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Surveillance Systems (427) Infectious Diseases (non-STD/STI) (1478) Outbreak and Pandemic Preparedness and Management (1673) JMIR Theme Issue: COVID-19 Special Issue (2388) Surveillance Reports (122) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1481) Public (e)Health, Digital Epidemiology and Public Health Informatics (636)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53551, first published .
Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in Europe: Longitudinal Trend Analysis

Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in Europe: Longitudinal Trend Analysis

Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in Europe: Longitudinal Trend Analysis

Authors of this article:

Alexander L Lundberg1, 2 Author Orcid Image ;   Scott A Wu3 Author Orcid Image ;   Alan G Soetikno3 Author Orcid Image ;   Claudia Hawkins4, 5 Author Orcid Image ;   Robert L Murphy4, 6 Author Orcid Image ;   Robert J Havey6, 7 Author Orcid Image ;   Egon A Ozer4, 8 Author Orcid Image ;   Charles B Moss9 Author Orcid Image ;   Sarah B Welch1, 2 Author Orcid Image ;   Maryann Mason1, 2 Author Orcid Image ;   Yingxuan Liu1, 2 Author Orcid Image ;   Lori A Post1, 2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Original Paper

    1Buehler Center for Health Policy and Economics, Robert J. Havey, MD Institute for Global Health, Northwestern University, Chicago, IL, United States

    2Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States

    3Feinberg School of Medicine, Northwestern University, Chicago, IL, United States

    4Department of Medicine, Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States

    5Center for Global Communicable and Emerging Infectious Diseases, Robert J. Havey, MD Institute for Global Health, Northwestern University, Chicago, IL, United States

    6Robert J. Havey, MD Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States

    7Department of Medicine, General Internal Medicine and Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States

    8Center for Pathogen Genomics and Microbial Evolution, Robert J. Havey, MD Institute for Global Health, Northwestern University, Chicago, IL, United States

    9Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL, United States

    Corresponding Author:

    Lori A Post, PhD

    Buehler Center for Health Policy and Economics

    Robert J. Havey, MD Institute for Global Health

    Northwestern University

    420 E. Superior

    Chicago, IL, 60611

    United States

    Phone: 1 3125031706

    Email: lori.post@northwestern.edu


    Background: In this study, we built upon our initial research published in 2020 by incorporating an additional 2 years of data for Europe. We assessed whether COVID-19 had shifted from the pandemic to endemic phase in the region when the World Health Organization (WHO) declared the end of the public health emergency of international concern on May 5, 2023.

    Objective: We first aimed to measure whether there was an expansion or contraction in the pandemic in Europe at the time of the WHO declaration. Second, we used dynamic and genomic surveillance methods to describe the history of the pandemic in the region and situate the window of the WHO declaration within the broader history. Third, we provided the historical context for the course of the pandemic in Europe in terms of policy and disease burden at the country and region levels.

    Methods: In addition to the updates of traditional surveillance data and dynamic panel estimates from the original study, this study used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data to identify the appearance and duration of variants of concern. We used Nextclade nomenclature to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2. Finally, we conducted a 1-tailed t test for whether regional weekly speed was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data across the sample period.

    Results: Speed for the region had remained below the outbreak threshold for 4 months by the time of the WHO declaration. Acceleration and jerk were also low and stable. While the 1-day and 7-day persistence coefficients remained statistically significant, the coefficients were moderate in magnitude (0.404 and 0.547, respectively; P<.001 for both). The shift parameters for the 2 weeks around the WHO declaration were small and insignificant, suggesting little change in the clustering effect of cases on future cases at the time. From December 2021 onward, Omicron was the predominant variant of concern in sequenced viral samples. The rolling t test of speed equal to 10 became insignificant for the first time in April 2023.

    Conclusions: While COVID-19 continues to circulate in Europe, the rate of transmission remained below the threshold of an outbreak for 4 months ahead of the WHO declaration. The region had previously been in a nearly continuous state of outbreak. The more recent trend suggested that COVID-19 was endemic in the region and no longer reached the threshold of the pandemic definition. However, several countries remained in a state of outbreak, and the conclusion that COVID-19 was no longer a pandemic in Europe at the time is unclear.

    JMIR Public Health Surveill 2024;10:e53551

    doi:10.2196/53551

    Keywords

    Europe; COVID-19; history of the pandemic; method of the moments; Arellano-Bond estimators; Albania; Andorra; Austria; Belarus; Belgium; Bosnia and Herzegovina; Bulgaria; Croatia; the Czech Republic; Denmark; Estonia; Finland; France; Germany; Greece; Greenland; Hungary; Iceland; Ireland; the Isle of Man; Italy; Latvia; Liechtenstein; Lithuania; Luxembourg; Moldova; Monaco; Montenegro; the Netherlands; Norway; Poland; Portugal; Romania; San Marino; Serbia; Slovakia; Slovenia; Spain; Sweden; Switzerland; Ukraine; the United Kingdom; the Vatican City 


    Background

    COVID-19, the disease caused by the virus SARS-CoV-2, was first detected in Wuhan, China, in the fall of 2019 [1-5]. The first European [6] case of COVID-19 was reported in France on January 24, 2020, with additional cases reported in Germany and Finland soon afterward [7,8]. Our research team conducted an analysis of the pandemic in Europe 1 year into the pandemic [9]; this study provides 2 additional years of updated surveillance and analysis for the region.

    We adopt the World Bank’s definition of Europe, which is based on economic development and geographical proximity, encompassing Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Greenland, Hungary, Iceland, Ireland, the Isle of Man, Italy, Latvia, Liechtenstein, Lithuania, Luxembourg, Moldova, Monaco, Montenegro, the Netherlands, Norway, Poland, Portugal, Romania, San Marino, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, the United Kingdom, and the Vatican City [6].

    The World Health Organization (WHO) and Director-General Tedros Ghebreyesus declared the end of COVID-19 as a public health emergency of international concern on May 5, 2023 [10-12], based on the recommendation of the COVID-19 Emergency Committee [12]. To that end, we compared how the pandemic was progressing before and after the declaration.

    Empirical Definition of Pandemic Versus Epidemic Versus Outbreak Versus Endemic

    Epidemiological terms, such as pandemic, epidemic, outbreak, and endemic, are used to describe the occurrence and spread of diseases [13,14]. The distinctions between these terms lie in their scope, geographic extent, and severity. An epidemic refers to a sudden increase in the number of disease cases in a specific population or region. If the epidemic spreads across several countries or continents, it becomes a pandemic. An outbreak, on the other hand, describes a sudden increase in a concentrated setting, usually involving a more limited geographic area than an epidemic. Endemic refers to the constant presence of a disease in a particular geographic region or population, with no sudden increases in case volume [15,16].

    Traditional Surveillance Versus Enhanced Surveillance

    Public health surveillance is the “ongoing, systematic collection, analysis, and interpretation of health-related data essential to planning and evaluation of public health practice” [17]. Surveillance not only explains the burden of death and disease due to a virus but also generates research questions and guides researchers on topics that require further investigation [18-32]. Surveillance allows us to compare the burden of disease between geographical regions and to understand which regions are most impacted. The impact can be measured through rates of how many people contract a disease and how many die, as well as the affiliated costs.

    However, traditional surveillance carries several limitations that this study had addressed. Traditional surveillance provides a snapshot of what has already happened [18-32], meaning surveillance is static and only considers the past. In the middle of a burgeoning pandemic, policy makers and public health practitioners also need to understand what is about to happen. Is an outbreak increasing? Will growth switch from linear to exponential? Are more people dying from that particular condition in one place than another? To inform health policy and practice, knowledge of what is about to happen is often more valuable than knowledge of what did happen. To that end, we have developed enhanced surveillance metrics that reflect the dynamics of a pandemic and inform imminent growth, most importantly, where along the epidemiological outbreak curve a particular region is situated. We have also included dynamic metrics about the speed of the pandemic at the national, regional, and global levels and measured how the acceleration of speed this week compared to last week, as well as how novel infections last week would predict new cases this week. We can think of the latter measure as the echoing forward of cases. These metrics were tested and validated in prior research [9,33-43].

    The novel metrics add acceleration, jerk, and 1-day and 7-day persistence to the traditional measure of speed. The rate of new COVID-19 cases per 100,000 population is the “speed” of the pandemic. Acceleration is the difference in speed from one unit of time to the next. A positive (negative) acceleration means cases are rising (falling), and an acceleration of 0 indicates an inflection point or stable speed. From physics nomenclature, “jerk” is the change in acceleration from one time interval to the next. A positive jerk may indicate explosive growth in a disease. Finally, 1-day and 7-day persistence measures capture the impact of the 1-day and 7-day lag of speed on current speed. These measures derive from an Arellano-Bond dynamic panel data model, and they capture the echo-forward effect of COVID-19 cases on future cases either 1 or 7 days later [44].

    This research team used these metrics to effectively analyze the role of economic reopening on COVID-19 transmissions [42]. These metrics also provided the status of the pandemic in global regions, including Europe [9]. Finally, they helped quickly identify the emergence of the Omicron variant, and they were used in policy briefs throughout the pandemic [45].

    For the purpose of this study, standard surveillance metrics explain what has already happened in Europe, while enhanced surveillance metrics speak to what is about to happen or where along an epidemiological curve a country may sit. We used both types of metrics to analyze the possible end to the pandemic.

    Objectives

    This study has 3 objectives. First, we aimed to measure whether there was an expansion or contraction in the pandemic in Europe when WHO declared the end of the COVID-19 pandemic as a public health emergency of international concern on May 5, 2023. At both the region and country levels, we used advanced surveillance and analytical techniques to describe the status of the pandemic in a 2-week window around the WHO declaration. From a public health perspective, we need to know whether the rate of new COVID-19 cases was increasing, decreasing, or stable from week to week and whether any changes in the transmission rate indicated an acceleration or deceleration of the pandemic. Statistical insignificance is significant; it can signal the epidemiological “end” to the pandemic if the rate of new cases is 0 (or very low) and stable, meaning the number of new cases is neither accelerating nor decelerating.

    Second, we used dynamic and genomic surveillance methods to describe the history of the pandemic in the region and situate the time window around the WHO declaration within the broader history. We included the ratio of COVID-19 deaths to the number of transmissions as a proxy for the mortality risk from infection at the population level. We also included a historical record of genomic surveillance from sequenced viral specimens to identify the appearance and spread of variants of concern in the region.

    Third, we aimed to provide historical context for the course of the pandemic in Europe. We addressed several questions. How did countries respond to the pandemic? How did the region fare in terms of disease burden? Furthermore, what social, economic, and political factors shaped the course of COVID-19 in the region? This context can provide important lessons for disease prevention and mitigation in future pandemics.


    Overview

    This study conducted trend analyses with longitudinal COVID-19 data from Our World in Data [46]. This study provides updates of traditional surveillance data and dynamic panel estimates from the original study by Post et al [9,41,42,47,48]. For the region of Europe, the data comprised an unbalanced panel of 44 countries and territories, running from August 14, 2020, to May 12, 2023. Because a number of countries around the world switched from daily to weekly reports at various points in 2023, we used a cubic spline to interpolate daily new cases and deaths if any country had 4 consecutive periods of nonzero new cases interspersed by 6 days of 0 new cases.

    To identify the appearance and duration of variants of concern, we also used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID), which is an effective and trusted web-based resource for sharing genetic, clinical, and epidemiological COVID-19 data [49-52]. We used Nextclade nomenclature [53] to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2 [54,55]. Metadata for the study period, which add geographic location to the clade designations, were collected on June 22, 2023. To avoid low-frequency or potentially erroneous samples, the data set was further filtered to exclude months with <100 available samples, variant groups with <5 samples in a month, and variant groups representing <0.5% of the total samples in a month. The final data set consisted of 184,386 total samples available on GISAID [49-52].

    We analyzed the potential “statistical end” to the pandemic with a 1-tailed t test for whether the mean of speed was equal to or greater than the outbreak threshold of 10. We ran the test on a rolling 6-month window over weekly speed for the region, and we plotted the P values from the test over time. All statistical analyses were conducted in R (version 4.2.1; R Foundation for Statistical Computing) with the plm package (version 2.6-2) [47,56].

    Ethical Considerations

    This study does not constitute research with human participants (as defined by 45CFR46:102) because all data are publicly available and contain no identifiable private information. The institutional review board’s review was therefore unsolicited.


    Dynamic Panel Estimates

    Table 1 presents the dynamic panel estimates for the week of May 5, 2023.

    Table 1. Arellano-Bond dynamic panel data estimates for COVID-19 dynamics at the country level in Europe for the week of May 5, 2023a.
    VariableValueP value
    1-day lag coefficient0.404<.001
    7-day lag coefficient0.547<.001
    Shift parameter week of April 280.032.42
    Shift parameter week of May 50.075.48
    Weekend effect−0.272.01

    aWald: χ26=6104; P<.001; Sargan: χ2540=40; P>.99.

    While the 1-day and 7-day lag coefficients were positive and statistically significant (P<.001 for both), they were moderate in magnitude (0.404 and 0.547, respectively). For example, the 7-day coefficient suggests a cluster effect in which 1 case on a given day predicts 0.547 cases 1 week later. The shift parameters for the weeks of April 28, 2023, and May 5, 2023, were small and statistically insignificant (P=.42 and P=.48, respectively), however, suggesting the cluster effect of cases remained stable around the window of the WHO declaration.

    The dynamic panel estimates have several advantages over the basic reproductive number, R0, which estimates the average number of people a contagious person will infect [57]. Foremost, R0 depends on many variables, such as social distancing, vaccination rates, demographics, and the transmissibility of a pathogen. Because the SARS-CoV-2 virus has mutated over time, so has its R0, but rapidly updated estimates for R0 are difficult to obtain. Vaccination campaigns and public health mitigation efforts have also evolved and thereby shaped R0. The dynamic panel estimates are based on a recent, 120-day window, so they can quickly adjust to new circumstances. The Arellano-Bond model is also robust to time-invariant, unobservable factors (in the application, stable differences between countries); corrects for autocorrelation; and allows for statistical tests of various model parameters [42].

    The Wald and Sargan tests can assess the validity of the dynamic panel model. The Wald hypothesis test checks whether the independent variables have explanatory power for the dependent variable. From Table 1, the Wald test was highly significant (P<.001), rejecting the null hypothesis of no explanatory power. The Sargan test checks the validity of the overidentifying restrictions of the model. A rejection of the null would be evidence against the validity, but the test failed to reject the null with P>.99.

    Statis Surveillance Metrics

    Static surveillance metrics for the weeks of April 28 and May 5, 2023, are provided in Tables 2 and 3.

    Table 2. Static COVID-19 surveillance metrics for European countries for the week of April 28, 2023.
    CountryNew COVID-19 cases, nCumulative COVID-19 cases, n7-day moving average of new casesWeekly transmission rate per 100,000 populationNew weekly deathsCumulative deaths7-day moving average of deathsDeath rate per 100,000 populationConditional death rate
    Albania0334,0900003604000.01
    Andorra048,0154.5700159000
    Austria9526,067,780614.4310.65322,4562.140.030
    Belgium3174,795,866256.432.72534,2804.570.040.01
    Bosnia and Herzegovina14402,9065.860.43116,3380.140.030.04
    Bulgaria1551,305,030132.572.29338,3391.860.040.03
    Croatia621,272,88669.8610.77018,1803.570.790.01
    Czech Republic934,640,81886.430.89042,7781.7100.01
    Denmark203,412,109720.347857250.120
    Estonia23618,29732.141.7303001000
    Finland2791,475,8412465.048961210.570.140.01
    France616138,946,4304,651.439.0957163,076350.080
    Germany149638,409,9451,058.291.799174,08616.860.010
    Greece17636,031,8681,870.86118.81036,8118.140.530.01
    Hungary562,201,82464.433.92048,7781.140.060.02
    Iceland0209,191000260000
    Ireland1831,710,80860.293.64389142.430.060.01
    Italy358025,809,2082,974.436.0631189,90423.710.050.01
    Latvia11977,73417.430.59063510.8600.01
    Liechtenstein021,46500087000
    Lithuania801,318,87170.142.911967110.040.01
    Luxembourg0319,9590001232000
    Malta9118,5241211.94083500.190.01
    Moldova17620,333213.73012,1130.140.040.02
    Monaco216,7631.715.48067000
    Montenegro24291,70226.713.8302826000.01
    Netherlands08,610,37200022,992000
    Norway771,483,93954.291.42054764.5700
    Poland2516,514,536136.290.632119,5591.140.010.02
    Portugal2385,582,561193.572.32926,6285.290.090
    Romania5043,393,902522.2917.93068,08960.160.02
    San Marino1524,18910.7144.520125000.01
    Serbia4722,536,159355.716.87018,0220.5700.01
    Slovakia271,866,47014.430021,167000.01
    Slovenia401,343,64631.431.89293363.140.090.01
    Spain169213,845,8031,461.1424.91184120,91726.290.390.01
    Sweden1402,707,171147.719.31024,17410.710.710.01
    Switzerland1344,403,491142.2910.75014,0040.710.080
    Ukraine11505,538,3571,258.5720.270112,21011.710.180.02
    United Kingdom167024,594,9651,615.572.470226,27814.4300.01
    Table 3. Static COVID-19 surveillance metrics for European countries for the week of May 5, 2023.
    CountryNew COVID-19 cases, nCumulative COVID-19 cases, n7-day moving average of new casesWeekly transmission rate per 100,000 populationNew weekly deathsCumulative deaths7-day moving average of deathsDeath rate per 100,000 populationConditional death rate
    Albania0334,0900003604000.01
    Andorra048,015000159000
    Austria4366,071,124477.714.88222,4793.290.020
    Belgium1844,797,400219.141.58134,3002.860.010.01
    Bosnia and Herzegovina0402,9242.570016,3400.2900.04
    Bulgaria861,305,755103.571.27138,3511.710.010.03
    Croatia431,273,25650.437.39018,2134.710.610.01
    Czech Republic744,641,29267.710.71242,7881.430.020.01
    Denmark1093,412,75091.571.85586196.710.090
    Estonia21618,49227.861.5803001000
    Finland2051,477,3392143.70696596.710.110.01
    France465738,973,5073,868.146.8728163,24223.710.040
    Germany105638,417,0471,014.571.2713174,16911.860.020
    Greece20486,044,5171,878.29138.05036,8637.430.500.01
    Hungary472,202,17950.143.29048,7820.570.020.02
    Iceland0209,191000260000
    Ireland541,711,28768.431.08089170.4300.01
    Italy259225,828,2522,720.574.3924190,08025.140.040.01
    Latvia9977,80510.140.491635810.050.01
    Liechtenstein121,4680.430087000
    Lithuania731,319,39574.862.65096720.1400.01
    Luxembourg0319,9590001232000
    Malta7118,5747.299.71083500.190.01
    Moldova12620,44314.292.58012,1150.290.110.02
    Monaco216,7731.435.48067000
    Montenegro5291,81716.430.80028270.1400.01
    The Netherlands08,610,37200022,992000
    Norway591,484,28749.711.0905497300
    Poland1136,515,449130.430.286119,5813.140.020.02
    Portugal2315,583,979202.572.25326,6716.140.030
    Romania4083,397,365461.4314.52068,11740.130.02
    San Marino524,2417.4314.840125000.01
    Serbia2722,538,142283.293.96118,0331.570.010.01
    Slovakia211,866,64724.570021,167000.01
    Slovenia271,343,83126.431.27193562.860.050.01
    Spain250213,845,8032,143.2936.820120,91700.250.01
    Sweden1332,708,118134.868.83024,24710.430.640.01
    Switzerland994,404,340115.437.94014,0121.140.060
    Ukraine8145,544,969950.2914.350112,2718.710.130.02
    United Kingdom141024,604,5291,366.292.090226,278000.01

    Most countries had a transmission rate considered low by the US Centers for Disease Control and Prevention [9,33-43,58]. Specifically, a “low” transmission is considered to be no more than 10 cases per 100,000 people per week. “Moderate” transmission is 10 to 50 cases per 100,000 people per week. “Substantial” transmission is 50 to 100 cases [58,59]. However, a number of countries were in a state of outbreak. In particular, Greece had a speed of 119 new cases per 100,000 population in the week of April 28, and San Marino had a speed of 45. Greece was therefore in a “substantial” outbreak, while San Marino was in a “moderate” outbreak. Austria, Croatia, Malta, Romania, Spain, Switzerland, and Ukraine were also in “moderate” outbreaks.

    By the following week, only Greece, Romania, San Marino, Spain, and Ukraine remained in an outbreak. While speed dropped notably for San Marino, speed alarmingly increased for Greece and Spain. We noted that speed in island nations often vacillates between high and low rates of transmission.

    Overall, the status of the pandemic around the WHO declaration in Europe is consistent with an “end” to the pandemic, but the distinction is muddied by continued outbreaks in several countries. The outbreaks in Greece and San Marino comprise a small portion of the overall European population, and speed is more variable in island nations. On the basis of the definition of a pandemic or an outbreak in several countries, the data suggest a shift from pandemic to endemic COVID-19, but the continued outbreaks in 3 other countries do caution that the conclusion may be premature.

    Comparing Tables 2 and 3 demonstrates a drop in transmission rates before and after the WHO declared an end to COVID-19 as a public health emergency. Overall, the United Kingdom, Italy, and Germany had the most cases of COVID-19 transmissions and deaths, but these ranks are largely a function of population size. Thus, a better measure of COVID-19 fatality risk is the number of COVID-19 cases and deaths per 100,000 people. Moreover, death is often a better proxy for the state of an outbreak than transmissions because deaths are less likely to be undercounted [60]. Undercounting may be due to poor public health infrastructure, home antigen testing, or a dearth of polymerase chain reaction testing or other resources. When we control for the risk of death given the number of COVID-19 transmissions, we find that Eastern European countries, such as Bosnia and Herzegovina, Moldova, and Poland, had the highest conditional death rates. For example, Bosnia and Herzegovina had the highest conditional death rate of 0.04 deaths per confirmed case. These disparities could be driven by differences in public health infrastructure, demographics, and the influx of refugees from the Russian invasion of Ukraine.

    Enhanced Dynamic Surveillance Metrics

    Tables 4 and 5 contain enhanced dynamic surveillance metrics for the weeks before and after May 5.

    We note that the figures in Tables 4 and 5 are not calculated as day-over-day averages across the week, as they are in Tables 2 and 3. Thus, the magnitudes of speed differ slightly across the tables. Again, by the week of May 5, speed was low for every country except Greece, Romania, San Marino, Spain, and Ukraine. The 7-day persistence effect on speed was also relatively high for these countries but low for others. Acceleration was almost uniformly negative, with the exception of Greece and Spain, which saw their outbreaks grow somewhat from the first week to the second. Across the board, jerk tended to be very small in magnitude, suggesting little change in acceleration rates.

    Table 6 compares the 7-day persistence effect on speed for the top 5 countries around the 2 weeks of the WHO declaration. These ranks largely reflect the speed in the countries with outbreaks in the prior tables.

    Table 4. Novel surveillance metrics for European countries for the week of April 28, 2023.
    CountrySpeedaAccelerationbJerkc7-day persistence effect on speedd
    Albania0000
    Andorra7.340−2.152.81
    Austria6.870.421.225.08
    Belgium2.20−0.12−0.031.79
    Bosnia and Herzegovina0.1800.060.19
    Bulgaria1.950.080.051.21
    Croatia12.12−0.37−0.057.44
    Czech Republic0.82−0.07−0.070.76
    Denmark1.22−0.17−0.170.84
    Estonia2.42−0.020.111.70
    Finland4.44−0.11−0.152.73
    France6.86−0.09−0.354.92
    Germany1.270.020.010.88
    Greece126.11−3.090.2675.57
    Hungary4.52−0.220.013.23
    Iceland0000
    Ireland1.200.280.730.84
    Italy5.04−0.48−1.353.03
    Latvia0.94−0.0600.55
    Liechtenstein0.36000.39
    Lithuania2.5500.041.76
    Luxembourg0000
    Malta15.67−1.28−0.0112.24
    Moldova4.49−0.300.034.01
    Monaco4.700.78−0.780.85
    Montenegro4.2600.212.97
    Netherlands0000
    Norway10.030.040.67
    Poland0.34−0.010.090.41
    Portugal1.88−0.05−0.320.99
    Romania18.59−0.480.1713.01
    San Marino31.802.975.9423.16
    Serbia5.180.030.023.57
    Slovakia0.40000.07
    Slovenia1.480.080.051.28
    Spain21.510.880.2010.81
    Sweden9.80−0.1705.91
    Switzerland11.39−0.17−0.036.54
    Ukraine22.19−0.56−0.0115.18
    United Kingdom2.39−0.02−0.051.32

    aNew COVID-19 cases per 100,000 people.

    bThe difference in speed from one week to the next.

    cThe change in acceleration from one week to the next.

    dThe impact of the 7-day lag of speed on current speed (the echo-forward effect of COVID-19 cases on future cases 7 days later).

    Table 5. Novel surveillance metrics for European countries for the week of May 5, 2023.
    CountrySpeedaAccelerationbJerkc7-day persistence effect on speedd
    Albania0000
    Andorra007.344.25
    Austria5.34−0.82−0.703.98
    Belgium1.88−0.160.011.27
    Bosnia and Herzegovina0.08−0.06−0.060.10
    Bulgaria1.53−0.15−0.081.13
    Croatia8.75−0.480.027.02
    Czech Republic0.65−0.030.010.48
    Denmark1.560.220.170.71
    Estonia2.10−0.020.091.40
    Finland3.86−0.190.162.57
    France5.70−0.320.113.97
    Germany1.22−0.080.010.74
    Greece126.612.750.7873.03
    Hungary3.50−0.090.022.62
    Iceland0000
    Ireland1.36−0.37−0.700.70
    Italy4.61−0.240.292.92
    Latvia0.55−0.020.120.55
    Liechtenstein1.09000.21
    Lithuania2.72−0.040.091.48
    Luxembourg0000
    Malta9.82−0.320.199.08
    Moldova3.04−0.160.012.60
    Monaco3.9100.782.72
    Montenegro2.62−0.43−0.432.47
    Netherlands0000
    Norway0.91−0.05−0.010.58
    Poland0.33−0.05−0.080.20
    Portugal1.97−0.010.091.09
    Romania16.43−0.49−0.1010.77
    San Marino22.05−4.24−3.3918.42
    Serbia4.12−0.42−0.053
    Slovakia0.45000.23
    Slovenia1.25−0.090.140.86
    Spain31.541.700.0512.46
    Sweden8.95−0.070.025.67
    Switzerland9.25−0.40−0.036.60
    Ukraine16.75−0.840.0212.85
    United Kingdom2.02−0.060.031.39

    aNew COVID-19 cases per 100,000 people.

    bThe difference in speed from one week to the next.

    cThe change in acceleration from one week to the next.

    dThe impact of the 7-day lag of speed on current speed (the echo-forward effect of COVID-19 cases on future cases 7 days later).

    Table 6. The European countries with the highest 7-day persistence estimate in the weeks of April 28 and May 5, 2023.
    RankCountry7-day persistence week 1 (April 28)7-day persistence week 2 (May 5)
    1Greece75.5773.03
    2San Marino23.1618.42
    3Ukraine15.1812.85
    4Romania13.0110.77
    4Spain10.8112.46
    5Malta12.249.08

    These metrics suggest that the pandemic may have ended in the region. Still, speed and persistence measures were moderate to high for several countries in outbreaks, and the region had not exited the pandemic with as much clarity as had several other global regions, such as South Asia or sub-Saharan Africa.

    Figure 1 plots regional speed, acceleration, jerk, and 7-day persistence metrics from August 14, 2020, to May 12, 2023. The dashed gray line denotes the informal US Centers for Disease Control and Prevention outbreak threshold of speed equal to 10. The region was in a nearly continuous state of outbreak for the entire period. However, speed fell below the outbreak threshold and remained below it from January 2023 onward.

    ‎
    Figure 1. Novel surveillance metrics (speed, acceleration, jerk, and 7-day persistence) for COVID-19 transmissions in Europe from August 2020 to May 2023.

    The region did see a slight bump in cases around the end of February 2023. Still, the bump did not cause the region to cross the outbreak threshold.

    Europe saw one especially pronounced outbreak over the course of the pandemic. The outbreak caused speed to reach a peak of 246 novel COVID-19 cases per 100,000 population in the last week of January 2022. Figure 2 plots variant groups as a proportion of all viral specimens collected and sequenced in the region (and made available through GISAID) each month. The outbreak occurred just after the arrival of the Omicron variant. Europe, like much of the rest of the world, saw a surge in cases amid the heightened transmissibility of Omicron [45]. Earlier outbreaks were driven by the ancestral, Alpha, and Delta variants.

    ‎
    Figure 2. Variants of concern (VOCs) as a proportion of all sequenced SARS-CoV-2 specimens from April 2020 to May 2023 in Europe.

    Another potential indication of the end of the pandemic is the continued dominance of the Omicron variant. Subclades of Omicron continue to circulate 4.5 months after WHO declared the end of the COVID-19 emergency [61]. Viral sequences have almost exclusively returned as Omicron and its subvariants ever since its arrival.

    Figure 3 plots P values from a series of 1-tailed t tests of whether speed for the region was equal to or greater than the threshold outbreak of 10. These tests were conducted over a rolling 6-month window of weekly regional speed. The dashed gray line denotes the least restrictive conventional significance level threshold of α=.10. The test strongly rejected the null in favor of the alternative until the very end of April 2023. While this more recent lack of statistical significance is consistent with the end of the pandemic in the region, its relative recency around the WHO declaration suggests prematurity in the conclusion that COVID-19 had transitioned from the pandemic to endemic phase in Europe.

    ‎
    Figure 3. P values from t tests of weekly COVID-19 transmissions per 100,000 people equal to 10 over a rolling, 6-month window in Europe.

    Figure 4 provides a time line of the onset of COVID-19 in Europe as well as vaccination programs and major events that likely shaped the course of disease control, such as the Next Generation European Union (NGEU) economic recovery package and the Russian invasion of Ukraine. Millions of refugees fled Ukraine, accelerating the spread of disease in the region. Mass human migration is affiliated with increased disease transmission [62].

    ‎
    Figure 4. Timeline of the COVID-19 pandemic in Europe. EU: European Union; VOC: variants of concern; WHO: World Health Organization.

    Principal Findings

    Multiple metrics suggest the pandemic had largely subsided in Europe by the time of the WHO declaration. Regional speed had remained below outbreak status for 4 months, and acceleration and jerk were both low and stable. The 1-day and 7-day persistence coefficients did remain statistically significant (P<.001 for both), but the coefficients were moderate in magnitude, and the shift parameters for the weeks around the WHO declaration were insignificant. This statistical insignificance implies no change in the clustering effect of cases on future cases at the time. The rolling t test of speed equal to 10 became insignificant for the first time in the month ahead of the declaration. Finally, Omicron had been the predominant variant of concern in sequenced viral samples for approximately 6 months.

    Still, among the 44 countries in Europe, 5 had surpassed the outbreak threshold at the time of the WHO declaration. While the region as a whole did not breach the outbreak threshold, 5 countries were classified as having epidemic status, with 4 being categorized as having mild epidemics. Our analysis of transmission rates, outbreak testing, and statistical findings suggests that the pandemic is likely concluding in Europe. Nevertheless, exercising caution, we refrain from definitively declaring the end of the pandemic at the time of the WHO declaration.

    History of Policy and Disease Burden

    For a brief history of policy and disease burden, differences in health outcomes across countries remained relatively minor despite heterogeneity in European COVID-19 mitigation efforts in terms of school closures, business restrictions, quarantines, social distancing, and mask mandates [63]. For instance, Denmark implemented the most stringent measures, while Sweden pursued a comparatively relaxed approach [63,64]. The environment and climate factors also factored into differential transmission and death rates [64,65]. Notably, widespread vaccination efforts proved effective in reducing the severity of COVID-19 cases and lowering mortality rates [66].

    Age structure and environmental risk factors such as pollution affected the COVID-19 pandemic at its onset [67]. Italy reported their first cases in March 2020 [68-70]. Hospitals and cities lacked preparedness, with the initial wave claiming 35,000 lives [68]. Similar challenges were observed in France, which struggled despite having robust health care resources [7,68,71-74]. Around 6 months into the pandemic, France had one of the highest prevalence rates of the virus in Europe, with >2 million cumulative confirmed cases at the time [73]. Despite benefiting from a universal health insurance system, centralized presidential regime with a strong public administration, and a relatively high number of health care professionals and hospital beds compared to other European countries, France was still underprepared for the pandemic’s onset, with the health care system soon overwhelmed by cases and fatalities [73,75]. During the peak months in 2020, Spain, Belgium, and Ireland had the highest incidence and mortality rates [76]. European health care systems improved as vaccines became available [77]. Despite difficulties, Europe’s health care system benefited from risk recognition, treatment availability, and increased vaccine access [78-81]. Throughout the pandemic, new variants and fluctuating social protection measures have led to additional waves of infection [82,83]. Since this time, case rates have fallen despite mild rebound periods, with the COVID-19 social and financial recovery in Europe benefiting from rapidly decreased social restrictions and unprecedented monetary investments [84].

    Policies Implemented to Control and Mitigate the Transmission of COVID-19

    When the WHO declared COVID-19 a pandemic [85], Europe was the epicenter [86]. The European Union (EU) responded by closing external borders [7,87], but public reactions to social restrictions were volatile, leading to protests [88,89]. Despite this, Europe adapted to new regulations and accelerated its digital transformation [90,91].

    The pandemic caused a 4% economic decline in Europe in 2020, with wide variation between countries [92]. To aid response, the EU allocated funds, including €140 million (US $159.6 million) in emergency funding [93]. Member states received continuous support through programs such as European instrument for temporary Support to mitigate Unemployment Risks in an Emergency [94,95]. In addition, in July 2020, the European Council, the European Commission’s sibling executive arm in the EU, agreed to an unprecedented €750 billion (US $855 billion) recovery package titled NGEU to be disseminated to member states through the end of 2023 [96]. NGEU, the largest stimulus package in European history, included special investments in future health care preparedness and modernization [97].

    The EU’s Health Security Committee held its first meeting regarding COVID-19 on January 17, 2020, with the first mobilization of COVID-19 research funds taking place on January 31, 2020 [94]. Early efforts were made in conjunction with member states to secure medical equipment and protective gear for health care workers and ensure the free movement of critical workers to and around the EU [94]. Lockdowns and travel restrictions put in place by mid-March 2020, which is approximately when Italy’s total death toll overtook China during the first wave of the pandemic, affected >250 million Europeans [98]. As the pandemic intensified in the spring and summer of 2020, the European Commission partnered with Global Citizen and the European Investment Bank to develop tests, treatments, and vaccines with >€10 billion (US $11.4 billion) in new funds [94]. The most stringent virus mitigation orders in the United Kingdom came from Prime Minister Boris Johnson and the House of Commons in late March 2020, which granted the prime minister emergency powers to enforce widespread lockdowns and travel restrictions [99].

    Vaccination Campaigns

    Europe’s vaccination strategy featured early collective bargaining, resulting in 2.8 billion vaccine doses secured for member states through an advance purchase agreement in June 2020 [93]. The United Kingdom administered its first vaccine dose on December 8, 2020 [100], and by August 2021, the EU had fully vaccinated 70% of its adult population, exporting >1 billion vaccine doses to low- and middle-income countries by October 2021 [94]. However, some Eastern European countries, such as Hungary, Poland, Romania, and Bulgaria, lagged behind with <65% of their populations fully vaccinated by February 2022 [101].

    In total, 4 vaccines received emergency use authorization and were widely used in Europe throughout the pandemic: BioNTech or Pfizer, Moderna, Oxford or AstraZeneca, and Johnson & Johnson [102-104]. Vaccine development, approval, and rollout occurred in rapid fashion due to collective efforts by regulatory agencies, private pharmaceutical companies, public universities, and public health agencies [102].

    Humanitarian Crises

    During the Omicron surge in early 2022, Russia’s attack on Ukraine displaced two-thirds of Ukrainians, leading to surges in Omicron and other infectious diseases, including tuberculosis. This conflict hindered COVID-19 vaccine distribution and access to essential medical services, such as HIV treatment [105-107]. Ukrainians faced obstacles in migrating without vaccination proof, as only 36% were vaccinated when the war began [108-113].

    Europe absorbed many refugees during the COVID-19 pandemic, including those fleeing political unrest and violence in the Middle East and Eastern Europe [114,115]. In addition to overcrowded and unsafe living conditions and limited access to financial or medical support, many migrants were unable to access timely COVID-19 vaccination [114,115]. Some countries, like Spain, prioritized migrant vaccination, while Portugal granted temporary residence to ensure equal access [115].

    Limitations

    The COVID-19 data had become less frequently reported around the world by the time the WHO declared an end to the pandemic public health emergency [116]. In addition, more people began to use at-home tests as the pandemic evolved [117], and the Russian invasion of Ukraine damaged public health infrastructure, which may have reduced the accuracy of reported cases in the region. Because the enhanced surveillance metrics of speed, acceleration, jerk, and 7-day persistence are based on rates, not total counts, statistical bias caused by countries dropping in or out of the sample is mitigated, but to the extent that a nonincluded country is unrepresentative of the region in disease burden; the omission of a country or territory can still influence historical data comparisons. Viral specimen tests for variants of concern in GISAID are also dependent on testing and sequencing capacity, which varied by country across the region.

    Conclusions

    While there is significant evidence indicating that the pandemic in Europe has transitioned to an endemic phase, the persistent risk of new COVID-19 variants underscores the need for vigilance, robust vaccination campaigns, and international cooperation to effectively curb the spread of coronavirus in the region [40]. As the data on transmissions become less frequent [117] and as pandemic fatigue grows [118], the challenge of vigilance also evolves. The public health lessons from European policy and disease burden can inform not only the continued challenge but also responses to inevitable future pandemics.

    Acknowledgments

    The research reported in this publication was generously supported by the Robert J. Havey, MD Institute for Global Health’s Global Innovation Challenge Award at Northwestern University, Feinberg School of Medicine. This study was also supported by Feed the Future through the US Agency for International Development, under the terms of contract 7200LA1800003 and Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (grant AID-OAA-L-14-00006). The opinions expressed herein are those of the author(s) and do not necessarily reflect the views of the US Agency for International Development or the authors’ organizations.

    The authors gratefully acknowledge all data contributors, that is, the authors and their originating laboratories responsible for obtaining the specimens and their submitting laboratories for generating the genetic sequence and metadata and sharing via the Global Initiative on Sharing All Influenza Data initiative on which this research is based.

    Data Availability

    The data sets analyzed during this study are available in the Our World in Data repository [46] and the Global Initiative on Sharing All Influenza Data repository [49].

    Conflicts of Interest

    None declared.

    1. Muralidar S, Ambi SV, Sekaran S, Krishnan UM. The emergence of COVID-19 as a global pandemic: understanding the epidemiology, immune response and potential therapeutic targets of SARS-CoV-2. Biochimie. Dec 2020;179:85-100. [CrossRef] [Medline]
    2. Sharma A, Ahmad Farouk I, Lal SK. COVID-19: a review on the novel coronavirus disease evolution, transmission, detection, control and prevention. Viruses. Jan 29, 2021;13(2):202. [CrossRef] [Medline]
    3. Chilamakuri R, Agarwal S. COVID-19: characteristics and therapeutics. Cells. Jan 21, 2021;10(2):206. [CrossRef] [Medline]
    4. Hu B, Guo H, Zhou P, Shi ZL. Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol. Mar 06, 2021;19(3):141-154. [CrossRef] [Medline]
    5. Seyed Hosseini E, Riahi Kashani N, Nikzad H, Azadbakht J, Hassani Bafrani H, Haddad Kashani H. The novel coronavirus disease-2019 (COVID-19): mechanism of action, detection and recent therapeutic strategies. Virology. Dec 2020;551:1-9. [CrossRef] [Medline]
    6. The world by region. The World Bank. URL: https://datatopics.worldbank.org/sdgatlas/archive/2017/the-world-by-region.html [accessed 2024-02-05]
    7. Linka K, Peirlinck M, Sahli Costabal F, Kuhl E. Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions. Comput Methods Biomech Biomed Engin. Aug 05, 2020;23(11):710-717. [CrossRef] [Medline]
    8. Nadeau SA, Vaughan TG, Scire J, Huisman JS, Stadler T. The origin and early spread of SARS-CoV-2 in Europe. Proc Natl Acad Sci U S A. Mar 02, 2021;118(9):e2012008118. [CrossRef] [Medline]
    9. Post L, Culler K, Moss CB, Murphy RL, Achenbach CJ, Ison MG, et al. Surveillance of the second wave of COVID-19 in Europe: longitudinal trend analyses. JMIR Public Health Surveill. Apr 28, 2021;7(4):e25695. [CrossRef] [Medline]
    10. Smith-Schoenwalder C. When does the COVID-19 pandemic end?: neither WHO nor any other organization decides when a pandemic begins or ends. U.S. News & World Report. May 12, 2023. URL: https://www.usnews.com/news/health-news/articles/2023-05-12/when-does-the-covid-19-pandemic-end [accessed 2023-09-02]
    11. Burki T. WHO ends the COVID-19 public health emergency. Lancet Respiratory Medicine. Jul 2023;11(7):588. [FREE Full text] [CrossRef]
    12. WHO chief declares end to COVID-19 as a global health emergency. United Nations. May 05, 2023. URL: https://news.un.org/en/story/2023/05/1136367 [accessed 2023-08-20]
    13. Lesson 1: Introduction to Epidemiology. Centers for Disease Control and Prevention. URL: https://www.cdc.gov/csels/dsepd/ss1978/lesson1/section11.html [accessed 2023-08-24]
    14. Epidemic, Endemic, Pandemic: What are the Differences? Columbia Mailman School of Public Health. Feb 19, 2021. URL: https:/​/www.​publichealth.columbia.edu/​public-health-now/​news/​epidemic-endemic-pandemic-what-are-differences [accessed 2023-08-19]
    15. The Lancet Infectious Diseases. Transitioning to endemicity with COVID-19 research. Lancet Infect Dis. Mar 2022;22(3):297. [CrossRef] [Medline]
    16. Britto D. Pandemic to Endemic: The Race Against Time. Tony Blair Institute for Global Change. Sep 19, 2021. URL: https://institute.global/policy/pandemic-endemic-race-against-time [accessed 2023-09-04]
    17. Hong R, Walker R, Hovan G, Henry L, Pescatore R. The power of public health surveillance. Dela J Public Health. Jul 2020;6(2):60-63. [CrossRef] [Medline]
    18. Teutsch SM. Considerations in planning a surveillance system. In: Teutsch SM, Churchill RE, editors. Principles and Practice of Public Health Surveillance. New York, NY. Oxford University Press; 2000:17-29.
    19. Teutsch SM. Considerations in planning a surveillance system. In: Lee LM, Teutsch SM, Thacker SB, St. Louis ME, editors. Principles & Practice of Public Health Surveillance. 3rd edition. Oxford, UK. Oxford University Press; 2010:18-31.
    20. Teutsch SM, Thacker SB. Planning a public health surveillance system. Epidemiol Bull. Mar 1995;16(1):1-6. [Medline]
    21. Thacker SB, Qualters JR, Lee LM, Centers for Disease Control and Prevention. Public health surveillance in the United States: evolution and challenges. MMWR Suppl. Jul 27, 2012;61(3):3-9. [Medline]
    22. Klaucke DN, Buehler JW, Thacker SB, Parrish RG, Trowbridge FL, Berkelman RL, et al. The Surveillance Coordination Group. Guidelines for evaluating surveillance systems. MMWR Morb Mortal Wkly Rep. May 06, 1988;37(S-5):1-18. [FREE Full text]
    23. Lee LM, Thacker SB. Public health surveillance and knowing about health in the context of growing sources of health data. Am J Prev Med. Dec 2011;41(6):636-640. [CrossRef] [Medline]
    24. Davis AM, Dunet DO, Keaton R, Snider DE, Sosin DM, Stroup DF, et al. CDC guidelines: improving the quality. Centers for Disease Control and Prevention. 1996. URL: https://stacks.cdc.gov/view/cdc/24407 [accessed 2023-09-05]
    25. Thacker SB, Stroup DF. Future directions for comprehensive public health surveillance and health information systems in the United States. Am J Epidemiol. Sep 01, 1994;140(5):383-397. [CrossRef] [Medline]
    26. Nsubuga P, White WE, Thacker SB, Anderson MA, Blount SB, Broome CV, et al. Public health surveillance: a tool for targeting and monitoring interventions. In: Jamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, Evans DB, et al, editors. Disease Control Priorities in Developing Countries. 2nd edition. New York, NY. Oxford University Press; Apr 13, 2006.
    27. Lee LM, Teutsch SM, Thacker SB, St. Louis ME. Principles & Practice of Public Health Surveillance. 3rd edition. New York, NY. Oxford University Press; Aug 26, 2010.
    28. Thacker SB, Stroup DF, Rothenberg RB, Brownson RC. Public health surveillance for chronic conditions: a scientific basis for decisions. Stat Med. Feb 28, 1995;14(5-7):629-641. [CrossRef] [Medline]
    29. Perry HN, McDonnell SM, Alemu W, Nsubuga P, Chungong S, Otten MW, et al. Planning an integrated disease surveillance and response system: a matrix of skills and activities. BMC Med. Aug 15, 2007;5(1):24. [CrossRef] [Medline]
    30. Koo D, Thacker SB. In snow's footsteps: commentary on shoe-leather and applied epidemiology. Am J Epidemiol. Sep 15, 2010;172(6):737-739. [CrossRef] [Medline]
    31. Romaguera RA, German RG, Klaucke DN. Evaluating Public Health Surveillance. In: Teutsch SM, Churchill RE, editors. Principles & Practice of Public Health Surveillance. New York, NY. Oxford University Press; 2000:176-193.
    32. Pappaioanou M, Malison M, Wilkins K, Otto B, Goodman RA, Churchill R, et al. Strengthening capacity in developing countries for evidence-based public health: the data for decision-making project. Soc Sci Med. Nov 2003;57(10):1925-1937. [CrossRef] [Medline]
    33. Post L, Boctor MJ, Issa TZ, Moss CB, Murphy RL, Achenbach CJ, et al. SARS-CoV-2 surveillance system in Canada: longitudinal trend analysis. JMIR Public Health Surveill. May 10, 2021;7(5):e25753. [CrossRef] [Medline]
    34. Post L, Marogi E, Moss CB, Murphy RL, Ison MG, Achenbach CJ, et al. SARS-CoV-2 surveillance in the Middle East and North Africa: longitudinal trend analysis. J Med Internet Res. Jan 15, 2021;23(1):e25830. [CrossRef] [Medline]
    35. Post L, Ohiomoba RO, Maras A, Watts SJ, Moss CB, Murphy RL, et al. Latin America and the Caribbean SARS-CoV-2 surveillance: longitudinal trend analysis. JMIR Public Health Surveill. Apr 27, 2021;7(4):e25728. [CrossRef] [Medline]
    36. Post LA, Argaw ST, Jones C, Moss CB, Resnick D, Singh LN, et al. A SARS-CoV-2 surveillance system in sub-Saharan Africa: modeling study for persistence and transmission to inform policy. J Med Internet Res. Nov 19, 2020;22(11):e24248. [CrossRef] [Medline]
    37. Post LA, Benishay ET, Moss CB, Murphy RL, Achenbach CJ, Ison MG, et al. Surveillance metrics of SARS-CoV-2 transmission in central Asia: longitudinal trend analysis. J Med Internet Res. Feb 03, 2021;23(2):e25799. [CrossRef] [Medline]
    38. Post LA, Issa TZ, Boctor MJ, Moss CB, Murphy RL, Ison MG, et al. Dynamic public health surveillance to track and mitigate the US COVID-19 epidemic: longitudinal trend analysis study. J Med Internet Res. Dec 03, 2020;22(12):e24286. [CrossRef] [Medline]
    39. Post LA, Lin JS, Moss CB, Murphy RL, Ison MG, Achenbach CJ, et al. SARS-CoV-2 wave two surveillance in east Asia and the Pacific: longitudinal trend analysis. J Med Internet Res. Feb 01, 2021;23(2):e25454. [CrossRef] [Medline]
    40. Post LA, Lorenzo-Redondo R. Omicron: fewer adverse outcomes come with new dangers. Lancet. Apr 2022;399(10332):1280-1281. [CrossRef]
    41. Oehmke JF, Moss CB, Singh LN, Oehmke TB, Post LA. Dynamic panel surveillance of COVID-19 transmission in the United States to inform health policy: observational statistical study. J Med Internet Res. Oct 05, 2020;22(10):e21955. [CrossRef] [Medline]
    42. Oehmke JF, Oehmke TB, Singh LN, Post LA. Dynamic panel estimate-based health surveillance of SARS-CoV-2 infection rates to inform public health policy: model development and validation. J Med Internet Res. Sep 22, 2020;22(9):e20924. [CrossRef] [Medline]
    43. Oehmke TB, Post LA, Moss CB, Issa TZ, Boctor MJ, Welch SB, et al. Dynamic panel data modeling and surveillance of COVID-19 in metropolitan areas in the United States: longitudinal trend analysis. J Med Internet Res. Feb 09, 2021;23(2):e26081. [CrossRef] [Medline]
    44. Arellano M, Bond S. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud. Apr 1991;58(2):277. [CrossRef]
    45. Lundberg AL, Lorenzo-Redondo R, Ozer EA, Hawkins CA, Hultquist JF, Welch SB, et al. Has Omicron changed the evolution of the pandemic? JMIR Public Health Surveill. Jan 31, 2022;8(1):e35763. [FREE Full text] [CrossRef] [Medline]
    46. Ritchie H, Mathieu E, Rodés-Guirao L, Appel C, Giattino C, Hasell J, et al. Coronavirus pandemic (COVID-19). Our World in Data. 2020. URL: https://ourworldindata.org/coronavirus [accessed 2024-04-02]
    47. Croissant Y, Millo G. Panel data econometrics in R: the plm package. J Stat Soft. 2008;27(2):1-43. [CrossRef]
    48. Hansen LP. Large sample properties of generalized method of moments estimators. Econometrica. Jul 1982;50(4):1029. [CrossRef]
    49. Global initiative on sharing all influenza data (GISAID). World Health Organization. URL: https://www.gisaid.org/ [accessed 2024-04-18]
    50. Khare S, Gurry C, Freitas L, Schultz MB, Bach G, Diallo A, et al. GISAID's role in pandemic response. China CDC Wkly. Dec 03, 2021;3(49):1049-1051. [CrossRef] [Medline]
    51. Shu Y, McCauley J. GISAID: global initiative on sharing all influenza data - from vision to reality. Euro Surveill. Mar 30, 2017;22(13):30494. [CrossRef] [Medline]
    52. Nasereddin A, Golan Berman H, Wolf DG, Oiknine-Djian E, Adar S. Identification of SARS-CoV-2 variants of concern using amplicon next-generation sequencing. Microbiol Spectr. Aug 31, 2022;10(4):e0073622. [CrossRef] [Medline]
    53. Huddleston J, Hadfield J, Sibley T, Lee J, Fay K, Ilcisin M, et al. Augur: a bioinformatics toolkit for phylogenetic analyses of human pathogens. J Open Source Softw. Jan 2021;6(57):2906. [CrossRef] [Medline]
    54. Rambaut A, Holmes EC, O'Toole Á, Hill V, McCrone JT, Ruis C, et al. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nat Microbiol. Nov 15, 2020;5(11):1403-1407. [CrossRef] [Medline]
    55. O'Toole Á, Scher E, Underwood A, Jackson B, Hill V, McCrone JT, et al. Assignment of epidemiological lineages in an emerging pandemic using the Pangolin tool. Virus Evol. 2021;7(2):veab064. [CrossRef] [Medline]
    56. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria.; 2023. URL: https://www.R-project.org/ [accessed 2024-04-14]
    57. Chen J. Pathogenicity and transmissibility of 2019-nCoV-a quick overview and comparison with other emerging viruses. Microbes Infect. Mar 2020;22(2):69-71. [CrossRef] [Medline]
    58. Levenson E, Firger J. What the CDC’s ‘substantial’ and ‘high’ levels of COVID-19 transmission actually mean. CNN Health. Jun 28, 2021. URL: https:/​/edition.​cnn.com/​2021/​07/​28/​health/​substantial-or-high-covid-19-transmission-wellness/​index.​html [accessed 2023-08-22]
    59. Christie A, Brooks JT, Hicks LA, Sauber-Schatz EK, Yoder JS, Honein MA, et al. CDC COVID-19 Response Team. Guidance for implementing COVID-19 prevention strategies in the context of varying community transmission levels and vaccination coverage. MMWR Morb Mortal Wkly Rep. Jul 27, 2021;70(30):1044-1047. [CrossRef] [Medline]
    60. Stoto MA. Public health assessment in the 1990s. Annu Rev Public Health. 1992;13:59-78. [CrossRef] [Medline]
    61. Katella K. What to know about EG.5 (Eris)—the latest coronavirus strain. Yale Medicine. Oct 5, 2023. URL: https://www.yalemedicine.org/news/covid-eg5-eris-latest-coronavirus-strain [accessed 2024-01-15]
    62. Buckee C, Noor A, Sattenspiel L. Thinking clearly about social aspects of infectious disease transmission. Nature. Jul 30, 2021;595(7866):205-213. [CrossRef] [Medline]
    63. Scheel-Hincke LL, Connolly FF, Olofsson J, Andersen-Ranberg K. 27 Two Nordic countries with different approaches to handling the COVID-19 pandemic: a comparison of Sweden and Denmark. In: Börsch-Supan A, Abramowska-Kmon A, Andersen-Ranberg K, Brugiavini A, Chlon-Dominczak A, Jusot F, et al, editors. Social, Health, and Economic Impacts of the COVID-19 Pandemic and the Epidemiological Control Measures. Berlin, Germany. De Gruyter; Oct 2023:281-290.
    64. Soriano-Arandes A, Brett A, Buonsenso D, Emilsson L, de la Fuente Garcia I, Gkentzi D, et al. Policies on children and schools during the SARS-CoV-2 pandemic in Western Europe. Front Public Health. Jul 25, 2023;11:1175444. [CrossRef] [Medline]
    65. Faruk MO, Rana MS, Jannat SN, Khanam Lisa F, Rahman MS. Impact of environmental factors on COVID-19 transmission: spatial variations in the world. Int J Environ Health Res. Sep 12, 2023;33(9):864-880. [CrossRef] [Medline]
    66. Cuschieri S, Cuschieri A, Farrugia E, Diacono E, Balzan E, Grupetta M, et al. A case for cross-border governance? A comparative trend assessment of COVID-19 transmission, vaccination, and outcomes among 35 nations in Europe across 18 months. Disaster Med Public Health Prep. May 02, 2022;17:e196. [CrossRef] [Medline]
    67. Falco A, Piscitelli P, Vito D, Pacella F, Franco C, Pulimeno M, et al. COVID-19 epidemic spread and green areas Italy and Spain between 2020 and 2021: an observational multi-country retrospective study. Environ Res. Jan 01, 2023;216(Pt 1):114089. [CrossRef] [Medline]
    68. Amante A, Balmer C. Why has Italy suffered so badly during the pandemic? World Economic Forum. Dec 17, 2020. URL: https:/​/www.​weforum.org/​agenda/​2020/​12/​italy-death-toll-pandemic-covid-coronavirus-health-population-europe/​ [accessed 2023-09-17]
    69. Rostami A, Sepidarkish M, Leeflang MM, Riahi SM, Nourollahpour Shiadeh M, Esfandyari S, et al. SARS-CoV-2 seroprevalence worldwide: a systematic review and meta-analysis. Clin Microbiol Infect. Mar 2021;27(3):331-340. [CrossRef] [Medline]
    70. Rivieccio BA, Luconi E, Boracchi P, Pariani E, Romanò L, Salini S, et al. Heterogeneity of COVID-19 outbreak in Italy. Acta Biomed. Apr 20, 2020;91(2):31-34. [CrossRef] [Medline]
    71. Scortichini M, Schneider Dos Santos R, De' Donato F, De Sario M, Michelozzi P, Davoli M, et al. Excess mortality during the COVID-19 outbreak in Italy: a two-stage interrupted time-series analysis. Int J Epidemiol. Jan 23, 2021;49(6):1909-1917. [CrossRef] [Medline]
    72. Cavanna L, Cremona G, Citterio C, Nunzio CD, Muroni M, Andena AM, et al. COVID-19 outbreak in Italy: report on the first 124 consecutive patients treated at home. Tohoku J Exp Med. Sep 2021;255(1):61-69. [CrossRef] [Medline]
    73. Or Z, Gandré C, Durand Zaleski I, Steffen M. France's response to the COVID-19 pandemic: between a rock and a hard place. Health Econ Policy Law. Jan 2022;17(1):14-26. [CrossRef] [Medline]
    74. Laffet K, Haboubi F, Elkadri N, Georges Nohra R, Rothan-Tondeur M. The early stage of the COVID-19 outbreak in Tunisia, France, and Germany: a systematic mapping review of the different national strategies. Int J Environ Res Public Health. Aug 15, 2021;18(16):8622. [CrossRef] [Medline]
    75. Durand GA, de Laval F, de Bonet d'Oléon A, Le Flem FX, Morin Y, Badaut C, et al. COVID-19 outbreak among French firefighters, Marseille, France, 2020. Euro Surveill. Oct 2021;26(41):2001676. [CrossRef] [Medline]
    76. Zach H, Hanová M, Letkovičová M. Distribution of COVID-19 cases and deaths in Europe during the first 12 peak weeks of outbreak. Cent Eur J Public Health. Mar 31, 2021;29(1):9-13. [CrossRef] [Medline]
    77. Mutch H, Young JJ, Sadiq F, Rose AM, Evans JM, European COVID-19 hospital surveillance analysis writing group. Enhanced surveillance of hospitalised COVID-19 patients in Europe: I-MOVE-COVID-19 surveillance network, February 2020 to December 2021. Euro Surveill. Jun 2023;28(26):2200669. [CrossRef] [Medline]
    78. Summanen M, Kosunen M, Kainu V, Cansel A, Niskanen S, Nurmi L, et al. COVID-19 hospitalisations and all-cause mortality by risk group in Finland. PLoS One. May 23, 2023;18(5):e0286142. [CrossRef] [Medline]
    79. Tallei TE, Alhumaid S, AlMusa Z, Fatimawali, Kusumawaty D, Alynbiawi A, et al. Update on the Omicron sub-variants BA.4 and BA.5. Rev Med Virol. Jan 2023;33(1):e2391. [FREE Full text] [CrossRef] [Medline]
    80. Wiemken TL, Khan F, Puzniak L, Yang W, Simmering J, Polgreen P, et al. Seasonal trends in COVID-19 cases, hospitalizations, and mortality in the United States and Europe. Sci Rep. Mar 08, 2023;13(1):3886. [CrossRef] [Medline]
    81. Flahault A, Calmy A, Costagliola D, Drapkina O, Eckerle I, Larson HJ, et al. No time for complacency on COVID-19 in Europe. Lancet. Jun 10, 2023;401(10392):1909-1912. [CrossRef] [Medline]
    82. Boehm E, Kronig I, Neher RA, Eckerle I, Vetter P, Kaiser L, et al. Geneva Centre for Emerging Viral Diseases. Novel SARS-CoV-2 variants: the pandemics within the pandemic. Clin Microbiol Infect. Aug 2021;27(8):1109-1117. [CrossRef] [Medline]
    83. Kahn F, Bonander C, Moghaddassi M, Rasmussen M, Malmqvist U, Inghammar M, et al. Risk of severe COVID-19 from the Delta and Omicron variants in relation to vaccination status, sex, age and comorbidities - surveillance results from southern Sweden, July 2021 to January 2022. Euro Surveill. Mar 2022;27(9):2200121. [CrossRef] [Medline]
    84. Cassetti G, Boitier B, Elia A, Le Mouël P, Gargiulo M, Zagamé P, et al. The interplay among COVID-19 economic recovery, behavioural changes, and the European Green Deal: an energy-economic modelling perspective. Energy (Oxf). Jan 15, 2023;263:125798. [CrossRef] [Medline]
    85. Mahase E. Covid-19: WHO declares pandemic because of "alarming levels" of spread, severity, and inaction. BMJ. Mar 12, 2020;368:m1036. [CrossRef] [Medline]
    86. Coronavirus: Europe now epicentre of the pandemic, says WHO. BBC. Mar 13, 2020. URL: https://www.bbc.com/news/world-europe-51876784 [accessed 2024-05-27]
    87. Goniewicz K, Khorram-Manesh A, Hertelendy AJ, Goniewicz M, Naylor K, Burkle FM. Current response and management decisions of the European Union to the COVID-19 outbreak: a review. Sustainability. May 08, 2020;12(9):3838. [FREE Full text] [CrossRef]
    88. Borbáth E, Hunger S, Hutter S, Oana IE. Civic and political engagement during the multifaceted COVID-19 crisis. Schweiz Z Polit. Jun 10, 2021;27(2):311-324. [CrossRef] [Medline]
    89. Jung S, Krüger TH. How the COVID-19 pandemic divides society: towards a better understanding of differences between supporters and opponents of the COVID-19 pandemic lockdown in Germany. J Psychiatr Res. Nov 2022;155:232-240. [CrossRef] [Medline]
    90. Contreras RR. COVID-19 and digitalisation. European Foundation for the Improvement of Living and Working Conditions. URL: https://www.eurofound.europa.eu/data/digitalisation/research-digests/covid-19-and-digitalisation [accessed 2023-11-19]
    91. Digitalisation in Europe 2021-2022: evidence from the EIB investment survey. European Investment Bank. May 02, 2022. URL: https://www.eib.org/en/publications/digitalisation-in-europe-2021-2022 [accessed 2023-10-06]
    92. Business resilience in the pandemic and beyond: adaptation, innovation, financing and climate action from Eastern Europe to Central Asia. European Investment Bank. May 18, 2022. URL: https://www.eib.org/en/publications/business-resilience-in-the-pandemic-and-beyond [accessed 2023-10-06]
    93. Policy responses to COVID-19. International Monetary Fund. Jul 2, 2021. URL: https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19#U [accessed 2023-10-11]
    94. Timeline of EU action. European Commission. Mar 31, 2023. URL: https://commission.europa.eu/strategy-and-policy/coronavirus-response/timeline-eu-action_en [accessed 2023-11-01]
    95. The European instrument for temporary Support to mitigate Unemployment Risks in an Emergency (SURE). European Commission. URL: https://economy-finance.ec.europa.eu/eu-financial-assistance/sure_en [accessed 2023-11-01]
    96. Special European Council, 17-21 July 2020. European Council, Council of the European Union. URL: https://www.consilium.europa.eu/en/meetings/european-council/2020/07/17-21/ [accessed 2023-11-01]
    97. Recovery plan for Europe. European Commission. URL: https://commission.europa.eu/strategy-and-policy/recovery-plan-europe_en [accessed 2023-11-01]
    98. Henley J, Oltermann P. Italy records its deadliest day of coronavirus outbreak with 475 deaths. The Guardian. URL: https://www.theguardian.com/world/2020/mar/18/coronavirus-lockdown-eu-belgium-germany-adopt-measures [accessed 2023-11-04]
    99. Prime minister's statement on coronavirus (COVID-19): 23 March 2020. Prime Minister's Office. Mar 23, 2023. URL: https://www.gov.uk/government/speeches/pm-address-to-the-nation-on-coronavirus-23-march-2020 [accessed 2023-11-19]
    100. COVID-19 vaccine: first person receives Pfizer jab in UK. BBC News. Dec 8, 2020. URL: https://www.bbc.com/news/uk-55227325 [accessed 2023-09-19]
    101. Vicente P, Suleman A. COVID-19 in Europe: from outbreak to vaccination. BMC Public Health. Dec 02, 2022;22(1):2245. [CrossRef] [Medline]
    102. Luxi N, Giovanazzi A, Capuano A, Crisafulli S, Cutroneo PM, Fantini MP, et al. Ilmiovaccino COVID19 collaborating group. COVID-19 vaccination in pregnancy, paediatrics, immunocompromised patients, and persons with history of allergy or prior SARS-CoV-2 infection: overview of current recommendations and pre- and post-marketing evidence for vaccine efficacy and safety. Drug Saf. Dec 05, 2021;44(12):1247-1269. [CrossRef] [Medline]
    103. Hall V, Foulkes S, Insalata F, Kirwan P, Saei A, Atti A, et al. SIREN Study Group. Protection against SARS-CoV-2 after COVID-19 vaccination and previous infection. N Engl J Med. Mar 31, 2022;386(13):1207-1220. [CrossRef] [Medline]
    104. Eyre DW, Taylor D, Purver M, Chapman D, Fowler T, Pouwels KB, et al. Effect of COVID-19 vaccination on transmission of Alpha and Delta variants. N Engl J Med. Feb 24, 2022;386(8):744-756. [CrossRef]
    105. Maggioni A, Gonzales-Zamora JA, Maggioni A, Peek L, McLaughlin SA, von Both U, et al. Cascading risks for preventable infectious diseases in children and adolescents during the 2022 invasion of Ukraine. Int J Environ Res Public Health. Jun 08, 2022;19(12):7005. [CrossRef] [Medline]
    106. Roberts L. Surge of HIV, tuberculosis and COVID feared amid war in Ukraine. Nature. Mar 15, 2022;603(7902):557-558. [CrossRef] [Medline]
    107. Kumar N, Acharya A, Gendelman HE, Byrareddy SN. The 2022 outbreak and the pathobiology of the monkeypox virus. J Autoimmun. Jul 2022;131:102855. [CrossRef] [Medline]
    108. Uwishema O, Sujanamulk B, Abbass M, Fawaz R, Javed A, Aboudib K, et al. Russia-Ukraine conflict and COVID-19: a double burden for Ukraine's healthcare system and a concern for global citizens. Postgrad Med J. Aug 2022;98(1162):569-571. [CrossRef] [Medline]
    109. Kovoor JG, Bacchi S, Gupta AK, Maddern GJ. COVID-19 and the Ukraine-Russia conflict: warnings from history. Br J Surg. Jul 15, 2022;109(8):777. [CrossRef] [Medline]
    110. Ramírez C, Durón RM. The Russia-Ukraine war could bring catastrophic public-health challenges beyond COVID-19. Int J Infect Dis. Jul 2022;120:44-45. [CrossRef] [Medline]
    111. Malchrzak W, Babicki M, Pokorna-Kałwak D, Doniec Z, Mastalerz-Migas A. COVID-19 vaccination and Ukrainian refugees in Poland during Russian-Ukrainian war-narrative review. Vaccines (Basel). Jun 16, 2022;10(6):955. [CrossRef] [Medline]
    112. Su Z, McDonnell D, Cheshmehzangi A, Ahmad J, Šegalo S, Pereira da Veiga C, et al. Public health crises and Ukrainian refugees. Brain Behav Immun. Jul 2022;103:243-245. [CrossRef] [Medline]
    113. Rahimi F, Talebi Bezmin Abadi A. The Ukrainian refugee crisis and the COVID-19 pandemic in Europe. Int J Surg. Jun 2022;102:106671. [CrossRef] [Medline]
    114. Crawshaw AF, Farah Y, Deal A, Rustage K, Hayward SE, Carter J, et al. Defining the determinants of vaccine uptake and undervaccination in migrant populations in Europe to improve routine and COVID-19 vaccine uptake: a systematic review. Lancet Infect Dis. Sep 2022;22(9):e254-e266. [CrossRef]
    115. Armocida B, Formenti B, Missoni E, D'Apice C, Marchese V, Calvi M, et al. Challenges in the equitable access to COVID-19 vaccines for migrant populations in Europe. Lancet Reg Health Eur. Jul 2021;6:100147. [CrossRef] [Medline]
    116. Stein R. As the pandemic ebbs, an influential COVID tracker shuts down. National Public Radio. 2023. URL: https:/​/www.​npr.org/​sections/​health-shots/​2023/​02/​10/​1155790201/​as-the-pandemic-ebbs-an-influential-covid-tracker-shuts-down [accessed 2023-08-27]
    117. Ritchey MD, Rosenblum HG, Del Guercio K, Humbard M, Santos S, Hall J, et al. COVID-19 self-test data: challenges and opportunities - United States, October 31, 2021-June 11, 2022. MMWR Morb Mortal Wkly Rep. Aug 12, 2022;71(32):1005-1010. [CrossRef] [Medline]
    118. Lilleholt L, Zettler I, Betsch C, Böhm R. Development and validation of the pandemic fatigue scale. Nat Commun. Oct 10, 2023;14(1):6352. [CrossRef] [Medline]

    ‎
    EU: European Union
    GISAID: Global Initiative on Sharing All Influenza Data
    NGEU: Next Generation European Union
    WHO: World Health Organization

    Edited by A Mavragani; submitted 10.10.23; peer-reviewed by CW Coia, C Argyropoulos; comments to author 28.01.24; revised version received 12.03.24; accepted 20.03.24; published 21.06.24.

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    ©Alexander L Lundberg, Scott A Wu, Alan G Soetikno, Claudia Hawkins, Robert L Murphy, Robert J Havey, Egon A Ozer, Charles B Moss, Sarah B Welch, Maryann Mason, Yingxuan Liu, Lori A Post. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 21.06.2024.

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