having knowledge and foresight about how a country might be able to contain the spread of COVID-19 will be of paramount importance to the government, policy makers, business partners and entrepreneurs. To help social and administrative decision making, a model that will be able to forecast when a country might be able to contain the spread of COVID-19 is needed.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The results obtained using our long short-term memory (LSTM) network-based model are promising as we validate our prediction model using New Zealand\u2019s data since they have been able to contain the spread of COVID-19 and bring the daily new cases tally to zero. Our proposed forecasting model was able to correctly predict the dates within which New Zealand was able to contain the spread of COVID-19. Similarly, the proposed model has been used to forecast the dates when other countries would be able to contain the spread of COVID-19.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The forecasted dates are only a prediction based on the existing situation. However, these forecasted dates can be used to guide actions and make informed decisions that will be practically beneficial in influencing the real future. The current forecasting trend shows that more stringent actions\/restrictions need to be implemented for most of the countries as the forecasting model shows they will take over three months before they can possibly contain the spread of COVID-19.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-021-04224-2","type":"journal-article","created":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T11:08:58Z","timestamp":1623323338000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Forecasting the spread of COVID-19 using LSTM network"],"prefix":"10.1186","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6145-1065","authenticated-orcid":false,"given":"Shiu","family":"Kumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronesh","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tatsuhiko","family":"Tsunoda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thirumananseri","family":"Kumarevel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alok","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,6,10]]},"reference":[{"issue":"1","key":"4224_CR1","first-page":"157","volume":"91","author":"D Cucinotta","year":"2020","unstructured":"Cucinotta D, Vanelli M. WHO declares COVID-19 a pandemic. Acta Biomed. 2020;91(1):157\u201360.","journal-title":"Acta Biomed"},{"issue":"8","key":"4224_CR2","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.3390\/v2081803","volume":"2","author":"PCY Woo","year":"2010","unstructured":"Woo PCY, Huang Y, Lau SKP, Yuen K-Y. Coronavirus genomics and bioinformatics analysis. Viruses. 2010;2(8):1804\u201320.","journal-title":"Viruses"},{"key":"4224_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10389-020-01258-3","volume":"1","author":"M Mohammadi","year":"2020","unstructured":"Mohammadi M, Meskini M, do Nascimento Pinto AL. 2019 Novel coronavirus (COVID-19) overview. J Public Health. 2020;1:1. https:\/\/doi.org\/10.1007\/s10389-020-01258-3.","journal-title":"J Public Health"},{"issue":"3","key":"4224_CR4","doi-asserted-by":"publisher","first-page":"e0231236","DOI":"10.1371\/journal.pone.0231236","volume":"15","author":"F Petropoulos","year":"2020","unstructured":"Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. PLoS ONE. 2020;15(3):e0231236.","journal-title":"PLoS ONE"},{"issue":"7","key":"4224_CR5","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.jiph.2020.06.001","volume":"13","author":"SI Alzahrani","year":"2020","unstructured":"Alzahrani SI, Aljamaan IA, Al-Fakih EA. Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions. J Infect Public Health. 2020;13(7):914\u20139.","journal-title":"J Infect Public Health"},{"issue":"1","key":"4224_CR6","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s11071-020-05757-6","volume":"101","author":"K Rajagopal","year":"2020","unstructured":"Rajagopal K, Hasanzadeh N, Parastesh F, Hamarash II, Jafari S, Hussain I. A fractional-order model for the novel coronavirus (COVID-19) outbreak. Nonlinear Dyn. 2020;101(1):711\u20138.","journal-title":"Nonlinear Dyn"},{"key":"4224_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.psep.2020.05.029","volume":"141","author":"AI Saba","year":"2020","unstructured":"Saba AI, Elsheikh AH. Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks. Process Saf Environ Prot Trans Inst Chem Eng B. 2020;141:1\u20138.","journal-title":"Process Saf Environ Prot Trans Inst Chem Eng B"},{"key":"4224_CR8","doi-asserted-by":"publisher","first-page":"138704","DOI":"10.1016\/j.scitotenv.2020.138704","volume":"727","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, Xie J, Huang F, Cao L. Association between short-term exposure to air pollution and COVID-19 infection: evidence from China. Sci Total Environ. 2020;727:138704.","journal-title":"Sci Total Environ"},{"key":"4224_CR9","unstructured":"Tiwari A, Gupta R, Chandra R: Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown. 2021."},{"key":"4224_CR10","doi-asserted-by":"crossref","unstructured":"Chandra R, Jain A, Chauhan DS: Deep learning via LSTM models for COVID-19 infection forecasting in India. 2021.","DOI":"10.1371\/journal.pone.0262708"},{"key":"4224_CR11","doi-asserted-by":"publisher","first-page":"109864","DOI":"10.1016\/j.chaos.2020.109864","volume":"135","author":"VKR Chimmula","year":"2020","unstructured":"Chimmula VKR, Zhang L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos Solitons Fractals. 2020;135:109864.","journal-title":"Chaos Solitons Fractals"},{"issue":"3","key":"4224_CR12","doi-asserted-by":"publisher","first-page":"165","DOI":"10.21037\/jtd.2020.02.64","volume":"12","author":"Z Yang","year":"2020","unstructured":"Yang Z, Zeng Z, Wang K, Wong S-S, Liang W, Zanin M, Liu P, Cao X, Gao Z, Mai Z, et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J Thorac Dis. 2020;12(3):165\u201374.","journal-title":"J Thorac Dis"},{"issue":"1","key":"4224_CR13","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1016\/j.aej.2020.09.037","volume":"60","author":"J Farooq","year":"2021","unstructured":"Farooq J, Bazaz MA. A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India. Alex Eng J. 2021;60(1):587\u201396.","journal-title":"Alex Eng J"},{"key":"4224_CR14","doi-asserted-by":"publisher","first-page":"138762","DOI":"10.1016\/j.scitotenv.2020.138762","volume":"728","author":"A Tomar","year":"2020","unstructured":"Tomar A, Gupta N. Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Sci Total Environ. 2020;728:138762.","journal-title":"Sci Total Environ"},{"key":"4224_CR15","unstructured":"COVID-19 CORONAVIRUS PANDEMIC. https:\/\/www.worldometers.info\/coronavirus\/. Accessed 15 Dec 2020."},{"key":"4224_CR16","unstructured":"Luo J: When will COVID-19 end? Data-driven prediction. 2020."},{"issue":"375","key":"4224_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fneur.2020.00375","volume":"11","author":"Y Gao","year":"2020","unstructured":"Gao Y, Gao B, Chen Q, Liu J, Zhang Y. Deep convolutional neural network-based epileptic electroencephalogram (EEG) signal classification. Front Neurol. 2020;11(375):1. https:\/\/doi.org\/10.3389\/fneur.2020.00375.","journal-title":"Front Neurol"},{"issue":"1","key":"4224_CR18","doi-asserted-by":"publisher","first-page":"9153","DOI":"10.1038\/s41598-019-45605-1","volume":"9","author":"S Kumar","year":"2019","unstructured":"Kumar S, Sharma A, Tsunoda T. Brain wave classification using long short-term memory network based OPTICAL predictor. Sci Rep. 2019;9(1):9153.","journal-title":"Sci Rep"},{"issue":"4","key":"4224_CR19","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1109\/TNSRE.2018.2813138","volume":"26","author":"S Chambon","year":"2018","unstructured":"Chambon S, Galtier MN, Arnal PJ, Wainrib G, Gramfort A. A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Trans Neural Syst Rehabil Eng. 2018;26(4):758\u201369.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04224-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-021-04224-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04224-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T16:07:40Z","timestamp":1659629260000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-021-04224-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,10]]},"references-count":19,"journal-issue":{"issue":"S6","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["4224"],"URL":"https:\/\/doi.org\/10.1186\/s12859-021-04224-2","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,10]]},"assertion":[{"value":"23 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"316"}}