{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T01:30:40Z","timestamp":1778549440801,"version":"3.51.4"},"reference-count":33,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T00:00:00Z","timestamp":1614211200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2021,2,25]]},"abstract":"<jats:p>COVID-19 has led to a pandemic, affecting almost all countries in a few months. In this work, we applied selected deep learning models including multilayer perceptron, random forest, and different versions of long short-term memory (LSTM), using three data sources to train the models, including COVID-19 occurrences, basic information like coded country names, and detailed information like population, and area of different countries. The main goal is to forecast the outbreak in nine countries (Iran, Germany, Italy, Japan, Korea, Switzerland, Spain, China, and the USA). The performances of the models are measured using four metrics, including mean average percentage error (MAPE), root mean square error (RMSE), normalized RMSE (NRMSE), and <jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:msup>\n                           <a:mrow>\n                              <a:mi>R<\/a:mi>\n                           <\/a:mrow>\n                           <a:mrow>\n                              <a:mn>2<\/a:mn>\n                           <\/a:mrow>\n                        <\/a:msup>\n                     <\/a:math>\n                  <\/jats:inline-formula>. The best performance was found for a modified version of LSTM, called M-LSTM (winner model), to forecast the future trajectory of the pandemic in the mentioned countries. For this purpose, we collected the data from January 22 till July 30, 2020, for training, and from 1 August 2020 to 31 August 2020, for the testing phase. Through experimental results, the winner model achieved reasonably accurate predictions (MAPE, RMSE, NRMSE, and <jats:inline-formula>\n                     <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:msup>\n                           <c:mrow>\n                              <c:mi>R<\/c:mi>\n                           <\/c:mrow>\n                           <c:mrow>\n                              <c:mn>2<\/c:mn>\n                           <\/c:mrow>\n                        <\/c:msup>\n                     <\/c:math>\n                  <\/jats:inline-formula> are 0.509, 458.12, 0.001624, and 0.99997, respectively). Furthermore, we stopped the training of the model on some dates related to main country actions to investigate the effect of country actions on predictions by the model.<\/jats:p>","DOI":"10.1155\/2021\/6927985","type":"journal-article","created":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T16:37:08Z","timestamp":1614357428000},"page":"1-16","source":"Crossref","is-referenced-by-count":46,"title":["COVID-19 in Iran: Forecasting Pandemic Using Deep Learning"],"prefix":"10.1155","volume":"2021","author":[{"given":"Rahele","family":"Kafieh","sequence":"first","affiliation":[{"name":"Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roya","family":"Arian","sequence":"additional","affiliation":[{"name":"Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Narges","family":"Saeedizadeh","sequence":"additional","affiliation":[{"name":"Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6495-1313","authenticated-orcid":true,"given":"Zahra","family":"Amini","sequence":"additional","affiliation":[{"name":"Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nasim Dadashi","family":"Serej","sequence":"additional","affiliation":[{"name":"Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shervin","family":"Minaee","sequence":"additional","affiliation":[{"name":"Snap Inc., Machine Learning Research Team, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9913-8442","authenticated-orcid":true,"given":"Sunil Kumar","family":"Yadav","sequence":"additional","affiliation":[{"name":"Nocturne GmbH, Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atefeh","family":"Vaezi","sequence":"additional","affiliation":[{"name":"Department of Community and Family Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nima","family":"Rezaei","sequence":"additional","affiliation":[{"name":"Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaghayegh","family":"Haghjooy Javanmard","sequence":"additional","affiliation":[{"name":"Applied Physiology Research Center, Isfahan Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1002\/jmv.25678"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.4178\/epih.e2020006"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijantimicag.2020.105924"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaut.2020.102433"},{"key":"5","volume-title":"Coronavirus disease 2019 (COVID-19) situation report \u2013 31","author":"Organization, WHO","year":"2020"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/4296806"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/7056285"},{"key":"8","article-title":"How many infections of COVID-19 there will be in the \"Diamond Princess\"-predicted by a virus transmission model based on the simulation of crowd flow","author":"Z. 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