{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T15:10:18Z","timestamp":1773933018939,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T00:00:00Z","timestamp":1603756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The epidemic of coronavirus-disease-2019 (COVID-19) started in Italy with the first official diagnosis on 21 February 2020; However, it is not known how many cases were already present in earlier days and weeks, thus limiting the possibilities of conducting any retrospective analysis. We hypothesized that an unbiased representation of COVID-19 diffusion in these early phases could be inferred by the georeferenced calls to the emergency number relevant to respiratory problems and by the following emergency medical services (EMS) interventions. Accordingly, the aim of this study was to identify the beginning of anomalous trends (change in the data morphology) in emergency calls and EMS ambulances dispatches and reconstruct COVID-19 spatiotemporal evolution on the territory of Lombardy region. Accordingly, a signal processing method, previously used to find morphological features on the electrocardiographic signal, was applied on a time series representative of territorial clusters of about 100,000 citizens. Both emergency calls and age- and gender-weighted ambulance dispatches resulted strongly correlated to COVID-19 casualties on a provincial level, and the identified local starting days anticipated the official diagnoses and casualties, thus demonstrating how these parameters could be effectively used as early indicators for the spatiotemporal evolution of the epidemic on a certain territory.<\/jats:p>","DOI":"10.3390\/ijgi9110639","type":"journal-article","created":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T09:22:45Z","timestamp":1603790565000},"page":"639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Mapping Spatiotemporal Diffusion of COVID-19 in Lombardy (Italy) on the Base of Emergency Medical Services Activities"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9658-5749","authenticated-orcid":false,"given":"Lorenzo","family":"Gianquintieri","sequence":"first","affiliation":[{"name":"Electronics, Information and Biomedical Engineering dpt, Politecnico di Milano, 20133 Milano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3161-5561","authenticated-orcid":false,"given":"Maria Antonia","family":"Brovelli","sequence":"additional","affiliation":[{"name":"Civil and Environmental Engineering dpt, Politecnico di Milano, 20133 Milano, Italy"},{"name":"Consiglio Nazionale delle Ricerche, Istituto per il Rilevamento Elettromagnetico dell\u2019Ambiente, 20133 Milano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Pagliosa","sequence":"additional","affiliation":[{"name":"Azienda Regionale Emergenza Urgenza (AREU), 20124 Milano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriele","family":"Dassi","sequence":"additional","affiliation":[{"name":"Azienda Regionale Emergenza Urgenza (AREU), 20124 Milano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piero Maria","family":"Brambilla","sequence":"additional","affiliation":[{"name":"Azienda Regionale Emergenza Urgenza (AREU), 20124 Milano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rodolfo","family":"Bonora","sequence":"additional","affiliation":[{"name":"Azienda Regionale Emergenza Urgenza (AREU), 20124 Milano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giuseppe Maria","family":"Sechi","sequence":"additional","affiliation":[{"name":"Azienda Regionale Emergenza Urgenza (AREU), 20124 Milano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1770-6486","authenticated-orcid":false,"given":"Enrico Gianluca","family":"Caiani","sequence":"additional","affiliation":[{"name":"Electronics, Information and Biomedical Engineering dpt, Politecnico di Milano, 20133 Milano, Italy"},{"name":"Consiglio Nazionale delle Ricerche, Istituto di Elettronica e di Ingegneria dell\u2019Informazione e delle Telecomunicazioni, 20133 Milano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,27]]},"reference":[{"key":"ref_1","unstructured":"(2020, August 31). Istituto Superiore di Sanit\u00e0, Roma\u2013Aggiornamento Nazionale 09 Marzo 2020. Available online: https:\/\/www.ansa.it\/documents\/1583864041148_Bollettino.pdf."},{"key":"ref_2","unstructured":"(2020, September 26). Jon Hopkins University of Medicine\u2013Coronavirus Research Center\u2013Global Map. Available online: https:\/\/coronavirus.jhu.edu\/map.html."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1152\/physiolgenomics.00029.2020","article-title":"Artificial intelligence and machine learning to fight COVID-19","volume":"52","author":"Alimadadi","year":"2020","journal-title":"Physiol. Genom."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bragazzi, N.L., Dai, H., Damiani, G., Behzadifar, M., Martini, M., and Wu, J. (2020). How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17093176"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e19104","DOI":"10.2196\/19104","article-title":"Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review","volume":"22","author":"Mohamed","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Simsek, M., and Kantarci, B. (2020). Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17103437"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e18980","DOI":"10.2196\/18980","article-title":"Global preparedness against COVID-19: We must leverage the power of digital health (Preprint)","volume":"6","author":"Mahmood","year":"2020","journal-title":"JMIR Public Health Surveill."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1038\/s41591-020-0824-5","article-title":"Digital technology and COVID-19","volume":"26","author":"Ting","year":"2020","journal-title":"Nat. Med."},{"key":"ref_9","unstructured":"Vafea, M.T., Atalla, E., Georgakas, J., Shehadeh, F., Mylona, E.K., Kalligeros, M., and Mylonakis, E. (2020). Emerging Technologies for Use in the Study, Diagnosis, and Treatment of Patients with COVID-19. Cell. Mol. Bioeng., 1\u20139."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e201","DOI":"10.1016\/S2589-7500(20)30026-1","article-title":"Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: A population-level observational study","volume":"2","author":"Sun","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ibrahim, N.K. (2020). Epidemiologic surveillance for controlling Covid-19 pandemic: Types, challenges and implications. J. Infect. Public Health.","DOI":"10.1016\/j.jiph.2020.07.019"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e122","DOI":"10.1017\/S0950268820001314","article-title":"The COVID-19 pandemic: A new challenge for syndromic surveillance","volume":"148","author":"Elliot","year":"2020","journal-title":"Epidemiology Infect."},{"key":"ref_13","first-page":"S166","article-title":"Frontline Field Epidemiology Training Programs as a Strategy to Improve Disease Surveillance and Response","volume":"23","author":"Lopez","year":"2017","journal-title":"Emerg. Infect. Dis."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"140033","DOI":"10.1016\/j.scitotenv.2020.140033","article-title":"Spatial analysis and GIS in the study of COVID-19. A review","volume":"739","author":"Napoletano","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1097\/CM9.0000000000000782","article-title":"Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China","volume":"133","author":"Chen","year":"2020","journal-title":"Chin. Med. J."},{"key":"ref_16","first-page":"114","article-title":"Application of Geographic Information System in Monitoring and Detecting the COVID-19 Outbreak","volume":"49","author":"Rezaei","year":"2020","journal-title":"Iran. J. Public Health"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Giuliani, D., Dickson, M.M., Espa, G., and Santi, F. (2020). Modelling and Predicting the Spatio-Temporal Spread of Coronavirus Disease 2019 (COVID-19) in Italy. SSRN Electron. J.","DOI":"10.2139\/ssrn.3559569"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lakhani, A. (2020). Introducing the Percent, Number, Availability, and Capacity [PNAC] Spatial Approach to Identify Priority Rural Areas Requiring Targeted Health Support in Light of COVID-19: A Commentary and Application. J. Rural. Health, 1\u20134.","DOI":"10.1111\/jrh.12436"},{"key":"#cr-split#-ref_20.1","unstructured":"Padula, W.V., and Davidson, P. (2020). Countries with High Registered Nurse"},{"key":"#cr-split#-ref_20.2","unstructured":"(RN) Concentrations Observe Reduced Mortality Rates of Coronavirus Disease 2019 (COVID-19). SSRN Electron. J., 3566190."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jella, T.K., Acu\u00f1a, A.J., Samuel, L.T., Jella, T.K., Mroz, T.E., and Kamath, A.F. (2020). Geospatial Mapping of Orthopaedic Surgeons Age 60 and Over and Confirmed Cases of COVID-19. J. Bone Jt. Surg. Am. Vol.","DOI":"10.2106\/JBJS.20.00577"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"138884","DOI":"10.1016\/j.scitotenv.2020.138884","article-title":"GIS-based spatial modeling of COVID-19 incidence rate in the continental United States","volume":"728","author":"Mollalo","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"138474","DOI":"10.1016\/j.scitotenv.2020.138474","article-title":"Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID","volume":"729","author":"Coccia","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"138835","DOI":"10.1016\/j.scitotenv.2020.138835","article-title":"Correlation between climate indicators and COVID-19 pandemic in New York, USA","volume":"728","author":"Bashir","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sajadi, M.M., Habibzadeh, P., Vintzileos, A., Shokouhi, S., Miralles-Wilhelm, F., and Amoroso, A. (2020). Temperature and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19. SSRN Electron. J., 3550308.","DOI":"10.2139\/ssrn.3550308"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1145\/3404820.3404824","article-title":"Mapping county-level mobility pattern changes in the United States in response to COVID-19","volume":"12","author":"Gao","year":"2020","journal-title":"SIGSPATIAL Spe\u0301c."},{"key":"ref_27","unstructured":"Warren, M.S., and Skillman, S.W. (2020). Mobility changes in response to COVID-19. arXiv."},{"key":"ref_28","unstructured":"Iacus, S.M., Natale, F., and Vespe, M. (2020). Flight restrictions from China during the COVID-2019 coronavirus outbreak. arXiv, 1\u20139."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.geosus.2020.03.005","article-title":"COVID-19: Challenges to GIS with Big Data","volume":"1","author":"Zhou","year":"2020","journal-title":"Geogr. Sustain."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xiong, Y., Guang, Y., Chen, F., and Zhu, F. (2020). Spatial statistics and influencing factors of the novel coronavirus pneumonia 2019 epidemic in Hubei Province, China. ResearchSquare.","DOI":"10.21203\/rs.3.rs-16858\/v2"},{"key":"ref_31","first-page":"316","article-title":"Special report: The simulations driving the world\u2019s response to COVID-19","volume":"580","author":"Adam","year":"2020","journal-title":"Nat. Cell Biol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1159\/000507587","article-title":"Epidemic Surveillance of Covid-19: Considering Uncertainty and Under-Ascertainment","volume":"38","author":"Peixoto","year":"2020","journal-title":"Port. J. Public Health"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"10484","DOI":"10.1073\/pnas.2004978117","article-title":"Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures","volume":"117","author":"Gatto","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1038\/s41591-020-0883-7","article-title":"Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy","volume":"26","author":"Giordano","year":"2020","journal-title":"Nat. Med."},{"key":"ref_35","first-page":"467","article-title":"How deadly is the coronavirus? Scientists are close to an answer","volume":"582","author":"Mallapaty","year":"2020","journal-title":"Nat. Cell Biol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1002\/jmv.25794","article-title":"Genomic characterization and phylogenetic analysis of SARS-COV-2 in Italy","volume":"92","author":"Zehender","year":"2020","journal-title":"J. Med Virol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"139652","DOI":"10.1016\/j.scitotenv.2020.139652","article-title":"First detection of SARS-CoV-2 in untreated wastewaters in Italy","volume":"736","author":"Iaconelli","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/1475-925X-10-77","article-title":"New approach for T-wave end detection on electrocardiogram: Performance in noisy conditions","volume":"10","author":"Neto","year":"2011","journal-title":"Biomed. Eng. Online"},{"key":"ref_39","unstructured":"(2020, August 31). World Health Organization-Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19) 28 February 2020. Available online: https:\/\/www.who.int\/publications\/i\/item\/report-of-the-who-china-joint-mission-on-coronavirus-disease-2019-(covid-19)."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gregori, D., Azzolina, D., Lanera, C., Prosepe, I., Destro, N., Lorenzoni, G., and Berchialla, P. (2020). A first estimation of the impact of public health actions against COVID-19 in Veneto (Italy). J. Epidemiol. Community Health.","DOI":"10.1136\/jech-2020-214209"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2000293","DOI":"10.2807\/1560-7917.ES.2020.25.12.2000293","article-title":"Potential short-term outcome of an uncontrolled COVID-19 epidemic in Lombardy, Italy, February to March 2020","volume":"25","author":"Guzzetta","year":"2020","journal-title":"Eurosurveillance"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s10654-020-00631-6","article-title":"Covid-19 epidemic in Italy: Evolution, projections and impact of government measures","volume":"35","author":"Sebastiani","year":"2020","journal-title":"Eur. J. Epidemiology"},{"key":"ref_43","unstructured":"Escolano, S. (2015). Sistemas de Informaci\u00f3n Geogr\u00e1fica: Una Introducci\u00f3n Para Estudiantes de Geograf\u00eda, Prensas de la Universidad de Zaragoza."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kost, G.J. (2020). Geospatial Hotspots Need Point-of-Care Strategies to Stop Highly Infectious Outbreaks: Ebola and Coronavirus. Arch. Pathol. Lab. Med.","DOI":"10.5858\/arpa.2020-0172-RA"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/11\/639\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:28:52Z","timestamp":1760178532000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/11\/639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,27]]},"references-count":45,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["ijgi9110639"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9110639","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,27]]}}}