Did COVID-19 surveillance system sensitivity change after Omicron? A retrospective observational study in England

Did COVID-19 surveillance system sensitivity change after Omicron? 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A retrospective observational study in England Julii Brainard, Iain R. Lake, Roger A. Morbey, Alex J. Elliot, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5889771/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 May, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 9 You are reading this latest preprint version Abstract 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. surveillance epidemic COVID-19 Omicron health care seeking Figures Figure 1 Figure 2 Figure 3 Introduction Surveillance systems (SS) were critical during the COVID-19 pandemic to manage, monitor and forecast health care demand, disease burden and the optimal timing of social distancing measures [1, 2]. COVID-19 SS facilitated identification of high-risk groups [3-6], informed modelling studies [7-10] and helped identification of distinct syndromic features of COVID-19 compared to other influenza-like illnesses [1, 11, 12]. To foster surveillance resilience and meet diverse information needs, multiple SS operated simultaneously in many jurisdictions, including in England UK [13]. As the UK COVID-19 activity progressed during the pandemic, it seems likely that the sensitivity of SS changed driven by multiple and complex pressures/factors. Universal vaccination programmes [14, 15] and widespread COVID-19 infections [16] are likely to have resulted in higher population resistance to developing symptomatic COVID-19 infection as well as reduced severity when symptoms were present. One key change in the pandemic occurred with the arrival of the Omicron (B.1.1.529) variant in England in November 2021. Omicron was associated with increased transmission rates [17] which were likely to affect surveillance sensitivity and specificity. Epidemiological evidence demonstrated that although Omicron sublineages were more transmissible than earlier COVID-19 variants [18], infection with Omicron variants were also associated with much milder illness [19]. Omicron sublineages becoming dominant coincided with reduced incubation periods [20] but higher risk of reinfection [18]. Laboratory evidence [21] indicated that Omicron had superior replication competence in the upper respiratory tract but poorer replication capacity in the lungs compared to earlier COVID-19 variants, potentially leading generally to more transmissible but milder illness and thus altered case presentation in infected persons. Using SS effectively and efficiently means understanding the strengths and limitations of each individual system [22, 23]. How the sensitivity of SS may change as pandemics develop is a key aspect of understanding these limitations. In addition to estimates of total cases, SS are relied upon to provide timely information about when case counts are rising or falling. If an SS is dependent on disease severity, then when typical case presentation changes, system sensitivity is likely to change also. Understanding of what proportion of cases an SS has identified could be faulty if sensitivity abruptly changes. In this retrospective observational study of COVID-19 SS in England, UK, we identified three best standard epidemic trackers that yielded daily estimates of COVID-19 case incidence and prevalence and compared these sources with case count estimates with each other and with other, complementary surveillance systems (CompSS). We apply simple and replicable methods to document how sensitive many SS were to estimating case counts in England when the dominant COVID-19 variant changed from Delta to Omicron BA.1 and then to Omicron BA.2. Methods In this study we use nine SS datasets that were previously described at length [13] as well as one additional surveillance dataset newly described here: case counts of persons hospitalised primarily for COVID-19. Below we provide a brief recap of each surveillance dataset used in this study, which are also summarised in Table 1. Best standard epidemic trackers We identified three potential ‘best standard’ epidemic trackers (BestET) for tracking cases in the English COVID-19 epidemic. The two least-biased case count estimators were daily case count estimates published by the Office for National Statistics (ONS) following random and stratified household sampling (Coronavirus Infection Survey for England, CISE). ONSCISE modelling allowed for demographic variations between the sampled and general population [24] to generate estimates of both incidence (ONSincid; new case count estimates) and prevalence (ONSprev: estimates of total concurrently infectious cases). The ONSCISE had high detection rates for asymptomatic cases because participants were swabbed at random, and high stability because data collection methods did not change and the modelling approach changed relatively little over time. However, the ONSCISE was not timely because of a typical > 10 day delay between swab collection and reporting date. ONS incidence estimates were not generated between 20 June and 19 November 2022 inclusive. Table 1. Best and complementary epidemic trackers Source Short name Description Primary purpose of dataset(s) Government testing framework Epidemic control and monitoring P12 Pillar 1 & 2 case counts Randomly chosen households for sampling ONSincid ONSCISE incidence estimates Estimate new infections, and prevalence ; epidemic monitoring ONSprev ONSCISE prevalence estimates NHS hospitalisation records HospAdm New admissions to hospital of patients who tested positive for COVID-19 Hospital services demand C19PRinH Count of persons who were in hospital primarily because of COVID-19 infection Syndromic datasets held by UKHSA, derived from actual health care usage records EDSS Emergency Department attendances Syndromic surveillance GPIH Consultations with GPs in usual opening hours 111calls NHS 111 telephone calls 111web NHS 111 website assessments The ZOE App ZOE Symptom tracker application Identify symptoms associated with COVID-19 infection status Notes: GP = General practice surgeries; NHS = National Health Service; ONSCISE = Office for National Statistics Coronavirus infection Survey for England; UKHSA = United Kingdom Health Security Agency. The most timely COVID-19 epidemic tracker was a combined count of laboratory-confirmed cases from two testing frameworks deployed during the COVID-19 epidemic in England. During our monitoring period, COVID-19 tests were available to health care workers and persons presenting with medical needs as part of what was designated a Pillar 1 testing framework. Tests to confirm COVID-19 infection were also available to wider members of the public with or without symptoms under a testing framework denoted as Pillar 2 tests. Combined case counts from Pillar 1 and 2 (P12) testing were widely used as most up-to-date estimates of daily total confirmed case counts during the English COVID-19 epidemic. P12 swab results [25, 26] were typically published < 24 hours after sample collection [27], and thus were very timely and had potentially high stability. P12 data strictly reflect incidence not prevalence. P12 data were likely to under-detect asymptomatic cases, because people who lacked symptoms had less reason to take a test. We therefore regard P12 data as a third ‘best standard’ epidemic tracker, and the most timely epidemic monitoring tracker, especially of symptomatic infection. Complementary surveillance systems/datasets This study compares the three BestET with seven other potential COVID-19 against CompSS concurrently available in England. We describe whether each dataset predominantly reflected incidence or prevalence or potentially both. The government website coronavirus.data.gov.uk provided counts of persons newly admitted to National Health Service (NHS) hospitals with SARS-CoV-2 positivity (HospAdm). Another epidemic tracker based on health service usage is counts of hospital inpatients in England who had COVID-19 as the primary reason for their hospitalisation (C19PRinH), which information was available from 18 June 2021 onwards. Cases in the HospAdm dataset were only counted once per infectious episode so align with COVID-19 incidence. However, hospitalisations while infected with Covid could be multiple for the same infectious episode in a single patient, and as a result the C19PRinH dataset could reflect incidence or prevalence. We used four CompSS which were based on national syndromic SS maintained by the UK Health Security Agency (UKHSA) real-time syndromic surveillance service. These syndromic CompSS describe patients accessing the NHS 111 telephone health advice (111calls) and NHS 111 online health assessments (111web) services; emergency department attendances (EDSS) and general practitioner in-hours (GPIH) consultations [2]. COVID-19-specific syndromic indicators for each of these syndromic CompSS were developed in 2020 [1]. All of these health service-record based indicators and counts of SARS-CoV-2 positive persons in hospital were typically reported within 24 hours so were timely. They were consistently defined during this monitoring period and thus had high stability. However, these CompSS were unlikely to capture asymptomatic or minimally symptomatic cases. These UKHSA datasets could reflect incidence or prevalence. The final CompSS what we used in our analysis was not based on actual health care demand but rather voluntary symptom-reporting. In a private sector project, the Zoe project (ZOE) produced COVID-19 incidence estimates [28]. ZOE was a nutritional and wellbeing digital application in development before 2020 which was adapted to support daily COVID-19 symptom tracking. ZOE produced estimates of community incidence that were derived from their own models that incorporated counts of their App users who reported new case status, with adjustments for demographic imbalances compared to the general population and the most recent ONSCISE estimates [29]. The precise algorithm(s) for how ZOE generated its incidence estimates and the completeness of the data collected are not published which means that stability of ZOE and its ascertainment biases are unknown. There were many ZOE model changes in their incidence estimates during the UK COVID-19 epidemic [29]. Previous research [13] found that in 2020-2021 the ZOE data correlated highly with the least biased and most timely epidemic trackers used in this article. Where available, we used uncorrected data (as first published) from each CompSS, because minimally verified data is what real-world decision makers usually have access to while an epidemic is active. We calculated correlation statistics between the BestET and the CompSS for both the full monitoring period and sub-periods. All SS and datasets apply to England only. More information about the data used in this study, cleaning, additional processing and sources is in [13]. Monitoring period We compared the datasets from 1 September 2021, until 19 June 2022. This period encompasses > 90 days before Omicron became dominant in England, the rapid transition from Delta to Omicron dominance in sequenced swab samples in late 2021, and transitions between several different Omicron waves before reporting of ONSCISE incidence estimates was paused on 20 June 2022. COVID-19 variants We consider SS sensitivity to track infections with regard to three main COVID-19 variants and their sublineages: Delta, Omicron BA.1 and Omicron BA.2. In the UK COVID-19 epidemic, the Delta variant was dominant among genetically sequenced test samples from 24 May to 14 December 2021. Omicron variant group BA.1 and sublineages dominated samples from 14 December 2021 through 20 February 2022, after which Omicron BA.2 sublineages were most sequenced samples until 7 June 2022. From 7-19 June 2022 no single SARS-Cov-2 variant dominated sequenced samples in England. Analysis: Quantitative comparisons To compare BestET and CompSS, we undertook visual and statistical comparisons that were simple and replicable. We separate some of the analysis between incidence and prevalence comparisons. The case counts suggested by BestET least biased and most timely best standard epidemic trackers were plotted in time series with CompSS, to visually discern if there was close correspondence. The two BestET that indicated incidence were compared to each other to see if the correlation between the most timely BestET (P12) and the least biased BestET (ONSincid) changed over time or when dominant variants changed. Spearman rho correlation statistics were calculated with 95% confidence intervals for the full monitoring periods. Spearman rho was appropriate because the time series had strongly nonparametric distributions. We wanted to test whether correlations between CompSS and BestET were especially low around the period when the dominant variant changed, from Delta to BA.1 or from BA.1 to BA.2 dominance. For this test, we calculated Spearman rho on day 16 in 31 day duration moving time windows, for six CompSS and P12 with ONSincid over the full period. The same was done for 7 CompSS against ONSprev. The Spearman rho value for day 16 in the moving window was plotted against calendar dates and with reference to which variant was dominant. These plots were visually assessed for apparent dips in correlation in transition periods. Daily ascertainment percentages for incidence were also calculated and reported as median values with IQR, to see how many cases each system seemingly detected daily, compared to the best standard SS. The resulting datasets (ascertainment percentages) were mostly non-parametric and lacked homoscedasticity between groups separated by variant dominance. We therefore tested for statistically significant (at p < 0.05) differences in the distribution of the ascertainnment values during each COVID-19 variant’s dominance, using Welch’s oneway ANOVA tests. Rstudio 2022.02.0 (R version 4.1.1) was used to generate plots and statistics. Results Overview Plots of each CompSS (rows) are shown against the BestET (columns) in Supplementary File S1. Two exemplar time series are reproduced as Figs. 1a and 1b in this article. Each individual figure has the complementary series plotted against left axis, and BestET against right axis, with approximate alignment for their peak values. These time series were smoothed (7 day moving average on central date) in these visualisations except the ONSincid and ONSprev. Raw (not smoothed) values were used for statistical comparisons and analysis. The period when each variant was dominant is indicated by multi-coloured lines at the bottom of each chart (green for Delta, cyan for BA.1 and purple for BA.2). Duration of dominance by each variant was : Delta for 104 days, BA.1 for 69 days and BA.2 for 107 days. There is strong visual similarity between many time series: many rises and falls happened about the same time. Comparisons between time series in all the figures are quantified statistically in Table 2 . Table 2 shows whole period correlation (Spearman rho with 95% confidence intervals) between BestET and CompSS. Most correlations were significant at p 0.70) with the BestET ONSincid and ONSprev, but were both much less correlated with P12 (95%CI < 0.70). EDSS was the CompSS most correlated with the most timely tracker (P12; rho 0.83, 95%CI 0.78–0.87). P12, EDSS and C19PRinH were each positively correlated with all other SS data. Given many patients tested under the Pillar 1 framework were attenders to ED, it is not surprising that P12 correlated highly with EDSS. GPIH and 111web had the weakest relationships with ONSincid or ONSprev or other trackers, except that the 111web data strongly correlated with 111calls counts (rho 0.79, 95%CI 0.73–0.83). Best epidemic trackers compared to each other Figure1a and 1b in this article show the P12 time series with ONSincid and ONSprev. Figure 1a and 1b are useful to focus on because they suggest that ascertainment by P12 compared to ONSincid declined over time. Ascertainment was higher during Delta or BA.1, but lower during BA.2 time periods. When the ascertainment ratio was calculated for P12 compared to ONS incidence the values were 42.5 (95%CI 37.0-49.1) during Delta, 30.39 (95%CI 23.9–38.1) in BA.1 and 8.93 (95%CI 7.0-11.3) in the BA.2 time period. This varying ascertainment relationship seems like an important reason why the Spearman rho correlation statistic over the full monitoring period between P12 and the ONSincid time series is positive but not high (rho 0.53, 95%CI 0.45–0.59). In our previous research for the same trackers in the period September 2020 to December 2021, this correlation was much higher (rho 0.94, 95%CI 0.92–0.95) [ 13 ]. Ascertainment of Complementary compared to BestET Table 3 gives subperiod ascertainment percentages of cases detected for CompSS incidence and prevalence trackers, compared to case counts reported by BestET, and separated by which variant was concurrently dominant. Data are reported as median ascertainment ratio (IQR). GPIH is not included because GPIH data were available as rates per 100,000 registered patients, and as such aren’t directly suitable for calculating ascertainment ratios. Ascertainment of prevalence or incidence suggested by P12 or CompSS compared to the ONS estimates was lower in BA.1 period compared to Delta dominant period. However, using ONSCISE outputs as Best ET, differences in ascertainment between the BA.1 period and the BA.2 period were mostly small. A noticeable exception is that P12 had very much lower ascertainment of the least biased incidence estimates in the BA.2 period (median 8.93%, IQR 7.0%-11.3%) than in BA.1 or Delta dominant periods (medians respectively 30.4% and 42.5% and IQR respectively 23.9%-38.1% and 37.0-49.1%). Ascertainment of total cases for each CompSS compared to P12 was highest during BA.2 and lowest during the BA.1 period. Because the ascertainment data were calculated daily, it was possible to statistically compare the distribution of daily ascertainment ratios during the dominance of each variant using Welch’s one-way ANOVA. This test yields an F-statistic with p-values; all between group differences (for ascertainment ratios across rows/variants in Table 3 ) were significant at p < 0.001 for the underlying data summarised in Table 3 . This statistical finding documents that no complementary SS was highly stable (consistent) in case detection rate across all three variant periods. Table 2 . Spearman rho correlations between SS with 95% confidence intervals. Table 3 Ascertainment (as %) during variant-dominant periods Comparison with ONS prevalence Complementary SS Delta BA.1 BA.2 C19PRinH 0.48 (0.4, 0.6) 0.21 (0.2, 0.2) 0.19 (0.2, 0.2) EDSS 0.04 (0.0, 0.0) 0.02 (0.0, 0.0) 0.01 (0.0, 0.0) 111calls 0.27 (0.2, 0.3) 0.09 (0.1, 0.1) 0.10 (0.1, 0.2) 111web 0.29 (0.3, 0.3) 0.06 (0.1, 0.1) 0.07 (0.1, 0.1) Comparison with ONS incidence Delta BA.1 BA.2 P12 42.5 (37.0, 49.1) 30.4 (23.9, 38.1) 8.93 (7.0, 11.3) HospAdm 0.87 (0.8, 1.1) 0.55 (0.4, 0.6) 0.48 (0.4, 0.7) ZOE 75.6 (67.6, 84.8) 53.1 (43.7, 66.4) 68.6 (54.4, 110.5) EDSS 0.42 (0.4, 0.5) 0.16 (0.1, 0.2) 0.11 (0.1, 0.1) 111calls 3.15 (2.8, 3.6) 0.86 (0.7, 1.1) 1.00 (0.6, 1.7) 111web 3.35 (2.8, 3.8) 0.64 (0.5, 0.8) 0.74 (0.4, 1.2) Comparison with P12 incidence Delta BA.1 BA.2 HospAdm 2.13 (1.9, 2.5) 1.83 (1.2, 2.3) 7.06 (3.5, 8.8) ZOE 176.4 (154, 207) 162.9 (121, 277) 949.4 (451, 1411) EDSS 1.03 (0.9, 1.3) 0.52 (0.5, 0.6) 1.40 (0.8, 1.8) 111calls 6.90 (6.1, 9.1) 2.80 (2.2, 4.1) 12.02 (5.4, 22.2) 111web 7.72 (6.1, 9.1) 2.08 (1.6, 2.8) 8.79 (3.9, 16.8) Notes: Values are for entire monitoring period, ascertainment percentage median (IQR). Tests for between group differences (in rows) using Welch’s one-way ANOVA were all significant at p < < 0.001. Moving 31 day period correlation between best and complementary SS The panels in Figs. 2a-3g plot the individual Spearman rho correlation statistics for complementary SS, compared to the two least biased epidemic trackers (ONSincid and ONSprev) in moving time windows. The same time series are plotted together in Supplementary File S1, Figures S2 and S3. The supplementary plots evidence that new hospital admissions for patients with Covid strongly related to ED attendances syndromically assigned as Covid-19, as indicated by high visual correspondence between these two time series on Figure S2. Otherwise, for most time series, they do not very obviously follow similar trajectories as each other on Figures S3 and S4. The plotted correlation values are for day 16 within each moving 31 day duration window. Concurrent dominant COVID-19 variant is also indicated by a colour-coded line that intersects the vertical axis at 0.5. Visually, on all the graphs the Spearman rho varies considerably over the study period with multiple cycles of low correlation followed by periods of higher correlation. This variability appears greater for complementary SS based upon hospital data (e.g EDSS, C19PRinH, HospAdm) than for other SS. Correlations with ONSprev are generally higher than with ONSincid. In many complementary SS, these dips to lower correlation appear to happen about the same time and don’t tend to occur at the same time as changes in the dominant variant. For instance, on multiple systems there appears to be a dip in correlations around November-December 2021 and another dip around February 2022 and the start of June 2022. HospAdm and EDSS correlate consistently well with ONSprev after February 2022 and apart from two short periods, ZOE correlates well with ONSprev over the entire study period. We hypothesised that changes in correlation would concur with changes in the dominant variant. However, visually, during the transition period from Delta to Omicron BA.1 (December 2021), correlations were rising or remained consistently high, there was no apparent dip. Around the time of variant transition from BA.1 to BA.2 (late February 2022), all complementary trackers had poor (generally declining) correlation with the ONSincid and ONSprev. However, these BA.1 to BA.2 dips were mostly small or similar to other fluctuations in correlation over the monitoring period shown in Figs. 2–3 panels. Discussion In a previous comparison of SS [2020–2021; 13] we found that P12 closely tracked the least biased indicator (ONSincid), with a whole-period Spearman rho of 0.95 (95%CI 0.92–0.95). Here we focus upon the period where the dominant COVID-19 variant changed from Delta to Omicron BA.1 and then to Omicron BA.2. We found a much lower whole-period correlation between P12 and ONSincid (0.53 (95%CI 0.45–0.59)) and the ascertainment ratio dropped from 42.9% at the start of the period when the Delta variant dominated to 8.9% by the time BA.2 became dominant. There are several likely reasons for reduction in sensitivity over time. It seems likely that fewer people chose to get tested in the late UK epidemic period, but each surveillance system also exhibited increased variability in January-June 2022. Bajaj et al. [ 30 ] found that counts of Pillar 2 PCR tests taken/100 persons per day noticeably fell between 11 January and 31 March 2022 when rapid antigen (lateral flow device) tests were still available for free but no-cost access to Pillar 2 PCR tests was withdrawn. Other reasons for declines in epidemic tracker sensitivity possibly include an increasingly vaccinated population experiencing mostly re-infections with variants (Omicron) which have been associated with less severe symptoms than Delta and earlier variants. Less severe symptoms would result in lower usage of health services, fewer cases detected through surveillance and reduced sensitivity of Pillar 1 tests [ 31 ]. Attitudes about individual duty to contribute to infection control probably also changed. On 21 February 2022 the UK announced a “Living with Covid” plan [ 32 , 33 ] which meant withdrawal of all free COVID-19 antigen or PCR tests by 31 March 2022, making it both harder to obtain tests and also seemingly less important to know one’s own case status. When the BestET were compared to CompSS, like in our previous analysis, ZOE was particularly correlated with ONSincid and ONSprev. ZOE also maintained relatively high ascertainment ratios with ONSincid and ONSprev. This is to be expected because ZOE incorporated recent ONS data to generate their estimates. Some CompSS that we used had ascertainment ratios lower than found in our previous study of 2020–2021 data. Their correlations with BestET also reduced over time in our current analysis. This is consistent with an infection that is becoming declining importance as a public health risk. By 11 February 2022 around 71% of the English population had been infected at least once with SARS-CoV-2 [ 34 ]. COVID-19 vaccine distribution and uptake were fast and high in England: > 90% of English adults age > 65 + received at least one COVID-19 vaccination by 17 May 2021 [ 35 ]. Vulnerable persons were encouraged to be vaccinated again repeatedly from autumn 2021 onwards. Hybrid immunity (achieved after both vaccination and natural infection) is widely believed to lead to milder clinical presentation for subsequent COVID-19 infections [ 36 – 40 ]. As a result, many if not most cases detected in our study period could have been from reinfections or vaccinated individuals. Most of CompSS in our analysis were highly sensitive to symptomatic illness and had low sensitivity to minimally symptomatic infections. This is likely to be explain the reduced ascertainment of EDSS, HospAdm and C19PRinH in comparison to ONSprev and ONSincid in contrast to 2020–2021 [ 13 ]. It may also help explain the additional reduction in ascertainment amongst these SS as the study period moves from Delta to BA.1 to BA.2. Some CompSS had an especially large reduction in ascertainment rates (e.g. 111calls). A plausible explanation is that in addition to changing severity, altered behaviour played a role. In February 2022 the UK government announced a “Living with Covid” plan [ 32 , 33 ] that described lifting COVID-19-linked legal restrictions on social contact and testing requirements. The plan meant cessation of free laboratory tests to confirm COVID-19 infection (such as rapid antigen and rtPCR tests) from March 2022; as a result, the Pillar 1 & 2 case detection strategy changed. Our analysis showed that other SS lacked stability over the period 1 September 2021 to 19 June 2022. The transition to “Living with Covid” seems likely to have reduced the number of people seeking health care advice for and/or reporting COVID-19 symptoms. This may help to explain the reductions for CompSS of less severe illness (111calls and 111web) suggestive of fewer individuals seeking medical advice for an illness perceived as a lower potential health threat. Hence it is unsurprising that sensitivity reduced over this time period when the dominant variant changes from Delta to BA.1 and BA.2. In spite of lower ascertainment over time, it is notable that several of these trackers have high correlation with ONSincid and ONSprev over the full period (see Table 2 ). Most notable is HospAdm which correlated with ONSincid and ONSprev at mean values 0.79 and 0.91 respectively. EDSS and C19PRinH have what could be considered moderate correlation with both ONS indicators (mean correlations ranging from 0.35 to 0.56, full confidence intervals always above zero). In Table 2 , other CompSS, such as 111calls and 111web and GPIH, which seem likely to be most sensitive to the least severe COVID-19 infections, generally correlated poorly with ONS trackers. One reason may be because for many of the SS systems, health conditions are assigned probabilistically, based on clinical presentation rather than being laboratory-confirmed [ 41 ]. Increasing amounts of minimally symptomatic COVID-19 may complicate this process. Furthermore, for most of 2020–2021, there was low circulation of respiratory viruses other than SARS-CoV-2 which made the COVID-19 syndrome assignment especially reliable. However, during our study period there were large resurgences in cases of non-COVID-19 respiratory virus infections in many high income countries, including England [ 42 – 44 ]. Hence, some patients infected with other respiratory viruses may have been syndromically assigned as COVID-19 while some patients with multiple infections (eg., influenza and COVID-19) may have been assigned only one of these conditions. These imprecise assignments may have contributed to lower correlations especially for presentations by patients with mild symptoms. It was notable that lower correlation with ONSincid and ONSprev did not associate with transition between variants. Across systems there were some periods when correlation coefficients appeared particularly poor (e.g. November 2021); identifying the reasons for that requires separate research. Strengths and Limitations Our analysis is novel by robustly quantifying that SS sensitivity for COVID-19 changed and varied in England in 2021–2022. This happened among multiple epidemic trackers. The purpose of surveillance data is to support decision-making that can facilitate rapid introduction of interventions and thus potentially less mortality and morbidity. Optimal use of surveillance data means understanding strengths and weaknesses of individual systems and indicators, and which trackers are best suited to support specific decisions made in real time. The value of complementary surveillance systems is both confirmatory and to reduce risk of over-reliance on individual trackers which may have unrecognised instability or limitations. Complementary systems have the potential to monitor and track disease burden across the breadth of the health care service, ensuring that all severities of presentation are captured. Identifying declines in sensitivity and instability, including temporal coincidence with some specific policy and public health changes, can inform better prospective decision-making during a future pandemic. We hypothesised why falls in surveillance sensitivity may have happened, and also propose that such changes in surveillance sensitivity may be expected as a normal development during an epidemic. We note that choices about which surveillance systems to implement are likely to at least partly depend on specific epidemic management strategies and priorities with corresponding cost-benefit evidence which is outside our study’s remit. Our analysis benefited from large and comprehensive datasets that described access to the NHS, which is the main health care provider for the English population. We also accessed estimates made by the ZOE project for COVID-19 incidence. Some CompSS such as HospAdm and C19PRinH were priority epidemic trackers because they informed resource allocation and as such were subject to priority verification. These indicators were consistently defined during this monitoring period and thus had high stability. However, because these definitions were developed before or when Delta was dominant, the surveillance systems may have had higher sensitivity to pre-Omicron variants. We did not assess if correlations improved by adjusting correlation using delays (lags) between time series, such as if increased visits to the NHS111 website correlated with rises in hospital admissions 7–10 days later. Our previous analysis [ 13 ] found that allowing for 0–14 day offsets did not much improve correlation between the same epidemic tracker time series that were used in this article. Our comparison is limited to only specific datasets. We have not considered many possible other surveillance datasets that could merit consideration as epidemic trackers, such as infected-person counts derived from wastewater sampling for SARS-CoV-2 virus. Jones et al. (2025) [ 45 ] undertook a thorough catchment-by-catchment comparison between ONSCISE infection counts and geographically coincident prevalence derived from waste water samples in England in 2020–2021. Jones et al. found that correlations were highly variable, but also concluded that SARS-CoV-2 concentrations in wastewater generally correlated well with ONSCISE prevalence. Conclusion From 1 September 2021 to 19 June 2022, CompSS greatly reduced in the proportion of COVID-19 cases they were detecting. This decrease was greatest in SS and indicators particularly focussed on less severe cases (e.g. presenting to the NHS 111 telephone helpline) which may reflect lower engagement with healthcare for COVID-19-like symptoms. However, despite this fall many indicators of more acute (and severe) COVID-19 presentations (e.g. HospAdm) continued to correlate well with the least biased epidemic trackers indicating that they generally reflected true trends in COVID-19 activity. For all CompSS, correlations tended to cycle over time indicating periods when they were all useful measures of COVID-19 trends. Factors other than which variant was dominant seem likely to have affected how well each tracker reflected true rises and falls in COVID-19 infections. Abbreviations 111calls counts of calls to the NHS111 telephone advice service 111web counts of website visits to the NHS111 web advice service ANOVA Analysis of variance BestET Best epidemic trackers C19PRinH Covid-19 was primary reason for being in hospital CompSS Complementary surveillance systems COVID-19 Coronavirus disease 2019 GPIH General practice in hours services, surveillance system EDSS Emergency department surveillance system HospAdm Hospital admissions NHS National health service ONS Office for National Statistics ONSCISE Office for National Statistics Coronavirus Infection Survey for England ONSincid ONSCISE estimate of incidence ONSprev ONSCISE estimate of prevalence P12 Count of COVID-19 cases confirmed under Pillar 1 and 2 test frameworks Sept September SS Surveillance systems UK United Kingdom UKHSA United Kingdom health security agency ZOE Zoe application data / Zoe application data custodians Declarations Ethics approval and consent to participate The research adhered to the Declaration of Helsinki 2024. The anonymised health data used in this study were either published (freely and fully in the public domain) or collected and aggregated as part of the public health function of the UK Health Security Agency (UKHSA). UKHSA has access to a range of data sources under Regulation 3 (Health Protection) of The Health Service (Control of Patient Information) Regulations 2002. Under the terms of that access, internal review by the the UKHSA Research Support and Governance Office agreed that this study fell outside the remit for ethical review. This decision was in accordance with the revised guidance in the Governance Arrangements for Research Ethics Committees that was released in September 2011. Consent for publication Not applicable Availability of data and materials None of the original datasets were collected by ourselves and therefore they should not be redistributed by us. Zoe application, ONSCISE, COVID-19 hospital admissions and Pillar 1 & 2 data all have been accessible in the public domain. Applications for requests to access relevant anonymised data included in this study held by UKSA should be submitted to the UKHSA Office for Data Release at: https://www.gov.uk/government/publications/accessing-ukhsa-protected-data/accessing-ukhsa-protected-data ). R scripts used for analysis and to generate plots are available at https://github.com/JuliiBrainard/SpotOmicron/tree/main. Funding This work was funded and authors were financially renumerated by the National Institute for Health and Care Research (NIHR) Health Protection Research Unit (HPRU) in Emergency Preparedness and Response at King’s College London in partnership with the UK Health Security Agency (UKHSA) in collaboration with the University of East Anglia. AJE is also affiliated with the NIHR HPRU in Gastrointestinal Infections at the University of Liverpool. The views expressed are those of the author(s) and not necessarily those of the NHS, NIHR, UEA, any HPRU, Department of Health or UKHSA. The funders had no role in writing the manuscript. Authors were not precluded from accessing data that supported this study and authors accept responsibility for this submission. Author Contributions PRH and IRL conceived of the study. PHR and JB extracted data; PRH, IRL and JB designed the data analysis strategy. JB and IRL wrote the draft protocol. IRL and JB designed the visualisations. JB assembled the datasets, analysed the data, wrote the first draft of the manuscript and assembled revisions. RAM and AJE facilitated access to and interpretation of the syndromic surveillance data. All authors commented on the draft manuscript, have read and approved of the final manuscript. Competing Interests We declare no competing interests. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of our funders. Acknowledgements We thank syndromic data providers: NHS 111 and NHS England supporting NHS 111 surveillance; emergency department clinicians and NHS Trusts and NHS England supporting the Emergency Department Syndromic Surveillance System; and participating TPP SystmOne practices supporting GP in-hours surveillance. The authors thank members of the UKHSA Real-time Syndromic Surveillance Team (Sally Harcourt, Sue Smith and other staff) for data verification and technical support in accessing the syndromic data for this study. References Elliot AJ, Harcourt SE, Hughes HE, Loveridge P, Morbey RA, Smith S, Soriano A, Bains A, Smith GE, Edeghere O: The COVID-19 pandemic: a new challenge for syndromic surveillance . Epidemiology & Infection 2020, 148 . 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Ito K, Piantham C, Nishiura H: Relative instantaneous reproduction number of Omicron SARS‐CoV‐2 variant with respect to the Delta variant in Denmark . Journal of medical virology 2022, 94 (5):2265-2268. Pulliam JR, van Schalkwyk C, Govender N, von Gottberg A, Cohen C, Groome MJ, Dushoff J, Mlisana K, Moultrie H: Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa . Science 2022, 376 (6593):eabn4947. Yu W, Guo Y, Zhang S, Kong Y, Shen Z, Zhang J: Proportion of asymptomatic infection and nonsevere disease caused by SARS‐CoV‐2 Omicron variant: A systematic review and analysis . Journal of Medical Virology 2022, 94 (12):5790-5801. Tanaka H, Ogata T, Shibata T, Nagai H, Takahashi Y, Kinoshita M, Matsubayashi K, Hattori S, Taniguchi C: Shorter incubation period among COVID-19 cases with the BA. 1 Omicron variant . International Journal of Environmental Research and Public Health 2022, 19 (10):6330. 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In . ; 2020. Elson R, Davies TM, Lake IR, Vivancos R, Blomquist PB, Charlett A, Dabrera G: The spatio-temporal distribution of COVID-19 infection in England between January and June 2020 . Epidemiology & Infection 2021, 149 :e73. Weekly statistics for NHS Test and Trace (England): 18 to 24 November 2021 [https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1038052/test-and-trace_week-78.pdf] COVID estimates revised after change to methodology [https://health-study.joinzoe.com/post/covid-estimates-revised-after-change-to-methodology] Zoe and King's College London: Zoe Health Study: Daily COVID Infections Report . In . : Kings College London; 2023: 30. Bajaj S, Chen S, Creswell R, Naidoo R, Tsui JLH, Kolade O, Nicholson G, Lehmann B, Hay JA, Kraemer MUG et al : COVID-19 testing and reporting behaviours in England across different sociodemographic groups: a population-based study using testing data and data from community prevalence surveillance surveys . The Lancet Digital Health 2024, 6 (11):e778-e790. Willyard C: Are repeat COVID infections dangerous? What the science says . In: Naturecom. 2023. PM statement on living with COVID: 21 February 2022 [https://www.gov.uk/government/speeches/pm-statement-on-living-with-covid-21-february-2022] COVID-19 Response: Living with COVID-19 [https://www.gov.uk/government/publications/covid-19-response-living-with-covid-19] Office for National Statistics: Coronavirus (COVID-19) Infection Survey technical article: Cumulative incidence of the number of people who have tested positive for COVID-19, UK: 22 April 2022 . In . ; 2022: 9. Tessier E, Rai Y, Clarke E, Lakhani A, Tsang C, Makwana A, Heard H, Rickeard T, Lakhani S, Roy P: Characteristics associated with COVID-19 vaccine uptake among adults aged 50 years and above in England (8 December 2020–17 May 2021): a population-level observational study . BMJ Open 2022, 12 (3):e055278. Flacco ME, Acuti Martellucci C, Baccolini V, De Vito C, Renzi E, Villari P, Manzoli L: Risk of reinfection and disease after SARS‐CoV‐2 primary infection: Meta‐analysis . European Journal of Clinical Investigation 2022, 52 (10):e13845. Mensah AA, Lacy J, Stowe J, Seghezzo G, Sachdeva R, Simmons R, Bukasa A, O'Boyle S, Andrews N, Ramsay M: Disease severity during SARS-COV-2 reinfection: a nationwide study . Journal of Infection 2022, 84 (4):542-550. Spinardi JR, Srivastava A: Hybrid immunity to SARS-CoV-2 from infection and vaccination—evidence synthesis and implications for new COVID-19 vaccines . Biomedicines 2023, 11 (2):370. Hui DS: Hybrid immunity and strategies for COVID-19 vaccination . The Lancet Infectious Diseases 2023, 23 (1):2-3. Šmíd M, Barusová T, Jarkovský J, Májek O, Pavlík T, Přibylová L, Weinerová J, Zajíček M, Trnka J: Post-vaccination, post-infection and hybrid immunity against severe cases of COVID-19 and long COVID after infection with SARS-CoV-2 Omicron subvariants, Czechia, December 2021 to August 2023 . Eurosurveillance 2024, 29 (35):2300690. Smith GE, Elliot AJ, Lake I, Edeghere O, Morbey R, Catchpole M, Heymann DL, Hawker J, Ibbotson S, McCloskey B: Syndromic surveillance: two decades experience of sustainable systems–its people not just data! Epidemiology & Infection 2019, 147 :e101. Bardsley M, Morbey RA, Hughes HE, Beck CR, Watson CH, Zhao H, Ellis J, Smith GE, Elliot AJ: Epidemiology of respiratory syncytial virus in children younger than 5 years in England during the COVID-19 pandemic, measured by laboratory, clinical, and syndromic surveillance: a retrospective observational study . The Lancet Infectious Diseases 2023, 23 (1):56-66. Lee SS, Viboud C, Petersen E: Understanding the rebound of influenza in the post COVID-19 pandemic period holds important clues for epidemiology and control . International Journal of Infectious Diseases 2022, 122 :1002-1004. Analysis reveals new insights into global surge of Strep A infections [https://www.imperial.ac.uk/news/253233/analysis-reveals-insights-into-global-surge/] Jones NR, Elson R, Wade MJ, McIntyre-Nolan S, Woods A, Lewis J, Hatziioanou D, Vivancos R, Hunter PR, Lake IR: Localised wastewater SARS-CoV-2 levels linked to COVID-19 cases: A long-term multisite study in England . Science of the Total Environment 2025, 962 :178455. Additional Declarations No competing interests reported. Supplementary Files appendixSpotOmicronReady.docx Cite Share Download PDF Status: Published Journal Publication published 29 May, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 12 May, 2025 Reviews received at journal 02 May, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 25 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 23 Apr, 2025 Submission checks completed at journal 21 Apr, 2025 First submitted to journal 21 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5889771","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447114678,"identity":"7b5dc104-9e7b-424d-b532-e455285bed41","order_by":0,"name":"Julii Brainard","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACxgYeIGkAYiYfgAkyE6slLYE4LQwMPDBGjgFxWpgbeI99+FFwR968PeebxIcaBnn+Bh5jA3xaGBv4kmf2GDwznHPm7TbJGccYDGcc4DFOwK+Fx5iBx+Aw4wyJ3G3SPGwMjBsYeIwPENLC+MfgsP0MiZxn0n/+MdgTpYUZaEsiUAubNGMbQyJIC36HNfMlM8sYHE6ewfPM2LK3TyJ5xmG2YrzeN2zvPcz45s9h2xnsyQ9v/PhmY9vf3rxZAq+WZlS+BOGIlCcgPwpGwSgYBaOAgQEADf9A4Re/YPcAAAAASUVORK5CYII=","orcid":"","institution":"University of East Anglia Norwich","correspondingAuthor":true,"prefix":"","firstName":"Julii","middleName":"","lastName":"Brainard","suffix":""},{"id":447114683,"identity":"9939ea98-6985-4ddf-8608-b77da5f498e4","order_by":1,"name":"Iain R. Lake","email":"","orcid":"","institution":"University of East Anglia Norwich","correspondingAuthor":false,"prefix":"","firstName":"Iain","middleName":"R.","lastName":"Lake","suffix":""},{"id":447114686,"identity":"8d35ecf4-cbd9-4ff1-8957-42c0e004d2c7","order_by":2,"name":"Roger A. Morbey","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Roger","middleName":"A.","lastName":"Morbey","suffix":""},{"id":447114689,"identity":"7cfc2a23-901e-4bde-ae11-80ca497b3e37","order_by":3,"name":"Alex J. Elliot","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"J.","lastName":"Elliot","suffix":""},{"id":447114693,"identity":"da071305-6e59-45b9-ab27-17b5f9fd98f7","order_by":4,"name":"Paul R. Hunter","email":"","orcid":"","institution":"University of East Anglia Norwich","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"R.","lastName":"Hunter","suffix":""}],"badges":[],"createdAt":"2025-01-23 16:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5889771/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5889771/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-11120-0","type":"published","date":"2025-05-29T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81541614,"identity":"444cba87-d261-4c21-b833-82c91765e27e","added_by":"auto","created_at":"2025-04-28 11:15:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90869,"visible":true,"origin":"","legend":"\u003cp\u003ePillar 1 and 2 case count time series overlain with ONS estimates of incidence and prevalence\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes\u003c/em\u003e: P12 is scaled to left side axis, ONS dataset counts are scaled to each right vertical axis. \u0026nbsp;Dates are 1 September 2021 to 19 June 2022.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5889771/v1/ff3c23e9523b870240a2fab9.png"},{"id":81544653,"identity":"99768b60-4d70-4bca-8741-879a7ae91363","added_by":"auto","created_at":"2025-04-28 11:31:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109263,"visible":true,"origin":"","legend":"\u003cp\u003eMoving 31 day correlations, complementary trackers against least biased incidence tracker (ONSincid)\u003c/p\u003e\n\u003cp\u003eNotes: Y-axis is Spearman rho; month on x-axis (1 September 2021 to 19 June 2022). Line to colour code concurrent variant dominance in each point intersects 0.5 on the vertical axis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5889771/v1/a3012c02c9b31a3be3e068e9.png"},{"id":81541617,"identity":"a42c2947-1366-42ce-ad1d-888a2c785935","added_by":"auto","created_at":"2025-04-28 11:15:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":95658,"visible":true,"origin":"","legend":"\u003cp\u003eMoving 30 day correlations, complementary trackers against least biased prevalence tracker (ONSprev)\u003c/p\u003e\n\u003cp\u003eNotes: Y-axis is Spearman rho; month on x-axis (1 September 2021 to 19 June 2022). Line to colour code concurrent variant dominance in each point intersects 0.5 on the vertical axis.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5889771/v1/96f0fd2371f1dadf1acd185f.png"},{"id":83782765,"identity":"3044e8b7-2777-46c5-ad45-0fa3b657f5bf","added_by":"auto","created_at":"2025-06-02 16:03:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3017897,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5889771/v1/947eb637-5902-43c0-87c1-974837fe083f.pdf"},{"id":81541634,"identity":"d3830340-03d1-4160-ad3b-9019a40b1a95","added_by":"auto","created_at":"2025-04-28 11:15:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":205318,"visible":true,"origin":"","legend":"","description":"","filename":"appendixSpotOmicronReady.docx","url":"https://assets-eu.researchsquare.com/files/rs-5889771/v1/eec034f33e09e8c23ae2171c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Did COVID-19 surveillance system sensitivity change after Omicron? A retrospective observational study in England","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSurveillance systems (SS) were critical during the COVID-19 pandemic to manage, monitor and forecast health care demand, disease burden and the optimal timing of social distancing measures [1, 2]. \u0026nbsp; COVID-19 SS facilitated identification of high-risk groups [3-6], informed modelling studies [7-10] and helped identification of distinct syndromic features of COVID-19 compared to other influenza-like illnesses [1, 11, 12]. \u0026nbsp;To foster surveillance resilience and meet diverse information needs, multiple SS operated simultaneously in many jurisdictions, including in England UK [13].\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs the UK COVID-19 activity progressed during the pandemic, it seems likely that the sensitivity of SS changed driven by multiple and complex pressures/factors. \u0026nbsp; Universal vaccination programmes [14, 15] and widespread COVID-19 infections [16] are likely to have resulted in higher population resistance to developing symptomatic COVID-19 infection as well as reduced severity when symptoms were present. \u0026nbsp;One key change in the pandemic occurred with the arrival of the Omicron (B.1.1.529) variant in England in November 2021. Omicron was associated with increased transmission rates [17] which were likely to affect surveillance sensitivity and specificity. \u0026nbsp;Epidemiological evidence demonstrated that although Omicron sublineages were more transmissible than earlier COVID-19 variants [18], infection with Omicron variants were also associated with much milder illness [19]. \u0026nbsp;Omicron sublineages becoming dominant coincided with reduced incubation periods [20]\u0026nbsp; but higher risk of reinfection [18]. \u0026nbsp;Laboratory evidence [21] indicated that Omicron \u0026nbsp;had superior replication competence in the upper respiratory tract but poorer replication capacity in the lungs compared to earlier COVID-19 variants, potentially leading generally to more transmissible but milder illness and thus altered case presentation in infected persons. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing SS effectively and efficiently means understanding the strengths and limitations of each individual system [22, 23]. \u0026nbsp;How the sensitivity of SS may change as pandemics develop is a key aspect of understanding these limitations. \u0026nbsp; In addition to estimates of total cases, SS are relied upon to provide timely information about when case counts are rising or falling. \u0026nbsp;If an SS is dependent on disease severity, then when typical case presentation changes, system sensitivity is likely to change also. \u0026nbsp;Understanding of what proportion of cases an SS has identified could be faulty if sensitivity abruptly changes. \u0026nbsp;In this retrospective observational study of COVID-19 SS in England, UK, we identified three best standard epidemic trackers that yielded daily estimates of COVID-19 case incidence and prevalence and compared these sources with case count estimates with each other and with other, complementary surveillance systems (CompSS). \u0026nbsp;We apply simple and replicable methods to document how sensitive many SS were to estimating case counts in England when the dominant COVID-19 variant changed from Delta to Omicron BA.1 and then to Omicron BA.2.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eIn this study we use nine SS datasets that were previously described at length [13] as well as one additional surveillance dataset newly described here: case counts of persons hospitalised primarily for COVID-19. \u0026nbsp;Below we provide a brief recap of each surveillance dataset used in this study, which are also summarised in Table 1.\u003c/p\u003e\n\u003ch2\u003eBest standard epidemic trackers\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe identified three potential \u0026lsquo;best standard\u0026rsquo; epidemic trackers (BestET) for tracking cases in the English COVID-19 epidemic. \u0026nbsp;The two \u003cem\u003eleast-biased\u003c/em\u003e case count estimators were daily case count estimates published by the Office for National Statistics (ONS) following random and stratified household sampling (Coronavirus Infection Survey for England, CISE). \u0026nbsp;ONSCISE modelling allowed for demographic variations between the sampled and general population [24] to generate estimates of both incidence (ONSincid; new case count estimates) and prevalence (ONSprev: estimates of total concurrently infectious cases). \u0026nbsp;The ONSCISE had high detection rates for asymptomatic cases because participants were swabbed at random, and high stability because data collection methods did not change and the modelling approach changed relatively little over time. \u0026nbsp;However, the ONSCISE was not timely because of a typical \u0026gt; 10 day delay between swab collection and reporting date. ONS incidence estimates were not generated between 20 June and 19 November 2022 inclusive. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. \u0026nbsp;Best and complementary epidemic trackers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShort name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary purpose of dataset(s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003eGovernment testing framework\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eEpidemic control and monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eP12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePillar 1 \u0026amp; 2 case counts\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eRandomly chosen households for sampling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eONSincid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eONSCISE incidence estimates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eEstimate new infections, and prevalence ; epidemic monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eONSprev\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eONSCISE prevalence estimates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHS hospitalisation records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eHospAdm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eNew admissions to hospital of patients who tested positive\u0026nbsp;\u003c/p\u003e\n \u003cp\u003efor COVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHospital services demand\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eC19PRinH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eCount of persons who were in hospital primarily because of COVID-19 infection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSyndromic datasets held by UKHSA, derived from actual health care usage records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEDSS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eEmergency Department attendances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSyndromic surveillance\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eGPIH\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eConsultations with GPs in usual opening hours\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e111calls\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eNHS 111 telephone calls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e111web\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eNHS 111 website assessments\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eThe ZOE\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eApp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eZOE \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSymptom tracker \u0026nbsp;application\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eIdentify symptoms associated with\u0026nbsp;COVID-19\u0026nbsp;infection status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: GP = General practice surgeries; NHS = National Health Service; ONSCISE = Office for National Statistics Coronavirus infection Survey for England; \u0026nbsp;UKHSA = United Kingdom Health Security Agency.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003emost timely\u003c/em\u003e COVID-19 epidemic tracker was a combined count of laboratory-confirmed cases from two testing frameworks deployed during the COVID-19 epidemic in England. \u0026nbsp; During our monitoring period, COVID-19 tests were available to health care workers and persons presenting with medical needs as part of what was designated a Pillar 1 testing framework. \u0026nbsp;Tests to confirm COVID-19 infection were also available to wider members of the public with or without symptoms under a testing framework denoted as Pillar 2 tests. Combined case counts from Pillar 1 and 2 (P12) testing were widely used as most up-to-date estimates of daily total confirmed case counts during the English COVID-19 epidemic. \u0026nbsp;P12 swab results [25, 26] were typically \u0026nbsp;published \u0026lt; 24 hours after sample collection \u0026nbsp;[27], and thus were very timely and had potentially high stability. \u0026nbsp;P12 data strictly reflect incidence not prevalence. \u0026nbsp;P12 data were likely to under-detect asymptomatic cases, because people who lacked symptoms had less reason to take a test. \u0026nbsp;We therefore regard P12 data as a third \u0026lsquo;best standard\u0026rsquo; epidemic tracker, and the \u003cem\u003emost timely\u003c/em\u003e epidemic monitoring tracker, especially of symptomatic infection. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eComplementary surveillance systems/datasets\u003c/h2\u003e\n\u003cp\u003eThis study compares the three BestET with seven other potential COVID-19 against CompSS concurrently available in England. \u0026nbsp;We describe whether each dataset predominantly reflected incidence or prevalence or potentially both. \u0026nbsp;The government website coronavirus.data.gov.uk provided counts of persons newly admitted to National Health Service (NHS) hospitals with SARS-CoV-2 positivity (HospAdm). \u0026nbsp; Another epidemic tracker based on health service usage is counts of hospital inpatients in England who had COVID-19 as the primary reason for their hospitalisation (C19PRinH), which information was available from 18 June 2021 onwards. \u0026nbsp;Cases in the HospAdm dataset were only counted once per infectious episode so align with COVID-19 incidence. \u0026nbsp;However, hospitalisations while infected with Covid \u003cem\u003ecould\u003c/em\u003e be multiple for the same infectious episode in a single patient, and as a result the C19PRinH dataset could reflect incidence or prevalence. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used four CompSS which were based on national syndromic SS maintained by the UK Health Security Agency (UKHSA) real-time syndromic surveillance service. These syndromic CompSS describe patients accessing the NHS 111 telephone health advice (111calls) and NHS 111 online health assessments (111web) services; emergency department attendances (EDSS) and general practitioner in-hours (GPIH) consultations [2]. \u0026nbsp;COVID-19-specific syndromic indicators for each of these syndromic CompSS were developed in 2020 [1]. \u0026nbsp;All of these health service-record based indicators and counts of SARS-CoV-2 positive persons in hospital were typically reported within 24 hours so were timely. \u0026nbsp;They were consistently defined during this monitoring period and thus had high stability. However, these CompSS were unlikely to capture asymptomatic or minimally symptomatic cases. These UKHSA datasets could reflect incidence or prevalence.\u003c/p\u003e\n\u003cp\u003eThe final CompSS what we used in our analysis was not based on actual health care demand but rather voluntary symptom-reporting. \u0026nbsp;In a private sector project, the Zoe project (ZOE) produced COVID-19 incidence estimates [28]. \u0026nbsp;ZOE was a nutritional and wellbeing digital application in development before 2020 which was adapted to support daily COVID-19 symptom tracking. \u0026nbsp;ZOE produced estimates of community incidence that were derived from their own models that incorporated counts of their App users who reported new case status, with adjustments for demographic imbalances compared to the general \u0026nbsp;population and the most recent ONSCISE estimates [29]. The precise algorithm(s) for how ZOE generated its incidence estimates and the completeness of the data collected are not published which means that stability of ZOE and its ascertainment biases are unknown. \u0026nbsp;There were many ZOE model changes in their incidence estimates during the UK COVID-19 epidemic [29]. \u0026nbsp;Previous research [13] found that in 2020-2021 the ZOE data correlated highly with the least biased and most timely epidemic trackers used in this article.\u003c/p\u003e\n\u003cp\u003eWhere available, we used uncorrected data (as first published) from each CompSS, because minimally verified data is what real-world decision makers usually have access to while an epidemic is active. \u0026nbsp;We calculated correlation statistics between the BestET and the CompSS for both the full monitoring period and sub-periods. \u0026nbsp;All SS and datasets apply to England only. \u0026nbsp; More information about the data used in this study, cleaning, additional processing and sources is in [13].\u003c/p\u003e\n\u003ch2\u003eMonitoring period\u003c/h2\u003e\n\u003cp\u003eWe compared the datasets from 1 September 2021, until 19 June 2022. \u0026nbsp;This period encompasses \u0026gt; 90 days before Omicron became dominant in England, the rapid transition from Delta to Omicron dominance in sequenced swab samples in late 2021, and transitions between several different Omicron waves before reporting of ONSCISE incidence estimates was paused on 20 June 2022. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOVID-19 variants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe consider SS sensitivity to track infections with regard to three main COVID-19 variants and their sublineages: Delta, Omicron BA.1 and Omicron BA.2. \u0026nbsp;In the UK COVID-19 epidemic, the Delta variant was dominant among genetically sequenced test samples from 24 May to 14 December 2021. \u0026nbsp; Omicron variant group BA.1 and sublineages dominated samples from 14 December 2021 through 20 February 2022, after which Omicron BA.2 sublineages were most sequenced samples until 7 June 2022. \u0026nbsp; From 7-19 June 2022 no single SARS-Cov-2 variant dominated sequenced samples in England.\u003c/p\u003e\n\u003ch2\u003eAnalysis: Quantitative comparisons\u003c/h2\u003e\n\u003cp\u003eTo compare BestET and CompSS, we undertook visual and statistical comparisons that were simple and replicable. \u0026nbsp;We separate some of the analysis between incidence and prevalence comparisons. \u0026nbsp;The case counts suggested by BestET least biased and most timely best standard epidemic trackers were plotted in time series with CompSS, to visually discern if there was close correspondence. \u0026nbsp;The two BestET that indicated incidence were compared to each other to see if the correlation between the most timely BestET (P12) and the least biased BestET (ONSincid) changed over time or when dominant variants changed. \u0026nbsp; Spearman \u003cem\u003erho\u003c/em\u003e correlation statistics were calculated with 95% confidence intervals for the full monitoring periods. \u0026nbsp;Spearman rho was appropriate because the time series had strongly nonparametric distributions. \u003c/p\u003e\n\u003cp\u003eWe wanted to test whether correlations between CompSS and BestET were especially low around the period when the dominant variant changed, from Delta to BA.1 or from BA.1 to BA.2 dominance. \u0026nbsp;For this test, we calculated Spearman rho on day 16 in 31 day duration moving time windows, for six CompSS and P12 with ONSincid over the full period. The same was done for 7 CompSS against ONSprev. \u0026nbsp;The Spearman rho value for day 16 in the moving window was plotted against calendar dates and with reference to which variant was dominant. \u0026nbsp;These plots were visually assessed for apparent dips in correlation in transition periods.\u003c/p\u003e\n\u003cp\u003eDaily ascertainment percentages for incidence were also calculated and reported as median values with IQR, to see how many cases each system seemingly detected daily, compared to the best standard SS. \u0026nbsp;The resulting datasets (ascertainment percentages) were mostly non-parametric and lacked homoscedasticity between groups separated by variant dominance. \u0026nbsp; We therefore tested for statistically significant (at p \u0026lt; 0.05) differences in the distribution of the ascertainnment values during each COVID-19 variant\u0026rsquo;s dominance, using Welch\u0026rsquo;s oneway ANOVA tests. \u0026nbsp;Rstudio 2022.02.0 (R version 4.1.1) was used to generate plots and statistics.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOverview\u003c/p\u003e\n\u003cp\u003ePlots of each CompSS (rows) are shown against the BestET (columns) in Supplementary File S1. Two exemplar time series are reproduced as Figs. 1a and 1b in this article. Each individual figure has the complementary series plotted against left axis, and BestET against right axis, with approximate alignment for their peak values. These time series were smoothed (7 day moving average on central date) in these visualisations except the ONSincid and ONSprev. Raw (not smoothed) values were used for statistical comparisons and analysis. The period when each variant was dominant is indicated by multi-coloured lines at the bottom of each chart (green for Delta, cyan for BA.1 and purple for BA.2). Duration of dominance by each variant was : Delta for 104 days, BA.1 for 69 days and BA.2 for 107 days. There is strong visual similarity between many time series: many rises and falls happened about the same time. Comparisons between time series in all the figures are quantified statistically in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows whole period correlation (Spearman rho with 95% confidence intervals) between BestET and CompSS. Most correlations were significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, meaning there was evidence of correlation, mostly positive. HospAdm and ZOE had the highest positive correlation (95% confidence intervals\u0026thinsp;\u0026gt;\u0026thinsp;0.70) with the BestET ONSincid and ONSprev, but were both much less correlated with P12 (95%CI\u0026thinsp;\u0026lt;\u0026thinsp;0.70). EDSS was the CompSS most correlated with the most timely tracker (P12; rho 0.83, 95%CI 0.78\u0026ndash;0.87). P12, EDSS and C19PRinH were each positively correlated with all other SS data. Given many patients tested under the Pillar 1 framework were attenders to ED, it is not surprising that P12 correlated highly with EDSS. GPIH and 111web had the weakest relationships with ONSincid or ONSprev or other trackers, except that the 111web data strongly correlated with 111calls counts (rho 0.79, 95%CI 0.73\u0026ndash;0.83).\u003c/p\u003e\n\u003cp\u003eBest epidemic trackers compared to each other\u003c/p\u003e\n\u003cp\u003eFigure1a and 1b in this article show the P12 time series with ONSincid and ONSprev. Figure\u0026nbsp;1a and 1b are useful to focus on because they suggest that ascertainment by P12 compared to ONSincid declined over time. Ascertainment was higher during Delta or BA.1, but lower during BA.2 time periods. When the ascertainment ratio was calculated for P12 compared to ONS incidence the values were 42.5 (95%CI 37.0-49.1) during Delta, 30.39 (95%CI 23.9\u0026ndash;38.1) in BA.1 and 8.93 (95%CI 7.0-11.3) in the BA.2 time period. This varying ascertainment relationship seems like an important reason why the Spearman rho correlation statistic over the full monitoring period between P12 and the ONSincid time series is positive but not high (rho 0.53, 95%CI 0.45\u0026ndash;0.59). In our previous research for the same trackers in the period September 2020 to December 2021, this correlation was much higher (rho 0.94, 95%CI 0.92\u0026ndash;0.95) [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eAscertainment of Complementary compared to BestET\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e gives subperiod ascertainment percentages of cases detected for CompSS incidence and prevalence trackers, compared to case counts reported by BestET, and separated by which variant was concurrently dominant. Data are reported as median ascertainment ratio (IQR). GPIH is not included because GPIH data were available as rates per 100,000 registered patients, and as such aren\u0026rsquo;t directly suitable for calculating ascertainment ratios.\u003c/p\u003e\n\u003cp\u003eAscertainment of prevalence or incidence suggested by P12 or CompSS compared to the ONS estimates was lower in BA.1 period compared to Delta dominant period. However, using ONSCISE outputs as Best ET, differences in ascertainment between the BA.1 period and the BA.2 period were mostly small. A noticeable exception is that P12 had very much lower ascertainment of the least biased incidence estimates in the BA.2 period (median 8.93%, IQR 7.0%-11.3%) than in BA.1 or Delta dominant periods (medians respectively 30.4% and 42.5% and IQR respectively 23.9%-38.1% and 37.0-49.1%). Ascertainment of total cases for each CompSS compared to P12 was highest during BA.2 and lowest during the BA.1 period.\u003c/p\u003e\n\u003cp\u003eBecause the ascertainment data were calculated daily, it was possible to statistically compare the distribution of daily ascertainment ratios during the dominance of each variant using Welch\u0026rsquo;s one-way ANOVA. This test yields an F-statistic with p-values; all between group differences (for ascertainment ratios across rows/variants in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) were significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for the underlying data summarised in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. This statistical finding documents that no complementary SS was highly stable (consistent) in case detection rate across all three variant periods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. \u0026nbsp;Spearman rho correlations between SS with 95% confidence intervals. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAscertainment (as %) during variant-dominant periods\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eComparison with ONS prevalence\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComplementary SS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDelta\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBA.1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBA.2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eC19PRinH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48 (0.4, 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21 (0.2, 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19 (0.2, 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEDSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e111calls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27 (0.2, 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09 (0.1, 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10 (0.1, 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e111web\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29 (0.3, 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06 (0.1, 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07 (0.1, 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparison with ONS incidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBA.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBA.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.5 (37.0, 49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.4 (23.9, 38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.93 (7.0, 11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospAdm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87 (0.8, 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55 (0.4, 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48 (0.4, 0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eZOE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.6 (67.6, 84.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.1 (43.7, 66.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.6 (54.4, 110.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEDSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42 (0.4, 0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16 (0.1, 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11 (0.1, 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e111calls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15 (2.8, 3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86 (0.7, 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.6, 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e111web\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.35 (2.8, 3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64 (0.5, 0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74 (0.4, 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparison with P12 incidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBA.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBA.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospAdm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13 (1.9, 2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83 (1.2, 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.06 (3.5, 8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eZOE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176.4 (154, 207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.9 (121, 277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e949.4 (451, 1411)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEDSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.9, 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52 (0.5, 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40 (0.8, 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e111calls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.90 (6.1, 9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80 (2.2, 4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.02 (5.4, 22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e111web\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.72 (6.1, 9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.08 (1.6, 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.79 (3.9, 16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: Values are for entire monitoring period, ascertainment percentage median (IQR). Tests for between group differences (in rows) using Welch\u0026rsquo;s one-way ANOVA were all significant at p\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n\u003cp\u003eMoving 31 day period correlation between best and complementary SS\u003c/p\u003e\n\u003cp\u003eThe panels in Figs.\u0026nbsp;2a-3g plot the individual Spearman rho correlation statistics for complementary SS, compared to the two least biased epidemic trackers (ONSincid and ONSprev) in moving time windows. The same time series are plotted together in Supplementary File S1, Figures S2 and S3. The supplementary plots evidence that new hospital admissions for patients with Covid strongly related to ED attendances syndromically assigned as Covid-19, as indicated by high visual correspondence between these two time series on Figure S2. Otherwise, for most time series, they do not very obviously follow similar trajectories as each other on Figures S3 and S4. The plotted correlation values are for day 16 within each moving 31 day duration window. Concurrent dominant COVID-19 variant is also indicated by a colour-coded line that intersects the vertical axis at 0.5.\u003c/p\u003e\n\u003cp\u003eVisually, on all the graphs the Spearman rho varies considerably over the study period with multiple cycles of low correlation followed by periods of higher correlation. This variability appears greater for complementary SS based upon hospital data (e.g EDSS, C19PRinH, HospAdm) than for other SS. Correlations with ONSprev are generally higher than with ONSincid. In many complementary SS, these dips to lower correlation appear to happen about the same time and don\u0026rsquo;t tend to occur at the same time as changes in the dominant variant. For instance, on multiple systems there appears to be a dip in correlations around November-December 2021 and another dip around February 2022 and the start of June 2022. HospAdm and EDSS correlate consistently well with ONSprev after February 2022 and apart from two short periods, ZOE correlates well with ONSprev over the entire study period.\u003c/p\u003e\n\u003cp\u003eWe hypothesised that changes in correlation would concur with changes in the dominant variant. However, visually, during the transition period from Delta to Omicron BA.1 (December 2021), correlations were rising or remained consistently high, there was no apparent dip. Around the time of variant transition from BA.1 to BA.2 (late February 2022), all complementary trackers had poor (generally declining) correlation with the ONSincid and ONSprev. However, these BA.1 to BA.2 dips were mostly small or similar to other fluctuations in correlation over the monitoring period shown in Figs. 2\u0026ndash;3 panels.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn a previous comparison of SS [2020\u0026ndash;2021; 13] we found that P12 closely tracked the least biased indicator (ONSincid), with a whole-period Spearman rho of 0.95 (95%CI 0.92\u0026ndash;0.95). Here we focus upon the period where the dominant COVID-19 variant changed from Delta to Omicron BA.1 and then to Omicron BA.2. We found a much lower whole-period correlation between P12 and ONSincid (0.53 (95%CI 0.45\u0026ndash;0.59)) and the ascertainment ratio dropped from 42.9% at the start of the period when the Delta variant dominated to 8.9% by the time BA.2 became dominant. There are several likely reasons for reduction in sensitivity over time. It seems likely that fewer people chose to get tested in the late UK epidemic period, but each surveillance system also exhibited increased variability in January-June 2022. Bajaj \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] found that counts of Pillar 2 PCR tests taken/100 persons per day noticeably fell between 11 January and 31 March 2022 when rapid antigen (lateral flow device) tests were still available for free but no-cost access to Pillar 2 PCR tests was withdrawn. Other reasons for declines in epidemic tracker sensitivity possibly include an increasingly vaccinated population experiencing mostly re-infections with variants (Omicron) which have been associated with less severe symptoms than Delta and earlier variants. Less severe symptoms would result in lower usage of health services, fewer cases detected through surveillance and reduced sensitivity of Pillar 1 tests [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Attitudes about individual duty to contribute to infection control probably also changed. On 21 February 2022 the UK announced a \u0026ldquo;Living with Covid\u0026rdquo; plan [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] which meant withdrawal of all free COVID-19 antigen or PCR tests by 31 March 2022, making it both harder to obtain tests and also seemingly less important to know one\u0026rsquo;s own case status. When the BestET were compared to CompSS, like in our previous analysis, ZOE was particularly correlated with ONSincid and ONSprev. ZOE also maintained relatively high ascertainment ratios with ONSincid and ONSprev. This is to be expected because ZOE incorporated recent ONS data to generate their estimates.\u003c/p\u003e \u003cp\u003eSome CompSS that we used had ascertainment ratios lower than found in our previous study of 2020\u0026ndash;2021 data. Their correlations with BestET also reduced over time in our current analysis. This is consistent with an infection that is becoming declining importance as a public health risk. By 11 February 2022 around 71% of the English population had been infected at least once with SARS-CoV-2 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. COVID-19 vaccine distribution and uptake were fast and high in England: \u0026gt; 90% of English adults age\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026thinsp;+\u0026thinsp;received at least one COVID-19 vaccination by 17 May 2021 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Vulnerable persons were encouraged to be vaccinated again repeatedly from autumn 2021 onwards. Hybrid immunity (achieved after both vaccination and natural infection) is widely believed to lead to milder clinical presentation for subsequent COVID-19 infections [\u003cspan additionalcitationids=\"CR37 CR38 CR39\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. As a result, many if not most cases detected in our study period could have been from reinfections or vaccinated individuals. Most of CompSS in our analysis were highly sensitive to symptomatic illness and had low sensitivity to minimally symptomatic infections. This is likely to be explain the reduced ascertainment of EDSS, HospAdm and C19PRinH in comparison to ONSprev and ONSincid in contrast to 2020\u0026ndash;2021 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It may also help explain the additional reduction in ascertainment amongst these SS as the study period moves from Delta to BA.1 to BA.2.\u003c/p\u003e \u003cp\u003eSome CompSS had an especially large reduction in ascertainment rates (e.g. 111calls). A plausible explanation is that in addition to changing severity, altered behaviour played a role. In February 2022 the UK government announced a \u0026ldquo;Living with Covid\u0026rdquo; plan [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] that described lifting COVID-19-linked legal restrictions on social contact and testing requirements. The plan meant cessation of free laboratory tests to confirm COVID-19 infection (such as rapid antigen and rtPCR tests) from March 2022; as a result, the Pillar 1 \u0026amp; 2 case detection strategy changed. Our analysis showed that other SS lacked stability over the period 1 September 2021 to 19 June 2022. The transition to \u0026ldquo;Living with Covid\u0026rdquo; seems likely to have reduced the number of people seeking health care advice for and/or reporting COVID-19 symptoms. This may help to explain the reductions for CompSS of less severe illness (111calls and 111web) suggestive of fewer individuals seeking medical advice for an illness perceived as a lower potential health threat. Hence it is unsurprising that sensitivity reduced over this time period when the dominant variant changes from Delta to BA.1 and BA.2.\u003c/p\u003e \u003cp\u003eIn spite of lower ascertainment over time, it is notable that several of these trackers have high correlation with ONSincid and ONSprev over the full period (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Most notable is HospAdm which correlated with ONSincid and ONSprev at mean values 0.79 and 0.91 respectively. EDSS and C19PRinH have what could be considered moderate correlation with both ONS indicators (mean correlations ranging from 0.35 to 0.56, full confidence intervals always above zero). In Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, other CompSS, such as 111calls and 111web and GPIH, which seem likely to be most sensitive to the least severe COVID-19 infections, generally correlated poorly with ONS trackers. One reason may be because for many of the SS systems, health conditions are assigned probabilistically, based on clinical presentation rather than being laboratory-confirmed [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Increasing amounts of minimally symptomatic COVID-19 may complicate this process. Furthermore, for most of 2020\u0026ndash;2021, there was low circulation of respiratory viruses other than SARS-CoV-2 which made the COVID-19 syndrome assignment especially reliable. However, during our study period there were large resurgences in cases of non-COVID-19 respiratory virus infections in many high income countries, including England [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Hence, some patients infected with other respiratory viruses may have been syndromically assigned as COVID-19 while some patients with multiple infections (eg., influenza and COVID-19) may have been assigned only one of these conditions. These imprecise assignments may have contributed to lower correlations especially for presentations by patients with mild symptoms.\u003c/p\u003e \u003cp\u003eIt was notable that lower correlation with ONSincid and ONSprev did not associate with transition between variants. Across systems there were some periods when correlation coefficients appeared particularly poor (e.g. November 2021); identifying the reasons for that requires separate research.\u003c/p\u003e \u003cp\u003eStrengths and Limitations\u003c/p\u003e \u003cp\u003eOur analysis is novel by robustly quantifying that SS sensitivity for COVID-19 changed and varied in England in 2021\u0026ndash;2022. This happened among multiple epidemic trackers. The purpose of surveillance data is to support decision-making that can facilitate rapid introduction of interventions and thus potentially less mortality and morbidity. Optimal use of surveillance data means understanding strengths and weaknesses of individual systems and indicators, and which trackers are best suited to support specific decisions made in real time. The value of complementary surveillance systems is both confirmatory and to reduce risk of over-reliance on individual trackers which may have unrecognised instability or limitations. Complementary systems have the potential to monitor and track disease burden across the breadth of the health care service, ensuring that all severities of presentation are captured. Identifying declines in sensitivity and instability, including temporal coincidence with some specific policy and public health changes, can inform better prospective decision-making during a future pandemic. We hypothesised why falls in surveillance sensitivity may have happened, and also propose that such changes in surveillance sensitivity may be expected as a normal development during an epidemic. We note that choices about which surveillance systems to implement are likely to at least partly depend on specific epidemic management strategies and priorities with corresponding cost-benefit evidence which is outside our study\u0026rsquo;s remit.\u003c/p\u003e \u003cp\u003eOur analysis benefited from large and comprehensive datasets that described access to the NHS, which is the main health care provider for the English population. We also accessed estimates made by the ZOE project for COVID-19 incidence. Some CompSS such as HospAdm and C19PRinH were priority epidemic trackers because they informed resource allocation and as such were subject to priority verification. These indicators were consistently defined during this monitoring period and thus had high stability. However, because these definitions were developed before or when Delta was dominant, the surveillance systems may have had higher sensitivity to pre-Omicron variants. We did not assess if correlations improved by adjusting correlation using delays (lags) between time series, such as if increased visits to the NHS111 website correlated with rises in hospital admissions 7\u0026ndash;10 days later. Our previous analysis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] found that allowing for 0\u0026ndash;14 day offsets did not much improve correlation between the same epidemic tracker time series that were used in this article. Our comparison is limited to only specific datasets. We have not considered many possible other surveillance datasets that could merit consideration as epidemic trackers, such as infected-person counts derived from wastewater sampling for SARS-CoV-2 virus. Jones \u003cem\u003eet al.\u003c/em\u003e (2025) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] undertook a thorough catchment-by-catchment comparison between ONSCISE infection counts and geographically coincident prevalence derived from waste water samples in England in 2020\u0026ndash;2021. Jones \u003cem\u003eet al.\u003c/em\u003e found that correlations were highly variable, but also concluded that SARS-CoV-2 concentrations in wastewater generally correlated well with ONSCISE prevalence.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFrom 1 September 2021 to 19 June 2022, CompSS greatly reduced in the proportion of COVID-19 cases they were detecting. This decrease was greatest in SS and indicators particularly focussed on less severe cases (e.g. presenting to the NHS 111 telephone helpline) which may reflect lower engagement with healthcare for COVID-19-like symptoms. However, despite this fall many indicators of more acute (and severe) COVID-19 presentations (e.g. HospAdm) continued to correlate well with the least biased epidemic trackers indicating that they generally reflected true trends in COVID-19 activity. For all CompSS, correlations tended to cycle over time indicating periods when they were all useful measures of COVID-19 trends. Factors other than which variant was dominant seem likely to have affected how well each tracker reflected true rises and falls in COVID-19 infections.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e111calls\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecounts of calls to the NHS111 telephone advice service\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e111web\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecounts of website visits to the NHS111 web advice service\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBestET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBest epidemic trackers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC19PRinH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCovid-19 was primary reason for being in hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCompSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComplementary surveillance systems\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOVID-19\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronavirus disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGPIH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral practice in hours services, surveillance system\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEDSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEmergency department surveillance system\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHospAdm\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHospital admissions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational health service\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eONS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOffice for National Statistics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eONSCISE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOffice for National Statistics Coronavirus Infection Survey for England\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eONSincid\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eONSCISE estimate of incidence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eONSprev\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eONSCISE estimate of prevalence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eP12\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCount of COVID-19 cases confirmed under Pillar 1 and 2 test frameworks\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSept\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSurveillance systems\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUKHSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited Kingdom health security agency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eZOE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eZoe application data / Zoe application data custodians\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research adhered to the Declaration of Helsinki 2024. \u0026nbsp;The anonymised health data used in this study were either published (freely and fully in the public domain) or collected and aggregated as part of the public health function of the UK Health Security Agency (UKHSA). \u0026nbsp;UKHSA has access to a range of data sources under Regulation 3 (Health Protection) of The Health Service (Control of Patient Information) Regulations 2002. \u0026nbsp;Under the terms of that access, internal review by the\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ethe UKHSA Research Support and Governance Office agreed that this study fell outside the remit for ethical review. This decision was in accordance with the revised guidance in the Governance Arrangements for Research Ethics Committees that was released in September 2011.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the original datasets were collected by ourselves and therefore they should not be redistributed by us. \u0026nbsp;Zoe application, ONSCISE, COVID-19 hospital admissions and Pillar 1 \u0026amp; 2 data all have been accessible in the public domain. \u0026nbsp; Applications for requests to access relevant anonymised data included in this study held by UKSA should be submitted to the UKHSA Office for Data Release at:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttps://www.gov.uk/government/publications/accessing-ukhsa-protected-data/accessing-ukhsa-protected-data ). \u0026nbsp;R scripts used for analysis and to generate plots are available at https://github.com/JuliiBrainard/SpotOmicron/tree/main.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded and authors were financially renumerated by the National Institute for Health and Care Research (NIHR) Health Protection Research Unit (HPRU) in Emergency Preparedness and Response at King\u0026rsquo;s College London in partnership with the UK Health Security Agency (UKHSA) in collaboration with the University of East Anglia. AJE is also affiliated with the NIHR HPRU in Gastrointestinal Infections at the University of Liverpool. The views expressed are those of the author(s) and not necessarily those of the NHS, NIHR, UEA, any HPRU, Department of Health or UKHSA. The funders had no role in writing the manuscript. \u0026nbsp;Authors were not precluded from accessing data that supported this study and authors accept responsibility for this submission.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003ePRH and IRL conceived of the study. \u0026nbsp; PHR and JB extracted data; PRH, IRL and JB designed the data analysis strategy. \u0026nbsp;JB and IRL wrote the draft protocol. \u0026nbsp;IRL and JB designed the visualisations. \u0026nbsp;JB assembled the datasets, analysed the data, wrote the first draft of the manuscript and assembled revisions. RAM and AJE facilitated access to and interpretation of the syndromic surveillance data. \u0026nbsp;All authors commented on the draft manuscript, have read and approved of the final manuscript. \u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe declare no competing interests. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of our funders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank syndromic data providers: NHS 111 and NHS England supporting NHS 111 surveillance; emergency department clinicians and NHS Trusts and NHS England supporting the Emergency Department Syndromic Surveillance System; and participating TPP SystmOne practices supporting GP in-hours surveillance. The authors thank members of the UKHSA Real-time Syndromic Surveillance Team (Sally Harcourt, Sue Smith and other staff) for data verification and technical support in accessing the syndromic data for this study. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eElliot AJ, Harcourt SE, Hughes HE, Loveridge P, Morbey RA, Smith S, Soriano A, Bains A, Smith GE, Edeghere O: \u003cstrong\u003eThe COVID-19 pandemic: a new challenge for syndromic surveillance\u003c/strong\u003e. \u003cem\u003eEpidemiology \u0026amp; Infection\u0026nbsp;\u003c/em\u003e2020, \u003cstrong\u003e148\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eFerraro CF, Findlater L, Morbey R, Hughes HE, Harcourt S, Hughes TC, Elliot AJ, Oliver I, Smith GE: \u003cstrong\u003eDescribing the indirect impact of COVID-19 on healthcare utilisation using syndromic surveillance systems\u003c/strong\u003e. \u003cem\u003eBMC Public Health\u0026nbsp;\u003c/em\u003e2021, 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T, Jarkovsk\u0026yacute; J, M\u0026aacute;jek O, Pavl\u0026iacute;k T, Přibylov\u0026aacute; L, Weinerov\u0026aacute; J, Zaj\u0026iacute;ček M, Trnka J: \u003cstrong\u003ePost-vaccination, post-infection and hybrid immunity against severe cases of COVID-19 and long COVID after infection with SARS-CoV-2 Omicron subvariants, Czechia, December 2021 to August 2023\u003c/strong\u003e. \u003cem\u003eEurosurveillance\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e29\u003c/strong\u003e(35):2300690.\u003c/li\u003e\n \u003cli\u003eSmith GE, Elliot AJ, Lake I, Edeghere O, Morbey R, Catchpole M, Heymann DL, Hawker J, Ibbotson S, McCloskey B: \u003cstrong\u003eSyndromic surveillance: two decades experience of sustainable systems\u0026ndash;its people not just data!\u003c/strong\u003e \u003cem\u003eEpidemiology \u0026amp; Infection\u0026nbsp;\u003c/em\u003e2019, \u003cstrong\u003e147\u003c/strong\u003e:e101.\u003c/li\u003e\n \u003cli\u003eBardsley M, Morbey RA, Hughes HE, Beck CR, Watson CH, Zhao H, Ellis J, Smith GE, Elliot AJ: \u003cstrong\u003eEpidemiology of respiratory syncytial virus in children younger than 5 years in England during the COVID-19 pandemic, measured by laboratory, clinical, and syndromic surveillance: a retrospective observational study\u003c/strong\u003e. \u003cem\u003eThe Lancet Infectious Diseases\u0026nbsp;\u003c/em\u003e2023, \u003cstrong\u003e23\u003c/strong\u003e(1):56-66.\u003c/li\u003e\n \u003cli\u003eLee SS, Viboud C, Petersen E: \u003cstrong\u003eUnderstanding the rebound of influenza in the post COVID-19 pandemic period holds important clues for epidemiology and control\u003c/strong\u003e. \u003cem\u003eInternational Journal of Infectious Diseases\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e122\u003c/strong\u003e:1002-1004.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAnalysis reveals new insights into global surge of Strep A infections\u0026nbsp;\u003c/strong\u003e[https://www.imperial.ac.uk/news/253233/analysis-reveals-insights-into-global-surge/]\u003c/li\u003e\n \u003cli\u003eJones NR, Elson R, Wade MJ, McIntyre-Nolan S, Woods A, Lewis J, Hatziioanou D, Vivancos R, Hunter PR, Lake IR: \u003cstrong\u003eLocalised wastewater SARS-CoV-2 levels linked to COVID-19 cases: A long-term multisite study in England\u003c/strong\u003e. \u003cem\u003eScience of the Total Environment\u0026nbsp;\u003c/em\u003e2025, \u003cstrong\u003e962\u003c/strong\u003e:178455.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"surveillance, epidemic, COVID-19, Omicron, health care seeking","lastPublishedDoi":"10.21203/rs.3.rs-5889771/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5889771/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTime 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. \u0026nbsp;Daily ascertainment percentages of incident cases that each SS detected during each variant’s dominance were calculated. \u0026nbsp;We tested for statistically significant (at p \u0026lt; 0.05) differences in the distribution of the ascertainment values during each COVID-19 variant’s dominance, using Welch’s oneway ANOVA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpearman rho correlation was significantly positive between most complementary and the best trackers over the whole period. \u0026nbsp;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 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.\u003c/p\u003e","manuscriptTitle":"Did COVID-19 surveillance system sensitivity change after Omicron? A retrospective observational study in England","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 11:15:31","doi":"10.21203/rs.3.rs-5889771/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-12T17:41:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-02T04:55:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T08:37:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106603522141489300255224369595920117579","date":"2025-04-25T19:25:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94630622820517810375004636858090886679","date":"2025-04-23T20:44:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324855887912777149123883858216336646455","date":"2025-04-23T17:27:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-23T15:45:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-22T01:22:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-04-21T10:58:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0efe3a9d-c490-43e6-9cee-7559ebac4cd8","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T15:58:29+00:00","versionOfRecord":{"articleIdentity":"rs-5889771","link":"https://doi.org/10.1186/s12879-025-11120-0","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-05-29 15:56:57","publishedOnDateReadable":"May 29th, 2025"},"versionCreatedAt":"2025-04-28 11:15:31","video":"","vorDoi":"10.1186/s12879-025-11120-0","vorDoiUrl":"https://doi.org/10.1186/s12879-025-11120-0","workflowStages":[]},"version":"v1","identity":"rs-5889771","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5889771","identity":"rs-5889771","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}