{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T10:17:15Z","timestamp":1776507435661,"version":"3.51.2"},"reference-count":29,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2018,8,21]],"date-time":"2018-08-21T00:00:00Z","timestamp":1534809600000},"content-version":"am","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2018,8,21]],"date-time":"2018-08-21T00:00:00Z","timestamp":1534809600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000774","name":"Defense Threat Reduction Agency","doi-asserted-by":"publisher","award":["HDTRA1\u201015\u2010C\u20100018"],"award-info":[{"award-number":["HDTRA1\u201015\u2010C\u20100018"]}],"id":[{"id":"10.13039\/100000774","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["GM110748"],"award-info":[{"award-number":["GM110748"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000066","name":"National Institute of Environmental Health Sciences","doi-asserted-by":"publisher","award":["ES009089"],"award-info":[{"award-number":["ES009089"]}],"id":[{"id":"10.13039\/100000066","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Influenza Resp Viruses"],"published-print":{"date-parts":[[2018,11]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Advance warning of influenza incidence levels from skillful forecasts could help public health officials and healthcare providers implement more timely preparedness and intervention measures to combat outbreaks. Compared to influenza predictions generated at regional and national levels, those generated at finer scales could offer greater value in determining locally appropriate measures; however, to date, the various influenza surveillance data that are collected by state and county departments of health have not been well utilized in influenza prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives<\/jats:title><jats:p>To assess whether an influenza forecast model system can be optimized to generate accurate forecasts using novel surveillance data streams.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Here, we generate retrospective influenza forecasts with a dynamic, compartmental model\u2010inference system using surveillance data for influenza\u2010like illness (<jats:styled-content style=\"fixed-case\">ILI<\/jats:styled-content>), laboratory\u2010confirmed cases, and pneumonia and influenza mortality at state and county levels. We evaluate how specification of 3 system inputs\u2014scaling, observational error variance (<jats:styled-content style=\"fixed-case\">OEV<\/jats:styled-content>), and filter divergence (lambda)\u2014affects forecast accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In retrospective forecasts, and across data types, there were no clear optimal combinations for the 3 system inputs; however, scaling was most critical to forecast accuracy, whereas <jats:styled-content style=\"fixed-case\">OEV<\/jats:styled-content> and lambda were not.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Forecasts using new data streams should be tested to determine an appropriate scaling value using historical data and analyzed for forecast accuracy.<\/jats:p><\/jats:sec>","DOI":"10.1111\/irv.12594","type":"journal-article","created":{"date-parts":[[2018,7,20]],"date-time":"2018-07-20T09:52:15Z","timestamp":1532080335000},"page":"755-764","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Influenza forecast optimization when using different surveillance data types and geographic scale"],"prefix":"10.1111","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9400-9521","authenticated-orcid":false,"given":"Haruka","family":"Morita","sequence":"first","affiliation":[{"name":"Department of Environmental Health Sciences Mailman School of Public Health Columbia University  New York City New York"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah","family":"Kramer","sequence":"additional","affiliation":[{"name":"Department of Environmental Health Sciences Mailman School of Public Health Columbia University  New York City New York"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandra","family":"Heaney","sequence":"additional","affiliation":[{"name":"Department of Environmental Health Sciences Mailman School of Public Health Columbia University  New York City New York"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harold","family":"Gil","sequence":"additional","affiliation":[{"name":"Marion County Public Health Department  Indianapolis Indiana"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7216-7809","authenticated-orcid":false,"given":"Jeffrey","family":"Shaman","sequence":"additional","affiliation":[{"name":"Department of Environmental Health Sciences Mailman School of Public Health Columbia University  New York City New York"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,8,21]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"Centers for Disease Control and Prevention.Disease Burden of Influenza.2017.https:\/\/www.cdc.gov\/flu\/about\/disease\/burden.htm. 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