{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T03:21:12Z","timestamp":1788837672123,"version":"build-2803163510"},"reference-count":28,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Public Health Surveill"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Reporting of notifiable infectious diseases was overall impacted by the COVID-19 pandemic. This could affect disease surveillance and thus, outbreak detection, as it is based on historical data. For effective outbreak detection, it is crucial that communicable disease control professionals can rely on accurate and valuable alarm thresholds to ensure timely and adequate response to potential outbreaks.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n In this study, we take the first steps in the development of a methodology that adjusts for the impact of the COVID-19 pandemic on the number of notifications of notifiable infectious diseases and provides corrected alarm thresholds for outbreak detection. We identify which infectious diseases were affected by the COVID-19 pandemic, assess the duration of these effects, and explore potential correction methods to improve outbreak detection.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We analyzed cases of 25 notifiable infectious diseases reported from 2015\u20102023 in the Netherlands. Negative binomial regression was used to calculate the incidence rate ratios for each period: pre-COVID-19 pandemic, COVID 2020, COVID 2021, COVID 2022, and post-COVID-19 pandemic. To address the decrease in notifications during the COVID-19 pandemic, we tested 3 correction methods: (1) recoding COVID-19 years as missing; (2) imputing with the last pre-COVID-19 pandemic observation; and (3) imputing the historical moving average.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Between 2015\u20102023, a total of 74,990 notifications were reported in the Netherlands, of which 9,836 notifications (13%) occurred during the COVID-19 pandemic. Malaria, typhoid fever, hepatitis A, meningococcal infection, paratyphoid fever, Q-fever, shigellosis, measles, mumps, and pertussis had significantly lower notifications during the COVID-19 pandemic, but the duration and magnitude of the effect differed among the infections. The effect of the COVID-19 pandemic on the notifications of malaria (incidence rate ratio [IRR] 0.17, 95% CI 0.08\u20100.36) and typhoid fever (IRR 0.12, 95% CI 0.02\u20100.55) was only seen in 2020, while the notifications for measles (IRR 0.34, 95% CI 0.15\u20100.75) and pertussis (IRR 0.34, 95% CI 0.20\u20100.60) were still significantly lower in the post-COVID-19 pandemic period. In addition, the newly calculated alarm thresholds showed a noticeable difference compared with the original unadjusted alarm thresholds. However, the variation among the 3 different corrected alarm thresholds was not substantial.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n During the COVID-19 pandemic, notifications of 10 infectious diseases declined significantly. The duration of this decline varied among infections, highlighting the need for pattern-specific adjustments. Our study demonstrates that accounting for the reduced notifications impacts alarm threshold calculations for outbreak detection. We therefore recommend amending the alarm thresholds to account for this impact to ensure reliable outbreak detection, so that communicable disease control professionals can act in a timely and data-driven manner. The next step in the development of the \u201cpandemic-proof\u201d methodology is to determine which correction method is most suitable. Further validation by communicable disease control professionals is essential to assess the applicability of the adjusted alarm thresholds for outbreak detection.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/73953","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T03:55:09Z","timestamp":1751342109000},"page":"e73953-e73953","source":"Crossref","is-referenced-by-count":1,"title":["A \u201cPandemic-Proof\u201d Methodology for Outbreak Detection Adapted From COVID-19\u2019s Impact on Notifications of Infectious Diseases in the Netherlands: Surveillance Study"],"prefix":"10.2196","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7643-7888","authenticated-orcid":false,"given":"Babette","family":"van Deursen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3471-0329","authenticated-orcid":false,"given":"Stijn","family":"Raven","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9565-0893","authenticated-orcid":false,"given":"Wolfer","family":"van den Bosch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4269-0041","authenticated-orcid":false,"given":"Cornelia H M","family":"van Jaarsveld","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5793-5443","authenticated-orcid":false,"given":"Aura","family":"Timen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,8,26]]},"reference":[{"issue":"34","key":"R1","doi-asserted-by":"publisher","first-page":"30003","DOI":"10.2807\/1560-7917.ES.2015.20.34.30003","article-title":"To notify or not to notify: decision aid for policy makers on whether to make an infectious disease mandatorily notifiable","volume":"20","author":"Bijkerk","journal-title":"Euro Surveill"},{"issue":"3","key":"R2","doi-asserted-by":"publisher","first-page":"547","DOI":"10.2307\/2983331","article-title":"A statistical algorithm for the early detection of outbreaks of infectious disease","volume":"159","author":"Farrington","journal-title":"J R Stat Soc Ser A Stat Soc"},{"issue":"3","key":"R3","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1093\/oxfordjournals.aje.a116684","article-title":"Evaluation of a method for detecting aberrations in public health surveillance data","volume":"137","author":"Stroup","journal-title":"Am J Epidemiol"},{"issue":"1","key":"R4","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1111\/j.1467-985X.2011.00714.x","article-title":"Statistical methods for the prospective detection of infectious disease outbreaks: a review","volume":"175","author":"Unkel","journal-title":"J R Stat Soc Ser A Stat Soc"},{"issue":"22","key":"R5","doi-asserted-by":"publisher","first-page":"3660","DOI":"10.1093\/bioinformatics\/btv418","article-title":"The application of a novel \u201crising activity, multi-level mixed effects, indicator emphasis\u201d (RAMMIE) method for syndromic surveillance in England","volume":"31","author":"Morbey","journal-title":"Bioinformatics"},{"issue":"7","key":"R6","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.1002\/sim.5595","article-title":"An improved algorithm for outbreak detection in multiple surveillance systems","volume":"32","author":"Noufaily","journal-title":"Stat Med"},{"issue":"3","key":"R7","doi-asserted-by":"publisher","first-page":"E274","DOI":"10.15167\/2421-4248\/jpmh2023.64.3.2904","article-title":"Impact of the COVID-19 pandemic on the infectious disease epidemiology","volume":"64","author":"Facciol\u00e0","journal-title":"J Prev Med Hyg"},{"issue":"1","key":"R8","doi-asserted-by":"publisher","first-page":"6923","DOI":"10.1038\/s41467-021-27292-7","article-title":"Changes in notifiable infectious disease incidence in China during the COVID-19 pandemic","volume":"12","author":"Geng","journal-title":"Nat Commun"},{"issue":"1","key":"R9","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-022-14796-0","article-title":"Impact of national COVID-19 restrictions on incidence of notifiable communicable diseases in England: an interrupted time series analysis","volume":"22","author":"Nash","journal-title":"BMC Public Health"},{"issue":"100103","key":"R10","doi-asserted-by":"publisher","DOI":"10.1016\/j.lanepe.2021.100103","article-title":"Impact of the COVID-19 pandemic and associated non-pharmaceutical interventions on other notifiable infectious diseases in Germany: An analysis of national surveillance data during week 1-2016 - 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