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Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53214, first published .
Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in East Asia and the Pacific Region: Longitudinal Trend Analysis

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

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

Authors of this article:

Alexander L Lundberg1, 2 Author Orcid Image ;   Alan G Soetikno3 Author Orcid Image ;   Scott A Wu3 Author Orcid Image ;   Egon Ozer4, 5 Author Orcid Image ;   Sarah B Welch1 Author Orcid Image ;   Yingxuan Liu1 Author Orcid Image ;   Claudia Hawkins4, 6 Author Orcid Image ;   Maryann Mason1, 2 Author Orcid Image ;   Robert Murphy4, 7 Author Orcid Image ;   Robert J Havey7, 8 Author Orcid Image ;   Charles B Moss9 Author Orcid Image ;   Chad J Achenbach4, 7 Author Orcid Image ;   Lori Ann 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 Pathogen Genomics and Microbial Evolution, Robert J. Havey, MD Institute for Global Health, Northwestern University, Chicago, IL, United States

    6Center for Global Communicable and Emerging Infectious Diseases, Robert J Havey, MD Institute for Global Health, Northwestern University, Chicago, IL, United States

    7Robert J Havey, MD Institute for Global Health, Northwestern University, Chicago, IL, United States

    8Department of Medicine, General Internal Medicine and Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States

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

    Corresponding Author:

    Lori Ann Post, PhD

    Buehler Center for Health Policy and Economics

    Robert J Havey, MD Institute for Global Health

    Northwestern University

    420 E Superior St

    Chicago, IL, 60611

    United States

    Phone: 1 3125031706

    Email: lori.post@northwestern.edu


    Background: This study updates the COVID-19 pandemic surveillance in East Asia and the Pacific region that we first conducted in 2020 with 2 additional years of data for the region.

    Objective: First, we aimed to measure whether there was an expansion or contraction of the pandemic in East Asia and the Pacific region when the World Health Organization (WHO) declared the end of the COVID-19 public health emergency of international concern on May 5, 2023. Second, we used dynamic and genomic surveillance methods to describe the dynamic history of the pandemic in the region and situate the window of the WHO declaration within the broader history. Finally, we aimed to provide historical context for the course of the pandemic in East Asia and the Pacific region.

    Methods: In addition to 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-sided t test to determine whether the 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: Several countries in East Asia and the Pacific region had COVID-19 transmission rates above an outbreak threshold at the point of the WHO declaration (Brunei, New Zealand, Australia, and South Korea). However, the regional transmission rate had remained below the outbreak threshold for 4 months. In the rolling 6-month window t test for regional outbreak status, the final P value ≤.10 implies a rejection of the null hypothesis (at the α=.10 level) that the region as a whole was not in an outbreak for the period from November 5, 2022, to May 5, 2023. From January 2022 onward, nearly every sequenced SARS-CoV-2 specimen in the region was identified as the Omicron variant.

    Conclusions: While COVID-19 continued to circulate in East Asia and the Pacific region, transmission rates had fallen below outbreak status by the time of the WHO declaration. Compared to other global regions, East Asia and the Pacific region had the latest outbreaks driven by the Omicron variant. COVID-19 appears to be endemic in the region, no longer reaching the threshold for a pandemic definition. However, the late outbreaks raise uncertainty about whether the pandemic was truly over in the region at the time of the WHO declaration.

    JMIR Public Health Surveill 2025;11:e53214

    doi:10.2196/53214

    Keywords

    SARS-CoV-2; COVID-19; East Asia; Pacific; American Samoa; Australia; Brunei Darussalam; Cambodia; China; Fiji; French Polynesia; Guam; Hong Kong; Indonesia; Japan; Kiribati; People’s Democratic Republic of Korea; Republic of Korea; Lao People’s Democratic Republic; Macao; Malaysia; Marshall Islands; Federated States of Micronesia; Mongolia; Myanmar; Nauru, New Caledonia; New Zealand; Northern Mariana Islands; Palau; Papua New Guinea; Philippines; Samoa; Singapore; Solomon Islands; Thailand; Timor-Leste; Tonga; Tuvalu; Vanuatu; Vietnam; pandemic; surveillance; public health; speed; acceleration; deceleration; jerk; dynamic panel; generalized method of moments; Arellano-Bond; 7-day lag 


    Background

    COVID-19, the disease caused by SARS-CoV-2, was first detected in Wuhan, China, in the fall of 2019 [1-5]. Our research team conducted an analysis of the pandemic in East Asia and the Pacific region one year into the pandemic [6]. This study provides 2 additional years of updated surveillance and analysis for the region.

    We adopted the World Bank’s definition of East Asia and the Pacific region, which is based on economic development and geographical proximity, encompassing American Samoa, Australia, Brunei Darussalam, Cambodia, China, Fiji, French Polynesia, Guam, Hong Kong, Indonesia, Japan, Kiribati, People’s Democratic Republic of Korea, Republic of Korea, Lao People’s Democratic Republic, Macao, Malaysia, Marshall Islands, Federated States of Micronesia, Mongolia, Myanmar, Nauru, New Caledonia, New Zealand, Northern Mariana Islands, Palau, Papua New Guinea, the Philippines, Samoa, Singapore, Solomon Islands, Thailand, Timor-Leste, Tonga, Tuvalu, Vanuatu, and Vietnam [7]. North Korea neither collects nor reports COVID-19 caseloads and deaths.

    The World Health Organization (WHO) and Director-General Ghebreyesus declared the end of COVID-19 as a public health emergency of international concern on May 5, 2023 [8-10], based on the recommendation of the COVID-19 Emergency Committee [10]. We compared the progression of the pandemic before and after this 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 disease [11,12]. 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 [13,14]. Field epidemiology defines these terms based on transmission metrics and geographical distribution and uses transmission metrics and geographical distribution to differentiate the nuances of these terms. Although public health surveillance data may not capture all cases, they are the best proxy for monitoring and tracking disease spread within a population in near real time. Surveillance is crucial for the timely response to health threats [15].

    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” [16]. Not only does surveillance explain the burden of death and disease transmission but also it generates research questions and guides investigators on topics that require further investigation [17-31]. 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 per capita rates of how many people contract a disease, how many die, and affiliated costs.

    However, traditional surveillance carries several limitations that this study has addressed. Traditional surveillance provides a snapshot of past events [17-31], meaning it is static and only reflects historical data. 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. In addition, we included dynamic metrics about the speed of the pandemic at the national, regional, and global level. We measured how acceleration of speed one week compared to the prior week, as well as how novel infections in a prior week predicted new cases the following week. We can think of the latter measure as the echoing forward of cases. These metrics were tested and validated in prior research [6,32-42].

    For the purpose of this study, standard surveillance metrics explain what has already happened in East Asia and the Pacific region, while enhanced surveillance metrics provide insights into future trends or the current position of a country along an epidemiological curve. We used both types of metrics to analyze the possible end to the pandemic in East Asia and the Pacific region.

    Objectives

    This study has 3 objectives. First, we aimed to measure whether there was an expansion or contraction in the pandemic in East Asia and the Pacific region at the time the WHO declared the end of the COVID-19 pandemic as a public health emergency of international concern on May 5, 2023 [43-51]. At both the region and country level, 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 needed to know whether the rate of new COVID-19 cases was increasing, decreasing, or stable from week to week, and if 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 East Asia and the Pacific region. We have addressed several questions. How did countries respond to the pandemic? How did the region fare in terms of disease burden? And 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

    We conducted trend analyses with longitudinal COVID-19 data from Our World in Data (OWID) [52]. This study provides updates of traditional surveillance data and dynamic panel estimates from the original work by Post et al [6,40,41,53-55]. For East Asia and the Pacific region, the data comprised an unbalanced panel of 36 countries and territories, running from August 14, 2020, to May 12, 2023. Because several 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 [56-58].

    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 [59-62]. We used Nextclade nomenclature [63] to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2 [64,65]. Metadata for the study period were collected on June 22, 2023. To avoid low frequency or potentially erroneous samples, the dataset was further filtered to exclude months with fewer than 100 available samples, variant groups with fewer than 5 samples in a month, and variant groups representing <0.5% of the total samples in a month. The final dataset consisted of 184,386 total samples available on GISAID [59-62].

    Traditional surveillance metrics include the speed of transmission or the rate of new COVID-19 cases per 100,000 population. Novel metrics include acceleration, jerk, and 1- and 7-day persistence estimates. Acceleration is the change in speed from one time unit to the next. Acceleration can measure whether transmission rates are rising, falling, or stable. Jerk is the change in acceleration from one time unit to the next. The term jerk is adopted from physics nomenclature, as a large jerk can signal explosive growth in transmissions. The 1- and 7-day persistence measures provide the impact of the 1- and 7-day lag of speed on current speed. For another interpretation, these estimates capture how COVID-19 cases echo forward to cases either 1 or 7 days later. They are coefficient estimates from an Arellano-Bond dynamic panel data model [66]:

    in which the dependent variable is speed, the independent variables include weekend and recent week indicators, αi is a country fixed effect, and uit is the idiosyncratic error term. Advantages of the Arellano-Bond estimates include a correction for time-invariant omitted variables and the ability to assess both the predictive ability of the model and validity of its specification [41].

    We analyzed the potential statistical end to the pandemic with a 1-sided 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. The test can assess whether the region as a whole was experiencing an outbreak over a defined time window, during which the speed may sometimes exceed the outbreak threshold and at other times remain below it. All statistical analyses were conducted in R (version 4.2.1; R Foundation for Statistical Computing) with the plm package (version 2.6-2) [53,54].

    Ethical Considerations

    All data used in this study are publicly available and contain no identifiable, private information. Therefore, the study is deemed exempt research with human subjects as defined by the US Government Code of Federal Regulations 45CFR46 and so was not submitted the Northwestern University Institutional Review Board. However, the authors note that anonymized COVID-19 data surveillance systems can generate local and global ethical questions beyond the scope of this study [67].


    Tables 1-6 present traditional (ie, static) and novel (ie, dynamic) COVID-19 surveillance metrics with OWID transmission and death data for East Asia and the Pacific region. These results focus on the 2-week period around the time when the WHO declared COVID-19 was no longer a public health emergency of international concern. The week before the declaration was April 28, 2023, and the week after was May 5, 2023.

    Table 1. Arellano-Bond dynamic panel data estimates of the number of daily COVID-19 infections reported by country in East Asia and the Pacific region from April 28 to May 12, 2023a,b.
    VariablesValuesP valuesc
    1-day lag coefficient0.171.46
    7-day lag coefficient0.877<.001
    Shift parameter, week of April 28−0.488<.001
    Shift parameter, week of May 50.491<.001
    Weekend effect−1.215.28

    aWald test: χ26=730,275, P≤.001.

    bSargan: χ2540=24, P=.99.

    cContains estimates from the model in equation (1).

    Figure 1 plots the novel surveillance metrics over time, from the start of the pandemic up to the WHO declaration in May 2023. Figure 2 uses GISAID data on sequenced SARS-CoV-2 variants to present the proportion of variants of concern returned among specimens over a similar period. Figure 3 returns to regional transmission data to present the results of a rolling 6-month window t test for whether the region was in a state of outbreak over each period. Finally, Figure 4 presents a qualitative timeline of key events in the COVID-19 pandemic in East Asia and the Pacific region.

    Table 1 presents the dynamic panel estimates for the most recent time window. The Wald test for the regression was significant (P<.001), and the Sargan test failed to reject the validity of the overidentification restrictions (P=.99). The 1-day lag coefficient was not statistically significant (P=.46), but the 7-day lag coefficient was (P<.001), suggesting a cluster effect in which cases on a given day impact cases 7 days later. The shift parameter for the week of April 28 was negative (−0.488), while the more recent shift parameter for the week of May 5 was positive (0.491). The roughly equal magnitudes suggest the echo-forward effect of cases remained roughly stable from the start to the end of the 2-week period.

    Standard surveillance metrics for the weeks of April 28 and May 5, 2023, are provided in Tables 2 and 3. Although most countries in the region had a small number of new COVID-19 cases, several countries had high rates of transmission. For the week of April 28, Brunei had by far the highest rate of new COVID-19 cases at 1431 per 100,000 population. The next highest rate was 204 in New Zealand, followed by 115 in Australia and 36 in South Korea. These rates exceeded the outbreak threshold according to the Centers for Disease Control and Prevention [32-42,68]. Specifically, low transmission is defined as no more than 10 cases per 100,000 people per week, moderate transmission ranges from 10 to 50 cases per 100,000 people per week, and substantial transmission is between 50 to 100 cases per 100,000 people per week [68,69]. Thus, Brunei, New Zealand, and Australia were in greater-than-substantial outbreaks, while South Korea was in a moderate outbreak.

    Outbreak status for countries in the region looked roughly the same for the week of May 5. Brunei had somewhat lower transmission rate than in the prior week, while Australia had a higher rate. The other exception is Guam, who entered an outbreak with a speed of 61 (Table 3). The speed in Guam was 17 for the week of April 21. The dip to zero in the intermediate week (Table 2) most likely represents a disruption in data reports, but it is also important to note that transmission speed can be more variable in small island territories. For the region as a whole, COVID-19 may have still met the pandemic definition at the time of the WHO declaration, with several countries experiencing sizeable outbreaks. However, transmission rates were low for the rest of the region, suggesting a transition from pandemic to endemic.

    Table 2. Static COVID-19 surveillance metrics for East Asia and Pacific countries in the week of April 28, 2023.
    CountryNew COVID-19 cases, nCumulative COVID-19 cases, n7-day moving average of new casesInfection rate per 100,000 individualsNew weekly deaths, nCumulative deaths, n7-day moving average of deathsDeath rate per 100,000 individualsConditional death rate
    Australia430111,272,3554273.57115.01020,613180.520
    Brunei918292,644721.291431.2001580.140.300
    Cambodia0138,7330.14003056000.02
    China46299,250,200511.140.037120,9845.8600
    Fiji068,921000883000.01
    French Polynesia678,518900649000.01
    Guam051,2408.7100413000.01
    Indonesia21226,784,1701871.140.7720161,40420.860.010.02
    Japan734333,766,95711,059.295.921874,614210.010
    Laos4218,0851.290.050671000
    Malaysia05,071,8407090037,0201.2900.01
    Mongolia191,008,0389.43002136000
    Myanmar208635,660140.570.38019,4920.1400.03
    New Caledonia080,058000314000
    New Zealand15092,261,1261423.57203.73027623.710.510
    Northern Mariana Islands313,8491.860041000
    Palau0600005.5309000
    Papua New Guinea146,8501.140.010670000.01
    Philippines11904,097,525932.711.03066,444000.02
    Singapore32012,391,7703316.71001722000
    South Korea18,75231,251,20315,477.5736.19634,5186.710.010
    Thailand2424,732,301252.292.36033,9571.430.010.01
    Timor223,4310.430.150138000.01
    Vietnam223311,567,7281836.142.27043,195100
    Table 3. Static COVID-19 surveillance metrics for East Asia and Pacific countries in the week of May 5, 2023.
    CountryNew COVID-19 cases, nCumulative COVID-19 cases, n7-day moving average of new casesInfection rate per 100,000 individualsNew weekly deaths, nCumulative deaths, n7-day moving average of deathsDeath rate per 100,000 individualsConditional death rate
    Australia507611,303,6714643.29135.74020,75119.710.430
    Brunei816299,505927.861271.7201600.290.540
    Cambodia0138,7360.43003056000.02
    China57299,254,488612.570.0412121,0489.1400
    Fiji068,921000883000.01
    French Polynesia378,5453.5700649000.01
    Guam10551,3451561.120413000.01
    Indonesia14716,795,2211578.710.5322161,57424.290.010.02
    Japan83333,803,5725230.710.672574,69411.430.020
    Laos4218,0961.570.050671000
    Malaysia05,079,4361085.140037,0281.1400.01
    Mongolia491,008,26536.43002136000
    Myanmar157636,967186.710.29019,4930.1400.03
    New Caledonia080,058000314000
    New Zealand15352,272,2291564.43207.28027924.290.890
    Northern Mariana Islands413,8723.570041000
    Palau160000.14009000
    Papua New Guinea046,864200670000.01
    Philippines19404,108,91416271.68066,4531.2900.02
    Singapore33862,414,3943277001722000
    South Korea19,98931,371,34717,163.4338.58834,59110.430.020
    Thailand3204,734,000275.433.12033,9671.430.030.01
    Timor123,4350.570.070138000.01
    Vietnam282311,585,3902523.142.88043,2000.7100

    A comparison of Tables 2 and 3 demonstrates little to no change before and after the WHO declared an end to COVID-19 as a public health emergency. China, Japan, and South Korea had the most cumulative cases of COVID-19 transmissions, but these ranks are a function of population size. Thus, a better measure is the number of COVID-19 cases and deaths per 100,000 population. Moreover, death is often a better proxy for the state of an outbreak than transmissions because deaths are less likely to be undercounted [70]. Undercounting may be due to poor public health infrastructure, increased use of home antigen testing, or a dearth in polymerase chain reaction testing or other resources. China, Japan, and South Korea reported <.01 deaths per population. When we control for risk of death given the number of COVID-19 transmissions, we find that Myanmar had the highest conditional death rate, at 0.03 deaths per 100,000 population, while Cambodia, Indonesia, and the Philippines each reported a rate of 0.02.

    Table 4. Novel COVID-19 surveillance metrics for East Asia and Pacific countries for the week of April 28, 2023.
    CountrySpeedAccelerationJerk7-day persistence effect on speed
    Australia114.280.31−0.0239.51
    Brunei1124.51111.65−4.97134.57
    Cambodia0000
    China0.04000.01
    Fiji0−0.05−0.050.02
    French Polynesia3.26000.51
    Guam5.07−2.490.752.52
    Indonesia0.680−0.030.15
    Japan8.92−0.51−0.902.88
    Laos0.020−0.010.01
    Malaysia2.09000.71
    Mongolia0.16000.04
    Myanmar0.260.0300.04
    New Caledonia0000.02
    New Zealand192.182.011.3172.76
    Northern Mariana Islands2.88002.54
    Palau0.790.790.790
    Papua New Guinea0.01000
    Philippines0.810.050.010.16
    Singapore59.340024.83
    South Korea29.871.37−0.178.68
    Thailand2.46−0.01−0.020.71
    Timor0.030.020.020.01
    Vietnam1.87−0.110.120.85

    Tables 4 and 5 present enhanced dynamic surveillance metrics for the weeks before and after May 5. For most countries, speed was low and stable, which implied values of acceleration and jerk close to 0. Four countries were in a state of outbreak: Australia, Brunei, New Zealand, and South Korea.

    From the first to the second week, acceleration was positive for Australia but negative for Brunei, which means the outbreak was worsening in Australia but improving in Brunei. The near-zero value of jerk for Australia suggests an inflection point of the outbreak, which may have been on the verge of reduction. Because acceleration was negative for Brunei, the negative value of jerk suggests a continued, but slowed, downward trend in the transmission rate. However, the 7-day persistence effect remained high for both countries.

    The outbreaks in New Zealand and South Korea remained stable, with near-zero acceleration and jerk, although the 7-day persistence effect showed a slight increase. Because 4 out of the 36 countries and territories were experiencing outbreaks, the distinction between pandemic and endemic for the region remains unclear. On the basis of trends in other global regions, these outbreaks are likely to be the last outbreaks of this magnitude for these 4 countries; however, the scale of these outbreaks means that transmissions could spread across borders, potentially leading to new outbreaks in neighboring countries.

    Finally, we note that the figures in Tables 4 and 5 are not calculated as day-over-day averages across the week, as in Tables 2 and 3. Thus, the magnitudes of speed may not exactly match those presented in Tables 2 and 3.

    Table 5. Novel COVID-19 surveillance metrics for East Asia and Pacific countries for the week of May 5, 2023.
    CountrySpeedAccelerationJerk7-day persistence effect on speed
    Australia124.162.960.6144.39
    Brunei1446.57−22.78−25.05436.81
    Cambodia0000
    China0.04000.01
    Fiji0000
    French Polynesia1.26001.27
    Guam8.738.738.731.97
    Indonesia0.57−0.030.050.26
    Japan4.22−0.751.173.47
    Laos0.0200.010.01
    Malaysia3.20000.81
    Mongolia0.95000.06
    Myanmar0.34−0.01−0.010.10
    New Caledonia0000
    New Zealand211.210.51−1.1274.65
    Northern Mariana Islands6.63001.12
    Palau0−0.79−0.790.31
    Papua New Guinea0.02000
    Philippines1.410.090.020.31
    Singapore57.340023.05
    South Korea33.120.340.1411.60
    Thailand2.690.110.030.96
    Timor0.04−0.01−0.010.01
    Vietnam2.570.09−0.100.73
    Table 6. East Asia and Pacific countries with the highest 7-day persistence estimate in the week of May 5, 2023.
    Country7-day persistence (May 5, 2023)
    Brunei436.81
    New Zealand74.65
    Australia44.39
    Singapore23.05
    South Korea11.60

    Table 6 compares the 7-day persistence effect on speed for the top 5 countries for the week of May 5. Unsurprisingly, persistence was highest for Brunei, which had the largest outbreak at the time. Interestingly, Singapore appears on the list despite not having reached outbreak status. The persistence suggests that an outbreak could have been imminent for Singapore. Alternatively, the persistence could signify a strong, continued presence of COVID-19 as endemic in the area.

    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 Centers for Disease Control and Prevention outbreak threshold of speed equal to 10. The region saw 4 outbreaks over the period, but the first and third were small, barely eclipsing a speed of 10. The first outbreak happened in August 2021. The second happened in February 2022, ultimately reaching a peak speed of 38. A third minor outbreak occurred in July 2022. The final outbreak began in December 2022, reaching a peak speed of 243 at the end of the year. This outbreak was one of the largest in the world over the course of the pandemic. However, since the end of January 2023, the region has not been in a state of outbreak.

    ‎
    Figure 1. Novel surveillance metrics (speed, acceleration, jerk, and 7-day persistence) for COVID-19 infections in East Asia and the Pacific region from August 2020 to May 2023.

    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 first outbreak occurred shortly after the appearance of the Delta variant. Each subsequent outbreak occurred after the dominant Omicron variant arrived. East Asia and the Pacific region, like much of the rest of the world, saw a surge in cases amid the heightened transmissibility of Omicron [71]. In contrast, the large outbreaks driven by Omicron did not occur until the end of 2022. This delay was achieved by the effective zero–COVID-19 policy in China, which was finally abandoned at that point. The outbreak hit in force shortly afterward.

    Another potential indication of the end to the pandemic is the continued dominance of the Omicron variant. While the region saw a mixture of the ancestral, Alpha, Beta, and Delta variants before the arrival of Omicron in November of 2021, viral sequences have almost exclusively been identified as Omicron and its subvariants ever since.

    Figure 3 plots P values from a series of 1-sided t tests of whether speed for the region was equal to or greater than the threshold outbreak of 10. These tests were conducted on a rolling 6-month window of weekly regional speed. The test can assess whether the region was in an outbreak over a period in which speed may sometimes eclipse an outbreak threshold and other times remain below the threshold. Statistical insignificance indicates no evidence against the null hypothesis of below-outbreak regional speed over a period. For example, at the α=.10 level, the final P value ≤.10 in the figure implies a rejection of the null hypothesis that the region as a whole was not in an outbreak for the period from November 5, 2022, to May 5, 2023.

    In Figure 3, the dashed gray line denotes the least restrictive conventional significance level threshold of α=.10. The test first rejected the null in favor of the alternative for the 6-month period ending in early May of 2021. The test lost significance again in mid-September, before regaining significance at the very end of 2022. This second period of rejection was driven by the large Omicron outbreak around the time. More recently, the test was on the verge of insignificance at the time of WHO declaration. Although the test failed to reject the null at the α=.10 level, it did reject at the α=.05 level. Compared to other global regions, East Asia and the Pacific region are the latest to potentially transition from the pandemic to endemic COVID-19 status.

    Within the historical context of enhanced surveillance metrics, the region may be at the end stage of the pandemic. Speed has not been this low for this long since the start of the pandemic. However, we do note that several countries in the region remained in substantial outbreaks at the time of the WHO declaration. This reality brings some uncertainty to the ostensible end to the pandemic in the region.

    ‎
    Figure 2. Variants of concern (VOCs) as a proportion of all sequenced SARS-CoV-2 specimens from March 2020 to May 2023 in East Asia and the Pacific region (n=1,234,493).
    ‎
    Figure 3. P values from t tests of weekly COVID-19 transmissions per 100,000 population equal to 10 over a rolling 6-month window in East Asia and the Pacific region.

    Figure 4 provides a timeline of the onset of COVID-19 in East Asia and the Pacific region as well as vaccination programs and major events that shaped the progression of the pandemic in the region. Most notably, the zero–COVID-19 policy in China effectively contained the disease in the country and therefore to an extent in the region. Upon the abandonment of the policy, a large wave of cases driven by Omicron finally hit the country, much later than in other parts of the world.

    ‎
    Figure 4. Timeline of the COVID-19 pandemic in East Asia and the Pacific region. VOC: variant of concern; WHO: World Health Organization.

    Principal Findings

    COVID-19 has affected all countries in East Asia and the Pacific region. As of March 16, 2023, the WHO had reported a total of 213,883,614 COVID-19 cases and 622,018 deaths in East Asia and the Pacific region [72]. At the same point, the WHO reported 760,360,956 confirmed cases of COVID-19, including 6,873,477 deaths worldwide [72].

    The results indicate a potential COVID-19 transition from pandemic to endemic at the time the WHO removed the designation of COVID-19 as a public health emergency of international concern on May 5, 2023. Regional speed had remained below the outbreak threshold of 10 for several months ahead of the WHO declaration. The rolling t test of speed, equal to the outbreak threshold, suggests that the region was in an outbreak over the six-month period starting at the end of 2022 when the Omicron variant drove a large increase in cases. However, the result was only significant at the α=.10 level, not the 0.05 level. However, several countries in the region had transmission rates consistent with a substantial outbreak in the 2 weeks around the WHO declaration. These outbreaks make an apparent end to the pandemic in the region unclear at the time.

    As another indicator, nearly all sequenced SARS-CoV-2 specimens in the region were identified as the Omicron variant from early 2022 onward. This transition is consistent with a more clearly defined end to the pandemic in other global regions [73-75]. The relative delay for East Asia and the Pacific region may be attributable to a late Omicron outbreak in the region when China relaxed its stringent and effective zero–COVID-19 policy [76].

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

    Many countries in the region experienced multiple waves of COVID-19 infections, with some waves more severe than others. During the initial outbreaks, countries in East Asia and the Pacific region were quick to respond with policies that were highly effective at curbing transmission rates and limiting mortality. Compared to other regions of the world, Asian countries experienced significantly lower mortality early in the pandemic (2.7 deaths per million in Asian countries vs 197 deaths per million in European countries) [77]. This disparity has been attributed to several factors.

    First, countries in the region were able to quickly formulate effective strategies that were guided by their prior experiences with other respiratory pandemics, such as the 2003 outbreak of SARS-CoV-1 and the 2015 outbreak of Middle East respiratory syndrome [78-80].

    Second, cultural differences between Eastern and Western societies may have allowed countries to more easily adopt face masking and social distancing policies [77]. Where Western countries such as the United States pride themselves on individual freedoms, many Asian countries have cultures that value collective societal well-being [81].

    Third, the region as a whole tended to implement strict containment policies, which typically included mask mandates, social distancing, mass testing requirements, contact tracing, quarantine protocols, border lockdowns, and eventually mass vaccination programs to control the virus.

    China initially implemented a strict zero–COVID-19 policy with the aim to keep cases as close to zero as possible. At times, entire cities, such as Shanghai, Chengdu, and Wuhan, entered citywide lockdown to prevent the spread of the virus [82-84]. Amid these lockdowns, economic activities were halted, public transportation was suspended, and residents were restricted to their homes. Meanwhile, individuals who tested positive for COVID-19 were taken to large isolation facilities to undergo quarantine [82,85].

    New Zealand and Taiwan also initially implemented strict COVID-19 policies including mask mandates, tight border control, contact tracing, and tightly targeted quarantines for local outbreaks. These strategies were highly effective in containing the virus during major outbreaks for both countries [86,87]. However, both eventually shifted away from these strategies with the emergence of more contagious variants such as Delta and Omicron. Instead, they focused more on achieving herd immunity through widespread vaccination campaigns and protecting populations [86,88].

    Vietnam, having learned lessons from the SARS outbreak in 2003, began preparing for COVID-19 weeks before the first cases were reported in their country. Knowing that their health care resources were limited, Vietnam’s strategic goal was to keep COVID-19 cases as low as possible. They began stockpiling personal protective equipment and created local medical response teams to address outbreaks. When Vietnam detected its first cases in the community, it acted decisively and swiftly by locking down areas and performing meticulous contact tracing [89,90]. Vietnam never relied on widespread testing to curb infections [89].

    The Philippines and Indonesia were COVID-19 hotspots throughout the pandemic due to limited public health responses by their governments. Both countries were late to institute COVID-19 containment measures, such as contact tracing, testing, and isolation, leading to high rates of infections and deaths relative to other countries in the region [91-94]. In the Philippines, the country’s limited health care infrastructure left it ill-prepared for the pandemic, leading to an exponential growth in cases during the first wave [94,95].

    Many Pacific Island countries were quick to implement strategies to prevent COVID-19 infections. Given their geographical isolation, these countries went almost completely free of COVID-19 cases during the initial wave of infections through the use of strict border closures and restrictions on international travel [96-98]. For example, the Solomon Islands reported its first COVID-19 case in October 2020 in a student who was returning from the Philippines [99]. However, it was not until 2022 that the Solomon Islands faced its first widespread community outbreak [100]. These strict border closures, while effective, came with a natural economic cost as many island nations rely heavily on tourism, which virtually evaporated during the pandemic [97].

    Subsequent waves of infection were primarily driven by local easing of COVID-19 precautions and the emergence of new and more contagious variants such as Delta and Omicron. For example, Japan has experienced several waves of COVID-19 infections, with the largest wave occurring in mid-2022 due to the Omicron variant [101]. China, on the other hand, has reported fewer waves of COVID-19 infections due to its strict containment measures, although it is not clear how accurate their data are given their lack of consistent reporting. However, when China eased their zero–COVID-19 policy in December 2022, it subsequently experienced an intense surge in cases and related deaths [76]. Other countries in Asia, such as Vietnam and Taiwan, have been relatively successful in controlling the virus and have experienced fewer waves of infection.

    While countries enjoyed relative success early on in controlling disease, they had to grapple with the economic and social consequences of their restrictive containment policies. These outbreaks and their associated governmental responses led to substantial sociopolitical turmoil throughout the region. Protests were widespread and focused on the government’s mishandling of economic recovery and overly restrictive COVID-19 policies. Thailand and Malaysia saw protests break out in 2021 due to the government’s perceived lackluster pandemic response [102,103]. The Philippines saw protesters calling for an end to quarantine rules, which led to President Duterte to issue a “shoot to kill” order against the protesters [104,105]. In China, antigovernment protests broke out throughout the country in late 2022 calling for an end to China’s zero–COVID-19 policy, which used strict and draconian city-wide lockdowns to curb the spread of the virus. In response, China eased their policy in December 2022 and subsequently experienced an intense surge in cases and deaths. The WHO criticized China for underrepresenting the true number of deaths, with one senior official stating that China was using too narrow of a definition to count deaths [106,107]. New Zealand saw protesters converge on Parliament to oppose COVID-19 vaccines mandates [108].

    In terms of the economy, East Asia and the Pacific region experienced a 0.2% decline in gross domestic product (GDP) in 2020 driven by containment measures that limited economic activity. However, as vaccines became more prevalent and countries began easing restrictions, economies rebounded with a GDP growth of 5.8% in 2021 [109].

    To combat the economic ramifications of their containment strategies, governments in the region focused on reopening their economies and using large fiscal packages to stimulate economic recovery. These fiscal packages typically included direct cash support for affected workers, tax relief for individuals and corporations, support for their strained health care systems, and economic investment in specific economic sectors and public works. In 2020, the Chinese government launched several fiscal measures estimated to be worth approximately 4.9 trillion RMB (4.7% of GDP; US $672.3 billion). Key aspects of these measures included tax relief, investment in epidemic prevention and control, and medical equipment [110]. In Vietnam, the government introduced a fiscal support package worth 291.7 trillion VND (3.6% of GDP; US $11.6 billion) with measures that included deferment of value-added, personal income, and corporate income taxes [110]. The Australian government approved a substantial fiscal stimulus package worth AUS $312 billion (15.75% of GDP; US $196 billion) to be used through the financial year 2025, which included an AUS $20 billion (US $12.5 billion) health response package to secure vaccines and strengthen the existing health care system [110]. Singapore announced an SGD $92 billion (US $67.9 billion) fiscal package that included a cash payout to all Singaporeans [110]. In 2020, Japan approved multiple stimulus packages, including the Emergency Economic Package against COVID-19 (worth ¥117.1 trillion; US $750.5 billion) and the Comprehensive Economic Measures to Secure People’s Lives and Livelihoods toward Relief Hope package (worth ¥73.6 trillion; US $471.7 billion). These measures were intended to protect businesses and support wages while also focusing on building a more resilient future economy [110].

    Vaccination Efforts

    Efforts to create a vaccine started immediately. SinoVac, a Chinese pharmaceutical company, began development of an inactivated COVID-19 vaccine called CoronaVac as early as January 2020. By fall 2020, SinoVac launched phase 3 trials in several countries, including Brazil, Indonesia, the Philippines, and Turkey [111]. The large phase 3 trial in Brazil, which used a 2-dose series administered 14 days apart, demonstrated an efficacy of 51% against symptomatic COVID-19 infections and 100% against severe infections [112]. By early 2021, many countries in East Asia and the Pacific region, as well as in South America and Eastern Europe, began mass vaccination programs using CoronaVac [113-118]. By April 2021, SinoVac had announced that it had production capacity to produce 2 billion doses per year [111]. On June 1, 2021, the WHO validated the vaccine for emergency use. Other countries, such as Taiwan, Japan, and Korea, purchased millions of vaccines from western countries, including the AstraZeneca, Pfizer-BioNTech, and Moderna vaccines in early 2021 [119-122].

    With many countries now having reached herd immunity through a combination of several available vaccines, there has been widespread easing of border restrictions, as seen in Japan, China, Taiwan, Singapore, Australia, and New Zealand [123-127].

    Conclusions

    Concerns about potential resurgences of the virus remain valid. As long as COVID-19 continues to spread and mutate, the possibility of new variants emerging remains. These variants could potentially be more transmissible, be resistant to vaccines, or cause more severe illness. This underlines the importance of continued vigilance, vaccination efforts, and global cooperation to control the spread of the virus [39].

    Limitations

    COVID-19 data became less frequently reported worldwide by the time the WHO declared an end to the pandemic as a public health emergency of international concern [128]. In addition, more people began using at-home tests as the pandemic evolved [129], and experts believe the Chinese government has underreported COVID-19 deaths [130]. Because the enhanced surveillance metrics of speed, acceleration, jerk, and 7-day persistence are based on rates and not total counts, statistical bias caused by countries dropping in or out of the sample is mitigated. However, if a nonincluded country is unrepresentative of the region in terms of disease burden, its omission can still influence historical data comparisons. Viral specimen tests for variants of concern in GISAID depend on testing and sequencing capacity, which varied by country across the region.

    Acknowledgments

    Research reported in this publication was generously supported by the Robert J Havey, MD, Institute for Global Health’s Innovation Challenge Award at Northwestern University Feinberg School of Medicine.

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

    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. [FREE Full text] [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. [FREE Full text] [CrossRef] [Medline]
    3. Chilamakuri R, Agarwal S. COVID-19: characteristics and therapeutics. Cells. Jan 21, 2021;10(2):206. [FREE Full text] [CrossRef] [Medline]
    4. Hu B, Guo H, Zhou P, Shi Z. Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol. Mar 06, 2021;19(3):141-154. [FREE Full text] [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. [FREE Full text] [CrossRef] [Medline]
    6. 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. [FREE Full text] [CrossRef] [Medline]
    7. East Asia and Pacific. The World Bank Group. URL: https://data.worldbank.org/region/east-asia-and-pacific [accessed 2024-04-29]
    8. 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. 2023. URL: https://www.usnews.com/news/health-news/articles/2023-05-12/when-does-the-covid-19-pandemic-end [accessed 2024-04-29]
    9. Burki T. WHO ends the COVID-19 public health emergency. Lancet Respir Med. Jul 2023;11(7):588. [FREE Full text] [CrossRef] [Medline]
    10. WHO chief declares end to COVID-19 as a global health emergency. United Nations. URL: https://news.un.org/en/story/2023/05/1136367 [accessed 2024-04-29]
    11. Lesson 1: introduction to epidemiology. Centers for Disease Control and Prevention. URL: https://archive.cdc.gov/#/details?url=https://www.cdc.gov/csels/dsepd/ss1978/lesson1/section11.html [accessed 2024-04-29]
    12. Epidemic, endemic, pandemic: what are the differences? Columbia University. Feb 19, 2021. URL: https:/​/www.​publichealth.columbia.edu/​public-health-now/​news/​epidemic-endemic-pandemic-what-are-differences [accessed 2024-04-29]
    13. The Lancet Infectious Diseases. Transitioning to endemicity with COVID-19 research. Lancet Infect Dis. Mar 2022;22(3):297. [FREE Full text] [CrossRef] [Medline]
    14. Britto D, Delorme J, Miller B, Sleat D, Summers H, Wain R. 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 2024-04-29]
    15. Gilbert R, Cliffe SJ. Public health surveillance. In: Regmi K, Gee I, editors. Public Health Intelligence. Cham, Switzerland. Springer; Apr 27, 2016:91-110.
    16. 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. [FREE Full text] [CrossRef] [Medline]
    17. Teutsch SM, Churchill RE. Principles and Practice of Public Health Surveillance 2nd Edition. Oxford, UK. Oxford University Press; 2000.
    18. 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 Academic Press; 2010:18-28.
    19. Teutsch SM, Thacker SB. Planning a public health surveillance system. Epidemiol Bull. Mar 1995;16(1):1-6. [Medline]
    20. Thacker SB, Qualters JR, Lee LM, Centers for Disease ControlPrevention. Public health surveillance in the United States: evolution and challenges. MMWR Suppl. Jul 27, 2012;61(3):3-9. [Medline]
    21. Centers for Disease Control (CDC). Guidelines for evaluating surveillance systems. MMWR Suppl. May 06, 1988;37(5):1-18. [Medline]
    22. 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]
    23. 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#moretextPAmods.subject_name [accessed 2024-04-29]
    24. 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]
    25. Nsubuga P, White M, Thacker S, Anderson M, Blount S, Broome C, 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; 2006.
    26. Lee LM, Thacker SB, St. Louis ME, Teutsch SM. Principles and Practice of Public Health Surveillance 3rd Edition. New York, NY. Oxford University Press; 2010.
    27. Thacker SB, Stroup DF, Rothenberg RB. Public health surveillance for chronic conditions: a scientific basis for decisions. Stat Med. Feb 28, 1995;14(5-7):629-641. [CrossRef] [Medline]
    28. 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. [FREE Full text] [CrossRef] [Medline]
    29. 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]
    30. Romaguera RA, German RR, Klaucke DN. Evaluating public health surveillance. In: Teutsch SM, Churchill RE, editors. Principles and Practice of Public Health Surveillance. Oxford, UK. Oxford University Press; 2000:176-193.
    31. 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]
    32. 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. [FREE Full text] [CrossRef] [Medline]
    33. 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. [FREE Full text] [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. [FREE Full text] [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. [FREE Full text] [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. [FREE Full text] [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. [FREE Full text] [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. [FREE Full text] [CrossRef] [Medline]
    39. Post LA, Lorenzo-Redondo R. Omicron: fewer adverse outcomes come with new dangers. Lancet. Apr 02, 2022;399(10332):1280-1281. [FREE Full text] [CrossRef] [Medline]
    40. 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. [FREE Full text] [CrossRef] [Medline]
    41. 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. [FREE Full text] [CrossRef] [Medline]
    42. 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. [FREE Full text] [CrossRef] [Medline]
    43. Zarocostas J. With the COVID-19 PHEIC over, what next? Lancet. May 20, 2023;401(10389):1642-1643. [FREE Full text] [CrossRef] [Medline]
    44. Sarker R, Roknuzzaman AS, Hossain MJ, Bhuiyan MA, Islam MR. The WHO declares COVID-19 is no longer a public health emergency of international concern: benefits, challenges, and necessary precautions to come back to normal life. Int J Surg. Sep 01, 2023;109(9):2851-2852. [FREE Full text] [CrossRef] [Medline]
    45. Fan VY, Cash R, Bertozzi S, Pate M. The when is less important than the what: an epidemic scale as an alternative to the WHO's Public Health Emergency of International Concern. Lancet Glob Health. Oct 2023;11(10):e1499-e1500. [FREE Full text] [CrossRef] [Medline]
    46. Torner N. The end of COVID-19 public health emergency of international concern (PHEIC): and now what? Vacunas. Jul 2023;24(3):164-165. [CrossRef]
    47. Wise J. COVID-19: WHO declares end of global health emergency. BMJ. May 09, 2023;381:1041. [CrossRef] [Medline]
    48. Harris E. WHO declares end of COVID-19 global health emergency. JAMA. Jun 06, 2023;329(21):1817. [CrossRef] [Medline]
    49. Okesanya OJ, Manirambona E, Buban J, Olabode ON, Lucero-Prisno DE. Coronavirus disease 2019 emergency is over but the pandemic is not: implications for a new global order. Int J Surg Glob Health. 2023;6(4):e0207. [CrossRef]
    50. Lenharo M. WHO declares end to COVID-19's emergency phase. Nature. May 05, 2023:882. [CrossRef] [Medline]
    51. Sarker R, Roknuzzaman AS, Nazmunnahar, Hossain MJ, Islam MR. Benefits and probable ill effects of WHO's declaration of end of COVID-19 pandemic: a way back to pandemic-free normal life. Ann Med Surg (Lond). Jun 2023;85(6):3199-3201. [FREE Full text] [CrossRef] [Medline]
    52. Ritchie H, Mathieu E, Rodés-Guirao L, Appel C, Gavrilov D, Giattino C, et al. Coronavirus pandemic (COVID-19). Our World in Data. URL: https://ourworldindata.org/coronavirus [accessed 2024-04-29]
    53. Croissant Y, Millo G. Panel data econometrics in R: the plm package. J Stat Soft. 2008;27(2):1-43. [CrossRef]
    54. R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2023. URL: http://www.R-project.org/ [accessed 2024-04-14]
    55. Hansen LP. Large sample properties of generalized method of moments estimators. Econometrica. Jul 1982;50(4):1029. [CrossRef]
    56. Eales O, Ainslie KE, Walters CE, Wang H, Atchison C, Ashby D, et al. Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number. Epidemics. Sep 2022;40:100604. [FREE Full text] [CrossRef] [Medline]
    57. Elliott P, Eales O, Steyn N, Tang D, Bodinier B, Wang H, et al. Twin peaks: the Omicron SARS-CoV-2 BA.1 and BA.2 epidemics in England. Science. Jun 24, 2022;376(6600):eabq4411. [FREE Full text] [CrossRef] [Medline]
    58. Shaddick G, Zidek JV, Schmidt AM. Spatio–Temporal Methods in Environmental Epidemiology with R. New York, NY. CRC Press; 2023.
    59. Global influenza surveillance and response system (GISRS). Global Initiative on Sharing All Influenza Data (GISAID). URL: https://www.gisaid.org/ [accessed 2024-04-29]
    60. 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. [FREE Full text] [CrossRef] [Medline]
    61. Shu Y, McCauley J. GISAID: global initiative on sharing all influenza data - from vision to reality. Euro Surveill. Mar 30, 2017;22(13):30494. [FREE Full text] [CrossRef] [Medline]
    62. 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. [FREE Full text] [CrossRef] [Medline]
    63. 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. [FREE Full text] [CrossRef] [Medline]
    64. 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. [FREE Full text] [CrossRef] [Medline]
    65. 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. [FREE Full text] [CrossRef] [Medline]
    66. 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):277-297. [CrossRef]
    67. Hu M. Pandemic Surveillance: Privacy, Security, and Data Ethics Elgar Law, Technology and Society Series. New York, NY. Monograph Book; 2022.
    68. Levenson E, Firger J. What the CDC’s ‘substantial’ and ‘high’ levels of COVID-19 transmission actually mean. CNN Health. 2021. URL: https:/​/edition.​cnn.com/​2021/​07/​28/​health/​substantial-or-high-covid-19-transmission-wellness/​index.​html [accessed 2024-04-29]
    69. Christie A, Brooks JT, Hicks LA, Sauber-Schatz EK, Yoder JS, Honein MA, et al. 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. [FREE Full text] [CrossRef] [Medline]
    70. Stoto MA. Public health assessment in the 1990s. Annu Rev Public Health. 1992;13:59-78. [CrossRef] [Medline]
    71. 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]
    72. Coronavirus disease (COVID-19) dashboard. World Health Organization. URL: https://covid19.who.int/table [accessed 2024-04-29]
    73. Post L, Wu SA, Soetikno AG, Ozer EA, Liu Y, Welch SB, et al. Updated surveillance metrics and history of the COVID-19 pandemic (2020-2023) in Latin America and the Caribbean: longitudinal trend analysis. JMIR Public Health Surveill. May 17, 2024;10:e44398. [FREE Full text] [CrossRef] [Medline]
    74. Lundberg AL, Wu SA, Soetikno AG, Hawkins C, Murphy RL, Havey RJ, et al. Updated surveillance metrics and history of the COVID-19 pandemic (2020-2023) in Europe: longitudinal trend analysis. JMIR Public Health Surveill. Jun 21, 2024;10:e53551. [FREE Full text] [CrossRef] [Medline]
    75. Soetikno AG, Lundberg AL, Ozer EA, Wu SA, Welch SB, Mason M, et al. Updated surveillance metrics and history of the COVID-19 pandemic (2020-2023) in the Middle East and North Africa: longitudinal trend analysis. JMIR Public Health Surveill. Jun 12, 2024;10:e53219. [FREE Full text] [CrossRef] [Medline]
    76. Woodruff J, Warsi Z. COVID rapidly spreads in China as government eases strict quarantine rules. Public Broadcasting Service. URL: https://tinyurl.com/yc3xb293 [accessed 2024-04-29]
    77. Landoni G, Maimeri N, Fedrizzi M, Fresilli S, Kuzovlev A, Likhvantsev V, et al. Why are Asian countries outperforming the western world in controlling COVID-19 pandemic? Pathog Glob Health. Feb 26, 2021;115(1):70-72. [FREE Full text] [CrossRef] [Medline]
    78. Acter T, Uddin N, Das J, Akhter A, Choudhury TR, Kim S. Evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as coronavirus disease 2019 (COVID-19) pandemic: a global health emergency. Sci Total Environ. Aug 15, 2020;730:138996. [FREE Full text] [CrossRef] [Medline]
    79. To KK, Sridhar S, Chiu KH, Hung DL, Li X, Hung IF, et al. Lessons learned 1 year after SARS-CoV-2 emergence leading to COVID-19 pandemic. Emerg Microbes Infect. Dec 2021;10(1):507-535. [FREE Full text] [CrossRef] [Medline]
    80. Khan M, Adil SF, Alkhathlan HZ, Tahir MN, Saif S, Khan M, et al. COVID-19: a global challenge with old history, epidemiology and progress so far. Molecules. Dec 23, 2020;26(1):39. [FREE Full text] [CrossRef] [Medline]
    81. Jennings R. How cultural differences help Asian countries beat COVID-19, while US struggles. Voice of America (VOA). URL: https://www.insidevoa.com/p/5831.html [accessed 2024-04-29]
    82. Brant R. Shanghai Covid: China announces largest city-wide lockdown. BBC News. Mar 27, 2022. URL: https://www.bbc.com/news/world-asia-china-60893070 [accessed 2024-04-29]
    83. Woo R. China's Chengdu exits full citywide Covid lockdown on Thursday. Reuters. Sep 14, 2022. URL: https://www.reuters.com/world/china/chinas-chengdu-lift-citywide-covid-lockdown-thursday-2022-09-14/ [accessed 2022-09-14]
    84. China ends 76-day lockdown of Wuhan city, where the virus outbreak started. CNBC. Apr 07, 2020. URL: https:/​/www.​cnbc.com/​2020/​04/​08/​coronavirus-china-ends-lockdown-of-wuhan-city-where-outbreak-started.​html [accessed 2024-04-29]
    85. What is China's 'zero covid' policy and why did it trigger protests? The Washington Post. Nov 09, 2022. URL: https://www.washingtonpost.com/world/2022/11/29/china-zero-covid-policy-protests-explained/ [accessed 2024-04-29]
    86. Chen SC. Taiwan's experience in fighting COVID-19. Nat Immunol. Apr 26, 2021;22(4):393-394. [CrossRef] [Medline]
    87. Qin A, Chien AC. Covid? What Covid? Taiwan thrives as a bubble of normality. The Washington Post. Aug 23, 2021. URL: https://www.nytimes.com/2021/03/13/world/asia/taiwan-covid.html [accessed 2024-04-29]
    88. Frost N. Battling delta, New Zealand abandons its zero-covid ambitions. The NewYork Times. Oct 04, 2021. URL: https://www.nytimes.com/2021/10/04/world/australia/new-zealand-covid-zero.html [accessed 2024-04-29]
    89. Gan N. How Vietnam managed to keep its coronavirus death toll at zero. CNN World. May 30, 2020. URL: https://edition.cnn.com/2020/05/29/asia/coronavirus-vietnam-intl-hnk/index.html [accessed 2024-04-29]
    90. Willoughby E. An ideal public health model? Vietnam’s state-led, preventative, low-cost response to COVID-19. Brookings. Jun 29, 2021. URL: https:/​/www.​brookings.edu/​blog/​order-from-chaos/​2021/​06/​29/​an-ideal-public-health-model-vietnams-state-led-preventative-low-cost-response-to-covid-19/​ [accessed 2024-04-29]
    91. Indonesia: weak public health response to COVID-19. Human Rights Watch. Jan 13, 2021. URL: https://www.hrw.org/news/2021/01/13/indonesia-weak-public-health-response-covid-19 [accessed 2024-04-29]
    92. Sullivan M. The Philippines becomes coronavirus hot spot in Southeast Asia. National Public Radio. Oct 08, 2020. URL: https:/​/www.​npr.org/​2020/​08/​10/​900279722/​the-philippines-becomes-coronavirus-hot-spot-in-southeast-asia [accessed 2024-04-29]
    93. Philippines now has worst COVID-19 outbreak in Southeast Asia. The Japan Times. Aug 07, 2020. URL: https://www.japantimes.co.jp/news/2020/08/07/asia-pacific/philippines-coronavirus-southeast-asia/ [accessed 2024-04-29]
    94. Amit AM, Pepito VC, Dayrit MM. Early response to COVID-19 in the 
Philippines. Western Pac Surveill Response J. Mar 31, 2021;12(1):56-60. [FREE Full text] [CrossRef] [Medline]
    95. -. COVID-19: an ongoing public health crisis in the Philippines. Lancet Reg Health West Pac. Apr 2021;9:100160. [FREE Full text] [CrossRef] [Medline]
    96. Ryall J. Many Pacific Island nations have kept Covid at bay. Deutsche Welle. May 19, 2020. URL: https://www.dw.com/en/zero-cases-how-pacific-islands-kept-coronavirus-at-bay/a-53495263 [accessed 2024-04-29]
    97. Addressing COVID-19's impacts and structural crisis in the Pacific. The Food and Agriculture Organization. May 06, 2020. URL: https:/​/www.​fao.org/​newsroom/​detail/​Addressing-COVID-19-s-impacts-and-structural-crisis-in-the-Pacific/​en [accessed 2024-04-29]
    98. McClure H. Surviving the pandemic: COVID-19 in the Pacific and health security. Development Bulletin. URL: https://pacificsecurity.net/wp-content/uploads/2021/10/DB82_Part17.pdf [accessed 2024-04-29]
    99. The Solomon Islands reports its first coronavirus case, a student returning from the Philippines. SBS News. Oct 04, 2020. URL: https:/​/www.​sbs.com.au/​news/​article/​the-solomon-islands-reports-its-first-coronavirus-case-a-student-returning-from-the-philippines/​x20eytmn1 [accessed 2024-04-30]
    100. Rising D. Solomon Islands' 1st virus outbreak causes growing concern. The Associated Press. Feb 17, 2022. URL: https:/​/apnews.​com/​article/​coronavirus-pandemic-health-solomon-islands-honiara-30d3df8cb0f809c9e3e5a0cc5c6f886f [accessed 2024-04-29]
    101. McCurry J. Japan urges ‘highest level of vigilance’ as Omicron subvariant drives record Covid surge. The Gurdian. Jul 22, 2022. URL: https:/​/www.​theguardian.com/​world/​2022/​jul/​22/​japan-urges-highest-level-of-vigilance-as-omicron-subvariant-drives-record-covid-surge [accessed 2024-04-29]
    102. Suhartono M, Beech H. Thai protesters are back, and angrier, as government fumbles on Covid. The NewYork Times. Sep 05, 2021. URL: https://www.nytimes.com/2021/09/05/world/asia/thailand-protesters-covid.html [accessed 2024-04-29]
    103. Malaysia: hundreds take to the streets in anti-government protest. Al Jazeera Media Network. Jul 21, 2021. URL: https:/​/www.​aljazeera.com/​news/​2021/​7/​31/​hundreds-take-to-streets-in-anti-government-protest-in-malaysia [accessed 2024-04-29]
    104. Philippines: president Duterte gives “shoot to kill” order amid pandemic response. Amnesty International. Apr 02, 2020. URL: https:/​/www.​amnesty.org/​en/​latest/​news/​2020/​04/​philippines-president-duterte-shoot-to-kill-order-pandemic/​ [accessed 2024-04-29]
    105. Lo B, Ott H. The Philippines has the worst coronavirus outbreak in Southeast Asia, and critics warn the government could use it to crack down on dissent. CBS News. Aug 07, 2020. URL: https:/​/www.​cbsnews.com/​news/​covid-19-philippines-worst-coronavirus-outbreak-in-southeast-asia-crackdown-on-dissent/​ [accessed 2024-04-29]
    106. Branswell H. Senior WHO official faults China for undercounting Covid deaths. STAT News. Jun 04, 2023. URL: https://www.statnews.com/2023/01/04/who-official-faults-china-undercounting-covid-deaths/ [accessed 2024-04-29]
    107. China might be underreporting its COVID-related death toll. Euronews. Dec 21, 2022. URL: https:/​/www.​euronews.com/​2022/​12/​21/​china-might-be-underreporting-its-covid-related-death-toll-experts [accessed 2024-04-29]
    108. Anti-mandate protesters converge on New Zealand parliament. VOA News. Aug 23, 2022. URL: https://www.voanews.com/a/anti-mandate-protesters-converge-on-new-zealand-parliament-/6712836.html [accessed 2024-04-29]
    109. GDP growth (annual %) - East Asia and Pacific. World Bank Group. URL: https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?locations=Z4 [accessed 2024-04-29]
    110. Policy responses to COVID-19. International Monetary Fund. URL: https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19#C [accessed 2024-04-29]
    111. Corum J, Zimmer C. How the Sinovac vaccine works. The NewYork Times. May 07, 2021. URL: https://www.nytimes.com/interactive/2020/health/sinovac-covid-19-vaccine.html [accessed 2024-04-29]
    112. The Sinovac-CoronaVac COVID-19 vaccine: what you need to know. World Health Organization. Jun 10, 2022. URL: https:/​/www.​who.int/​news-room/​feature-stories/​detail/​the-sinovac-covid-19-vaccine-what-you-need-to-know#:~:text=The%20vaccine%20is%20safe%20and,COVID%2D19%20in%20the%20past [accessed 2024-04-29]
    113. Lyudmila K. Vaccination with CoronaVac launched in Ukraine on April 13 – Health minister. UNIAN. Apr 13, 2021. URL: https:/​/www.​unian.info/​society/​covid-19-vaccination-with-coronavac-launched-in-ukraine-on-april-13-health-minister-11386060.​html [accessed 2024-04-29]
    114. Miranda NA. Chile receives 2 million-dose first delivery of Sinovac COVID-19 vaccine. Reuters. Jan 28, 2021. URL: https:/​/www.​reuters.com/​business/​healthcare-pharmaceuticals/​chile-receives-2-million-dose-first-delivery-sinovac-covid-19-vaccine-2021-01-28/​ [accessed 2024-04-29]
    115. Mano A, Simões E. Chinese vaccine draws demand across Latin America, say Brazilian officials. Reuters. Dec 10, 2020. URL: https:/​/www.​reuters.com/​article/​business/​healthcare-pharmaceuticals/​chinese-vaccine-draws-demand-across-latin-america-say-brazilian-officials-idUSKBN28K1IJ/​ [accessed 2024-04-29]
    116. Nguyen A. Thailand finally kicks off COVID vaccinations: 5 things to know. NIKKEI Asia. Mar 01, 2021. URL: https:/​/asia.​nikkei.com/​Spotlight/​Coronavirus/​COVID-vaccines/​Thailand-finally-kicks-off-COVID-vaccinations-5-things-to-know [accessed 2024-04-29]
    117. Indonesia starts mass COVID vaccinations over vast territory. The Associated Press. Jan 13, 2021. URL: https:/​/apnews.​com/​article/​asia-pacific-indonesia-coronavirus-pandemic-joko-widodo-3ff30c85dc6943e2a06db470a9d04931 [accessed 2024-04-29]
    118. Brazil health ministry says plans to order 30 million more Coronavac doses. Reuters. Feb 19, 2021. URL: https:/​/www.​reuters.com/​article/​business/​healthcare-pharmaceuticals/​brazil-health-ministry-says-plans-to-order-30-million-more-coronavac-doses-idUSKBN2AJ1D3/​ [accessed 2024-04-29]
    119. Takenaka K. Japan begins COVID-19 vaccination in 'first major step' to halt pandemic. Reuters. Feb 16, 2021. URL: https:/​/www.​reuters.com/​article/​world/​japan-begins-covid-19-vaccination-in-first-major-step-to-halt-pandemic-idUSKBN2AH00O/​ [accessed 2024-04-29]
    120. Cha S. S.Korea to secure more mRNA vaccines for COVID-19 booster shot. Reuters. Jun 29, 2021. URL: https:/​/www.​reuters.com/​world/​asia-pacific/​skorea-secure-more-mrna-vaccines-covid-19-booster-shot-2021-06-29/​ [accessed 2024-04-29]
    121. Lee IC. Taiwan begins COVID-19 vaccinations. Taipei Times. Mar 23, 2021. URL: https://www.taipeitimes.com/News/front/archives/2021/03/23/2003754315 [accessed 2024-04-24]
    122. Taiwan clears AstraZeneca vaccine, shots might start on Monday. Al Jazeera. Mar 18, 2021. URL: https:/​/www.​aljazeera.com/​news/​2021/​3/​18/​taiwan-clears-astrazeneca-vaccine-shots-might-start-on-monday [accessed 2024-04-29]
    123. Travelers rush to enter China as it eases pandemic border restrictions. PBS News. Jan 08, 2023. URL: https:/​/www.​pbs.org/​newshour/​world/​travelers-rush-to-enter-china-as-it-eases-pandemic-border-restrictions [accessed 2024-04-29]
    124. Coronavirus (COVID-19) measures by the government. Japan National Tourism Organization. May 09, 2023. URL: https://www.japan.travel/en/practical-coronavirus-information/government-measures/ [accessed 2024-04-29]
    125. COVID-19 and the border. Government of Australia. Mar 11, 2023. URL: https:/​/www.​homeaffairs.gov.au/​portfolio-functions/​travel-and-crossing-the-border/​covid-19-and-the-border [accessed 2024-04-29]
    126. You want to travel to New Zealand. New Zealand Customs. URL: https:/​/covid19.​govt.nz/​international-travel/​#:~:text=Travellers%20and%20crew%20do%20not,still%20require%20proof%20of%20vaccination [accessed 2024-04-29]
    127. COVID-19 travel information. Ministry of Foreign Affairs Singapore. URL: https://www.mfa.gov.sg/Consular-Services/Singapore-Citizens/COVID-19-Information [accessed 2024-04-29]
    128. Stein R. As the pandemic ebbs, an influential COVID tracker shuts down. KERA News. Feb 10, 2023. URL: https://www.keranews.org/2023-02-10/as-the-pandemic-ebbs-an-influential-covid-tracker-shuts-down [accessed 2024-04-29]
    129. 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. [FREE Full text] [CrossRef] [Medline]
    130. Brown RB. Biases in COVID-19 case and death definitions: potential causes and consequences. Disaster Med Public Health Prep. Dec 12, 2022;17:e313. [FREE Full text] [CrossRef] [Medline]

    ‎
    GDP: gross domestic product
    GISAID: Global Initiative on Sharing All Influenza Data
    OWID: Our World in Data
    VOC: variant of concern
    WHO: World Health Organization

    Edited by A Mavragani; submitted 03.10.23; peer-reviewed by S Thawillarp, A Coristine; comments to author 09.10.24; revised version received 06.01.25; accepted 12.01.25; published 21.02.25.

    Copyright

    ©Alexander L Lundberg, Alan G Soetikno, Scott A Wu, Egon Ozer, Sarah B Welch, Yingxuan Liu, Claudia Hawkins, Maryann Mason, Robert Murphy, Robert J Havey, Charles B Moss, Chad J Achenbach, Lori Ann Post. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 21.02.2025.

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