{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T17:29:04Z","timestamp":1793813344398,"version":"4.6.1"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,11,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Backfill is the process of revising case data, often by retrospectively assigning or reassigning newly reported cases to earlier symptom onset dates. Time- and space-varying delays in backfill may compromise real-time surveillance and forecasting efforts by obscuring true transmission patterns. Using Michigan COVID-19 case data, we developed a statistical mixture model to describe backfill as well as geographic and temporal variations. The model combined an exponential process (case reporting delay) with a gamma-distributed process (case reassignment to onset date). Parameters were estimated by a regularized maximum likelihood method, and the Akaike Information Criterion was used to determine the necessity of the reassignment component for each date. We estimated the exponential reporting speed over time and space and, if appropriate, the weight, transient peak, and time of case reassignment. We found that case reporting improved over the course of the pandemic: reporting speed increased over time (with substantial day-to-day variation), and case reassignments were processed faster. We also identified potential regional disparities: regions with population densities below 50 people\/km2 had slower backfill speeds. These findings provide critical insights into the evolution of case reporting and backfill dynamics that can be leveraged for \u201cnowcasting\u201d models to enhance real-time surveillance data, ultimately improving outbreak preparedness and response.<\/jats:p>","DOI":"10.1093\/aje\/kwag170","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T11:53:20Z","timestamp":1786017200000},"page":"2793-2801","source":"Crossref","is-referenced-by-count":0,"title":["Characterizing spatiotemporal patterns of case reporting backfill: a case study of COVID-19 reporting in Michigan, 2020-2024"],"prefix":"10.1093","volume":"195","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5145-3683","authenticated-orcid":false,"given":"Yannan","family":"Niu","sequence":"first","affiliation":[{"name":"Department of Epidemiology, University of Michigan , Ann Arbor, MI ,","place":["United 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