Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review.

BACKGROUND: The COVID-19 pandemic has prompted the deployment of digital technologies for public health surveillance globally. The rapid development and use of these technologies have curtailed opportunities to fully consider their potential impacts (eg, for human rights, civil liberties, privacy, and marginalization of vulnerable groups). OBJECTIVE: We conducted a scoping review of peer-reviewed and gray literature to identify the types and applications of digital technologies used for surveillance during the COVID-19 pandemic and the predicted and witnessed consequences of digital surveillance. METHODS: Our methodology was informed by the 5-stage methodological framework to guide scoping reviews: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the findings. We conducted a search of peer-reviewed and gray literature published between December 1, 2019, and December 31, 2020. We focused on the first year of the pandemic to provide a snapshot of the questions, concerns, findings, and discussions emerging from peer-reviewed and gray literature during this pivotal first year of the pandemic. Our review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines. RESULTS: We reviewed a total of 147 peer-reviewed and 79 gray literature publications. Based on our analysis of these publications, we identified a total of 90 countries and regions where digital technologies were used for public health surveillance during the COVID-19 pandemic. Some of the most frequently used technologies included mobile phone apps, location-tracking technologies, drones, temperature-scanning technologies, and wearable devices. We also found that the literature raised concerns regarding the implications of digital surveillance in relation to data security and privacy, function creep and mission creep, private sector involvement in surveillance, human rights, civil liberties, and impacts on marginalized groups. Finally, we identified recommendations for ethical digital technology design and use, including proportionality, transparency, purpose limitation, protecting privacy and security, and accountability. CONCLUSIONS: A wide range of digital technologies was used worldwide to support public health surveillance during the COVID-19 pandemic. The findings of our analysis highlight the importance of considering short- and long-term consequences of digital surveillance not only during the COVID-19 pandemic but also for future public health crises. These findings also demonstrate the ways in which digital surveillance has rendered visible the shifting and blurred boundaries between public health surveillance and other forms of surveillance, particularly given the ubiquitous nature of digital surveillance. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-https://doi.org/10.1136/bmjopen-2021-053962.

Surveillance system integration: reporting the results of a global multicountry survey.

OBJECTIVES: Currently, there is no comprehensive picture of the global surveillance landscape. This survey examines the current state of surveillance systems, levels of integration, barriers and opportunities for the integration of surveillance systems at the country level, and the role of national public health institutes (NPHIs). STUDY DESIGN: This was a cross-sectional survey of NPHIs. METHODS: A web-based survey questionnaire was disseminated to 110 NPHIs in 95 countries between July and August 2022. Data were descriptively analysed, stratified by World Health Organization region, World Bank Income Group, and self-reported Integrated Disease Surveillance (IDS) maturity status. RESULTS: Sixty-five NPHIs responded. Systems exist to monitor notifiable diseases and vaccination coverage, but less so for private, pharmaceutical, and food safety sectors. While Ministries of Health usually lead surveillance, in many countries, NPHIs are also involved. Most countries report having partially developed IDS. Surveillance data are frequently inaccessible to the lead public health agency and seldomly integrated into a national public health surveillance system. Common challenges to establishing IDS include information technology system issues, financial constraints, data sharing and ownership limitations, workforce capacity gaps, and data availability. CONCLUSIONS: Public health surveillance systems across the globe, although built on similar principles, are at different levels of maturity but face similar developmental challenges. Leadership, ownership and governance, supporting legal mandates and regulations, as well as adherence to mandates, and enforcement of regulations are critical components of effective surveillance. In many countries, NPHIs play a significant role in integrated disease surveillance.

Person-centered hospital discharge data: Essential existing infrastructure to enhance public health surveillance of maternal substance use disorders in the midst of a national maternal overdose crisis.

OBJECTIVES: As crises of drug-related maternal harms escalate, US public health surveillance capacity remains suboptimal for drug-related maternal morbidities. Most state hospital discharge databases (HDDs) are encounter-based, and thus limit ascertainment of morbidities to delivery visits and ignoring those occurring during the 21 months spanning pregnancy and postpartum year. This study analyzes data from a state that curates person-centered HDD to compare patterns of substance use disorder (SUD) diagnoses at delivery vs. the full 21 pregnancy/postpartum months, overall and by maternal social position. METHODS: Among people who experienced an in-hospital birth in New York State between 9/1/2016 and 1/1/2018 (N = 330,872), we estimated SUD diagnosis (e.g., opioids, stimulants, benzodiazepines, cannabis) prevalence at delivery; across the full 9 months of pregnancy and 12 postpartum months; and by trimester and postpartum quarter. Risk ratio and risk difference estimated disparities by race/ethnicity, age, rurality, and payor. RESULTS: The 21-month SUD prevalence rate per 100,000 was 2671 (95% CI 2616-2726), with 31% (29.5%-31.5%) missing SUD indication when ascertained at delivery only (1866; 95% CI 1820-1912). Quarterly rates followed a roughly J-shaped trajectory. Structurally marginalized individuals suffered the highest 21-month SUD prevalence (e.g., Black:White risk ratio=1.80 [CI:1.73-1.88]). CONCLUSION: By spanning the full 21 months of pregnancy/postpartum, person-centered HDD reveal than the maternal SUD crisis is far greater than encounter-based delivery estimates had revealed. Generating person-centered HDD will improve efforts to tailor interventions to help people who use drugs survive while pregnant and postpartum, and eliminate inequities.

Assessing the performance of the integrated disease surveillance and response systems: a systematic review of global evidence.

OBJECTIVES: Public health surveillance systems are critical for detecting and responding to health threats. This review aims to analyze international literature on the performance of these systems in terms of core, support, and attributes of surveillance system. STUDY DESIGN: Systematic review. METHODS: Following the preregistered protocol (PROSPERO: CRD42022366051), a systematic search was conducted on PubMed/MEDLINE, CINHAL, CABI, Web of Science, and Google Scholar for articles evaluating Public Health Surveillance System performance from inception to July 21, 2023. Various study designs were included, and quality assessment was performed. Thematic analysis categorized findings into key surveillance system functions. RESULTS: Nine studies from different countries assessed core and supportive functions, as well as surveillance attributes. Performance varied among countries, with some excelling overall and others showing poor performance in specific areas. Many countries' surveillance systems had inadequate performance in key measures in terms of the core and supportive functions, as well as the attributes of the surveillance system. CONCLUSION: This review shows significant variations in the performance of public health surveillance systems across countries. Further research is needed to understand underperformance reasons and inform global policymaking for strengthening surveillance systems.

Assessment of the integrated disease surveillance and response system implementation in health zones at risk for viral hemorrhagic fever outbreaks in North Kivu, Democratic Republic of the Congo, following a major Ebola outbreak, 2021.

BACKGROUND: The Democratic Republic of the Congo (DRC) experienced its largest Ebola Virus Disease Outbreak in 2018-2020. As a result of the outbreak, significant funding and international support were provided to Eastern DRC to improve disease surveillance. The Integrated Disease Surveillance and Response (IDSR) strategy has been used in the DRC as a framework to strengthen public health surveillance, and full implementation could be critical as the DRC continues to face threats of various epidemic-prone diseases. In 2021, the DRC initiated an IDSR assessment in North Kivu province to assess the capabilities of the public health system to detect and respond to new public health threats. METHODS: The study utilized a mixed-methods design consisting of quantitative and qualitative methods. Quantitative assessment of the performance in IDSR core functions was conducted at multiple levels of the tiered health system through a standardized questionnaire and analysis of health data. Qualitative data were also collected through observations, focus groups and open-ended questions. Data were collected at the North Kivu provincial public health office, five health zones, 66 healthcare facilities, and from community health workers in 15 health areas. RESULTS: Thirty-six percent of health facilities had no case definition documents and 53% had no blank case reporting forms, limiting identification and reporting. Data completeness and timeliness among health facilities were 53% and 75% overall but varied widely by health zone. While these indicators seemingly improved at the health zone level at 100% and 97% respectively, the health facility data feeding into the reporting structure were inconsistent. The use of electronic Integrated Disease Surveillance and Response is not widely implemented. Rapid response teams were generally available, but functionality was low with lack of guidance documents and long response times. CONCLUSION: Support is needed at the lower levels of the public health system and to address specific zones with low performance. Limitations in materials, resources for communication and transportation, and workforce training continue to be challenges. This assessment highlights the need to move from outbreak-focused support and funding to building systems that can improve the long-term functionality of the routine disease surveillance system.

A Mortality Surveillance Collaboration Between a Health System and Public Health Department.

We describe a collaboration between a health system and public health department to create a mortality surveillance system. The collaboration enabled the health system to identify more than six times the number of deaths identified through local system medical records alone. This powerful epidemiological process, combining the nuanced data captured through clinical care in health systems with subsequent data on mortality, drives quality improvement, scientific research, and epidemiology that can be of particular benefit to underserved communities. (Am J Public Health. 2023;113(9):943-946. https://doi.org/10.2105/AJPH.2023.307335).

Detection of SARS-CoV-2 RNA in wastewater from an enclosed college campus serves as an early warning surveillance system.

SARS-CoV-2, the causative agent of Covid-19, is shed from infected persons in respiratory droplets, feces, and urine. Using quantitative PCR (qPCR), our group hypothesized that we could detect SARS-CoV-2 in wastewater samples collected on a university campus prior to the detection of the virus in individuals on campus. Wastewater samples were collected 3 times a week from 5 locations on the main campus of the University of North Carolina Wilmington (UNCW) from July 24, 2020 to December 21, 2020. Post-collection, total RNA was extracted and SARS-CoV-2 RNA in the samples was detected by qPCR. SARS-CoV-2 signal was detected on campus beginning on August 19 as classes began and the signal increased in both intensity and breadth as the Fall semester progressed. A comparison of two RNA extraction methods from wastewater showed that SARS-CoV-2 was detected more frequently on filter samples versus the direct extracts. Aligning our wastewater data with the reported SARS-CoV-2 cases on the campus Covid-19 dashboard showed the virus signal was routinely detected in the wastewater prior to clusters of individual cases being reported. These data support the testing of wastewater for the presence of SARS-CoV-2 and may be used as part of a surveillance program for detecting the virus in a community prior to an outbreak occurring and could ultimately be incorporated with other SARS-CoV-2 metrics to better inform public health enabling a quick response to contain or mitigating spread of the virus.

Effectiveness of Public Health Digital Surveillance Systems for Infectious Disease Prevention and Control at Mass Gatherings: Systematic Review.

BACKGROUND: Mass gatherings (MGs; eg, religious, sporting, musical, sociocultural, and other occasions that draw large crowds) pose public health challenges and concerns related to global health. A leading global concern regarding MGs is the possible importation and exportation of infectious diseases as they spread from the attendees to the general population, resulting in epidemic outbreaks. Governments and health authorities use technological interventions to support public health surveillance and prevent and control infectious diseases. OBJECTIVE: This study aims to review the evidence on the effectiveness of public health digital surveillance systems for infectious disease prevention and control at MG events. METHODS: A systematic literature search was conducted in January 2022 using the Ovid MEDLINE, Embase, CINAHL, and Scopus databases to examine relevant articles published in English up to January 2022. Interventional studies describing or evaluating the effectiveness of public health digital surveillance systems for infectious disease prevention and control at MGs were included in the analysis. Owing to the lack of appraisal tools for interventional studies describing and evaluating public health digital surveillance systems at MGs, a critical appraisal tool was developed and used to assess the quality of the included studies. RESULTS: In total, 8 articles were included in the review, and 3 types of MGs were identified: religious (the Hajj and Prayagraj Kumbh), sporting (the Olympic and Paralympic Games, the Federation International Football Association World Cup, and the Micronesian Games), and cultural (the Festival of Pacific Arts) events. In total, 88% (7/8) of the studies described surveillance systems implemented at MG events, and 12% (1/8) of the studies described and evaluated an enhanced surveillance system that was implemented for an event. In total, 4 studies reported the implementation of a surveillance system: 2 (50%) described the enhancement of the system that was implemented for an event, 1 (25%) reported a pilot implementation of a surveillance system, and 1 (25%) reported an evaluation of an enhanced system. The types of systems investigated were 2 syndromic, 1 participatory, 1 syndromic and event-based, 1 indicator- and event-based, and 1 event-based surveillance system. In total, 62% (5/8) of the studies reported timeliness as an outcome generated after implementing or enhancing the system without measuring its effectiveness. Only 12% (1/8) of the studies followed the Centers for Disease Control and Prevention guidelines for evaluating public health surveillance systems and the outcomes of enhanced systems based on the systems' attributes to measure their effectiveness. CONCLUSIONS: On the basis of the review of the literature and the analysis of the included studies, there is limited evidence of the effectiveness of public health digital surveillance systems for infectious disease prevention and control at MGs because of the absence of evaluation studies.

Após três anos de COVID-19, vigilância e vacinação são a chave para acabar com a pandemia nas Américas

À medida que a pandemia da COVID-19 entra em seu terceiro ano na Região das Américas, o diretor da Organização Pan-Americana da Saúde (OPAS), Jarbas Barbosa, pediu aos países que fortaleçam a vigilância e reduzam as lacunas na cobertura vacinal para acabar com a emergência e se preparar melhor para futuras crises de saúde.

States' Methods for Capturing and Reporting Local Responses to Suspected Nonfatal Drug Overdoses.

This study examines the available data used to surveil state-level drug overdoses and characterizes them based on their timeliness, drug involvement, and specification of nonfatal outcomes.

Surveillance of communicable diseases using social media: A systematic review.

BACKGROUND: Communicable diseases pose a severe threat to public health and economic growth. The traditional methods that are used for public health surveillance, however, involve many drawbacks, such as being labor intensive to operate and resulting in a lag between data collection and reporting. To effectively address the limitations of these traditional methods and to mitigate the adverse effects of these diseases, a proactive and real-time public health surveillance system is needed. Previous studies have indicated the usefulness of performing text mining on social media. OBJECTIVE: To conduct a systematic review of the literature that used textual content published to social media for the purpose of the surveillance and prediction of communicable diseases. METHODOLOGY: Broad search queries were formulated and performed in four databases. Both journal articles and conference materials were included. The quality of the studies, operationalized as reliability and validity, was assessed. This qualitative systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: Twenty-three publications were included in this systematic review. All studies reported positive results for using textual social media content to surveille communicable diseases. Most studies used Twitter as a source for these data. Influenza was studied most frequently, while other communicable diseases received far less attention. Journal articles had a higher quality (reliability and validity) than conference papers. However, studies often failed to provide important information about procedures and implementation. CONCLUSION: Text mining of health-related content published on social media can serve as a novel and powerful tool for the automated, real-time, and remote monitoring of public health and for the surveillance and prediction of communicable diseases in particular. This tool can address limitations related to traditional surveillance methods, and it has the potential to supplement traditional methods for public health surveillance.

Innovations in Public Health Surveillance for Emerging Infections.

Public health surveillance is defined as the ongoing, systematic collection, analysis, and interpretation of health data and is closely integrated with the timely dissemination of information that the public needs to know and upon which the public should act. Public health surveillance is central to modern public health practice by contributing data and information usually through a national notifiable disease reporting system (NNDRS). Although early identification and prediction of future disease trends may be technically feasible, more work is needed to improve accuracy so that policy makers can use these predictions to guide prevention and control efforts. In this article, we review the advantages and limitations of the current NNDRS in most countries, discuss some lessons learned about prevention and control from the first wave of COVID-19, and describe some technological innovations in public health surveillance, including geographic information systems (GIS), spatial modeling, artificial intelligence, information technology, data science, and the digital twin method. We conclude that the technology-driven innovative public health surveillance systems are expected to further improve the timeliness, completeness, and accuracy of case reporting during outbreaks and also enhance feedback and transparency, whereby all stakeholders should receive actionable information on control and be able to limit disease risk earlier than ever before.

Enhancing Surveillance Protocols for Acute Hepatitis C Virus Infection, Utah, 2014-2019.

During 2014-2019, the Utah Department of Health (UDOH) enhanced its surveillance program for acute hepatitis C virus (HCV) infections by mandating electronic reporting of negative HCV test results in 2015 and liver function test results in 2016. UDOH also engaged with blood and plasma donation centers beginning in 2014 and syringe exchange programs in 2018 to encourage manual reporting of negative HCV test results from facilities without electronic reporting capabilities. UDOH hepatitis surveillance staff also provided training for case investigations in 2017. The number of cases detected increased 14-fold, from 9 during 2012 to 127 during 2019. In 2019, of 127 cases, 55% (n = 70) were detected through negative HCV test results reported electronically before positive test results (ie, recent seroconversions), 25% (n = 32) through positive HCV test results and elevated liver function test results, 18% (n = 23) through manually reported negative HCV test results, and 2% (n = 2) through positive HCV test results and clinical evidence. Challenges to surveillance included accessing patients for investigations and engaging donation centers in reporting negative test results. Utah's experience demonstrates practical considerations for improving surveillance of acute HCV infections.

Análise espacial de casos prováveis de dengue no município de São Luís, Maranhão, Brasil / Spatial analysis of probable dengue cases in the municipality of São Luís, Maranhão, Brazil / Análisis espacial de los casos probables de dengue en el municipio de São Luís, Maranhão, Brasil

INTRODUÇÃO: A dengue é considerada uma das principais arboviroses mundiais, caracterizada no Brasil pelo aumento de casos graves e óbitos. OBJETIVO: realizar análise espacial dos casos prováveis de dengue em São Luís - MA. MÉTODOS: Estudo ecológico de base populacional dos casos prováveis de dengue, notificados no Sistema de Informação de Agravos de Notificação (SINAN) em 2015 e 2016, ocorridos no município de São Luís ­ MA. Foram georreferenciados 4.681 casos prováveis de dengue por setores censitários, calculadas as taxas de incidência e ajustadas através do estimador bayesiano empírico local. Foi utilizado o estimador de densidade de Kernel e Moran Global e Local para a análise espacial. RESULTADOS: Evidenciou-se através do estimador de densidade de Kernel, áreas quentes (alta-densidade) nos setores censitários da região noroeste do município. As taxas de incidência foram ajustadas pela aplicação do método bayesiano empírico local, identificando-se maior quantidade de setores com média e alta incidência. A partir do índice de Moran global foi evidenciada autocorrelação espacial positiva estatisticamente significativa para as taxas de incidência de dengue (I=0,69; p<0,001) e para as taxas de incidência ajustadas pelo método bayesiano (I=0,80; p<0,001). De acordo com o índice de Moran local, identificou-se clusters de setores de alta incidência de dengue em áreas com alta densidade populacional na região nordeste e noroeste do município. CONCLUSÃO: A pesquisa demonstrou que os estimadores bayesianos ajudaram a minimizar os problemas de subnotificação e da influência do tamanho populacional nos setores censitários.

INTRODUCTION: Dengue is considered one of the main arboviruses in the world, characterized in Brazil by the increase in severe cases and deaths. OBJECTIVE: to perform spatial analysis of probable dengue cases in São Luís - MA. METHODS: Population-based ecological study of probable dengue cases, reported in the Notifiable Diseases Information System (SINAN) in 2015 and 2016, which took place in the city of São Luís - MA. 4,681 probable dengue cases were georeferenced by census sectors, incidence rates were calculated and adjusted using the local empirical Bayesian estimator. The Kernel and Moran Global and Local density estimator was used for spatial analysis. RESULTS: Hot areas (high-density) in the census sectors of the northwest region of the municipality were evidenced through the Kernel density estimator. Incidence rates were adjusted by applying the local empirical Bayesian method, identifying a greater number of sectors with medium and high incidence. From the global Moran index, statistically significant positive spatial autocorrelation was evidenced for the dengue incidence rates (I = 0.69; p <0.001) and for the incidence rates adjusted by the Bayesian method (I = 0.80; p <0.001). According to the local Moran index, clusters of sectors with a high incidence of dengue were identified in areas with high population density in the northeast and northwest regions of the municipality. CONCLUSION: The research demonstrated that Bayesian estimators helped to minimize the problems of underreporting and the influence of population size on census tracts.

INTRODUCCIÓN: El dengue es considerado una de las principales arbovirosis a nivel mundial, caracterizada en Brasil por el aumento de casos graves y muertes. OBJETIVO: Realizar un análisis espacial de los casos probables de dengue en São Luís - MA. MÉTODOS: Estudio ecológico de base poblacional de los casos probables de dengue, notificados en el Sistema de Informação de Agravos de Notificação (SINAN) en 2015 y 2016, ocurridos en el municipio de São Luís - MA. Se georreferenciaron 4.681 casos probables de dengue por sectores censales, se calcularon las tasas de incidencia y se ajustaron mediante el estimador empírico bayesiano local. Para el análisis espacial se utilizó el estimador de densidad Kernel y Moran global y local. RESULTADOS: Se evidenció a través del estimador de densidad Kernel, áreas calientes (de alta densidad) en los sectores censales de la región noroeste del municipio. Las tasas de incidencia se ajustaron mediante la aplicación del método bayesiano empírico local, identificándose una mayor cantidad de setores con incidencia media y alta. A partir del índice global de Moran se evidenció una autocorrelación espacial positiva estadísticamente significativa para las tasas de incidencia de dengue (I=0,69; p<0,001) y para las tasas de incidencia ajustadas por el método bayesiano (I=0,80; p<0,001). Según el índice local de Moran, se identificaron clusters de sectores de alta incidencia de dengue en áreas con alta densidad de población en las regiones noreste y noroeste del municipio. CONCLUSIÓN: La investigación demostró que los estimadores bayesianos ayudaron a minimizar los problemas de infradeclaración y la influencia del tamaño de la población en los sectores censales.

The COVID-19 Dashboard for Real-time Tracking of the Pandemic: The Lasker-Bloomberg Public Service Award.

In this Viewpoint, Lauren Gardner, winner of the 2022 Lasker-Bloomberg Public Service Award for creating the COVID-19 Dashboard, discusses the development of the Dashboard and the factors that contributed to its success.

Establishing event-based surveillance system in Nigeria: a complementary information generating platform for improved public health performance, 2016.

Introduction: event-based surveillance (EBS) is a surveillance method involving systematic and prompt data collection on incidents of public health importance, and complements the current indicator-based surveillance system and the Integrated Disease Surveillance and Response System (IDSR). It also promotes a rapid assessment and response to public health emergencies in Nigeria, although there is a lack of information regarding the status of EBS among Public Health Stakeholders in Nigeria; hence our study aimed to assess the awareness, availability, and utility of EBS among Nigerian public health stakeholders. Methods: we conducted a cross-sectional study to assess the awareness, availability, functionality, and utilization of EBS in the 36 States in Nigeria, plus the Federal Capital Territory (FCT). We interviewed 53 stakeholders in disease surveillance and response using a self-administered, semi-structured questionnaire to obtain responses on the awareness of the event-based surveillance system, availability, and functionality. We also assessed the common structures used to report health-related events and the availability of minimum requirements for an event-based surveillance system. We performed descriptive statistics for the data obtained. Results: the majority of respondents were males and 37.7% were disease surveillance and notification officers (DSNOs). Awareness of EBS was poor with about half, 49% of the respondents reported hearing of EBS, but only 17% described it correctly. The overall level of availability of the EBS reporting structure was inadequate, 28.2% and poorly utilised in the States. Conclusion: the awareness, availability, and utilization of event-based surveillance systems are low in Nigeria. The government should improve the feasibility and utility of EBS in the States to enhance early disease detection and response.

Overlapping Delta and Omicron Outbreaks During the COVID-19 Pandemic: Dynamic Panel Data Estimates.

BACKGROUND: The Omicron variant of SARS-CoV-2 is more transmissible than prior variants of concern (VOCs). It has caused the largest outbreaks in the pandemic, with increases in mortality and hospitalizations. Early data on the spread of Omicron were captured in countries with relatively low case counts, so it was unclear how the arrival of Omicron would impact the trajectory of the pandemic in countries already experiencing high levels of community transmission of Delta. OBJECTIVE: The objective of this study is to quantify and explain the impact of Omicron on pandemic trajectories and how they differ between countries that were or were not in a Delta outbreak at the time Omicron occurred. METHODS: We used SARS-CoV-2 surveillance and genetic sequence data to classify countries into 2 groups: those that were in a Delta outbreak (defined by at least 10 novel daily transmissions per 100,000 population) when Omicron was first sequenced in the country and those that were not. We used trend analysis, survival curves, and dynamic panel regression models to compare outbreaks in the 2 groups over the period from November 1, 2021, to February 11, 2022. We summarized the outbreaks in terms of their peak rate of SARS-CoV-2 infections and the duration of time the outbreaks took to reach the peak rate. RESULTS: Countries that were already in an outbreak with predominantly Delta lineages when Omicron arrived took longer to reach their peak rate and saw greater than a twofold increase (2.04) in the average apex of the Omicron outbreak compared to countries that were not yet in an outbreak. CONCLUSIONS: These results suggest that high community transmission of Delta at the time of the first detection of Omicron was not protective, but rather preluded larger outbreaks in those countries. Outbreak status may reflect a generally susceptible population, due to overlapping factors, including climate, policy, and individual behavior. In the absence of strong mitigation measures, arrival of a new, more transmissible variant in these countries is therefore more likely to lead to larger outbreaks. Alternately, countries with enhanced surveillance programs and incentives may be more likely to both exist in an outbreak status and detect more cases during an outbreak, resulting in a spurious relationship. Either way, these data argue against herd immunity mitigating future outbreaks with variants that have undergone significant antigenic shifts.

Syndromic Surveillance Systems for Mass Gatherings: A Scoping Review.

Syndromic surveillance involves the near-real-time collection of data from a potential multitude of sources to detect outbreaks of disease or adverse health events earlier than traditional forms of public health surveillance. The purpose of the present study is to elucidate the role of syndromic surveillance during mass gathering scenarios. In the present review, the use of syndromic surveillance for mass gathering scenarios is described, including characteristics such as methodologies of data collection and analysis, degree of preparation and collaboration, and the degree to which prior surveillance infrastructure is utilized. Nineteen publications were included for data extraction. The most common data source for the included syndromic surveillance systems was emergency departments, with first aid stations and event-based clinics also present. Data were often collected using custom reporting forms. While syndromic surveillance can potentially serve as a method of informing public health policy regarding specific mass gatherings based on the profile of syndromes ascertained, the present review does not indicate that this form of surveillance is a reliable method of detecting potentially critical public health events during mass gathering scenarios.