{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:56:49Z","timestamp":1773799009885,"version":"3.50.1"},"reference-count":48,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T00:00:00Z","timestamp":1626134400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T00:00:00Z","timestamp":1626134400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Grant No. 11875002"],"award-info":[{"award-number":["Grant No. 11875002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"crossref","award":["ErUM-Data funding"],"award-info":[{"award-number":["ErUM-Data funding"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2021,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>As the COVID-19 pandemic continues to ravage the world, it is critical to assess the COVID-19 risk timely on multi-scale. To implement it and evaluate the public health policies, we develop a machine learning assisted framework to predict epidemic dynamics from the reported infection data. It contains a county-level spatio-temporal epidemiological model, which combines spatial cellular automata (CA) with time sensitive-undiagnosed-infected-removed (SUIR) model, and is compatible with the existing risk prediction models. The CA-SUIR model shows the multi-scale risk to the public and reveals the transmission modes of coronavirus in different scenarios. Through transfer learning, this new toolbox is used to predict the prevalence of multi-scale COVID-19 in all 412 counties in Germany. A t-day-ahead risk forecast as well as assessment of the non-pharmaceutical intervention policies is presented. We analyzed the situation at Christmas of 2020, and found that the most serious death toll could be 34.5. However, effective policy could control it below 21thousand, which provides a quantitative basis for evaluating the public policies implemented by the government. Such intervening evaluation process would help to improve public health policies and restart the economy appropriately in pandemics.<\/jats:p>","DOI":"10.1088\/2632-2153\/ac0314","type":"journal-article","created":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T22:32:10Z","timestamp":1621463530000},"page":"035031","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk"],"prefix":"10.1088","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3757-3403","authenticated-orcid":false,"given":"Lingxiao","family":"Wang","sequence":"first","affiliation":[]},{"given":"Tian","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Till","family":"Stoecker","sequence":"additional","affiliation":[]},{"given":"Horst","family":"Stoecker","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1916-6073","authenticated-orcid":false,"given":"Yin","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9859-1758","authenticated-orcid":false,"given":"Kai","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2021,7,13]]},"reference":[{"key":"mlstac0314bib1","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1126\/science.abb4557","article-title":"Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China","volume":"368","author":"Maier","year":"2020","journal-title":"Science"},{"key":"mlstac0314bib2","doi-asserted-by":"publisher","first-page":"2020","DOI":"10.1001\/jama.2020.6572","article-title":"From mitigation to containment of the COVID-19 Pandemic: putting the SARS-CoV-2Genie back in the bottle","volume":"323","author":"Walensky","year":"1889","journal-title":"JAMA"},{"key":"mlstac0314bib3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0238559","article-title":"Modeling the spread of COVID-19 in Germany: early assessment and possible scenarios","volume":"15","author":"Barbarossa","year":"2020","journal-title":"PLoS One"},{"key":"mlstac0314bib4","doi-asserted-by":"crossref","DOI":"10.1101\/2020.04.08.20056630","article-title":"A first study on the impact of current and future control measures on the spread of COVID-19 in Germany","author":"Barbarossa","year":"2020"},{"key":"mlstac0314bib5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2020.07.005","article-title":"COVID-19 and SARS-CoV-2. Modeling the present, looking at the future","volume":"869","author":"Estrada","year":"2020","journal-title":"Phys. Rep."},{"key":"mlstac0314bib6","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1038\/s42254-020-0178-4","article-title":"Modelling COVID-19","volume":"2","author":"Vespignani","year":"2020","journal-title":"Nat. Rev. Phys."},{"key":"mlstac0314bib7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijantimicag.2020.105948","article-title":"Review of the 2019 novel coronavirus (SARS-CoV-2) based on current evidence","volume":"55","author":"Wang","year":"2020","journal-title":"Int. J. Antimicr. Agents"},{"key":"mlstac0314bib8","doi-asserted-by":"crossref","DOI":"10.1101\/2020.06.10.20126771","article-title":"Meso-scale modeling of COVID-19 spatio-temporal outbreak dynamics in Germany","author":"Kergassner","year":"2020"},{"key":"mlstac0314bib9","article-title":"The foreshadow of a second wave: an analysis of current COVID-19 fatalities in Germany","author":"Linden","year":"2020"},{"key":"mlstac0314bib10","article-title":"Multifractal scaling analyses of the spatial diffusion pattern of COVID-19 pandemic in Chinese mainland","author":"Long","year":"2020"},{"key":"mlstac0314bib11","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1038\/s41562-020-01000-9","article-title":"Real-time, interactive website for US-county-level COVID-19 event risk assessment","volume":"4","author":"Chande","year":"2020","journal-title":"Nat. Hum. Behav."},{"key":"mlstac0314bib12","doi-asserted-by":"publisher","first-page":"1829","DOI":"10.1038\/s41591-020-1104-0","article-title":"Crowding and the shape of COVID-19 epidemics","volume":"26","author":"Rader","year":"2020","journal-title":"Nat. Med."},{"key":"mlstac0314bib13","article-title":"Global dynamics of a SUIR model with predicting COVID-19","author":"Wang","year":"2020"},{"key":"mlstac0314bib14","doi-asserted-by":"publisher","DOI":"10.1162\/99608f92.79e1f45e","article-title":"A spatiotemporal epidemiological prediction model to inform county-level COVID-19 risk in the United States","author":"Zhou","year":"2020","journal-title":"Harv. Data Sci. Rev."},{"key":"mlstac0314bib15","doi-asserted-by":"publisher","first-page":"5033","DOI":"10.1038\/s41467-020-18684-2","article-title":"Machine learning based early warning system enables accurate mortality risk prediction for COVID-19","volume":"11","author":"Gao","year":"2020","journal-title":"Nat. Commun."},{"key":"mlstac0314bib16","doi-asserted-by":"publisher","DOI":"10.2196\/19421","article-title":"Using reports of symptoms and diagnoses on social media to predict COVID-19 case counts in Mainland China: observational infoveillance study","volume":"22","author":"Shen","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"mlstac0314bib17","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110210","article-title":"Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm","volume":"140","author":"Ye\u015filkanat","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"mlstac0314bib18","doi-asserted-by":"crossref","DOI":"10.1101\/2020.05.24.20111989","article-title":"Epidemic model guided machine learning for COVID-19 forecasts in the United States","author":"Zou","year":"2020"},{"key":"mlstac0314bib19","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.amc.2006.06.126","article-title":"Modeling epidemics using cellular automata","volume":"186","author":"White","year":"2007","journal-title":"Appl. Math. Comput."},{"key":"mlstac0314bib20","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1016\/j.simpat.2008.05.015","article-title":"Modelling SIR-type epidemics by ODEs, PDEs, difference equations and cellular automata\u2013a comparative study","volume":"16","author":"Schneckenreither","year":"2008","journal-title":"Simul. Model. Practice Theory"},{"key":"mlstac0314bib21","doi-asserted-by":"publisher","first-page":"375","DOI":"10.12178\/1001-0548.2020083","article-title":"Assessment and prediction of COVID-19 based on SEIR model with undiscovered people","volume":"49","author":"Jun-feng","year":"2020","journal-title":"J. Univ. Electron. Sci. Technol. China"},{"key":"mlstac0314bib22","doi-asserted-by":"publisher","first-page":"3279","DOI":"10.1109\/TNSE.2020.3024723","article-title":"A time-dependent SIR model for COVID-19 with Undetectable infected persons","volume":"7","author":"Chen","year":"2020","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"mlstac0314bib23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cnsns.2020.105303","article-title":"Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China","volume":"88","author":"Ivorra","year":"2020","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"mlstac0314bib24","first-page":"pp 802","article-title":"ConvolutionalLSTM Network: A machine learning approach for precipitation nowcasting","author":"Shi","year":"2015"},{"key":"mlstac0314bib25","doi-asserted-by":"publisher","first-page":"A11","DOI":"10.1051\/0004-6361\/201730783","article-title":"DeepVel: deep learning for the estimation of horizontal velocities at the solar surface","volume":"604","author":"Ramos","year":"2017","journal-title":"A & A"},{"key":"mlstac0314bib26","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.121777","article-title":"Escape dynamics based on bounded rationality","volume":"531","author":"Wang","year":"2019","journal-title":"Phys. A"},{"key":"mlstac0314bib27","article-title":"Covid-19 across European regions: the role of border controls, type SSRN scholarly paper number ID 3688126","author":"Eckardt","year":"2020"},{"key":"mlstac0314bib28","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.arcontrol.2020.09.006","article-title":"In-host mathematical modelling of COVID-19 in humans","volume":"50","author":"Hernandez-Vargas","year":"2020","journal-title":"Ann. Rev. Control"},{"key":"mlstac0314bib29","article-title":"Rapid and lasting generation of B-cell memory toSARS-CoV-2 spike and nucleocapsid proteins in COVID-19 disease and convalescence","author":"Hartley","year":"2020"},{"key":"mlstac0314bib30","doi-asserted-by":"publisher","first-page":"4725","DOI":"10.1038\/s41598-021-84055-6","article-title":"In efficiency of SIR models in forecasting COVID-19 epidemic: a case study of Isfahan","volume":"11","author":"Moein","year":"2021","journal-title":"Sci. Rep."},{"key":"mlstac0314bib31","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1016\/S0140-6736(21)00575-4","article-title":"Assessment of protection against reinfection with SARS-CoV-2 among 4 million PCR-tested individuals in Denmark in 2020: a population-level observational study","volume":"397","author":"Hansen","year":"2021","journal-title":"Lancet"},{"key":"mlstac0314bib32","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110022","article-title":"Influence of isolation measures for patients with mild symptoms on the spread of COVID-19","volume":"139","author":"Pan","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"mlstac0314bib33","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2021.125993","article-title":"The effect of quarantine measures for close contacts on the transmission of emerging infectious diseases with infectivity in incubation period","volume":"574","author":"Pan","year":"2021","journal-title":"Phys. A"},{"key":"mlstac0314bib34","article-title":"Containment strategies after the first wave of COVID-19 using mobility data","author":"G\u00f6sgens","year":"2020"},{"key":"mlstac0314bib35","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1038\/s41579-020-00461-z","article-title":"Considerations for diagnostic COVID-19 tests","volume":"19","author":"Vandenberg","year":"2020","journal-title":"Nat. Rev. Microbiol."},{"key":"mlstac0314bib36","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1002\/jmv.25786","article-title":"Stability issues of RT-PCR testing of SARS-CoV-2 for hospitalized patients clinically diagnosed with COVID-19","volume":"92","author":"Li","year":"2020","journal-title":"J. Med Virol."},{"key":"mlstac0314bib37","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1126\/science.aba9757","article-title":"The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak","volume":"368","author":"Chinazzi","year":"2020","journal-title":"Science"},{"key":"mlstac0314bib38","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1126\/science.abb6105","article-title":"An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China","volume":"368","author":"Tian","year":"2020","journal-title":"Science"},{"key":"mlstac0314bib39","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1038\/s41562-020-0884-z","article-title":"Using social and behavioural science to support COVID-19 pandemic response","volume":"4","author":"Bavel","year":"2020","journal-title":"Nat. Hum. Behav."},{"key":"mlstac0314bib40","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1038\/s41586-020-2404-8","article-title":"The effect of large-scale anti-contagion policies on the COVID-19 pandemic","volume":"584","author":"Hsiang","year":"2020","journal-title":"Nature"},{"key":"mlstac0314bib41","article-title":"Modeling COVID-19 dynamics in Illinois under nonpharmaceutical interventions","volume":"10","author":"Wong","year":"2020","journal-title":"Phys. Rev. X"},{"key":"mlstac0314bib42","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1038\/s41586-020-2923-3","article-title":"Mobility network models of COVID-19 explain inequities and inform reopening","volume":"589","author":"Chang","year":"2020","journal-title":"Nature"},{"key":"mlstac0314bib43","doi-asserted-by":"publisher","first-page":"m1395","DOI":"10.1136\/bmj.m1395","article-title":"Covid-19: why Germany\u2019s case fatality rate seems so low","volume":"369","author":"Stafford","year":"2020","journal-title":"BMJ"},{"key":"mlstac0314bib44","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110072","article-title":"Stability analysis and numerical simulation ofSEIR model for pandemic COVID-19 spread in Indonesia","volume":"139","author":"Annas","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"mlstac0314bib45","article-title":"Centralized and decentralized isolation strategies and their impact on the COVID-19 pandemic dynamics","author":"Topirceanu","year":"2020"},{"key":"mlstac0314bib46","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.102.010401","article-title":"Self-isolation or borders closing: what prevents the spread of the epidemic better?","volume":"102","author":"Valba","year":"2020","journal-title":"Phys. Rev. E"},{"key":"mlstac0314bib47","article-title":"Modeling the heterogeneous disease-behavior-information dynamics during epidemics","author":"Ye","year":"20220"},{"key":"mlstac0314bib48","doi-asserted-by":"publisher","first-page":"eabc3054","DOI":"10.1126\/sciadv.abc3054","article-title":"Social connections with COVID-19-affected areas increase compliance with mobility restrictions","volume":"6","author":"Charoenwong","year":"2020","journal-title":"Sci. Adv."}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac0314","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac0314\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac0314","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac0314\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac0314\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac0314\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac0314\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac0314\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T16:07:40Z","timestamp":1639411660000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac0314"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,13]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,7,13]]},"published-print":{"date-parts":[[2021,9,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ac0314","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,13]]},"assertion":[{"value":"Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2021 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2021-03-07","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-05-19","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-07-13","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}