{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:46:24Z","timestamp":1775144784797,"version":"3.50.1"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Mass vaccination is a cornerstone of public health emergency preparedness and response. However, injudicious placement of vaccination sites can lead to the formation of long waiting lines or\n                      <jats:italic>queues<\/jats:italic>\n                      , which discourages individuals from waiting to be vaccinated and may thus jeopardize the achievement of public health targets. Queueing theory offers a framework for modeling queue formation at vaccination sites and its effect on vaccine uptake.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed an algorithm that integrates queueing theory within a spatial optimization framework to optimize the placement of mass vaccination sites. The algorithm was built and tested using data from a mass dog rabies vaccination campaign in Arequipa, Peru. We compared expected vaccination coverage and losses from queueing (i.e., attrition) for sites optimized with our queue-conscious algorithm to those used in a previous vaccination campaign, as well as to sites obtained from a queue-na\u00efve version of the same algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Sites placed by the queue-conscious algorithm resulted in 9\u201332% less attrition and 11\u201312% higher vaccination coverage compared to previously used sites and 9\u201319% less attrition and 1\u20132% higher vaccination coverage compared to sites placed by the queue-na\u00efve algorithm. Compared to the queue-na\u00efve algorithm, the queue-conscious algorithm placed more sites in densely populated areas to offset high arrival volumes, thereby reducing losses due to excessive queueing. These results were not sensitive to misspecification of queueing parameters or relaxation of the constant arrival rate assumption.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>One should consider losses from queueing to optimally place mass vaccination sites, even when empirically derived queueing parameters are not available. Due to the negative impacts of excessive wait times on participant satisfaction, reducing queueing attrition is also expected to yield downstream benefits and improve vaccination coverage in subsequent mass vaccination campaigns.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2024.1440673","type":"journal-article","created":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T01:36:32Z","timestamp":1735004192000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing mass vaccination programs with queueing theory and spatial optimization"],"prefix":"10.3389","volume":"12","author":[{"given":"Sherrie","family":"Xie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Rieders","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Srisa","family":"Changolkar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bhaswar B.","family":"Bhattacharya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elvis W.","family":"Diaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael Z.","family":"Levy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ricardo","family":"Castillo-Neyra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,12,24]]},"reference":[{"key":"ref1","first-page":"393","article-title":"Wait times, patient satisfaction scores, and the perception of care","volume":"20","author":"Bleustein","year":"2014","journal-title":"Am J Manag Care"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1186\/s12913-017-2281-5","article-title":"\u2018Waiting for\u2019 and \u2018waiting in\u2019 public and private hospitals: a qualitative study of patient trust in South Australia","volume":"17","author":"Ward","year":"2017","journal-title":"BMC Health Serv Res"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1186\/s12889-022-13127-7","article-title":"Barriers to and strategies to address COVID-19 testing hesitancy: a rapid scoping review","volume":"22","author":"Embrett","year":"2022","journal-title":"BMC Public Health"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"e67","DOI":"10.1056\/NEJMp2102535","article-title":"Mass-vaccination sites \u2014 an essential innovation to curb the Covid-19 pandemic","volume":"384","author":"Goralnick","year":"2021","journal-title":"N Engl J Med"},{"key":"ref5","volume-title":"Hours-long waits reported at Javits center COVID vaccine site in NYC. 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