{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:47:38Z","timestamp":1782841658492,"version":"3.54.5"},"reference-count":52,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Public Health Surveill"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Early detection of health threats is an objective of public health surveillance, and event-based surveillance (EBS) using unstructured information from diverse sources has played an increasingly important role in achieving this objective. However, the evaluation of EBS systems has been hindered by the lack of reference data on outbreak onsets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We introduce the concept of an \u201cearly detection interval\u201d and create a dataset of these intervals across multiple countries for the epidemic caused by the Omicron variant of SARS-CoV-2.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We defined the early detection interval as the time between the date of introduction of an infectious agent to a country and the date at which an increase is detectable in traditional public health surveillance data. To determine the date of the introduction of the Omicron variant, we analyzed phylogenetic studies and genome databases. We estimated the end of the interval by applying Bayesian online change point detection to reported COVID-19 case counts. In addition to the early detection intervals, this dataset also contains variables indicating data quality. To further understand the variation in the lengths of the early detection intervals, stratified analysis and univariate Cox proportional hazards were implemented.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>This dataset contains early detection intervals for the Omicron variant in 117 countries. The intervals have a median length of 28 (IQR 18-44) days, with a median beginning date of November 27, 2021 (IQR November 17, 2021, to December 12, 2021), and a median ending date of January 2, 2022 (IQR December 19, 2021, to January 9, 2022). Countries with high sequencing availability tend to have earlier start dates with a maximum difference across data sources of only 15 (IQR 7\u201039) days and consequently a prolonged interval length with a median length of 29 (20-47) days. Countries with low incomes were underrepresented in this dataset, with only 12 (29.27%) out of 41 included, and they tended to have shorter intervals with a median duration of 16 (IQR 12\u201023) days. The univariate Cox proportional hazards ratio regression analysis confirmed prolonged interval length in countries with high sequencing availability (hazard ratio 0.59, 95% CI 0.38\u20100.92) and shortened interval length in low-income countries (hazard ratio 2.37, 95% CI 1.29\u20104.36).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The dataset of early detection intervals created in this study can serve as reference data and facilitate the evaluation of the timeliness of alerts generated by EBS systems. Systematic and comprehensive evaluation of EBS is important to guide the development of EBS and motivate the integration of EBS into public health practice. Our study also highlights cross-country disparities in data quality, particularly for genomic evidence, and the need for data collection and sharing focused on low-resource settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/87030","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T20:13:03Z","timestamp":1778011983000},"page":"e87030-e87030","source":"Crossref","is-referenced-by-count":0,"title":["Early Detection Intervals for Evaluating Event-Based Surveillance System: Reference Dataset Development Study"],"prefix":"10.2196","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3389-4157","authenticated-orcid":false,"given":"Yannan","family":"Shen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7984-2088","authenticated-orcid":false,"given":"Philip","family":"AbdelMalik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1071-0914","authenticated-orcid":false,"given":"Russell J","family":"Steele","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1817-5047","authenticated-orcid":false,"given":"David L","family":"Buckeridge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,5,5]]},"reference":[{"issue":"10359","key":"R1","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.1016\/S0140-6736(22)01585-9","article-title":"The Lancet Commission on lessons for the future from the COVID-19 pandemic","volume":"400","author":"Sachs","journal-title":"Lancet"},{"issue":"4","key":"R2","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1038\/s41579-021-00639-z","article-title":"Infectious disease in an era of global change","volume":"20","author":"Baker","journal-title":"Nat Rev Microbiol"},{"key":"R3","unstructured":"WHO COVID-19 dashboard. 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