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The majority of available vaccines elicit strong immune responses when administered as prime-boost regimens. Since the immunological response to the first (\u201cprime\u201d) dose may provide already a substantial reduction in infectiousness and protection against severe disease, it may be more effective\u2014under certain immunological and epidemiological conditions\u2014to vaccinate as many people as possible with only one dose instead of administering a person a second (\u201cbooster\u201d) dose. Such a vaccination campaign may help to more effectively slow down the spread of SARS-CoV-2 and reduce hospitalizations and fatalities. The conditions that make prime-first vaccination favorable over prime-boost campaigns, however, are not well understood. By combining epidemiological modeling, random-sampling techniques, and decision tree learning, we find that prime-first vaccination is robustly favored over prime-boost vaccination campaigns even for low single-dose efficacies. For epidemiological parameters that describe the spread of coronavirus disease 2019 (COVID-19), recent data on new variants included, we show that the difference between prime-boost and single-shot waning rates is the only discriminative threshold, falling in the narrow range of 0.01\u20130.02\u2009day\u22121 below which prime-first vaccination should be considered.<\/jats:p>","DOI":"10.1063\/5.0066992","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T17:40:51Z","timestamp":1634751651000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":11,"title":["Decisive conditions for strategic vaccination against SARS-CoV-2"],"prefix":"10.1063","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1700-1897","authenticated-orcid":false,"given":"Lucas","family":"B\u00f6ttcher","sequence":"first","affiliation":[{"name":"Computational Social Science, Centre for Human and Machine Intelligence, Frankfurt School of Finance & Management 1 , 60322 Frankfurt am Main, Germany"},{"name":"Department of Computational Medicine, University of California, Los Angeles 2 , Los Angeles, California 90095-1766, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7872-7726","authenticated-orcid":false,"given":"Jan","family":"Nagler","sequence":"additional","affiliation":[{"name":"Deep Dynamics Group, Centre for Human and Machine Intelligence, Frankfurt School of Finance & Management 3 , 60322 Frankfurt am Main, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"317","published-online":{"date-parts":[[2021,10,20]]},"reference":[{"key":"2024031601160499200_c1","author":"World Health Organization"},{"key":"2024031601160499200_c2"},{"key":"2024031601160499200_c3","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s10654-021-00748-2","article-title":"Using excess deaths and testing statistics to determine estimates of COVID-19 mortalities","volume":"36","year":"2021","journal-title":"Eur. 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