{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T14:19:02Z","timestamp":1769091542227,"version":"3.49.0"},"reference-count":90,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T00:00:00Z","timestamp":1653868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100018227","name":"National Research Foundation of Ukraine","doi-asserted-by":"publisher","award":["2020.02\/0404"],"award-info":[{"award-number":["2020.02\/0404"]}],"id":[{"id":"10.13039\/100018227","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>COVID-19 has become the largest pandemic in recent history to sweep the world. This study is devoted to developing and investigating three models of the COVID-19 epidemic process based on statistical machine learning and the evaluation of the results of their forecasting. The models developed are based on Random Forest, K-Nearest Neighbors, and Gradient Boosting methods. The models were studied for the adequacy and accuracy of predictive incidence for 3, 7, 10, 14, 21, and 30 days. The study used data on new cases of COVID-19 in Germany, Japan, South Korea, and Ukraine. These countries are selected because they have different dynamics of the COVID-19 epidemic process, and their governments have applied various control measures to contain the pandemic. The simulation results showed sufficient accuracy for practical use in the K-Nearest Neighbors and Gradient Boosting models. Public health agencies can use the models and their predictions to address various pandemic containment challenges. Such challenges are investigated depending on the duration of the constructed forecast.<\/jats:p>","DOI":"10.3390\/computation10060086","type":"journal-article","created":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T10:05:14Z","timestamp":1653905114000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Investigation of Statistical Machine Learning Models for COVID-19 Epidemic Process Simulation: Random Forest, K-Nearest Neighbors, Gradient Boosting"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2623-3294","authenticated-orcid":false,"given":"Dmytro","family":"Chumachenko","sequence":"first","affiliation":[{"name":"Mathematical Modelling and Artificial Intelligence Department, National Aerospace University \u201cKharkiv Aviation Institute\u201d, 71072 Kharkiv, Ukraine"}]},{"given":"Ievgen","family":"Meniailov","sequence":"additional","affiliation":[{"name":"Mathematical Modelling and Artificial Intelligence Department, National Aerospace University \u201cKharkiv Aviation Institute\u201d, 71072 Kharkiv, Ukraine"}]},{"given":"Kseniia","family":"Bazilevych","sequence":"additional","affiliation":[{"name":"Mathematical Modelling and Artificial Intelligence Department, National Aerospace University \u201cKharkiv Aviation Institute\u201d, 71072 Kharkiv, Ukraine"}]},{"given":"Tetyana","family":"Chumachenko","sequence":"additional","affiliation":[{"name":"Epidemiology Department, Kharkiv National Medical University, 61000 Kharkiv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1707-843X","authenticated-orcid":false,"given":"Sergey","family":"Yakovlev","sequence":"additional","affiliation":[{"name":"Mathematical Modelling and Artificial Intelligence Department, National Aerospace University \u201cKharkiv Aviation Institute\u201d, 71072 Kharkiv, Ukraine"},{"name":"Institute of Information Technology, Lodz University of Technology, 90-924 Lodz, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1038\/s41577-021-00522-1","article-title":"The first 12 months of COVID-19: A timeline of immunological insights","volume":"21","author":"Carvalho","year":"2021","journal-title":"Nat. Rev. Immunol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1016\/j.jiph.2020.08.001","article-title":"From SARS to COVID-19: What lessons have we learned?","volume":"13","author":"Liu","year":"2020","journal-title":"J. Infect. Public Health"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Khan, M., Adil, S.F., Alkhathlan, H.Z., Tahir, M.N., Saif, S., Khan, M., and Khan, S.T. (2020). COVID-19: A global challenge with old history, epidemiology and progress so far. Molecules, 26.","DOI":"10.3390\/molecules26010039"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.bj.2020.05.021","article-title":"Fighting COVID-19: A quick review of diagnoses, therapies, and vaccines","volume":"43","author":"Shih","year":"2020","journal-title":"Biomed. J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, X., Gong, W., Wu, X., and Zhao, W. (2021). Estimating economic losses caused by COVID-19 under multiple control measure scenarios with a coupled infectious disease-economic model: A case study in Wuhan, China. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph182211753"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s12187-021-09865-6","article-title":"COVID-19 pandemic and the second lockdown: The 3rd wave of the disease through the voice of youth","volume":"15","author":"Branquinho","year":"2022","journal-title":"Child Indic. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1080\/14760584.2021.1915140","article-title":"The emergence of new strains of SARS-CoV-2. What does it mean for COVID-19 vaccines?","volume":"20","author":"Hossain","year":"2021","journal-title":"Expert Rev. Vaccines"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2878","DOI":"10.1111\/1462-2920.15549","article-title":"COVID-19: Vaccination problems","volume":"23","year":"2021","journal-title":"Environ. Microbiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1111\/tbed.13804","article-title":"Challenges and prospects of COVID-19 vaccine development based on the progress made in SARS and MERS vaccine development","volume":"68","author":"Begum","year":"2021","journal-title":"Transbound. Emerg. Dis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e22287","DOI":"10.2196\/22287","article-title":"The COVID-19 pandemic: A pandemic of lockdown loneliness and the role of digital technology","volume":"22","author":"Shan","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"150","DOI":"10.32620\/reks.2021.1.13","article-title":"Method and information technology to research the component architecture of products to justify investments of high-tech enterprise","volume":"1","author":"Fedorovich","year":"2021","journal-title":"Radioelectron. Comput. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s42521-020-00021-3","article-title":"COVID-19 contagion and digital finance","volume":"2","author":"Agosto","year":"2020","journal-title":"Digit. Financ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fedushko, S., and Ustyianovych, T. (2022). E-commerce customers behavior research using cohort analysis: A case study of COVID-19. J. Open Innov. Technol. Mark. Complex., 8.","DOI":"10.3390\/joitmc8010012"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Davidich, N., Chumachenko, I., Davidich, Y., Hanieva, T., Artsybasheva, N., and Melenchuk, T. (2020, January 14\u201317). Advanced traveller information systems to optimizing freight driver route selection. Proceedings of the 2020 13th International Conference on Developments in eSystems Engineering (DeSE), Liverpool, UK.","DOI":"10.1109\/DeSE51703.2020.9450763"},{"key":"ref_15","first-page":"555","article-title":"Computer vision mobile system for education using augmented reality technology","volume":"17","author":"Misiuk","year":"2021","journal-title":"J. Mob. Multimed."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nechyporenko, A., Reshetnik, V., Shyian, D., Yurevych, N., Alekseeva, V., Nazaryan, R., and Gargin, V. (2021, January 6\u20139). Comparative characteristics of the anatomical structures of the ostiomeatal complex obtained by 3D modeling. Proceedings of the 2020 IEEE International Conference on Problems of Infocommunications Science and Technology, Kharkiv, Ukraine.","DOI":"10.1109\/PICST51311.2020.9468111"},{"key":"ref_17","first-page":"65","article-title":"Intelligent evaluation of the informative features of cardiac studies diagnostic data using Shannon method","volume":"3003","author":"Bazilevych","year":"2021","journal-title":"CEUR Workshop Proc."},{"key":"ref_18","first-page":"749","article-title":"An approach towards missing data management using improved GRNN-SGTM ensemble method","volume":"24","author":"Izonin","year":"2021","journal-title":"Eng. Sci. Technol."},{"key":"ref_19","first-page":"265","article-title":"The concept of developing a decision support system for the epidemic morbidity control","volume":"2753","author":"Yakovlev","year":"2020","journal-title":"CEUR Workshop Proc."},{"key":"ref_20","first-page":"204","article-title":"An application of the theory of probabilities to the study of a priori pathometry","volume":"92","author":"Ross","year":"1916","journal-title":"Proc. R. Soc. Lond."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/S0140-6736(01)80187-2","article-title":"The Milroy lectures. On epidemic disease in England\u2014The evidence of variability and of persistency of type","volume":"167","author":"Hamer","year":"1906","journal-title":"Lancet"},{"key":"ref_22","first-page":"700","article-title":"Contribution to the mathematical theory to epidemics","volume":"115","author":"Kermack","year":"1927","journal-title":"Proc. R. Soc. Lond."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/S0169-409X(01)00118-1","article-title":"Compartment modeling","volume":"48","author":"Holz","year":"2001","journal-title":"Adv. Drug Deliv. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"16571","DOI":"10.1038\/s41598-021-95913-8","article-title":"Mathematical analysis of a measles transmission dynamics model in Bangladesh with double dose vaccination","volume":"11","author":"Kuddus","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_25","first-page":"202","article-title":"Forecasting seasonal influenza with a state-space SIR model","volume":"11","author":"Ostus","year":"2017","journal-title":"Ann. Appl. Stat."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/BF01120008","article-title":"Deterministic models of epidemics for a territory with a transport network","volume":"3","author":"Baroyan","year":"1967","journal-title":"Cybern. Syst. Snalysis"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"136263","DOI":"10.1155\/2014\/136263","article-title":"On fractional SIRC model with Salmonella bacterial infection","volume":"2014","author":"Rihan","year":"2014","journal-title":"Abstr. Appl. Anal."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1086\/505115","article-title":"A mathematical model of Hepatitis A transmission in the United States indicates value of universal childhood immunization","volume":"43","author":"Zink","year":"2006","journal-title":"Clin. Infect. Dis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6545179","DOI":"10.1155\/2022\/6545179","article-title":"Global stability and parameter estimation for a diphtheria model: A case study of an epidemic in Rohingya refugee camp in Bangladesh","volume":"2022","author":"Islam","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6550","DOI":"10.1016\/j.apm.2016.01.054","article-title":"Modelling and stability of HIV\/AIDS epidemic model with treatment","volume":"40","author":"Huo","year":"2016","journal-title":"Appl. Math. Model."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"150681","DOI":"10.1155\/2013\/150681","article-title":"Transmission model of Hepatitis B virus with the migration effect","volume":"2013","author":"Khan","year":"2013","journal-title":"BioMed Res. Int."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100378","DOI":"10.1016\/j.epidem.2019.100378","article-title":"Dynamic modeling of Hepatitis C transmission among people who inject drugs","volume":"30","author":"Stocks","year":"2020","journal-title":"Epidemics"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e2105292118","DOI":"10.1073\/pnas.2105292118","article-title":"A simple model for control of COVID-19 infections on an urban campus","volume":"118","author":"Brown","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"012009","DOI":"10.1088\/1742-6596\/1735\/1\/012009","article-title":"The SEIR model of COVID-19 forecasting rates of infections in New York city","volume":"1735","author":"Zha","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"21256","DOI":"10.1038\/s41598-020-77628-4","article-title":"Management strategies in a SEIR-type model of COVID-19 community spread","volume":"10","author":"Radulescu","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2954519","DOI":"10.1155\/2020\/2954519","article-title":"Effectiveness of the strategies implemented in Sri Lanka for controlling the COVID-19 outbreak","volume":"2020","author":"Erandi","year":"2020","journal-title":"J. Appl. Math."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Alexandrou, C., Harmandaris, V., Irakleous, A., Koutsou, G., and Savva, N. (2020). Modeling the evolution of COVID-19 via compartmental and particle-based approaches: Application to the Cyprus case. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0250709"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"202327","DOI":"10.1098\/rsos.202327","article-title":"Estimating the state of the COVID-19 epidemic in France using a model with memory","volume":"8","author":"Forien","year":"2021","journal-title":"R. Soc. Open Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Nistal, R., de la Sen, M., Gabirondo, J., Alonso-Quesada, S., Garrido, A.J., and Garrido, I. (2021). A Study on COVID-19 Incidence in Europe through Two SEIR Epidemic Models Which Consider Mixed Contagions from Asymptomatic and Symptomatic Individuals. Appl. Sci., 11.","DOI":"10.3390\/app11146266"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"110057","DOI":"10.1016\/j.chaos.2020.110057","article-title":"A SIR model assumption for the spread of COVID-19 in difference communities","volume":"139","author":"Cooped","year":"2020","journal-title":"Chaos Solut. Fractals"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"20200036","DOI":"10.1515\/em-2020-0036","article-title":"Applying SEIR model without vaccination for COVID-19 in case of the United States, Russia, the United Kingdom, Brazil, France, and India","volume":"10","author":"Solieva","year":"2021","journal-title":"Epidemiol. Methods"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"125564","DOI":"10.1016\/j.physa.2020.125564","article-title":"Exact properties of SIQR model for COVID-19","volume":"564","author":"Odagaki","year":"2021","journal-title":"Phys. A"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7787624","DOI":"10.1155\/2021\/7787624","article-title":"Extended epidemiological models for weak economic region: Case studies of the spreading of COVID-19 in the South Asian subcontinental countries","volume":"2021","author":"Mugdha","year":"2021","journal-title":"BioMed Res. Int."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Rahimi, I., Gandomi, A.H., Asteris, P.G., and Chen, F. (2021). Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases. Information, 12.","DOI":"10.3390\/info12030109"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"100490","DOI":"10.1016\/j.epidem.2021.100490","article-title":"COVID-19 Belgium: Extended SEIR-QD model with nursing homes and long-term scenarios-based forecasts","volume":"37","author":"Franco","year":"2021","journal-title":"Epidemics"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ghostine, R., Gharamti, M., Hassrouny, S., and Hoteit, I. (2021). An Extended SEIR Model with Vaccination for Forecasting the COVID-19 Pandemic in Saudi Arabia Using an Ensemble Kalman Filter. Mathematics, 9.","DOI":"10.3390\/math9060636"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1007\/s10559-022-00438-1","article-title":"Substantiating the diffusion model of innovation implementation and its application to vaccine propagation","volume":"58","author":"Gorbachuk","year":"2022","journal-title":"Cybern. Syst. Anal."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.arcontrol.2020.09.009","article-title":"Transport effect of COVID-19 pandemic in France","volume":"50","author":"Guan","year":"2020","journal-title":"Annu. Rev. Control"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"10","DOI":"10.17509\/ijost.v5i2.24432","article-title":"Application of SEIR model in COVID-19 and the effect of lockdown on reducing the number of active cases","volume":"5","author":"Putra","year":"2020","journal-title":"Indones. J. Sci. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"8191","DOI":"10.1038\/s41598-021-86873-0","article-title":"A compartmental model that predicts the effect of social distancing and vaccination on controlling COVID-19","volume":"11","author":"Dashtbali","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"207","DOI":"10.3390\/epidemiologia2020016","article-title":"Mask-Ematics: Modeling the Effects of Masks in COVID-19 Transmission in High-Risk Environments","volume":"2","author":"Morciglio","year":"2021","journal-title":"Epidemiologia"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"110885","DOI":"10.1016\/j.chaos.2021.110885","article-title":"Sensitivity assessment and optimal economic evaluation of a new COVID-19 compartmental epidemic model with control interventions","volume":"146","author":"Asamoah","year":"2021","journal-title":"Chaos Solut. Fractals"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"104731","DOI":"10.1016\/j.rinp.2021.104731","article-title":"Mathematical modeling of COVID-19 transmission dynamics between healthcare workers and community","volume":"29","author":"Masandawa","year":"2021","journal-title":"Results Phys."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"S86","DOI":"10.1016\/j.jfma.2021.05.002","article-title":"Evaluating medical capacity for hospitalization and intensive care unit of COVID-19: A queue model approach","volume":"120","author":"Jen","year":"2021","journal-title":"J. Formos. Med. Assoc."},{"key":"ref_55","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","author":"Dong","year":"2020","journal-title":"Lancet Infect. Dis."},{"key":"ref_56","unstructured":"(2022, April 25). Robert Koch-Institut: COVID-19 Dashboard. Available online: https:\/\/experience.arcgis.com\/experience\/478220a4c454480e823b17327b2bf1d4."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1007\/s00103-021-03394-x","article-title":"Die verschiedenen Phasen der COVID-19-Pandemie in Deutschland: Eine deskriptive Analyse von Januar 2020 bis Februar 2021","volume":"64","author":"Schilling","year":"2021","journal-title":"Bundesgesundheitsblat\u2014Gesundh. Gesundh."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"125","DOI":"10.5414\/CP204191","article-title":"Effect of lockdown and vaccination on the course of the COVID-19 pandemic in Germany","volume":"60","author":"Braun","year":"2022","journal-title":"Int. J. Clin. Pharmacol. Ther."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s12519-021-00511-3","article-title":"Role of Omicron variant of SARS-CoV-2 in children in Germany","volume":"18","author":"Bittmann","year":"2022","journal-title":"World J. Pediatrics"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s40258-020-00632-2","article-title":"How Many Intensive Care Beds are Justifiable for Hospital Pandemic Preparedness? A Cost-effectiveness Analysis for COVID-19 in Germany","volume":"19","author":"Gandjour","year":"2021","journal-title":"Appl. Health Econ. Health Policy"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Leithauser, N., Schneider, J., Johann, S., Krumke, S.O., Schmidt, E., Streicher, M., and Scholz, S. (2021). Quantifying Covid19-vaccine location strategies for Germany. BMC Health Serv. Res., 21.","DOI":"10.1186\/s12913-021-06587-x"},{"key":"ref_62","first-page":"100288","article-title":"Japanese travel behavior trends and change under COVID-19 state-of-emergency declaration: Nationwide observation by mobile phone location data","volume":"9","author":"Hara","year":"2021","journal-title":"Transp. Res. Interdiscip. Perspect."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1007\/s42399-020-00547-y","article-title":"Critical care medical centers may play an important role in reducing the risk of COVID-19 death in Japan","volume":"2","author":"Ishikawa","year":"2020","journal-title":"SN Compr. Clin. Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1097\/JCMA.0000000000000270","article-title":"The outbreak of COVID-19: An overview","volume":"83","author":"Wu","year":"2020","journal-title":"J. Chin. Med. Assoc."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.tra.2022.03.009","article-title":"Role of stay-at-home requests and travel restrictions in preventing the spread of COVID-19 in Japan","volume":"159","author":"Liu","year":"2022","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Machida, M., Nakamura, I., Kojima, T., Saito, R., Nakaya, T., Hanibuchi, T., Takamiya, T., Odagiri, Y., Fukushima, N., and Kikuchi, H. (2021). Acceptance of a COVID-19 vaccine in Japan during the COVID-19 pandemic. Vaccines, 9.","DOI":"10.3390\/vaccines9030210"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1016\/j.healthpol.2021.07.003","article-title":"COVID-19 Apps as a digital intervention police: A longitudinal panel data analysis in South Korea","volume":"125","author":"Kim","year":"2021","journal-title":"Health Policy"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e2021068","DOI":"10.4178\/epih.e2021068","article-title":"Reconstructing a COVID-19 outbreak within a religious group using social network analysis simulation in Korea","volume":"43","author":"Kim","year":"2021","journal-title":"Epidemiol. Health"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1093\/aje\/kwaa217","article-title":"Flattening the curve on COVID-19: South Korea\u2019s measures in tackling initial outbreak of Coronavirus","volume":"190","author":"Lee","year":"2021","journal-title":"Am. J. Epidemiol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"349","DOI":"10.3343\/alm.2020.40.5.349","article-title":"COVID-19 testing in South Korea: Current status and the need for faster diagnostics","volume":"40","author":"Kim","year":"2020","journal-title":"Ann. Lab. Med."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Choi, Y., Kim, J.S., Kim, J.E., Choi, H., and Lee, C.H. (2021). Vaccination prioritization strategies for COVID-19 in Korea: A mathematical modeling approach. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18084240"},{"key":"ref_72","first-page":"105087","article-title":"Investigating the first stage of the COVID-19 pandemic in Ukraine using epidemiological and genomic data","volume":"95","author":"Gankin","year":"2021","journal-title":"Infect. Genet. Evol. J. Mol. Epidemiol. Evol. Genet. Infect. Dis."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.36740\/WLek202105139","article-title":"Healthcare in Ukraine during the pandemic: Difficulties, challenges and solutions","volume":"74","author":"Korolchuk","year":"2021","journal-title":"Wiad. Lek."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"AO2","DOI":"10.22323\/2.19050202","article-title":"The landscape of disinformation of health crisis communication during the COVID-19 pandemic in Ukraine: Hybrid warfare tactics, fake media news and review of evidence","volume":"19","author":"Patel","year":"2020","journal-title":"J. Sci. Commun."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"5252","DOI":"10.1002\/jmv.27091","article-title":"COVID-19 vaccination in Ukraine: An update on the status of vaccination and the challenges at hand","volume":"93","author":"Matiashova","year":"2021","journal-title":"J. Med. Virol."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Guerard, J.B. (2013). Regression analysis and forecasting models. Introduction to Financial Forecasting in Investment Analysis, Springer.","DOI":"10.1007\/978-1-4614-5239-3"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random Forest: A classification and regression tool for compound classification and QSAR modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Kumar, T. (2015, January 13\u201314). Solution of linear and non linear regression problem by K Nearest Neighbour approach: By using three sigma rule. Proceedings of the 2015 IEEE International Conference on Computational Intelligence & Communication Technology, Ghaziabad, India.","DOI":"10.1109\/CICT.2015.110"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Singh, U., Rizwan, M., Alaraj, M., and Alsaidan, I. (2021). A machine learning-based gradient boosting regression approach for wind power production forecasting: A step towards smart grid environments. Energies, 14.","DOI":"10.3390\/en14165196"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Chen, C., Twycross, J., and Garibaldi, J.M. (2017). A new accuracy measure based on bounded relative error for time series forecasting. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0174202"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Seligman, B., Ferranna, M., and Bloom, D.E. (2021). Social determinants of mortality from COVID-19: A simulation study using NHANES. PLoS Med., 18.","DOI":"10.1371\/journal.pmed.1003888"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"593417","DOI":"10.3389\/fpubh.2021.593417","article-title":"Facilitating understanding, modeling and simulation of infectious disease epidemics in the age of COVID-19","volume":"9","author":"Rubin","year":"2021","journal-title":"Front. Public Health"},{"key":"ref_84","first-page":"55","article-title":"Density of COVID-19 and mass population movement during long holiday: Simulation comparing between using holiday postponement and no holiday postponement","volume":"25","author":"Wiwanitkit","year":"2020","journal-title":"J. Res. Med. Sci. Off. J. Isfahan Univ. Med. Sci."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"577","DOI":"10.7326\/M20-0504","article-title":"The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: Estimation and application","volume":"172","author":"Lauer","year":"2020","journal-title":"Ann. Intern. Med."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"3717","DOI":"10.1038\/s41598-021-83166-4","article-title":"Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China","volume":"11","author":"Chen","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/978-3-030-63761-3_5","article-title":"The incubation period of COVID-19: Current understanding and modeling technique","volume":"1318","author":"Leung","year":"2021","journal-title":"Adv. Exp. Med. Biol."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Zeinalnezhad, M., Chofreh, A.G., Goni, F.A., Klemes, J.J., and Sari, E. (2020). Simulation and improvement of patients\u2019 workflow in heart clinics during COVID-19 pandemic using timed coloured Petri nets. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17228577"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Karabay, A., Kuzdeuov, A., and Varol, H.A. (2021, January 1\u20135). COVID-19 vaccination strategies considering hesitancy using particle-based epidemic simulation. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Mexico, online.","DOI":"10.1101\/2021.09.26.21264153"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1080\/19371918.2020.1856750","article-title":"Role of the health system in combating COVID-19: Cross-section analysis and artificial neural network simulation for 124 country cases","volume":"36","author":"Bayraktar","year":"2021","journal-title":"Soc. Work Public Health"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/10\/6\/86\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:22:07Z","timestamp":1760138527000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/10\/6\/86"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,30]]},"references-count":90,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["computation10060086"],"URL":"https:\/\/doi.org\/10.3390\/computation10060086","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,30]]}}}