COVID-19 Transmission During the Winter 2023-24 Surge: A Comparative Analysis of Surveillance Estimates in the U.S., Canada, and the U.K.

COVID-19 Transmission During the Winter 2023-24 Surge: A Comparative Analysis of Surveillance Estimates in the U.S., Canada, and the U.K. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article COVID-19 Transmission During the Winter 2023-24 Surge: A Comparative Analysis of Surveillance Estimates in the U.S., Canada, and the U.K. Michael Hoerger, James I. Gerhart, Tristen Peyser, Nicole Pyke, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5786667/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Better estimates of COVID-19 transmission are needed since testing has declined. The present investigation examined the correspondence among estimates of COVID-19 transmission during the winter 2023-24 surge using wastewater-derived estimates for the U.S. and Canada and testing-derived estimate in the U.K. to evaluate validity and provide vital public health data on transmission levels. Methods: The study used data from wastewater-derived estimates of COVID-19 transmission in the U.S. (Pandemic Mitigation Collaborative dashboard) and Canada (COVID-19 Resources Canada dashboard) and testing-based surveillance in the U.K. (Health Security Agency). Data sets were linked by date and relative to the peak transmission date within each data set. Analyses focused on the UKHSA study period of November 2023 to March 2024. Analyses 1) described transmission on the peak day, 2) examined relative agreement in the patterns of transmission via correlations, 3) examined absolute agreement on the proportion of the population actively infectious across the two months of peak transmission, and 4) described estimates of the proportion of populations infected during the peak two months. Results: On the peak day of infections, an estimated 1.95 million people were infected in the U.S., 148 thousand in Canada, and 431 thousand in the U.K., meaning an estimated 2.5%-4.5% of these populations were actively infectious. Estimates showed high relative agreement in the patterns of transmission throughout the wave, especially between the U.S. and U.K. (r=.974, p100 million people were infected in the U.S., Canada, and the U.K. during the two peak months, or 20.9%-26.0% of each population. Discussion: Findings support the ongoing public health significance of COVID-19 by documenting high levels of transmission during the winter 2023-24 surge. Transmission estimates had high agreement across methodologies and nations. More resources are needed to prevent transmission and diagnose and treat long-term health sequelae. Epidemiology Infectious Diseases COVID-19 pandemics wastewater surveillance epidemiology Figures Figure 1 Introduction COVID-19 continues to transmit at high rates during periodic waves, and understanding the extent of transmission can help define the public health burden of COVID-19 and inform the allocation of public health and research funding to support prevention, diagnostics, and treatments. From 2020-21, publicly available testing programs documented transmission levels and yielded key metrics such as positivity ratios and reported daily cases, which informed the public on when to escalate precautions and guided public health policy. 1 Although reported case counts from opt-in testing programs in the U.S. underestimated true cases 4- to 8-fold throughout much of 2020-21, according to the Institute for Health Metrics and Evaluation (IHME) true-case estimation model, 2 they nonetheless provided a dependable metric to inform the public. Testing dropped off as public testing programs became sparse, making reported daily case counts less meaningful. 1,3 From January 2022 to April 2023, true cases were estimated to be 17-33x higher than reported cases. 2 Accordingly, since 2022, there has been a public health need for better metrics to track levels of COVID-19 transmission given the considerable public health and economic burden of this ongoing illness particularly among vulnerable populations such as people with cancer, primary immunodeficiency, and chronic illness. In the U.S. and Canada, and increasingly elsewhere, wastewater-based surveillance programs gained popularity as a passive method of monitoring viral levels in sewerage wastewater at the population level without the need for individual testing. 4-12 Wastewater surveillance data indicate when transmission is higher or lower and have been shown to correspond to case rates and other metrics. 6-11 Academic research teams in the U.S. 4 and Canada 5 have run dashboards estimating true daily infections, the proportion of the population actively infectious, and other metrics using publicly available wastewater surveillance data. Such estimates require layers of multi-disciplinary science from physical wastewater measurements to estimates of viral concentrations to population-level estimates, requiring rigor and inference at each layer. 8,12,13 As such, wastewater-derived estimates of transmission are potentially less readily understood and accepted by the public than simple counts and percentages from testing programs. As opt-in testing programs underestimate transmission and wastewater monitoring systems lack public familiarity, the U.K. Health Security Agency (UKHSA) conducted a winter 2023-24 COVID-19 testing-based surveillance study to estimate the population’s transmission levels. 14 The main challenges of a testing-based surveillance program are sampling a representative population and adjusting observed estimates based on potential confounders, such as noncompliance with testing protocols. 14 Surveillance testing programs yield statistics like the proportion of the population infectious that are familiar to the public based on earlier opt-in testing programs and reported positivity ratios. The present investigation examined the correspondence among estimates of COVID-19 transmission during the winter 2023-24 surge using wastewater-derived estimates for the U.S. 4 and Canada 5 and testing-derived estimate in the U.K. 14 Similarities in estimates would support the validity of estimation models, which is key for drawing reasonable conclusions about the infection burden of COVID-19. The aims of the research were to 1) document the estimated timing and peaks of the waves in the U.S., Canada, and the U.K., 2) examine the relative agreement across these nations on the timing of high versus low levels of transmission, 3) examine the absolute agreement on the proportion of the population estimated to be actively infectious, and 4) estimate the proportion of each population infected during the surge. Method Overview The analyses examined three data sets tracking winter 2023-24 COVID-19 transmission. The UHKSA 14 tracked COVID-19 transmission in England and Scotland from November 14, 2023, through March 6, 2024. Analyses linked the U.S. 4 and Canada 5 data sets to the UKHSA data sets in two ways, either based on the calendar date or based on the relative number of days before or after the estimated peak day of transmission within each data set (e.g., day “0” refers to the peak, day “-5” five days before the peak, and day “20” as twenty days after the peak). The UKHSA data happened to track transmission from day − 38 to day 75, relative to the peak. The date-based analyses used the full date range of the UKHSA study. The U.S. and Canada COVID-19 dashboards began before and continued after the UKHSA data, so the peak-based analyses used data from November 12, 2023, through April 2, 2024, to track transmission 38 days before and 75 days after the peaks, like in the UKHSA study. This ensured that each data file would have complete data for analysis regardless of whether it was linked by calendar date or relative to the peak. Data Sources U.S. Data. The U.S. data were from the Pandemic Mitigation Collaborative (PMC) dashboard, 4 led by Dr. Michael Hoerger, Director of Population Sciences and Disparities, Tulane Cancer Center, Tulane University. The PMC dashboard converts U.S. national wastewater data into estimates of infections. The full methodology of the dashboard is posted within each weekly report during the study period. 4 Briefly, the dashboard began in August 2023 and has continued through the time of this analysis. At launch, the PMC dashboard used national wastewater surveillance data from the U.S. Centers for Disease Control and Prevention (CDC) contractor, Biobot Analytics, as reported publicly on Biobot’s dashboard. 15 The U.S. CDC switched to a different contractor in late 2023, 16 shortly before the current study window, but Biobot continued to report data publicly on their dashboard throughout the study window and has in a more limited capacity to this day. 15 The PMC dashboard estimated new daily infections by linking Biobot-derived wastewater levels to the IHME 2 multi-faceted model of true (not merely reported) daily infections in the U.S., which reported estimates from February 4, 2020, through April 1, 2023. During the time period from the pandemic declaration onset of March 11, 2020, through April 1, 2023, Biobot wastewater levels and IHME estimates of new daily infections correlated an estimated r = .927. 4 For each data point in that date range, a multiplier value was computed as the number of IHME new daily infections divided by Biobot wastewater levels. A 10% trimmed mean (trimming the 5% largest and smallest values) was used to provide a conversion factor for estimating new daily infections from wastewater levels. Sensitivity analyses using the mean, median, or regression coefficients yielded similar estimates that hovered above or below the multiplier. Specifically, 1 copy/mL of SARS-Cov-2 in Biobot wastewater data was estimated as 1,455 new daily infections. 4 An underlying assumption was that Biobot would standardize estimates of viral concentration reasonably over time and region to provide a relatively stable indicator of transmission. The research team extracted two versions of data from the PMC model – real-time and retroactively-corrected estimates of the proportion of the population actively infectious. In real-time, note that Biobot typically had a reporting lag of approximately a week. The PMC dashboard used a forecasting model to post estimates of the current day’s level of infections, based on the lagged data coming in. 4 Retroactively, these values were corrected to account for minor errors in the short-term PMC forecast as well as minor errors on Biobot’s side in the reported estimation of wastewater levels. The dashboard computed the percentage of the U.S. population actively infectious by taking the estimate of new daily infections, multiplying it by the average infectious period of approximately 7 days, 17 and dividing it by the U.S. population estimate of 334,565,848 18 on the final day of the IHME reports on April 1, 2023. 2 Canada Data. The Canada data were from the COVID-19 Resources Canada (CRC) dashboard, 5 led by Dr. Tara Moriarty, Associate Professor of Dentistry, Laboratory Medicine, and Pathobiology at the University of Toronto. Although our research team could not identify a specific conversion formula on their dashboard website, a summary of their general methodology is posted on their dashboard. 5 Briefly, COVID-19 Resources Canada model uses wastewater data to explain variation in estimates of infection that are based on a combination of seroprevalence data, test positivity, and survey data on testing results. Our research team extracted two versions of data from the CRC model – real-time and retroactively-corrected estimates of the proportion of the population actively infectious. The retroactively-corrected estimates differed from the real-time estimates considerably based on retroactive corrections to the underlying wastewater data as well as changes to the COVID-19 Resources Canada case estimation model, which was revised in 2024 and applied retroactively to all data. Our research team could not identify details on the specific changes to the case estimation model. The retroactively-corrected data were downloaded from the dashboard website on December 7, 2024. 5 The real-time reports were not found to be archived on the dashboard website but were available from Dr. Moriarty’s Tweets 19 and manually entered in December 2024. U.K. Data. The UKHSA study 14 assessed SARS-CoV-2 infections among 150,000 people in England and Scotland using surveys on surveillance testing results from lateral flow devices (LFDs, also called rapid tests), led by Dr. Alex Glaser, Infectious Disease Modelling Team, All Hazards Intelligence, Data Analytics and Surveillance, UK Health Security Agency. Data were collected from November 14, 2023, through March 6, 2024. The sample agreed to participate in the context of a research study that had ethical approval. They included adults ages 16 and older and children ages 3–15 who had a consenting parent. Each participant received 14 tests. Each month, they were asked to test and complete a survey. Participants testing positive were asked to complete an additional survey and continue to test every other day until getting two negative tests. Estimates of incidence and prevalence were weighted based on demographics using a Bayesian multilevel regression and post-stratification approach to improve precision among undersampled subgroups. Models accounted for testing sensitivity and specificity for the 1-month testing interval, duration of positivity, and the level of promptness of participants in completing the test and survey after the requested date. The complete details of the methodology and reports are on the UKHSA website. 14 Data were downloaded directly from the website. Data Quality Double data entry was used to ensure data quality. Specifically, two research assistants each entered all of the data independently, and the lead author checked their work for agreement. The UKHSA data and retroactively-corrected versions of the U.S. and Canada data were available via data files. The real-time estimates for the U.S. data were extracted from the weekly reports on the dashboard website. 4 The real-time estimates for the Canada data were manually extracted from Dr. Moriarty’s Tweets 19 since they were not observed to be archived on the dashboard website. The corresponding author checked the accuracy of all data, found no inaccuracies other than two rounding errors easily resolved, and approved the data file for analysis. Data Analysis The data were analyzed and portrayed graphically using Microsoft Excel version 2411 and IBM SPSS Statistics 27. The data files for the U.K. and Canada retroactively-corrected data included daily estimates. The other data files include weekly or semi-weekly estimates, so a spline function was used to estimate values for intermediary dates. Each analysis spanned 114 days, corresponding to the UKHSA study, with the U.S. and Canada data linked to the UKHSA data either based on the calendar date or the day relative to the estimated peak of transmission within each data set. The analyses included the estimate of the population actively infectious with Covid within the following populations: U.S. (real-time), U.S. (retroactively corrected), Canada (real-time), Canada (retroactively corrected), U.K., England, and Scotland (all posted as final shortly after the assessment interval). First, analyses examined descriptive statistics and the relative agreement among estimates. Descriptive statistics included the timing and level of the wave peaks. The proportion of the population actively infectious (prevalence) was also converted to new daily cases (incidence) to report the maximum new daily infections at the peak. Individuals are infectious for approximately 7 days on average, 17 so the prevalence was divided by 7 to find the daily incidence, which was multiplied by the population to indicate new daily infections. For such analyses, the research team used the population estimates for January 1, 2024, which were 335,893,238 (U.S.), 20 40,769,890 (Canada), 21 and 68,596,400 (U.K., 22 with 57,979,200 from England, 5,511,650 from Scotland, and the remainder from Northern Ireland and Wales). The U.S. and Canada provide daily population estimates, whereas the U.K. estimates were extended linearly from the midpoint of 2022 to the midpoint of 2023 by an additional 6 months. Second, analyses examined relative agreement in the proportion of the population infections across regions using correlations, reported as statistically significant with a two-tailed alpha level of .05. Analyses included data sets linked by calendar date as well as when linked by the relative number of days from the region’s peak. Third, analyses examined the absolute agreement in estimates across regions. Each region (U.S., Canada, England, Scotland) was compared with the overall estimate of the U.K. for the proportion of the population actively infectious on each day for the dataset that linked regions by their peak date of infections. Absolute agreement was defined by the absolute value of the difference between the region’s estimate of the proportion infectious and the corresponding U.K. estimate. For example, a 0% difference would represent perfect absolute agreement, whereas a 1% difference would mean that the region’s estimate was either 1% higher or lower in absolute terms (e.g., 5% versus 4%) than the estimate of the proportion of the population infectious in the U.K. on a given day of the wave. Experts who analyze COVID-19 data on a daily to weekly basis (JE, MH, SAW) categorized each region’s level of absolute agreement on each day of the wave using 7 bins labeled and defined by their expert consensus: 0.00–0.50% difference from the U.K. in the proportion actively infectious (almost perfect agreement), 0.51–1.00% difference (excellent agreement), 1.01–1.50% (high agreement), 1.51–2.00% (moderate agreement), 2.01–2.50% (fair agreement), 2.51–3.00% (low agreement), and > 3.00% (poor agreement). The descriptive labels for each bin were inspired by similar approaches elsewhere for qualitatively describing levels of a consistency. 23 , 24 For each region, each day was classified into one of these bins, and analyses summarize the proportion of days within the peak two months (61 days, which included the peak and 30 days before and after) in each bin to provide a summary indicator of absolute agreement across the wave. Finally, analyses described the levels of infections during the peak one and two months of each region’s wave. Specifically, analyses indicated the mean and standard deviation (SD) of infections during the peak one and two months, total infections during that time period, and the proportion of the population infected during the corresponding time period (one- and two-month incidence). Results Descriptive Statistics The winter COVID-19 waves for the U.S., Canada, and the U.K. are depicted in Fig. 1 with summary statistics in Table 1 . The U.S. and Canada data include real-time and retroactively corrected estimates. The U.K. estimates were reported approximately every two weeks, shortly after the end date of reporting, and included subgroup estimates for England and Scotland. For the U.S., real-time estimates were that the peak occurred on January 5, 2024, at 4.5% of the population actively infectious, later retroactively corrected to a peak of December 29, 2023, at 4.1% infectious. For Canada, real-time estimates were that the peak occurred on January 18, 2024, at 8.1% of the population actively infectious, later retroactively corrected to a lower and earlier peak of December 20, 2023, at 2.5% infectious. The UKHSA study found that the peak was December 22, 2023, for the U.K. at 4.4% infectious, including in England at 4.5% and Scotland at 3.8%. Final estimates of the maximum daily infections were 1.95 million in the U.S., 148 thousand in Canada, and 431 thousand in the U.K. Table 1 Peak Transmission in the U.S., Canada, and the U.K. During the Winter 2023-24 COVID-19 Surge Data Source Population Size Peak Date Maximum Proportion Infectious Maximum Daily Infections U.S. 335,893,238 Real time January 5, 2024 4.5% 2,142,758 Retroactive December 29, 2023 4.1% 1,947,441 Canada 40,769,890 Real time January 18, 2024 8.1% 472,474 Retroactive December 20, 2024 2.5% 148,159 U.K. a 68,596,400 December 22, 2024 4.4% 431,177 England a 57,979,200 December 22, 2024 4.5% 372,723 Scotland a 5,511,650 December 22, 2024 3.8% 29,920 Note. Population size estimates were for January 1, 2024, near the wave peaks, from official government sources that included the U.S. Census, Statistics Canada, and the U.K. Office for National Statistics. The total population of the U.K. includes England, Scotland, Wales, and Northern Ireland, but the UKHSA study only samples in England and Scotland. The peak in the retroactive data from Canada differed arbitrarily (<.05%) from December 12 to December 28, whereas Figure 1 shows the other data sources had defined peaks. a The UKHSA study only reported final (retroactive) results, approximately every two weeks. It did not report real-time interim estimates that required later correction. Relative Agreement Across Regions The correlations among transmission estimates are shown in Table 2 . The average intercorrelation among estimates was r = .826 ( p < .001, r s from.141 to .999) when linked by calendar date and r = .948 ( p < .001, r s from .686 to .999) when linked by peak. The real-time U.S. estimates correlated r = .929 ( p < .001) with retroactively-corrected estimates when linked by calendar date and r = .942 ( p < .001) when linked by peak. The real-time estimates for Canada correlated only r = .141 ( p = .135) with retroactively-corrected estimates, owing to the 29 day difference in the estimated peak; the correlation was r = .898 ( p < .001) when linking by peak. The U.K. and constituent regions of England and Scotland had near-perfect intercorrelations ( r s = .999, p < .001). Linking by peak, real-time and retroactively-corrected estimates in the U.S. correlated r = .952 ( p < .001) and r = .974 ( p < .001) with the estimates for the U.K. Also linking by peak, real-time and retroactively-corrected estimates in Canada correlated r = .873 ( p < .001) and r = .768 ( p < .001) with the estimates for the U.K. Table 2 Relative Agreement: Correlations Among Transmission Estimates in the U.S., Canada, and the U.K., with Data Linked by the Calendar Date (Below Diagonal) or Relative to the Peak Transmission Date (Above Diagonal) Data Source U.S., real time U.S., retroactive Canada, real time Canada, retroactive U.K. England Scotland U.S., real time .942*** .822*** .796*** .952*** .952*** .954*** U.S., retroactive .929*** a .786*** .686*** .974*** .975*** .970*** Canada, real time .612*** .551*** .898*** .873*** .873*** .879*** Canada, retroactive .376*** .430*** .141 a .769*** .768*** .777*** U.K. .626*** .787*** .266** .760*** .999*** b .999*** b England .626*** .789*** .266** .758*** .999*** b .999*** b Scotland .614*** .776*** .250** .769*** .999*** b .999*** b Note. N = 114 days. Correlations below the diagonal are linked based on the calendar data, like in Table 1 A. Correlations above the diagonal are linked based on the peak transmission date, like in Table 1 B. a Accuracy coefficient, showing the correlation between real-time and retroactively corrected data. b Data sources had the same peak, so correlations were the same above and below the diagonal. *p < .05 **p < .01 ***p < .001 Absolute Agreement Across Regions The levels of absolute agreement in transmission during the winter 2023-24 surge are shown in Table 3 . Of the real-time estimates in the U.S., 93.5% had excellent or almost perfect agreement with the U.K. estimates, and 100.0% of retroactively-corrected U.S. estimates had excellent or better agreement with the U.K. estimates. For Canada, 0.0% of real-time estimates had excellent or almost perfect agreement with the U.K., whereas 68.8% of retroactively-corrected estimates were in excellent or almost perfect agreement with the U.K. England and Scotland corresponded closely with the overall U.K. estimate with 100.0% of daily levels of transmission in excellent or almost perfect agreement. Table 3 Absolute Agreement: Differences in Levels of Transmission in the U.S., Canada, and the U.K. During the Winter 2023-24 COVID-19 Surge Data Source Almost Perfect Agreement, 0.00-0.50% Difference Excellent Agreement, 0.51-1.00% Difference High Agreement, 1.01–1.50% Difference Moderate Agreement, 1.51-2.00% Difference Fair Agreement, 2.01–2.50% Difference Low Agreement, 2.51-3.00% Difference Poor Agreement, >3.00% Difference U.S. Real time 37 (60.7%) 20 (32.8%) 4 (6.6%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Retroactive 46 (75.4%) 15 (24.6%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Canada Real time 0 (0.0%) 0 (0.0%) 0 (0.0%) 2 (3.3%) 2 (3.3%) 4 (6.6%) 53 (86.9%) Retroactive 26 (42.6%) 16 (26.2%) 8 (13.1%) 11 (18.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) U.K. (reference) -- -- -- -- -- -- -- England 61 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Scotland 11 (18.0%) 50 (82.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Note . Levels of agreement are in reference to the overall estimate for the U.K. Infection Burden The international infection burden of the winter 2023-24 COVID-19 surge is shown in Table 4 . The models estimate that a total of 67,576,328 people were infected in the peak month and 112,721,033 in the peak two months of the winter 2023-24 COVID-19 surge across the U.S., Canada, and the U.K. (1-month: 51,952,438 + 4,511,273 + 11,112,617; 2-month: 87,393,734 + 8,501,582 + 16,825,717). The total proportion of the population infected during the peak month ranged from 11.1%-16.4% across the U.S., Canada, England, and Scotland, and 20.9%-26.0% during the peak two months in these regions. Table 4 Transmission Levels Within the Month and Two Months of Peak Transmission in the U.S., Canada, and the U.K. During the Winter 2023-24 COVID-19 Surge Peak Month Peak Two Months Data Source Daily Infections M (SD) Total Infections Proportion Infected Daily Infections M (SD) Total Infections Proportion Infected U.S. Real time 1,756,868 (291,951) 54,462,902 16.2% 1,510,223 (330,508) 92,123,624 27.4% Retroactive 1,675,885 (206,923) 51,952,438 15.5% 1,432,684 (293,106) 87,393,734 26.0% Canada Real time 448,078 (22,118) 13,890,407 34.1% 381,961 (79,727) 23,299,604 57.1% Retroactive 145,525 (2,286) 4,511,273 11.1% 139,370 (7,883) 8,501,582 20.9% U.K. 358,472 (60,608) 11,112,617 16.2% 275,831 (97,254) 16,825,717 24.5% England 307,530 (52,330) 9,533,437 16.4% 236,805 (83,328) 14,445,104 24.9% Scotland 24,815 (4,259) 769,269 14.0% 19,065 (6,764) 1,162,958 21.1% Note. Population size estimates were for January 1, 2024, near the wave peaks, from official government sources that included the U.S. Census, Statistics Canada, and the U.K. Office for National Statistics. The total population of the U.K. includes England, Scotland, Wales, and Northern Ireland, but the UKHSA study only sampled in England and Scotland. The peak in the retroactive data from Canada differed arbitrarily (< .05%) from December 12 to December 28, whereas Fig. 1 shows the other data sources had defined peaks. The peak month is defined as 31 days, including the peak, 15 days before, and 15 days after. The peak two months are defined as 61 days, including the peak, 30 days before, and 30 days after. Discussion This research documents the continued international infection burden of COVID-19 through the winter 2023-24 wave, using multiple surveillance methodologies and data spanning the U.S., Canada, and the U.K. The U.S. and U.K. estimates were in close agreement that at the wave’s peak approximately 4% of the people in each nation were actively infectious with COVID-19, ranging from 3.8–4.5% across regions and estimates (Table 1 ). Estimated levels in Canada were higher than this range in real-time estimates and lower in retroactively-corrected estimates. During the peak two months of infections, estimates were that 87.4 million people in the U.S., 23.3 million people in Canada, and 16.8 million people in the U.K. became infected. These estimates were similar on a per-capita basis, with a 2-month incidence of infection of 20.9–26.0% across populations. The results warrant a nuanced interpretation that accounts for variation across methodologies and nations. Findings have implications for infectious disease surveillance, case estimation modeling across different nations, and the public health significance of COVID-19. U.S. wastewater-derived estimates of infections corresponded closely with those in the testing-based surveillance program in the U.K. The real-time and retroactive estimates of transmission in the U.S. correlated near perfectly with each other, and both correlated near perfectly with the U.K. estimates when data were linked by peak date (Table 2 ). The real-time U.S. estimates of the peak proportion of the population infectious (4.5%) nearly matched those of the U.K. (4.4%), and the retroactively-corrected estimates for the U.S. (4.1%) fell directly between the estimates for England (4.5%) and Scotland (3.8%). During the wave, the U.S. and U.K. estimates showed excellent absolute agreement on the levels of transmission (Table 3 ). According to the U.S. model, 26.0% of the population got infected during the two months of the surge with highest transmission, which closely matched the 24.5% reported for the U.K. In summary, the U.S. model showed excellent evidence of external validity in international comparisons with the U.K. during the winter 2023-24 surge. The retroactively-corrected estimates in Canada showed moderate evidence of agreement in international comparisons. In Canada, the peak was estimated in real time as occurring on January 18, 2024, with 8.1% actively infectious, while retroactive estimates were that the peak occurred nearly a month earlier and at about 1/3 the level at 2.5% actively infectious. The corrected estimates correlated highly with those in the U.S. and U.K. (Table 2 ). It is telling that 0% of real-time estimates and 68.8% of retroactively-corrected estimates for Canada had excellent or better agreement with estimates of levels of transmission for the U.K. Whereas the U.S. retroactive corrections occurred mainly in the 1–2 weeks of real-time reporting, the timeline of the corrections to the model for Canada are unclear and suggest larger changes to the case estimation model beyond merely incorporating newly adjusted wastewater data. If permitted to speculate, one might wonder whether the real-time model was too aggressive in estimating levels of transmission and was updated with a more conservative model that would stick close to average levels of transmission. A conservative model would underinform the public regarding peaks and lulls but would still yield reasonable estimates over longer windows of time. For example, the estimate that 20.9% of people in Canada were infected during the peak two months of the surge was not far off the estimate for Scotland (21.1%) and only several percentage points below the estimates for the U.K. overall (24.5%) and U.S. (26.0%). Alternatively, it is possible that transmission was merely flatter (more platykurtic) in Canada during the analytic window. Overall, the estimates in Canada had strengths as well as limitations that could be further reduced through additional model calibration or additional analyses suggestive of a flatter wave relative to U.S. and U.K. estimates. More programs like the UKHSA testing-based surveillance program are needed to monitor ongoing health threats. The UKHSA program was methodologically rigorous. Relative to wastewater-derived estimates, testing-based estimates are more straightforward to interpret, likely more easily understood by consumers, and require less inference. In being concrete and straightforward, they have the potential to increase consumer trust, which is vital for the long-term strategy of public health. It was remarkable that estimates of transmission correlated almost perfectly between England and Scotland ( r = .999, Table 2 ). Estimates demonstrated excellent international convergence with the U.S. and Canada (Tables 1 – 4 ), particularly when centered by peak rather than date to account for variation in wave timing. These observed correlations may be artificially reduced relative to real associations because the analysis focuses on solely the months of high transmission, rather than including relative lulls too. Overall, there was strong evidence of validity when considering the nuanced limitations of wastewater-derived estimates. Overall, the findings document the international COVID-19 infection burden. The three countries had an estimated 112,721,033 infections during the peak two months of the surge. Such infections represent 25.3% of the total populations of the three countries when weighted by population size and 23.8% when each country is given equal weight regardless of population size. The infection burden contributes to worker and student absences, 25 , 26 hospitalizations, 7 gaps in treatment for people with serious medical conditions like cancer, 27 Long Covid (the umbrella term for symptoms and conditions persisting or emerging > 3 months post-infection), caregiver duties, 25 mortality, 28 family bereavement and psychiatric symptoms, 29 , 30 and reductions in the gross domestic product. 25 , 31 The public health and economic costs of the high incidence of COVID-19 infections warrant further attention. As an example, consider Long Covid. Each infection is thought to carry a 5–40% risk of Long Covid, 32 – 35 with the low estimate representing well-documented and clinically significant new comorbidities and the high estimate reflecting more so lingering symptoms that may or may not affect daily functioning. The 112 million estimated infections observed in these three countries during the two months of peak transmission would be estimated to translate into 5.6 million to 45.1 million new resulting Long Covid conditions (U.S.: 4.4 to 35.0 million, Canada: 0.4 to 3.4 million, U.K.: 0.8 to 6.7 million). The generalizability of waves across other countries warrants further investigation. If 23.8–25.3% of the world’s population of 8.0 billion (January 1, 2024) 20 were to experience an infection during the peak two months of a surge, this would represent 1.9 to 2.0 billion infections and, assuming a 5–40% risk, 95 to 760 million new Long Covid conditions. Better worldwide COVID-19 surveillance would help to shed light on the international infection burden and Long Covid. This body of research has several strengths and limitations. Strengths include that the data were from three countries, transmission was assessed frequently, the work was conducted by public health officials and uncompensated volunteers during a pandemic, the data and analyses involved scientists spanning multiple specialized disciplines, and the findings involved integrating multiple data sources. Several limitations are noted. The analysis is premised upon different regions having similar patterns of transmission during the winter 2023-24 surge. Analyses account for variation in the timing of the peak of the surge. However, an underlying assumption is that differences in peak levels of transmission during specific time periods might be similar. The analyses largely bear that out. Next, analyses may underestimate transmission in children, especially young children. Wastewater-derived case estimation may underestimate child cases, as children produce less waste proportionate to body weight and potentially less virus in wastewater. 36 The UKHSA study did not include children under 3 years old. Transmission among the youngest children could be higher due to lower access to and utilization of vaccines, the inability to mask well or at all in that age group, and often exposures in childcare environments with poor indoor air quality. Finally, reporting lags and delayed retroactive corrections undermine the public health impact of surveillance programs, as timely and accurate data are needed to reasonably inform real-world decision making. In the future, public health would benefit from improved infrastructure surrounding infectious disease surveillance. First, this research shows that wastewater surveillance can be used to derive statistics similar to those from testing programs – such as the proportion of the population actively infectious – that matter to the public. However, there remains a need for improved methodologic standards surrounding wastewater surveillance, including timelines for reporting as well as how to standardize data across regions and time points. Although this research used high-quality data from top-tier national sources, many local and regional wastewater surveillance programs have opportunities to improve rigor through better methodologic standards. Second, the expansion of wastewater surveillance programs to include more regions would offer tremendous advantages. 37 Surveillance in more regions within countries and across the globe would better inform decision making and strengthen trust in public health. Third, community-based COVID-19 testing-based surveillance programs have been limited and should also be expanded. The existence of testing-based surveillance programs would allow for the continued monitoring of the validity of estimates derived from wastewater surveillance programs and models. They would also inform efforts to calibrate wastewater models of transmission. For example, if a testing-based surveillance program identified the levels of transmission during the peaks of surges and the valleys of lulls, wastewater models could be calibrated to yield similar estimates. Thus, there are many benefits to improving the rigor and availability of infectious disease surveillance programs. In closing, COVID-19 transmitted at high rates during the winter 2023-24 surges in the U.S., Canada, and the U.K., with high convergence across methodologies and nations. About ¼ of the population in these nations were estimated to have been infected during the peak two months of the surge, meaning > 100 million infections. These infections continue to have significant health, social, educational, and economic implications. The increased allocation of resources toward prevention, diagnostics, and treatments appears warranted. References Tancredi S, Cullati S, Chioléro A (2024) Surveillance bias in the assessment of the size of COVID-19 epidemic waves: a case study. Public Health 234:98–104 Institute for Health Metrics and Evaluation. COVID-19 Projections (2023) https://covid19.healthdata.org/global Dotson T, Price B, Witrick B et al (2024) Factors Associated With Surveillance Testing in Individuals With COVID-19 Symptoms During the Last Leg of the Pandemic: Multivariable Regression Analysis. JMIR Public Health Surveillance 10(1):e52762 Hoerger M (2024) Pandemic Mitigation Collaborative - COVID-19 Forecasting Model. http://www.pmc19.com/data Moriarty T (2024) COVID-19 Resources Canada - Canadian COVID-19 Hazard Index. https://covid19resources.ca/covid-hazard-index/ Ciannella S, González-Fernández C, Gomez-Pastora J (2023) Recent progress on wastewater-based epidemiology for COVID-19 surveillance: A systematic review of analytical procedures and epidemiological modeling. Sci Total Environ 878:162953 Li X, Liu H, Gao L et al (2023) Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties. Nat Commun 14(1):4548 Shah S, Gwee SXW, Ng JQX, Lau N, Koh J, Pang J (2022) Wastewater surveillance to infer COVID-19 transmission: A systematic review. Sci Total Environ 804:150060 McMahan CS, Self S, Rennert L et al (2021) COVID-19 wastewater epidemiology: a model to estimate infected populations. Lancet Planet Health 5(12):e874–e881 Bogler A, Packman A, Furman A et al (2020) Rethinking wastewater risks and monitoring in light of the COVID-19 pandemic. Nat Sustain 3(12):981–990 Kuhn KG, Jarshaw J, Jeffries E et al (2022) Predicting COVID-19 cases in diverse population groups using SARS-CoV-2 wastewater monitoring across Oklahoma City. Sci Total Environ 812:151431 Chen C, Wang Y, Kaur G et al (2024) Wastewater-based epidemiology for COVID-19 surveillance and beyond: A survey. Epidemics 49:100793 Sun J, Yang MI, Peng J et al (2024) Underestimation of SARS-CoV-2 in wastewater due to single or double mutations in the N1 qPCR probe binding region. Water Res X 22:100221 UK Health Security Agency. Winter Coronavirus (COVID-19) Infection Study: estimates of epidemiological characteristics, England and Scotland: 2023 to 2024 (2024) https://www.gov.uk/government/statistics/winter-coronavirus-covid-19-infection-study-estimates-of-epidemiological-characteristics-england-and-scotland-2023-to-2024 BioBot Analytics. Biobot's Wastewater Data and Wastewater Intelligence Platform (2024) https://biobot.io/data/ Stone J (2023) CDC Improves Its Covid-19 Reporting With A New Wastewater Dashboard. Forbes Hakki S, Zhou J, Jonnerby J et al (2022) Onset and window of SARS-CoV-2 infectiousness and temporal correlation with symptom onset: a prospective, longitudinal, community cohort study. Lancet Respiratory Med 10(11):1061–1073 United States Census Bureau. U.S. and World Population Clock (2024) https://www.census.gov/popclock/ Moriarty T (2024) Twitter / X: @MoriartyLab. https://twitter.com/MoriartyLab United States Census Bureau. Census Bureau Projects U.S. and World Populations on New Year’s Day (2023) https://www.census.gov/newsroom/press-releases/2023/population-new-years-day.html Statistics Canada. Canada's population estimates: Strong population growth in 2023 (2024) https://www150.statcan.gc.ca/n1/daily-quotidien/240327/dq240327c-eng.htm UK Office for National Statistics. Population estimates for the UK, England, Wales, Scotland and Northern Ireland: mid-2023 (2024) https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/bulletins/annualmidyearpopulationestimates/mid2023 McHugh ML (2012) Interrater reliability: the kappa statistic. Biochemia Med 22(3):276–282 Gliem JA, Gliem RR (2003) Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales. Paper presented at: Midwest research-to-practice conference in adult, continuing, and community education Kwon J, Milne R, Rayner C et al (2024) Impact of Long COVID on productivity and informal caregiving. Eur J Health Econ 25(7):1095–1115 Thampi N, Schwartz KL, Brown KA (2023) School-Based Mandatory Masking Policies and Absenteeism in Ottawa, Canada, in 2022. JAMA Netw Open 6(7):e2325799–e2325799 Llanos AA, Ashrafi A, Ghosh N et al (2023) Evaluation of inequities in cancer treatment delay or discontinuation following SARS-CoV-2 infection. JAMA Netw Open 6(1):e2251165–e2251165 Msemburi W, Karlinsky A, Knutson V, Aleshin-Guendel S, Chatterji S, Wakefield J (2023) The WHO estimates of excess mortality associated with the COVID-19 pandemic. Nature 613(7942):130–137 Snyder M, Alburez-Gutierrez D, Williams I, Zagheni E (2022) Estimates from 31 countries show the significant impact of COVID-19 excess mortality on the incidence of family bereavement. Proceedings of the National Academy of Sciences. ;119(26):e2202686119 Sarigiannis KA, Tringali JJ, Vu J et al (2023) Symptoms of anxiety, depression, and stress among families of critically ill patients with COVID-19: a longitudinal clinical trial. Annals Am Thorac Soc 20(5):705–712 Cutler DM (2022) The economic cost of long COVID: an update. https://scholar.harvard.edu/sites/scholar.harvard.edu/files/cutler/files/long_covid_update_7-22.pdf Geng LN, Erlandson KM, Hornig M et al (2025) 2024 Update of the RECOVER-Adult Long COVID Research Index. JAMA Al-Aly Z, Davis H, McCorkell L et al (2024) Long COVID science, research and policy. Nat Med 30(8):2148–2164 Xie Y, Choi T, Al-Aly Z (2024) Postacute sequelae of SARS-CoV-2 infection in the pre-delta, delta, and omicron eras. N Engl J Med 391(6):515–525 Bowe B, Xie Y, Al-Aly Z (2022) Acute and postacute sequelae associated with SARS-CoV-2 reinfection. Nat Med 28(11):2398–2405 Rose C, Parker A, Jefferson B, Cartmell E (2015) The characterization of feces and urine: a review of the literature to inform advanced treatment technology. Crit Rev Environ Sci Technol 45(17):1827–1879 Naughton CC, Roman FA Jr, Alvarado AGF et al (2023) Show us the data: global COVID-19 wastewater monitoring efforts, equity, and gaps. FEMS microbes 4:xtad003 Additional Declarations The authors declare potential competing interests as follows: Dr. Maria C. Swartz notes funding from Pack Health, LLC. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5786667","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":399319315,"identity":"e6c3ac12-0703-4046-8010-1da11d61b04b","order_by":0,"name":"Michael Hoerger","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACCSjNz8DAeADCTCBSi2QbAwOJWgyOEatFd3bzwccVv2zyje/3GBz4ucOGgZ89xwCvFrM7x5INz/alWW47xmNwsPdMGoNkzxsCWm7kmEk29hw2MANqOcDbdpjB4AYhW27kmP9s7PlvYNwGtOVv238GeyK0mDE2/DhgYMDGY3CYt+0Ag4EEQS1pyZKNDckGEsfSCg7LtiXzSJx5VkBAS/LBjw1/7Az4mw9vfPi2zU6Ovz15A14tYMDYhmDzEFYOBn+IVDcKRsEoGAUjEwAAwT1LORbNIbYAAAAASUVORK5CYII=","orcid":"","institution":"Tulane University","correspondingAuthor":true,"prefix":"","firstName":"Michael","middleName":"","lastName":"Hoerger","suffix":""},{"id":399319316,"identity":"0bdc2e82-ed0e-4e0c-bfbe-beae083a3c24","order_by":1,"name":"James I. Gerhart","email":"","orcid":"","institution":"Ohio University","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"I.","lastName":"Gerhart","suffix":""},{"id":399319401,"identity":"a3b1fd37-069b-4836-9c21-510169963ab3","order_by":2,"name":"Tristen Peyser","email":"","orcid":"","institution":"Tulane University","correspondingAuthor":false,"prefix":"","firstName":"Tristen","middleName":"","lastName":"Peyser","suffix":""},{"id":399319470,"identity":"8bf70c43-ebfb-4407-8779-932637a7ba39","order_by":3,"name":"Nicole Pyke","email":"","orcid":"","institution":"Tulane University","correspondingAuthor":false,"prefix":"","firstName":"Nicole","middleName":"","lastName":"Pyke","suffix":""},{"id":399320081,"identity":"d76b6546-9b70-427d-92cd-41e97ec4f6cd","order_by":4,"name":"Nicole Garg","email":"","orcid":"","institution":"Tulane University","correspondingAuthor":false,"prefix":"","firstName":"Nicole","middleName":"","lastName":"Garg","suffix":""},{"id":399320082,"identity":"57f0697d-be5e-4407-9e00-3b298c0f5251","order_by":5,"name":"Carly J. Hall","email":"","orcid":"","institution":"Tulane University","correspondingAuthor":false,"prefix":"","firstName":"Carly","middleName":"J.","lastName":"Hall","suffix":""},{"id":399320225,"identity":"af157a81-5671-482d-b926-6016581a3284","order_by":6,"name":"Maria C. Swartz","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"C.","lastName":"Swartz","suffix":""},{"id":399325613,"identity":"4b73b61d-addf-4794-9124-064a3ac73055","order_by":7,"name":"Michael D. Swartz","email":"","orcid":"","institution":"The University of Texas Health Science Center at Houston","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"D.","lastName":"Swartz","suffix":""},{"id":399325614,"identity":"df790e42-1916-4c95-b2cd-79b727e0383c","order_by":8,"name":"Sara Anne Willette","email":"","orcid":"","institution":"New Brunswick, New Jersey","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"Anne","lastName":"Willette","suffix":""},{"id":399326137,"identity":"dc3ec3b1-fb6b-42c7-b3e2-cd2ccbf038eb","order_by":9,"name":"Courtney N. Baker","email":"","orcid":"","institution":"Tulane University","correspondingAuthor":false,"prefix":"","firstName":"Courtney","middleName":"N.","lastName":"Baker","suffix":""},{"id":399326138,"identity":"67f82d7f-357b-4ca2-ba8f-d84db70b6d1a","order_by":10,"name":"Joseph L. Eastman","email":"","orcid":"","institution":"Bovey, Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"L.","lastName":"Eastman","suffix":""}],"badges":[],"createdAt":"2025-01-08 07:31:29","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5786667/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5786667/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73357375,"identity":"a51d5a54-2454-4c9d-8248-b8da7151dd33","added_by":"auto","created_at":"2025-01-09 08:18:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":282482,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstimated Proportion of the Population Actively Infectious During the Winter 2023-24 COVID-19 Surge in the U.S., Canada, and the U.K. \u003c/strong\u003eFigure 1A shows transmission estimates linked by the calendar date (y axis, November 14, 2023, through March 6, 2024). Figure 1B shows estimates linked by peak transmission date (y axis, day 0 defined as the peak).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5786667/v1/02bd9e7e3b8d17cbe5232815.png"},{"id":73360418,"identity":"4f07a4f0-c1a2-40b8-b55d-b03efd7d04f9","added_by":"auto","created_at":"2025-01-09 08:42:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1239129,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5786667/v1/d1b0b42f-d056-4f5c-9185-796d65f3a464.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: Dr. Maria C. Swartz notes funding from Pack Health, LLC. \n","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCOVID-19 Transmission During the Winter 2023-24 Surge: A Comparative Analysis of Surveillance Estimates in the U.S., Canada, and the U.K.\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCOVID-19 continues to transmit at high rates during periodic waves, and understanding the extent of transmission can help define the public health burden of COVID-19 and inform the allocation of public health and research funding to support prevention, diagnostics, and treatments. From 2020-21, publicly available testing programs documented transmission levels and yielded key metrics such as positivity ratios and reported daily cases, which informed the public on when to escalate precautions and guided public health policy.\u003csup\u003e1\u003c/sup\u003e Although reported case counts from opt-in testing programs in the U.S. underestimated true cases 4- to 8-fold throughout much of 2020-21, according to the Institute for Health Metrics and Evaluation (IHME) true-case estimation model,\u003csup\u003e2\u003c/sup\u003e they nonetheless provided a dependable metric to inform the public. Testing dropped off as public testing programs became sparse, making reported daily case counts less meaningful.\u003csup\u003e1,3\u003c/sup\u003e From January 2022 to April 2023, true cases were estimated to be 17-33x higher than reported cases.\u003csup\u003e2\u003c/sup\u003e Accordingly, since 2022, there has been a public health need for better metrics to track levels of COVID-19 transmission given the considerable public health and economic burden of this ongoing illness particularly among vulnerable populations such as people with cancer, primary immunodeficiency, and chronic illness.\u003c/p\u003e\n\u003cp\u003eIn the U.S. and Canada, and increasingly elsewhere, wastewater-based surveillance programs gained popularity as a passive method of monitoring viral levels in sewerage wastewater at the population level without the need for individual testing.\u003csup\u003e4-12\u003c/sup\u003e Wastewater surveillance data indicate when transmission is higher or lower and have been shown to correspond to case rates and other metrics.\u003csup\u003e6-11\u003c/sup\u003e Academic research teams in the U.S.\u003csup\u003e4\u003c/sup\u003e and Canada\u003csup\u003e5\u003c/sup\u003e have run dashboards estimating true daily infections, the proportion of the population actively infectious, and other metrics using publicly available wastewater surveillance data. Such estimates require layers of multi-disciplinary science from physical wastewater measurements to estimates of viral concentrations to population-level estimates, requiring rigor and inference at each layer.\u003csup\u003e8,12,13\u003c/sup\u003e As such, wastewater-derived estimates of transmission are potentially less readily understood and accepted by the public than simple counts and percentages from testing programs.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;As opt-in testing programs underestimate transmission and wastewater monitoring systems lack public familiarity, the U.K. Health Security Agency (UKHSA) conducted a winter 2023-24 COVID-19 testing-based surveillance study to estimate the population\u0026rsquo;s transmission levels.\u003csup\u003e14\u003c/sup\u003e The main challenges of a testing-based surveillance program are sampling a representative population and adjusting observed estimates based on potential confounders, such as noncompliance with testing protocols.\u003csup\u003e14\u003c/sup\u003e Surveillance testing programs yield statistics like the proportion of the population infectious that are familiar to the public based on earlier opt-in testing programs and reported positivity ratios.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The present investigation examined the correspondence among estimates of COVID-19 transmission during the winter 2023-24 surge using wastewater-derived estimates for the U.S.\u003csup\u003e4\u003c/sup\u003e and Canada\u003csup\u003e5\u003c/sup\u003e and testing-derived estimate in the U.K.\u003csup\u003e14\u003c/sup\u003e Similarities in estimates would support the validity of estimation models, which is key for drawing reasonable conclusions about the infection burden of COVID-19. The aims of the research were to 1) document the estimated timing and peaks of the waves in the U.S., Canada, and the U.K., 2) examine the relative agreement across these nations on the timing of high versus low levels of transmission, 3) examine the absolute agreement on the proportion of the population estimated to be actively infectious, and 4) estimate the proportion of each population infected during the surge.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eOverview\u003c/h2\u003e \u003cp\u003eThe analyses examined three data sets tracking winter 2023-24 COVID-19 transmission. The UHKSA\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e tracked COVID-19 transmission in England and Scotland from November 14, 2023, through March 6, 2024. Analyses linked the U.S.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and Canada\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e data sets to the UKHSA data sets in two ways, either based on the calendar date or based on the relative number of days before or after the estimated peak day of transmission within each data set (e.g., day \u0026ldquo;0\u0026rdquo; refers to the peak, day \u0026ldquo;-5\u0026rdquo; five days before the peak, and day \u0026ldquo;20\u0026rdquo; as twenty days after the peak). The UKHSA data happened to track transmission from day \u0026minus;\u0026thinsp;38 to day 75, relative to the peak. The date-based analyses used the full date range of the UKHSA study. The U.S. and Canada COVID-19 dashboards began before and continued after the UKHSA data, so the peak-based analyses used data from November 12, 2023, through April 2, 2024, to track transmission 38 days before and 75 days after the peaks, like in the UKHSA study. This ensured that each data file would have complete data for analysis regardless of whether it was linked by calendar date or relative to the peak.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003e \u003cb\u003eU.S. Data.\u003c/b\u003e The U.S. data were from the Pandemic Mitigation Collaborative (PMC) dashboard,\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e led by Dr. Michael Hoerger, Director of Population Sciences and Disparities, Tulane Cancer Center, Tulane University. The PMC dashboard converts U.S. national wastewater data into estimates of infections. The full methodology of the dashboard is posted within each weekly report during the study period.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Briefly, the dashboard began in August 2023 and has continued through the time of this analysis. At launch, the PMC dashboard used national wastewater surveillance data from the U.S. Centers for Disease Control and Prevention (CDC) contractor, Biobot Analytics, as reported publicly on Biobot\u0026rsquo;s dashboard.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e The U.S. CDC switched to a different contractor in late 2023,\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e shortly before the current study window, but Biobot continued to report data publicly on their dashboard throughout the study window and has in a more limited capacity to this day.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe PMC dashboard estimated new daily infections by linking Biobot-derived wastewater levels to the IHME\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e multi-faceted model of true (not merely reported) daily infections in the U.S., which reported estimates from February 4, 2020, through April 1, 2023. During the time period from the pandemic declaration onset of March 11, 2020, through April 1, 2023, Biobot wastewater levels and IHME estimates of new daily infections correlated an estimated \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.927.\u003csup\u003e4\u003c/sup\u003e For each data point in that date range, a multiplier value was computed as the number of IHME new daily infections divided by Biobot wastewater levels. A 10% trimmed mean (trimming the 5% largest and smallest values) was used to provide a conversion factor for estimating new daily infections from wastewater levels. Sensitivity analyses using the mean, median, or regression coefficients yielded similar estimates that hovered above or below the multiplier. Specifically, 1 copy/mL of SARS-Cov-2 in Biobot wastewater data was estimated as 1,455 new daily infections.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e An underlying assumption was that Biobot would standardize estimates of viral concentration reasonably over time and region to provide a relatively stable indicator of transmission.\u003c/p\u003e \u003cp\u003eThe research team extracted two versions of data from the PMC model \u0026ndash; real-time and retroactively-corrected estimates of the proportion of the population actively infectious. In real-time, note that Biobot typically had a reporting lag of approximately a week. The PMC dashboard used a forecasting model to post estimates of the current day\u0026rsquo;s level of infections, based on the lagged data coming in.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Retroactively, these values were corrected to account for minor errors in the short-term PMC forecast as well as minor errors on Biobot\u0026rsquo;s side in the reported estimation of wastewater levels. The dashboard computed the percentage of the U.S. population actively infectious by taking the estimate of new daily infections, multiplying it by the average infectious period of approximately 7 days,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and dividing it by the U.S. population estimate of 334,565,848\u003csup\u003e18\u003c/sup\u003e on the final day of the IHME reports on April 1, 2023.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eCanada Data.\u003c/b\u003e The Canada data were from the COVID-19 Resources Canada (CRC) dashboard,\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e led by Dr. Tara Moriarty, Associate Professor of Dentistry, Laboratory Medicine, and Pathobiology at the University of Toronto. Although our research team could not identify a specific conversion formula on their dashboard website, a summary of their general methodology is posted on their dashboard.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Briefly, COVID-19 Resources Canada model uses wastewater data to explain variation in estimates of infection that are based on a combination of seroprevalence data, test positivity, and survey data on testing results.\u003c/p\u003e \u003cp\u003eOur research team extracted two versions of data from the CRC model \u0026ndash; real-time and retroactively-corrected estimates of the proportion of the population actively infectious. The retroactively-corrected estimates differed from the real-time estimates considerably based on retroactive corrections to the underlying wastewater data as well as changes to the COVID-19 Resources Canada case estimation model, which was revised in 2024 and applied retroactively to all data. Our research team could not identify details on the specific changes to the case estimation model. The retroactively-corrected data were downloaded from the dashboard website on December 7, 2024.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e The real-time reports were not found to be archived on the dashboard website but were available from Dr. Moriarty\u0026rsquo;s Tweets\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and manually entered in December 2024.\u003c/p\u003e \u003cp\u003e \u003cb\u003eU.K. Data.\u003c/b\u003e The UKHSA study\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e assessed SARS-CoV-2 infections among 150,000 people in England and Scotland using surveys on surveillance testing results from lateral flow devices (LFDs, also called rapid tests), led by Dr. Alex Glaser, Infectious Disease Modelling Team, All Hazards Intelligence, Data Analytics and Surveillance, UK Health Security Agency. Data were collected from November 14, 2023, through March 6, 2024. The sample agreed to participate in the context of a research study that had ethical approval. They included adults ages 16 and older and children ages 3\u0026ndash;15 who had a consenting parent. Each participant received 14 tests. Each month, they were asked to test and complete a survey. Participants testing positive were asked to complete an additional survey and continue to test every other day until getting two negative tests. Estimates of incidence and prevalence were weighted based on demographics using a Bayesian multilevel regression and post-stratification approach to improve precision among undersampled subgroups. Models accounted for testing sensitivity and specificity for the 1-month testing interval, duration of positivity, and the level of promptness of participants in completing the test and survey after the requested date. The complete details of the methodology and reports are on the UKHSA website.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Data were downloaded directly from the website.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Quality\u003c/h3\u003e\n\u003cp\u003eDouble data entry was used to ensure data quality. Specifically, two research assistants each entered all of the data independently, and the lead author checked their work for agreement. The UKHSA data and retroactively-corrected versions of the U.S. and Canada data were available via data files. The real-time estimates for the U.S. data were extracted from the weekly reports on the dashboard website.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The real-time estimates for the Canada data were manually extracted from Dr. Moriarty\u0026rsquo;s Tweets\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e since they were not observed to be archived on the dashboard website. The corresponding author checked the accuracy of all data, found no inaccuracies other than two rounding errors easily resolved, and approved the data file for analysis.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThe data were analyzed and portrayed graphically using Microsoft Excel version 2411 and IBM SPSS Statistics 27. The data files for the U.K. and Canada retroactively-corrected data included daily estimates. The other data files include weekly or semi-weekly estimates, so a spline function was used to estimate values for intermediary dates. Each analysis spanned 114 days, corresponding to the UKHSA study, with the U.S. and Canada data linked to the UKHSA data either based on the calendar date or the day relative to the estimated peak of transmission within each data set. The analyses included the estimate of the population actively infectious with Covid within the following populations: U.S. (real-time), U.S. (retroactively corrected), Canada (real-time), Canada (retroactively corrected), U.K., England, and Scotland (all posted as final shortly after the assessment interval).\u003c/p\u003e \u003cp\u003eFirst, analyses examined descriptive statistics and the relative agreement among estimates. Descriptive statistics included the timing and level of the wave peaks. The proportion of the population actively infectious (prevalence) was also converted to new daily cases (incidence) to report the maximum new daily infections at the peak. Individuals are infectious for approximately 7 days on average,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e so the prevalence was divided by 7 to find the daily incidence, which was multiplied by the population to indicate new daily infections. For such analyses, the research team used the population estimates for January 1, 2024, which were 335,893,238 (U.S.),\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e 40,769,890 (Canada),\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and 68,596,400 (U.K.,\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e with 57,979,200 from England, 5,511,650 from Scotland, and the remainder from Northern Ireland and Wales). The U.S. and Canada provide daily population estimates, whereas the U.K. estimates were extended linearly from the midpoint of 2022 to the midpoint of 2023 by an additional 6 months.\u003c/p\u003e \u003cp\u003e Second, analyses examined relative agreement in the proportion of the population infections across regions using correlations, reported as statistically significant with a two-tailed alpha level of .05. Analyses included data sets linked by calendar date as well as when linked by the relative number of days from the region\u0026rsquo;s peak.\u003c/p\u003e \u003cp\u003eThird, analyses examined the absolute agreement in estimates across regions. Each region (U.S., Canada, England, Scotland) was compared with the overall estimate of the U.K. for the proportion of the population actively infectious on each day for the dataset that linked regions by their peak date of infections. Absolute agreement was defined by the absolute value of the difference between the region\u0026rsquo;s estimate of the proportion infectious and the corresponding U.K. estimate. For example, a 0% difference would represent perfect absolute agreement, whereas a 1% difference would mean that the region\u0026rsquo;s estimate was either 1% higher or lower in absolute terms (e.g., 5% versus 4%) than the estimate of the proportion of the population infectious in the U.K. on a given day of the wave. Experts who analyze COVID-19 data on a daily to weekly basis (JE, MH, SAW) categorized each region\u0026rsquo;s level of absolute agreement on each day of the wave using 7 bins labeled and defined by their expert consensus: 0.00\u0026ndash;0.50% difference from the U.K. in the proportion actively infectious (almost perfect agreement), 0.51\u0026ndash;1.00% difference (excellent agreement), 1.01\u0026ndash;1.50% (high agreement), 1.51\u0026ndash;2.00% (moderate agreement), 2.01\u0026ndash;2.50% (fair agreement), 2.51\u0026ndash;3.00% (low agreement), and \u0026gt;\u0026thinsp;3.00% (poor agreement). The descriptive labels for each bin were inspired by similar approaches elsewhere for qualitatively describing levels of a consistency.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e For each region, each day was classified into one of these bins, and analyses summarize the proportion of days within the peak two months (61 days, which included the peak and 30 days before and after) in each bin to provide a summary indicator of absolute agreement across the wave.\u003c/p\u003e \u003cp\u003eFinally, analyses described the levels of infections during the peak one and two months of each region\u0026rsquo;s wave. Specifically, analyses indicated the mean and standard deviation (SD) of infections during the peak one and two months, total infections during that time period, and the proportion of the population infected during the corresponding time period (one- and two-month incidence).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e\n \u003cp\u003eThe winter COVID-19 waves for the U.S., Canada, and the U.K. are depicted in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e with summary statistics in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The U.S. and Canada data include real-time and retroactively corrected estimates. The U.K. estimates were reported approximately every two weeks, shortly after the end date of reporting, and included subgroup estimates for England and Scotland. For the U.S., real-time estimates were that the peak occurred on January 5, 2024, at 4.5% of the population actively infectious, later retroactively corrected to a peak of December 29, 2023, at 4.1% infectious. For Canada, real-time estimates were that the peak occurred on January 18, 2024, at 8.1% of the population actively infectious, later retroactively corrected to a lower and earlier peak of December 20, 2023, at 2.5% infectious. The UKHSA study found that the peak was December 22, 2023, for the U.K. at 4.4% infectious, including in England at 4.5% and Scotland at 3.8%. Final estimates of the maximum daily infections were 1.95 million in the U.S., 148 thousand in Canada, and 431 thousand in the U.K.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePeak Transmission in the U.S., Canada, and the U.K. During the Winter 2023-24 COVID-19 Surge\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4015%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData\u003cbr\u003e\u0026nbsp;Source\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003cbr\u003e\u0026nbsp;Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.0502%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak\u003cbr\u003e\u0026nbsp;Date\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003cbr\u003e\u0026nbsp;Proportion\u003cbr\u003e\u0026nbsp;Infectious\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003cbr\u003e\u0026nbsp;Daily\u003cbr\u003e\u0026nbsp;Infections\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4015%;\"\u003e\n \u003cp\u003eU.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e335,893,238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.0502%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4015%;\"\u003e\n \u003cp\u003e\u0026nbsp; Real time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.0502%;\"\u003e\n \u003cp\u003eJanuary 5, 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e2,142,758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4015%;\"\u003e\n \u003cp\u003e\u0026nbsp; Retroactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.0502%;\"\u003e\n \u003cp\u003eDecember 29, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e4.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e1,947,441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4015%;\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e40,769,890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.0502%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4015%;\"\u003e\n \u003cp\u003e\u0026nbsp; Real time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.0502%;\"\u003e\n \u003cp\u003eJanuary 18, 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e8.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e472,474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4015%;\"\u003e\n \u003cp\u003e\u0026nbsp; Retroactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.0502%;\"\u003e\n \u003cp\u003eDecember 20, 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e2.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e148,159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4015%;\"\u003e\n \u003cp\u003eU.K. \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e68,596,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.0502%;\"\u003e\n \u003cp\u003eDecember 22, 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e4.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e431,177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4015%;\"\u003e\n \u003cp\u003e\u0026nbsp; England \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e57,979,200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.0502%;\"\u003e\n \u003cp\u003eDecember 22, 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e372,723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4015%;\"\u003e\n \u003cp\u003e\u0026nbsp; Scotland\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e5,511,650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.0502%;\"\u003e\n \u003cp\u003eDecember 22, 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8494%;\"\u003e\n \u003cp\u003e29,920\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Population size estimates were for January 1, 2024, near the wave peaks, from official government sources that included the U.S. Census, Statistics Canada, and the U.K. Office for National Statistics. The total population of the U.K. includes England, Scotland, Wales, and Northern Ireland, but the UKHSA study only samples in England and Scotland. The peak in the retroactive data from Canada differed arbitrarily (\u0026lt;.05%) from December 12 to December 28, whereas Figure 1 shows the other data sources had defined peaks.\u003c/p\u003e\u003csup\u003ea\u003c/sup\u003eThe UKHSA study only reported final (retroactive) results, approximately every two weeks. It did not report real-time interim estimates that required later correction.\u0026nbsp;\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eRelative Agreement Across Regions\u003c/h2\u003e\n \u003cp\u003eThe correlations among transmission estimates are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The average intercorrelation among estimates was \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.826 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003er\u003c/em\u003es from.141 to .999) when linked by calendar date and \u003cem\u003er\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.948 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003er\u003c/em\u003es from .686 to .999) when linked by peak. The real-time U.S. estimates correlated \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.929 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) with retroactively-corrected estimates when linked by calendar date and \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.942 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) when linked by peak. The real-time estimates for Canada correlated only \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.141 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.135) with retroactively-corrected estimates, owing to the 29 day difference in the estimated peak; the correlation was \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.898 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) when linking by peak. The U.K. and constituent regions of England and Scotland had near-perfect intercorrelations (\u003cem\u003er\u003c/em\u003es\u0026thinsp;=\u0026thinsp;.999, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Linking by peak, real-time and retroactively-corrected estimates in the U.S. correlated \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.952 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.974 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) with the estimates for the U.K. Also linking by peak, real-time and retroactively-corrected estimates in Canada correlated \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.873 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.768 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) with the estimates for the U.K.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRelative Agreement: Correlations Among Transmission Estimates in the U.S., Canada, and the U.K., with Data Linked by the Calendar Date (Below Diagonal) or Relative to the Peak Transmission Date (Above Diagonal)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData Source\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eU.S.,\u003c/p\u003e\n \u003cp\u003ereal time\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eU.S.,\u003c/p\u003e\n \u003cp\u003eretroactive\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCanada,\u003c/p\u003e\n \u003cp\u003ereal time\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCanada,\u003c/p\u003e\n \u003cp\u003eretroactive\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eU.K.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEngland\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScotland\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\u003eU.S.,\u003c/p\u003e\n \u003cp\u003ereal time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.942***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.822***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.796***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.952***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.952***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.954***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eU.S.,\u003c/p\u003e\n \u003cp\u003eretroactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.929*** \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.786***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.686***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.974***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.975***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.970***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanada,\u003c/p\u003e\n \u003cp\u003ereal time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.612***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.551***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.898***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.873***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.873***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.879***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanada, retroactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.376***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.430***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.141 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.769***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.768***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.777***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eU.K.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.626***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.787***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.266**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.760***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.999*** \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.999*** \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEngland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.626***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.789***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.266**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.758***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.999*** \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.999*** \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScotland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.614***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.776***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.250**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.769***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.999*** \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.999*** \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003eNote.\u003c/em\u003e N\u0026thinsp;=\u0026thinsp;114 days. Correlations below the diagonal are linked based on the calendar data, like in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA. Correlations above the diagonal are linked based on the peak transmission date, like in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e Accuracy coefficient, showing the correlation between real-time and retroactively corrected data.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e Data sources had the same peak, so correlations were the same above and below the diagonal.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;.05 **p\u0026thinsp;\u0026lt;\u0026thinsp;.01 ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003ch3\u003eAbsolute Agreement Across Regions\u003c/h3\u003e\n \u003cp\u003eThe levels of absolute agreement in transmission during the winter 2023-24 surge are shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Of the real-time estimates in the U.S., 93.5% had excellent or almost perfect agreement with the U.K. estimates, and 100.0% of retroactively-corrected U.S. estimates had excellent or better agreement with the U.K. estimates. For Canada, 0.0% of real-time estimates had excellent or almost perfect agreement with the U.K., whereas 68.8% of retroactively-corrected estimates were in excellent or almost perfect agreement with the U.K. England and Scotland corresponded closely with the overall U.K. estimate with 100.0% of daily levels of transmission in excellent or almost perfect agreement.\u003c/p\u003e\n \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\u003eAbsolute Agreement: Differences in Levels of Transmission in the U.S., Canada, and the U.K. During the Winter 2023-24 COVID-19 Surge\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData\u003c/p\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlmost Perfect Agreement,\u003c/p\u003e\n \u003cp\u003e0.00-0.50%\u003c/p\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExcellent Agreement,\u003c/p\u003e\n \u003cp\u003e0.51-1.00%\u003c/p\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh Agreement,\u003c/p\u003e\n \u003cp\u003e1.01\u0026ndash;1.50%\u003c/p\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModerate Agreement,\u003c/p\u003e\n \u003cp\u003e1.51-2.00%\u003c/p\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFair Agreement,\u003c/p\u003e\n \u003cp\u003e2.01\u0026ndash;2.50%\u003c/p\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow Agreement,\u003c/p\u003e\n \u003cp\u003e2.51-3.00%\u003c/p\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePoor Agreement,\u003c/p\u003e\n \u003cp\u003e\u0026gt;3.00%\u003c/p\u003e\n \u003cp\u003eDifference\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\u003eU.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReal time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (60.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (32.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetroactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (75.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReal time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (86.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetroactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (42.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (26.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eU.K. (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEngland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScotland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (82.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e. Levels of agreement are in reference to the overall estimate for the U.K.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003ch3\u003e\u003cbr\u003e\u003c/h3\u003e\n \u003ch3\u003eInfection Burden\u003c/h3\u003e\n \u003cp\u003eThe international infection burden of the winter 2023-24 COVID-19 surge is shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The models estimate that a total of 67,576,328 people were infected in the peak month and 112,721,033 in the peak two months of the winter 2023-24 COVID-19 surge across the U.S., Canada, and the U.K. (1-month: 51,952,438\u0026thinsp;+\u0026thinsp;4,511,273\u0026thinsp;+\u0026thinsp;11,112,617; 2-month: 87,393,734\u0026thinsp;+\u0026thinsp;8,501,582\u0026thinsp;+\u0026thinsp;16,825,717). The total proportion of the population infected during the peak month ranged from 11.1%-16.4% across the U.S., Canada, England, and Scotland, and 20.9%-26.0% during the peak two months in these regions.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTransmission Levels Within the Month and Two Months of Peak Transmission in the U.S., Canada, and the U.K. During the Winter 2023-24 COVID-19 Surge\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 7.947%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" style=\"width: 24.9401%;\"\u003e\n \u003cp\u003ePeak Month\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" style=\"width: 26.8846%;\"\u003e\n \u003cp\u003ePeak Two Months\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 7.947%;\"\u003e\n \u003cp\u003eData\u003c/p\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.9906%;\"\u003e\n \u003cp\u003eDaily Infections\u003c/p\u003e\n \u003cp\u003eM (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eInfections\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 7.5243%;\"\u003e\n \u003cp\u003eProportion Infected\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 9.0461%;\"\u003e\n \u003cp\u003eDaily Infections\u003c/p\u003e\n \u003cp\u003eM (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eInfections\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 11.4978%;\"\u003e\n \u003cp\u003eProportion Infected\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\" style=\"width: 7.947%;\"\u003e\n \u003cp\u003eU.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9906%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.3407%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5243%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0461%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.3407%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4978%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.947%;\"\u003e\n \u003cp\u003eReal time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9906%;\"\u003e\n \u003cp\u003e1,756,868 (291,951)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e54,462,902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.5243%;\"\u003e\n \u003cp\u003e16.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0461%;\"\u003e\n \u003cp\u003e1,510,223 (330,508)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e92,123,624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 11.4978%;\"\u003e\n \u003cp\u003e27.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.947%;\"\u003e\n \u003cp\u003eRetroactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9906%;\"\u003e\n \u003cp\u003e1,675,885 (206,923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e51,952,438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.5243%;\"\u003e\n \u003cp\u003e15.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0461%;\"\u003e\n \u003cp\u003e1,432,684 (293,106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e87,393,734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 11.4978%;\"\u003e\n \u003cp\u003e26.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.947%;\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9906%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.3407%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5243%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0461%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.3407%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4978%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.947%;\"\u003e\n \u003cp\u003eReal time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9906%;\"\u003e\n \u003cp\u003e448,078 (22,118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e13,890,407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.5243%;\"\u003e\n \u003cp\u003e34.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0461%;\"\u003e\n \u003cp\u003e381,961 (79,727)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e23,299,604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 11.4978%;\"\u003e\n \u003cp\u003e57.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.947%;\"\u003e\n \u003cp\u003eRetroactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9906%;\"\u003e\n \u003cp\u003e145,525 (2,286)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e4,511,273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.5243%;\"\u003e\n \u003cp\u003e11.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0461%;\"\u003e\n \u003cp\u003e139,370 (7,883)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e8,501,582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 11.4978%;\"\u003e\n \u003cp\u003e20.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.947%;\"\u003e\n \u003cp\u003eU.K.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9906%;\"\u003e\n \u003cp\u003e358,472 (60,608)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e11,112,617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.5243%;\"\u003e\n \u003cp\u003e16.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0461%;\"\u003e\n \u003cp\u003e275,831 (97,254)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e16,825,717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 11.4978%;\"\u003e\n \u003cp\u003e24.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.947%;\"\u003e\n \u003cp\u003eEngland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9906%;\"\u003e\n \u003cp\u003e307,530 (52,330)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e9,533,437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.5243%;\"\u003e\n \u003cp\u003e16.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0461%;\"\u003e\n \u003cp\u003e236,805 (83,328)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e14,445,104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 11.4978%;\"\u003e\n \u003cp\u003e24.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 7.947%;\"\u003e\n \u003cp\u003eScotland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9906%;\"\u003e\n \u003cp\u003e24,815 (4,259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e769,269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.5243%;\"\u003e\n \u003cp\u003e14.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0461%;\"\u003e\n \u003cp\u003e19,065 (6,764)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.3407%;\"\u003e\n \u003cp\u003e1,162,958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 11.4978%;\"\u003e\n \u003cp\u003e21.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 62.8153%;\"\u003e\u003cem\u003eNote.\u003c/em\u003e Population size estimates were for January 1, 2024, near the wave peaks, from official government sources that included the U.S. Census, Statistics Canada, and the U.K. Office for National Statistics. The total population of the U.K. includes England, Scotland, Wales, and Northern Ireland, but the UKHSA study only sampled in England and Scotland. The peak in the retroactive data from Canada differed arbitrarily (\u0026lt;\u0026thinsp;.05%) from December 12 to December 28, whereas Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the other data sources had defined peaks. The peak month is defined as 31 days, including the peak, 15 days before, and 15 days after. The peak two months are defined as 61 days, including the peak, 30 days before, and 30 days after.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research documents the continued international infection burden of COVID-19 through the winter 2023-24 wave, using multiple surveillance methodologies and data spanning the U.S., Canada, and the U.K. The U.S. and U.K. estimates were in close agreement that at the wave\u0026rsquo;s peak approximately 4% of the people in each nation were actively infectious with COVID-19, ranging from 3.8\u0026ndash;4.5% across regions and estimates (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Estimated levels in Canada were higher than this range in real-time estimates and lower in retroactively-corrected estimates. During the peak two months of infections, estimates were that 87.4\u0026nbsp;million people in the U.S., 23.3\u0026nbsp;million people in Canada, and 16.8\u0026nbsp;million people in the U.K. became infected. These estimates were similar on a per-capita basis, with a 2-month incidence of infection of 20.9\u0026ndash;26.0% across populations. The results warrant a nuanced interpretation that accounts for variation across methodologies and nations. Findings have implications for infectious disease surveillance, case estimation modeling across different nations, and the public health significance of COVID-19.\u003c/p\u003e \u003cp\u003eU.S. wastewater-derived estimates of infections corresponded closely with those in the testing-based surveillance program in the U.K. The real-time and retroactive estimates of transmission in the U.S. correlated near perfectly with each other, and both correlated near perfectly with the U.K. estimates when data were linked by peak date (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The real-time U.S. estimates of the peak proportion of the population infectious (4.5%) nearly matched those of the U.K. (4.4%), and the retroactively-corrected estimates for the U.S. (4.1%) fell directly between the estimates for England (4.5%) and Scotland (3.8%). During the wave, the U.S. and U.K. estimates showed excellent absolute agreement on the levels of transmission (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). According to the U.S. model, 26.0% of the population got infected during the two months of the surge with highest transmission, which closely matched the 24.5% reported for the U.K. In summary, the U.S. model showed excellent evidence of external validity in international comparisons with the U.K. during the winter 2023-24 surge.\u003c/p\u003e \u003cp\u003eThe retroactively-corrected estimates in Canada showed moderate evidence of agreement in international comparisons. In Canada, the peak was estimated in real time as occurring on January 18, 2024, with 8.1% actively infectious, while retroactive estimates were that the peak occurred nearly a month earlier and at about 1/3 the level at 2.5% actively infectious. The corrected estimates correlated highly with those in the U.S. and U.K. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It is telling that 0% of real-time estimates and 68.8% of retroactively-corrected estimates for Canada had excellent or better agreement with estimates of levels of transmission for the U.K. Whereas the U.S. retroactive corrections occurred mainly in the 1\u0026ndash;2 weeks of real-time reporting, the timeline of the corrections to the model for Canada are unclear and suggest larger changes to the case estimation model beyond merely incorporating newly adjusted wastewater data. If permitted to speculate, one might wonder whether the real-time model was too aggressive in estimating levels of transmission and was updated with a more conservative model that would stick close to average levels of transmission. A conservative model would underinform the public regarding peaks and lulls but would still yield reasonable estimates over longer windows of time. For example, the estimate that 20.9% of people in Canada were infected during the peak two months of the surge was not far off the estimate for Scotland (21.1%) and only several percentage points below the estimates for the U.K. overall (24.5%) and U.S. (26.0%). Alternatively, it is possible that transmission was merely flatter (more platykurtic) in Canada during the analytic window. Overall, the estimates in Canada had strengths as well as limitations that could be further reduced through additional model calibration or additional analyses suggestive of a flatter wave relative to U.S. and U.K. estimates.\u003c/p\u003e \u003cp\u003eMore programs like the UKHSA testing-based surveillance program are needed to monitor ongoing health threats. The UKHSA program was methodologically rigorous. Relative to wastewater-derived estimates, testing-based estimates are more straightforward to interpret, likely more easily understood by consumers, and require less inference. In being concrete and straightforward, they have the potential to increase consumer trust, which is vital for the long-term strategy of public health. It was remarkable that estimates of transmission correlated almost perfectly between England and Scotland (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.999, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Estimates demonstrated excellent international convergence with the U.S. and Canada (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), particularly when centered by peak rather than date to account for variation in wave timing. These observed correlations may be artificially reduced relative to real associations because the analysis focuses on solely the months of high transmission, rather than including relative lulls too. Overall, there was strong evidence of validity when considering the nuanced limitations of wastewater-derived estimates.\u003c/p\u003e \u003cp\u003eOverall, the findings document the international COVID-19 infection burden. The three countries had an estimated 112,721,033 infections during the peak two months of the surge. Such infections represent 25.3% of the total populations of the three countries when weighted by population size and 23.8% when each country is given equal weight regardless of population size. The infection burden contributes to worker and student absences,\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e hospitalizations,\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e gaps in treatment for people with serious medical conditions like cancer, \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Long Covid (the umbrella term for symptoms and conditions persisting or emerging\u0026thinsp;\u0026gt;\u0026thinsp;3 months post-infection), caregiver duties,\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e mortality,\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e family bereavement and psychiatric symptoms,\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and reductions in the gross domestic product.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e The public health and economic costs of the high incidence of COVID-19 infections warrant further attention.\u003c/p\u003e \u003cp\u003eAs an example, consider Long Covid. Each infection is thought to carry a 5\u0026ndash;40% risk of Long Covid,\u003csup\u003e\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e with the low estimate representing well-documented and clinically significant new comorbidities and the high estimate reflecting more so lingering symptoms that may or may not affect daily functioning. The 112\u0026nbsp;million estimated infections observed in these three countries during the two months of peak transmission would be estimated to translate into 5.6\u0026nbsp;million to 45.1\u0026nbsp;million new resulting Long Covid conditions (U.S.: 4.4 to 35.0\u0026nbsp;million, Canada: 0.4 to 3.4\u0026nbsp;million, U.K.: 0.8 to 6.7\u0026nbsp;million). The generalizability of waves across other countries warrants further investigation. If 23.8\u0026ndash;25.3% of the world\u0026rsquo;s population of 8.0\u0026nbsp;billion (January 1, 2024)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e were to experience an infection during the peak two months of a surge, this would represent 1.9 to 2.0\u0026nbsp;billion infections and, assuming a 5\u0026ndash;40% risk, 95 to 760\u0026nbsp;million new Long Covid conditions. Better worldwide COVID-19 surveillance would help to shed light on the international infection burden and Long Covid.\u003c/p\u003e \u003cp\u003eThis body of research has several strengths and limitations. Strengths include that the data were from three countries, transmission was assessed frequently, the work was conducted by public health officials and uncompensated volunteers during a pandemic, the data and analyses involved scientists spanning multiple specialized disciplines, and the findings involved integrating multiple data sources. Several limitations are noted. The analysis is premised upon different regions having similar patterns of transmission during the winter 2023-24 surge. Analyses account for variation in the timing of the peak of the surge. However, an underlying assumption is that differences in peak levels of transmission during specific time periods might be similar. The analyses largely bear that out. Next, analyses may underestimate transmission in children, especially young children. Wastewater-derived case estimation may underestimate child cases, as children produce less waste proportionate to body weight and potentially less virus in wastewater.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e The UKHSA study did not include children under 3 years old. Transmission among the youngest children could be higher due to lower access to and utilization of vaccines, the inability to mask well or at all in that age group, and often exposures in childcare environments with poor indoor air quality. Finally, reporting lags and delayed retroactive corrections undermine the public health impact of surveillance programs, as timely and accurate data are needed to reasonably inform real-world decision making.\u003c/p\u003e \u003cp\u003eIn the future, public health would benefit from improved infrastructure surrounding infectious disease surveillance. First, this research shows that wastewater surveillance can be used to derive statistics similar to those from testing programs \u0026ndash; such as the proportion of the population actively infectious \u0026ndash; that matter to the public. However, there remains a need for improved methodologic standards surrounding wastewater surveillance, including timelines for reporting as well as how to standardize data across regions and time points. Although this research used high-quality data from top-tier national sources, many local and regional wastewater surveillance programs have opportunities to improve rigor through better methodologic standards. Second, the expansion of wastewater surveillance programs to include more regions would offer tremendous advantages.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Surveillance in more regions within countries and across the globe would better inform decision making and strengthen trust in public health. Third, community-based COVID-19 testing-based surveillance programs have been limited and should also be expanded. The existence of testing-based surveillance programs would allow for the continued monitoring of the validity of estimates derived from wastewater surveillance programs and models. They would also inform efforts to calibrate wastewater models of transmission. For example, if a testing-based surveillance program identified the levels of transmission during the peaks of surges and the valleys of lulls, wastewater models could be calibrated to yield similar estimates. Thus, there are many benefits to improving the rigor and availability of infectious disease surveillance programs.\u003c/p\u003e \u003cp\u003eIn closing, COVID-19 transmitted at high rates during the winter 2023-24 surges in the U.S., Canada, and the U.K., with high convergence across methodologies and nations. About \u0026frac14; of the population in these nations were estimated to have been infected during the peak two months of the surge, meaning\u0026thinsp;\u0026gt;\u0026thinsp;100\u0026nbsp;million infections. These infections continue to have significant health, social, educational, and economic implications. The increased allocation of resources toward prevention, diagnostics, and treatments appears warranted.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTancredi S, Cullati S, Chiol\u0026eacute;ro A (2024) Surveillance bias in the assessment of the size of COVID-19 epidemic waves: a case study. Public Health 234:98\u0026ndash;104\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstitute for Health Metrics and Evaluation. 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Canada's population estimates: Strong population growth in 2023 (2024) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www150.statcan.gc.ca/n1/daily-quotidien/240327/dq240327c-eng.htm\u003c/span\u003e\u003cspan address=\"https://www150.statcan.gc.ca/n1/daily-quotidien/240327/dq240327c-eng.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUK Office for National Statistics. 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Nat Med 30(8):2148\u0026ndash;2164\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Y, Choi T, Al-Aly Z (2024) Postacute sequelae of SARS-CoV-2 infection in the pre-delta, delta, and omicron eras. N Engl J Med 391(6):515\u0026ndash;525\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowe B, Xie Y, Al-Aly Z (2022) Acute and postacute sequelae associated with SARS-CoV-2 reinfection. Nat Med 28(11):2398\u0026ndash;2405\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRose C, Parker A, Jefferson B, Cartmell E (2015) The characterization of feces and urine: a review of the literature to inform advanced treatment technology. Crit Rev Environ Sci Technol 45(17):1827\u0026ndash;1879\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaughton CC, Roman FA Jr, Alvarado AGF et al (2023) Show us the data: global COVID-19 wastewater monitoring efforts, equity, and gaps. FEMS microbes 4:xtad003\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Tulane University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, pandemics, wastewater surveillance, epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-5786667/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5786667/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Better estimates of COVID-19 transmission are needed since testing has declined. The present investigation examined the correspondence among estimates of COVID-19 transmission during the winter 2023-24 surge using wastewater-derived estimates for the U.S. and Canada and testing-derived estimate in the U.K. to evaluate validity and provide vital public health data on transmission levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The study used data from wastewater-derived estimates of COVID-19 transmission in the U.S. (Pandemic Mitigation Collaborative dashboard) and Canada (COVID-19 Resources Canada dashboard) and testing-based surveillance in the U.K. (Health Security Agency). Data sets were linked by date and relative to the peak transmission date within each data set. Analyses focused on the UKHSA study period of November 2023 to March 2024. Analyses 1) described transmission on the peak day, 2) examined relative agreement in the patterns of transmission via correlations, 3) examined absolute agreement on the proportion of the population actively infectious across the two months of peak transmission, and 4) described estimates of the proportion of populations infected during the peak two months.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e On the peak day of infections, an estimated 1.95 million people were infected in the U.S., 148 thousand in Canada, and 431 thousand in the U.K., meaning an estimated 2.5%-4.5% of these populations were actively infectious. Estimates showed high relative agreement in the patterns of transmission throughout the wave, especially between the U.S. and U.K. (r=.974, p\u0026lt;.001). During the wave, 93.5% of U.S. estimates and 68.8% of Canada estimates had excellent or better agreement with the U.K. data. An estimated \u0026gt;100 million people were infected in the U.S., Canada, and the U.K. during the two peak months, or 20.9%-26.0% of each population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion: \u003c/strong\u003eFindings support the ongoing public health significance of COVID-19 by documenting high levels of transmission during the winter 2023-24 surge. Transmission estimates had high agreement across methodologies and nations. More resources are needed to prevent transmission and diagnose and treat long-term health sequelae.\u003c/p\u003e","manuscriptTitle":"COVID-19 Transmission During the Winter 2023-24 Surge: A Comparative Analysis of Surveillance Estimates in the U.S., Canada, and the U.K.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-09 08:18:11","doi":"10.21203/rs.3.rs-5786667/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec708d54-9c89-42d8-9bfc-a35721956aef","owner":[],"postedDate":"January 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42546442,"name":"Epidemiology"},{"id":42546443,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2025-01-09T08:18:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-09 08:18:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5786667","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5786667","identity":"rs-5786667","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}