{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T18:41:30Z","timestamp":1787164890021,"version":"build-2736575974"},"reference-count":30,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000147","name":"Division of Civil, Mechanical and Manufacturing Innovation","doi-asserted-by":"publisher","award":["1537379"],"award-info":[{"award-number":["1537379"]}],"id":[{"id":"10.13039\/100000147","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Med Decis Making"],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:p>It is long perceived that the more data collection, the more knowledge emerges about the real disease progression. During emergencies like the H1N1 and the severe acute respiratory syndrome coronavirus 2 pandemics, public health surveillance requested increased testing to address the exacerbated demand. However, it is currently unknown how accurately surveillance portrays disease progression through incidence and confirmed case trends. State surveillance, unlike commercial testing, can process specimens based on the upcoming demand (e.g., with testing restrictions). Hence, proper assessment of accuracy may lead to improvements for a robust infrastructure. Using the H1N1 pandemic experience, we developed a simulation that models the true unobserved influenza incidence trend in the State of Michigan, as well as trends observed at different data collection points of the surveillance system. We calculated the growth rate, or speed at which each trend increases during the pandemic growth phase, and we performed statistical experiments to assess the biases (or differences) between growth rates of unobserved and observed trends. We highlight the following results: 1) emergency-driven high-risk perception increases reporting, which leads to reduction of biases in the growth rates; 2) the best predicted growth rates are those estimated from the trend of specimens submitted to the surveillance point that receives reports from a variety of health care providers; and 3) under several criteria to queue specimens for viral subtyping with limited capacity, the best-performing criterion was to queue first-come, first-serve restricted to specimens with higher hospitalization risk. Under this criterion, the lab released capacity to subtype specimens for each day in the trend, which reduced the growth rate bias the most compared to other queuing criteria. Future research should investigate additional restrictions to the queue.<\/jats:p>","DOI":"10.1177\/0272989x211022276","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T05:31:01Z","timestamp":1626413461000},"page":"1004-1016","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Accuracy of State-Level Surveillance during Emerging Outbreaks of Respiratory Viruses: A Model-Based Assessment"],"prefix":"10.1177","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0862-7276","authenticated-orcid":false,"given":"Yuwen","family":"Gu","sequence":"first","affiliation":[{"name":"Western Michigan University, Kalamazoo, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elise","family":"DeDoncker","sequence":"additional","affiliation":[{"name":"Western Michigan University, Kalamazoo, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"VanEnk","sequence":"additional","affiliation":[{"name":"Bronson Methodist Hospital, Portage, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajib","family":"Paul","sequence":"additional","affiliation":[{"name":"University of North Carolina Charlotte College of Health and Human Services, Charlotte, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan","family":"Peters","sequence":"additional","affiliation":[{"name":"Michigan State University College of Human Medicine, East Lansing, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gillian","family":"Stoltman","sequence":"additional","affiliation":[{"name":"Western Michigan University Homer Stryker MD School of Medicine, Kalamazoo, MI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4939-0786","authenticated-orcid":false,"given":"Diana","family":"Prieto","sequence":"additional","affiliation":[{"name":"Johns Hopkins University Carey Business School, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"key":"bibr1-0272989X211022276","doi-asserted-by":"publisher","DOI":"10.1016\/j.puhe.2010.12.007"},{"issue":"6","key":"bibr2-0272989X211022276","volume":"67","author":"Flannery B","year":"2018","journal-title":"MMWR Morbid Mortal Wkly Rep"},{"key":"bibr3-0272989X211022276","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-024-1263-5_14"},{"key":"bibr4-0272989X211022276","unstructured":"World Health Organization. WHO global technical consultation: global standards and tools for influenza surveillance, March 2011."},{"key":"bibr5-0272989X211022276","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.26.4.1069"},{"issue":"251","key":"bibr6-0272989X211022276","volume":"12","author":"Prieto D","year":"2012","journal-title":"BMC Public Health"},{"key":"bibr7-0272989X211022276","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004513"},{"key":"bibr8-0272989X211022276","doi-asserted-by":"publisher","DOI":"10.1038\/srep25732"},{"issue":"480","key":"bibr9-0272989X211022276","volume":"14","author":"Biggerstaff M","year":"2014","journal-title":"BMC Public Health"},{"key":"bibr10-0272989X211022276","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-016-0260-x"},{"key":"bibr11-0272989X211022276","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1002472"},{"key":"bibr12-0272989X211022276","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0092945"},{"key":"bibr13-0272989X211022276","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0173099"},{"key":"bibr14-0272989X211022276","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0082183"},{"key":"bibr15-0272989X211022276","doi-asserted-by":"publisher","DOI":"10.1017\/S0950268814003495"},{"key":"bibr16-0272989X211022276","unstructured":"US Census Bureau. 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