{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:52:17Z","timestamp":1782841937950,"version":"3.54.5"},"reference-count":22,"publisher":"JMIR Publications Inc.","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"\n Background<\/jats:title>\n The COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The aim of this study was to develop a precise spread model of COVID-19 with time-dependent parameters via deep learning to respond promptly to the dynamic situation of the outbreak and proactively minimize damage.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In this study, we investigated a mathematical model with time-dependent parameters via deep learning based on forward-inverse problems. We used data from the Korea Centers for Disease Control and Prevention (KCDC) and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for Korea and the other countries, respectively. Because the data consist of confirmed, recovered, and deceased cases, we selected the susceptible-infected-recovered (SIR) model and found approximated solutions as well as model parameters. Specifically, we applied fully connected neural networks to the solutions and parameters and designed suitable loss functions.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n We developed an entirely new SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional Runge-Kutta fourth order model to confirm its convergent nature. In addition, we evaluated our model based on the real-world situation reported from the KCDC, the Korean government, and news media. We also crossvalidated our model using data from the CSSE for Italy, Sweden, and the United States.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The methodology and new model of this study could be employed for short-term prediction of COVID-19, which could help the government prepare for a new outbreak. In addition, from the perspective of measuring medical resources, our model has powerful strength because it assumes all the parameters as time-dependent, which reflects the exact status of viral spread.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/19907","type":"journal-article","created":{"date-parts":[[2020,9,2]],"date-time":"2020-09-02T14:25:18Z","timestamp":1599056718000},"page":"e19907","source":"Crossref","is-referenced-by-count":19,"title":["Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation"],"prefix":"10.2196","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9946-8807","authenticated-orcid":false,"given":"Se Young","family":"Jung","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4637-8722","authenticated-orcid":false,"given":"Hyeontae","family":"Jo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6630-2832","authenticated-orcid":false,"given":"Hwijae","family":"Son","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3678-2687","authenticated-orcid":false,"given":"Hyung Ju","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2020,9,9]]},"reference":[{"key":"ref1","unstructured":"Spanish fluWikipedia2020-04-06https:\/\/en.wikipedia.org\/w\/index.php?title=Spanish_flu&oldid=949335031"},{"key":"ref2","unstructured":"KassraieASpanish Flu: How America Fought a Pandemic a Century AgoAARP202003202020-04-15http:\/\/www.aarp.org\/politics-society\/history\/info-2020\/spanish-flu-pandemic.html"},{"key":"ref3","unstructured":"StrochlicNChampineRDHow some cities \u2018flattened the curve\u2019 during the 1918 flu pandemicNational Geographic202003272020-04-15https:\/\/www.nationalgeographic.com\/history\/2020\/03\/how-cities-flattened-curve-1918-spanish-flu-pandemic-coronavirus\/"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.25561\/77482"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1093\/jtm\/taaa021"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijid.2020.02.058"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3201\/eid2501.171901"},{"key":"ref8","unstructured":"8CYLuPChangCA Time-dependent SIR model for COVID-19 with Undetectable Infected PersonsarXiv q-bio202002282020-09-02http:\/\/arxiv.org\/abs\/2003.00122"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijheh.2020.113555"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1101\/2020.04.03.20052084"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1038\/s41421-020-0148-0"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3934\/mbe.2020205"},{"key":"ref13","unstructured":"Korea Centers for Disease Control and Prevention. Webpage in Korean2020-09-02http:\/\/www.cdc.kr\/"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"ref15","unstructured":"14JHSonHHwangHDeep Neural Network Approach to Forward-Inverse ProblemsarXiv math202007272020-09-02http:\/\/arxiv.org\/abs\/1907.12925"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1098\/rspb.2006.3754"},{"key":"ref17","unstructured":"Flattening the curve on COVID-19: How Korea responded to a pandemic using ICTKorea Ministry of the Interior and Safety202004162020-04-20https:\/\/mois.go.kr\/eng\/bbs\/type002\/commonSelectBoardArticle.do?bbsId=BBSMSTR_000000000022&nttId=76748"},{"key":"ref18","unstructured":"ParkHNMeasures on Daegu hospital shortage coming: Prime ministerThe Korea Herald202003012020-04-22http:\/\/www.koreaherald.com\/view.php?ud=20200301000210"},{"key":"ref19","unstructured":"Thousands wait for hospital beds in South Korea as coronavirus cases surgeThe Japan Times202003042020-04-22https:\/\/www.japantimes.co.jp\/news\/2020\/03\/04\/asia-pacific\/thousands-wait-hospital-beds-south-korea-coronavirus-cases-surge\/"},{"key":"ref20","unstructured":"KimYMCommunity treatment centers save medical resources in pandemicKorea Biomedical Review202004012020-04-22http:\/\/www.koreabiomed.com\/news\/articleView.html?idxno=7897"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijid.2020.03.031"},{"key":"ref22","unstructured":"YoonDHow South Korea Solved Its Acute Hospital-Bed ShortageThe Wall Street Journal2020-04-15https:\/\/www.wsj.com\/articles\/how-south-korea-solved-its-acute-hospital-bed-shortage-11584874801"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jmir.org\/2020\/9\/e19907","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:26:56Z","timestamp":1782840416000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.jmir.org\/2020\/9\/e19907\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,9]]},"references-count":22,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020]]}},"URL":"https:\/\/doi.org\/10.2196\/19907","relation":{"has-preprint":[{"id-type":"doi","id":"10.2196\/preprints.19907","asserted-by":"object"}]},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,9]]}}}