{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T03:20:31Z","timestamp":1761621631859,"version":"3.41.0"},"reference-count":24,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2022,7,31]],"date-time":"2022-07-31T00:00:00Z","timestamp":1659225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Digit. Gov.: Res. Pract."],"published-print":{"date-parts":[[2022,7,31]]},"abstract":"<jats:p>The 2020 COVID-19 pandemic radically changed the world and how people interact, move, and behave. Following a lockdown that was imposed worldwide, although with different timing, Mobile Contact Tracing Apps (MCTAs) were proposed to digitally trace contacts between individuals while gradually releasing mobility constraints mandated to contain the spread of disease. General concern for privacy regarding the use of GPS data shifted the efforts toward distributed applications, which use Bluetooth technology to trace proximity and potential infections. Nonetheless, GPS data would help more health operators to understand where hotbeds are and to what extent the spread is progressing and at what pace. In addition to these issues, in this work we take a closer look at the major pillars of MCTA: Penetration, Privacy, Position, and Performance. We focus on (i) how the penetration rate affects the ability of a tracing application to work; (ii) the proposal of a novel method of tracing, which builds on the GPS technology; (iii) how the position of infections is beneficial to rapidly reduce the infection; and (iv) the discussion of the effects of such paradigms in different scenarios.<\/jats:p>","DOI":"10.1145\/3532669","type":"journal-article","created":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T09:01:13Z","timestamp":1657270873000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Location Contact Tracing: Penetration, Privacy, Position, and Performance"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9993-4046","authenticated-orcid":false,"given":"Luca","family":"Bedogni","sequence":"first","affiliation":[{"name":"University of Modena and Reggio Emilia, Modena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9943-4209","authenticated-orcid":false,"given":"Federico","family":"Montori","sequence":"additional","affiliation":[{"name":"University of Bologna, Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1237-1664","authenticated-orcid":false,"given":"Flora","family":"Salim","sequence":"additional","affiliation":[{"name":"University of New South Wales, Kensington NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,14]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Nadeem Ahmed Regio A. 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Modelling memory for individual re-identification in decentralised mobile contact tracing applications. arxiv:cs.CY\/2010.05514."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1142\/S1793524518500936"},{"key":"e_1_3_2_9_2","article-title":"An agent-based model to evaluate the COVID-19 transmission risks in facilities","volume":"121","author":"Cuevas Erik","year":"2020","unstructured":"Erik Cuevas. 2020. An agent-based model to evaluate the COVID-19 transmission risks in facilities. Computers in Biology and Medicine 121 (2020), 103827.","journal-title":"Computers in Biology and Medicine"},{"key":"e_1_3_2_10_2","article-title":"Stemming the flow: How much can the Australian smartphone app help to control COVID-19?","author":"Currie Danielle","year":"2020","unstructured":"Danielle Currie, Cindy Peng, David Lyle, Brydie Jameson, and Michael Frommer. 2020. Stemming the flow: How much can the Australian smartphone app help to control COVID-19? Public Health Research & Practice (2020). Retrieved July 16, 2022 from https:\/\/www.phrp.com.au\/issues\/june-2020-volume-30-issue-2\/stemming-the-flow-how-much-can-the-australian-smartphone-app-help-to-control-covid-19\/.","journal-title":"Public Health Research & Practice"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0245101"},{"key":"e_1_3_2_12_2","unstructured":"France24. [n.d.]. France\u2019s COVID-19 tracing app fails to engage chalking up roughly 1.5 million userss. https:\/\/www.france24.com\/en\/20200623-france-s-covid-19-tracing-app-fails-to-engage-chalking-up-roughly-1-5-million-users. Accessed: 2021-11-05."},{"key":"e_1_3_2_13_2","unstructured":"The Guardian. [n.d.]. Australia\u2019s Covidsafe coronavirus tracing app works as few as one in four times for some devices. 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