tetano
Editor, Senior Moderator
BMC Infect Dis
. 2025 May 29;25(1):770.
doi: 10.1186/s12879-025-11120-0. Did COVID-19 surveillance system sensitivity change after Omicron? a retrospective observational study in England
Julii Brainard[SUP] 1 2 [/SUP], Iain R Lake[SUP] 3 4 [/SUP], Roger A Morbey[SUP] 3 5 [/SUP], Alex J Elliot[SUP] 3 5 [/SUP], Paul R Hunter[SUP] 6 3 [/SUP]
Affiliations
Background: During the COVID-19 pandemic in England, increases and falls in COVID-19 cases were monitored using many surveillance systems (SS). However, surveillance sensitivity may have changed as different variants were introduced to the population, due to greater disease-resistance after comprehensive vaccination programmes and widespread natural infection or for other reasons.
Methods: Time series data from ten epidemic trackers in England that were available Sept 2021-June 2022 were compared to each other using Spearman correlation statistics. Least biased and most timely SS in England were identified as 'best' standard epidemic trackers, while other COVID-19 tracking datasets we denote as complementary trackers. We compared the best standard trackers with each other and with the complementary trackers. Correlation calculations with 95% confidence intervals were made between complementary and best standard epidemic trackers. We tested the hypothesis that correlation with the best trackers was especially poor during transition periods when Delta, Omicron BA.1 and Omicron BA.2 sublineages were each dominant. Daily ascertainment percentages of incident cases that each SS detected during each variant's dominance were calculated. We tested for statistically significant (at p < 0.05) differences in the distribution of the ascertainment values during each COVID-19 variant's dominance, using Welch's oneway ANOVA.
Results: Spearman rho correlation was significantly positive between most complementary and the best trackers over the whole period. There was no apparent visual indication that correlations were especially poor during transition period from Delta to BA.1. There were falls in correlation in the transition period from BA.1 to BA.2 but these falls were relatively small compared to correlation fluctuations over the full period. Ascertainment was highest in the Delta period for complementary systems against the least biased tracker of incidence. Ascertainment was statistically different between the three variant-dominant periods.
Conclusions: From September 2021 to June 2022, complementary SS generally reflected case rises and falls. Ascertainment was highest in the Delta-dominant period but no complementary tracker was highly stable. Factors other than which variant was dominant seem likely to have affected how well each tracker reflected true case rises and falls.
Keywords: COVID-19; Epidemic; Health care seeking; Omicron; Surveillance.
. 2025 May 29;25(1):770.
doi: 10.1186/s12879-025-11120-0. Did COVID-19 surveillance system sensitivity change after Omicron? a retrospective observational study in England
Julii Brainard[SUP] 1 2 [/SUP], Iain R Lake[SUP] 3 4 [/SUP], Roger A Morbey[SUP] 3 5 [/SUP], Alex J Elliot[SUP] 3 5 [/SUP], Paul R Hunter[SUP] 6 3 [/SUP]
Affiliations
- PMID: 40442584
- DOI: 10.1186/s12879-025-11120-0
Background: During the COVID-19 pandemic in England, increases and falls in COVID-19 cases were monitored using many surveillance systems (SS). However, surveillance sensitivity may have changed as different variants were introduced to the population, due to greater disease-resistance after comprehensive vaccination programmes and widespread natural infection or for other reasons.
Methods: Time series data from ten epidemic trackers in England that were available Sept 2021-June 2022 were compared to each other using Spearman correlation statistics. Least biased and most timely SS in England were identified as 'best' standard epidemic trackers, while other COVID-19 tracking datasets we denote as complementary trackers. We compared the best standard trackers with each other and with the complementary trackers. Correlation calculations with 95% confidence intervals were made between complementary and best standard epidemic trackers. We tested the hypothesis that correlation with the best trackers was especially poor during transition periods when Delta, Omicron BA.1 and Omicron BA.2 sublineages were each dominant. Daily ascertainment percentages of incident cases that each SS detected during each variant's dominance were calculated. We tested for statistically significant (at p < 0.05) differences in the distribution of the ascertainment values during each COVID-19 variant's dominance, using Welch's oneway ANOVA.
Results: Spearman rho correlation was significantly positive between most complementary and the best trackers over the whole period. There was no apparent visual indication that correlations were especially poor during transition period from Delta to BA.1. There were falls in correlation in the transition period from BA.1 to BA.2 but these falls were relatively small compared to correlation fluctuations over the full period. Ascertainment was highest in the Delta period for complementary systems against the least biased tracker of incidence. Ascertainment was statistically different between the three variant-dominant periods.
Conclusions: From September 2021 to June 2022, complementary SS generally reflected case rises and falls. Ascertainment was highest in the Delta-dominant period but no complementary tracker was highly stable. Factors other than which variant was dominant seem likely to have affected how well each tracker reflected true case rises and falls.
Keywords: COVID-19; Epidemic; Health care seeking; Omicron; Surveillance.