Academic literature on the topic 'Event-Based Surveillance (EBS)'
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Journal articles on the topic "Event-Based Surveillance (EBS)"
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Kuehne, Anna, Patrick Keating, Jonathan Polonsky, Christopher Haskew, Karl Schenkel, Olivier Le Polain de Waroux, and Ruwan Ratnayake. "Event-based surveillance at health facility and community level in low-income and middle-income countries: a systematic review." BMJ Global Health 4, no. 6 (December 2019): e001878. http://dx.doi.org/10.1136/bmjgh-2019-001878.
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BackgroundThe International Health Regulations require member states to establish “capacity to detect, assess, notify and report events”. Event-based surveillance (EBS) can contribute to rapid detection of acute public health events. This is particularly relevant in low-income and middle-income countries (LMICs) which may have poor public health infrastructure. To identify best practices, we reviewed the literature on the implementation of EBS in LMICs to describe EBS structures and to evaluate EBS systems.MethodsWe conducted a systematic literature search of six databases to identify articles that evaluated EBS in LMICs and additionally searched for grey literature. We used a framework approach to facilitate qualitative data synthesis and exploration of patterns across and within articles.ResultsWe identified 778 records, of which we included 15 studies concerning 13 different EBS systems. The 13 EBS systems were set up as community-based surveillance, health facility-based surveillance or open surveillance (ie, notification by non-defined individuals and institutions). Four systems were set up in outbreak settings and nine outside outbreaks. All EBS systems were integrated into existing routine surveillance systems and pre-existing response structures to some extent. EBS was described as useful in detecting a large scope of events, reaching remote areas and guiding outbreak response.ConclusionHealth facility and community-based EBS provide valuable information that can strengthen the early warning function of national surveillance systems. Integration into existing early warning and response systems was described as key to generate data for action and to facilitate rapid verification and response. Priority in its implementation should be given to settings that would particularly benefit from EBS strengths. This includes areas most prone to outbreaks and where traditional ‘routine’ surveillance is suboptimal.
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Abbood, Auss, Alexander Ullrich, Rüdiger Busche, and Stéphane Ghozzi. "EventEpi—A natural language processing framework for event-based surveillance." PLOS Computational Biology 16, no. 11 (November 20, 2020): e1008277. http://dx.doi.org/10.1371/journal.pcbi.1008277.
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According to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of public health agents sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Reading the articles, discussing their relevance, and putting key information into a database is a time-consuming process. To support EBS, but also to gain insights into what makes an article and the event it describes relevant, we developed a natural language processing framework for automated information extraction and relevance scoring. First, we scraped relevant sources for EBS as done at the RKI (WHO Disease Outbreak News and ProMED) and automatically extracted the articles’ key data: disease, country, date, and confirmed-case count. For this, we performed named entity recognition in two steps: EpiTator, an open-source epidemiological annotation tool, suggested many different possibilities for each. We extracted the key country and disease using a heuristic with good results. We trained a naive Bayes classifier to find the key date and confirmed-case count, using the RKI’s EBS database as labels which performed modestly. Then, for relevance scoring, we defined two classes to which any article might belong: The article is relevant if it is in the EBS database and irrelevant otherwise. We compared the performance of different classifiers, using bag-of-words, document and word embeddings. The best classifier, a logistic regression, achieved a sensitivity of 0.82 and an index balanced accuracy of 0.61. Finally, we integrated these functionalities into a web application called EventEpi where relevant sources are automatically analyzed and put into a database. The user can also provide any URL or text, that will be analyzed in the same way and added to the database. Each of these steps could be improved, in particular with larger labeled datasets and fine-tuning of the learning algorithms. The overall framework, however, works already well and can be used in production, promising improvements in EBS. The source code and data are publicly available under open licenses.
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Ndegwa, Linus, Philip Ngere, Lyndah Makayotto, Neha N. Patel, Liku Nzisa, Nancy Otieno, Eric Osoro, et al. "Kenya’s experience implementing event-based surveillance during the COVID-19 pandemic." BMJ Global Health 8, no. 12 (December 2023): e013736. http://dx.doi.org/10.1136/bmjgh-2023-013736.
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Event-based surveillance (EBS) can be implemented in most settings for the detection of potential health threats by recognition and immediate reporting of predefined signals. Such a system complements existing case-based and sentinel surveillance systems. With the emergence of the COVID-19 pandemic in early 2020, the Kenya Ministry of Health (MOH) modified and expanded an EBS system in both community and health facility settings for the reporting of COVID-19-related signals. Using an electronic reporting tool, m-Dharura, MOH recorded 8790 signals reported, with 3002 (34.2%) verified as events, across both community and health facility sites from March 2020 to June 2021. A subsequent evaluation found that the EBS system was flexible enough to incorporate the addition of COVID-19-related signals during a pandemic and maintain high rates of reporting from participants. Inadequate resources for follow-up investigations to reported events, lack of supportive supervision for some community health volunteers and lack of data system interoperability were identified as challenges to be addressed as the EBS system in Kenya continues to expand to additional jurisdictions.
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Kasamatsu, Ayu, Masayuki Ota, Tomoe Shimada, Munehisa Fukusumi, Takuya Yamagishi, Anita Samuel, Manami Nakashita, et al. "Enhanced event-based surveillance for imported diseases during the Tokyo 2020 Olympic and Paralympic Games." Western Pacific Surveillance and Response Journal 12, no. 4 (October 1, 2021): 13–19. http://dx.doi.org/10.5365/wpsar.2021.12.4.903.
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In 2021, the National Institute of Infectious Diseases, Japan, undertook enhanced event-based surveillance (EBS) for infectious diseases occurring overseas that have potential for importation (excluding coronavirus disease 2019 [COVID-19]) for the Tokyo 2020 Olympic and Paralympic Summer Games (the Games). The pre-existing EBS system was enhanced using the World Health Organization Epidemic Intelligence from Open Sources system and the BlueDot Epidemic Intelligence platform. The enhanced EBS before and during the Games did not detect any major public health event that would warrant action for the Games. However, information from multiple sources helped us identify events, characterize risk and improve confidence in risk assessment. The collaboration also reduced the surveillance workload of the host country, while ensuring the quality of surveillance, even during the COVID-19 pandemic.
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Vaman, Raman Swathy, Sunil Solomon, Francisco Averhoff, Alan L. Landay, Jeromie Wesley Vivian Thangaraj, Rizwan Suliankatchi Abdulkader, Flory Joseph, Gavin Cloherty, and Manoj V. Murhekar. "Piloting an event-based surveillance model in private hospitals for early detection of disease clusters, Kerala, India." Indian Journal of Medical Research 161 (February 14, 2025): 54–63. https://doi.org/10.25259/ijmr_1395_2024.
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Background & objectives Event-based surveillance (EBS) is a critical component of early warning systems for detecting and responding to infectious disease outbreaks. While EBS is widely used in public health settings, its integration into private healthcare facilities remains limited. This study undertook to pilot an EBS in private hospitals in Kasaragod, Kerala and to assess its added value in early detection of disease clusters. Methods Clinical nurses abstracted the data on hospitalisation dates, places of residence, and presenting illnesses from case records of patients with acute febrile illness (AFI) admitted in six private hospitals. A software algorithm analysed the data to identify spatiotemporal clustering of case-patients or deaths (signals), for syndromes of interest [acute febrile illness with rash (AFIR), acute encephalitis syndrome (AES), acute febrile illness with haemorrhage (AFIH) and severe acute respiratory illness (SARI)]. The District Surveillance Unit (DSU) verified these signals, flagged verified signals as events, and conducted a risk assessment to determine if the events were outbreaks. Results From May to December 2023, data from 3294 (73%) of 4512 AFI patients were analysed using the EBS algorithm. Of the 88 signals identified, 67 (76%) were due to SARI, 9 (10.3%) were due to AES, and 9 (9%) were due to AFIR. Ten signals were verified as events, of which nine were classified as outbreaks (dengue-1, H1N1-3, H3N2-1, H1N1 and H3N2 - 1, H1N1 and SARS-COV2 – 1, no pathogen detected– 2). Five outbreaks were not detected by the existing indicator-based surveillance (IBS). Interpretation & conclusions EBS pilot in private health facilities complemented the IBS system by early detecting outbreaks. This EBS model has the potential for implementation in other districts, especially in districts at higher risk of zoonotic spillover.
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Valentin, Sarah, Bahdja Boudoua, Kara Sewalk, Nejat Arınık, Mathieu Roche, Renaud Lancelot, and Elena Arsevska. "Dissemination of information in event-based surveillance, a case study of Avian Influenza." PLOS ONE 18, no. 9 (September 5, 2023): e0285341. http://dx.doi.org/10.1371/journal.pone.0285341.
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Event-Based Surveillance (EBS) tools, such as HealthMap and PADI-web, monitor online news reports and other unofficial sources, with the primary aim to provide timely information to users from health agencies on disease outbreaks occurring worldwide. In this work, we describe how outbreak-related information disseminates from a primary source, via a secondary source, to a definitive aggregator, an EBS tool, during the 2018/19 avian influenza season. We analysed 337 news items from the PADI-web and 115 news articles from HealthMap EBS tools reporting avian influenza outbreaks in birds worldwide between July 2018 and June 2019. We used the sources cited in the news to trace the path of each outbreak. We built a directed network with nodes representing the sources (characterised by type, specialisation, and geographical focus) and edges representing the flow of information. We calculated the degree as a centrality measure to determine the importance of the nodes in information dissemination. We analysed the role of the sources in early detection (detection of an event before its official notification) to the World Organisation for Animal Health (WOAH) and late detection. A total of 23% and 43% of the avian influenza outbreaks detected by the PADI-web and HealthMap, respectively, were shared on time before their notification. For both tools, national and local veterinary authorities were the primary sources of early detection. The early detection component mainly relied on the dissemination of nationally acknowledged events by online news and press agencies, bypassing international reporting to the WAOH. WOAH was the major secondary source for late detection, occupying a central position between national authorities and disseminator sources, such as online news. PADI-web and HealthMap were highly complementary in terms of detected sources, explaining why 90% of the events were detected by only one of the tools. We show that current EBS tools can provide timely outbreak-related information and priority news sources to improve digital disease surveillance.
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Ngere, Philip, Radhika Gharpure, Stella Mamuti, Peninah Munyua, M. Kariuki Njenga, Lyndah Makayotto, Linus Ndegwa, et al. "Early warning and response systems for respiratory disease outbreaks: lessons learnt from cluster-associated cases of acute respiratory illnesses in Gilgil subcounty, Nakuru County, Kenya, 2021." BMJ Global Health 10, no. 2 (February 2025): e016418. https://doi.org/10.1136/bmjgh-2024-016418.
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Investigating acute respiratory illnesses (ARIs) is difficult due to non-specific symptoms, varied health-seeking behaviors, and resource limitations; yet early detection is critical to global health security. Kenya's Ministry of Health (MOH) uses the Integrated Disease Surveillance strategy for public health surveillance, incorporating event-based surveillance (EBS) and indicator-based surveillance (IBS) for early warning system. MOH, supported by the US-CDC, established Influenza Sentinel Surveillance (ISS) in 2006 and later launched community EBS (CEBS) and health facility EBS (HEBS) pilots to enhance surveillance for COVID-19. On March 2, 2021, the CEBS system detected a signal of “Two or more people presenting with similar signs and symptoms in a community within a week” in a county. Investigations launched on March 4, 2021, investigations revealed unreported ARI cases which had been missed by both the ISS and IBS. A total of 176 ARI cases were line-listed with 91/176 (51.7%) aged <5-years and 46/176 (26.1%) hospitalized. RT-PCR tests confirmed 34/79 (43.0%) SARS-CoV-2 and 1/7 (14.3%) A/H3N2 cases. Of the CEBS, HEBS, IBS, and ISS systems deployed by the county to strengthen the early warning for respiratory diseases, CEBS detected a signal of unreported ARIs that facilitated further investigations and response.
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Osman, Fatma, A. Kandi, S. Elrefaey, S. Elshourbagy, H. Abuelsoud, M. Taha, and A. Gehad. "Enforcement of Functionality and Effectiveness of Event-Based Surveillance System (EBS), Egypt, April-September 2017." Iproceedings 4, no. 1 (March 29, 2018): e10609. http://dx.doi.org/10.2196/10609.
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Cook, Meghan A., and Nicholas Brooke. "Event-Based Surveillance of Poisonings and Potentially Hazardous Exposures over 12 Months of the COVID-19 Pandemic." International Journal of Environmental Research and Public Health 18, no. 21 (October 22, 2021): 11133. http://dx.doi.org/10.3390/ijerph182111133.
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The COVID-19 pandemic has seen people and governments utilise an array of chemical and pharmaceutical substances in an attempt to prevent and treat COVID-19 infections. The Centre for Radiation, Chemicals and Environmental Hazards (CRCE) at Public Health England (PHE) routinely undertakes Event-Based Surveillance (EBS) to monitor public health threats and incidents related to chemicals and poisons. From April 2020, EBS functions were expanded to screen international media for potentially hazardous exposures associated with the COVID-19 pandemic. Media sources reported that poisons centres were experiencing increased enquiries associated with the use and misuse of household cleaners and alcohol-based hand sanitiser (HS). There were also media reports of people self-medicating with over-the-counter supplements and traditional or herbal remedies. Public figures who directly or indirectly facilitated misinformation were sometimes reported to be associated with changes in poisoning trends. Border closures were also believed to have been associated with increasingly toxic illicit drug supplies in Canada, and record numbers of opioid-related deaths were reported. In other countries, where the sale of alcohol was banned or limited, home-brewing and methanol-based supplies resulted in a number of fatalities. At least two chemical incidents also occurred at industrial sites in India, after sites were left unattended or were closed and reopened due to lockdown measures. Reports of poisoning identified in the international media were provided to the UK National Poisons Information Service (NPIS) and contributed to the UK COVID-19 public health response.
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Bustamante, N. D., K. A. Coggeshall, J. A. Fuller, C. Hercik, C. Blanton, A. Couture, D. Bensyl, and R. Arthur. "Sensitivity and timeliness of CDC’S Global Disease Detection Operations Center (GDDOC) event-based surveillance (EBS) system: a pilot study." Annals of Epidemiology 40 (December 2019): 40. http://dx.doi.org/10.1016/j.annepidem.2019.08.025.
Full textDissertations / Theses on the topic "Event-Based Surveillance (EBS)"
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Alencar, Medeiros Gabriel Henrique. "ΡreDiViD Τοwards the Ρredictiοn οf the Disseminatiοn οf Viral Disease cοntagiοn in a pandemic setting." Electronic Thesis or Diss., Normandie, 2025. http://www.theses.fr/2025NORMR005.
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Les systèmes de surveillance basés sur les événements (EBS) sont essentiels pour détecter et suivre les phénomènes de santé émergents tels que les épidémies et crises sanitaires. Cependant, ils souffrent de limitations, notamment une forte dépendance à l’expertise humaine, des difficultés à traiter des données textuelles hétérogènes et une prise en compte insuffisante des dynamiques spatio-temporelles. Pour pallier ces limites, nous proposons une approche hybride combinant des méthodologies guidées par les connaissances et les données, ancrée dans l’ontologie des phénomènes de propagation (PropaPhen) et le cadre Description-Detection-Prediction Framework (DDPF), afin d’améliorer la description, la détection et la prédiction des phénomènes de propagation. PropaPhen est une ontologie FAIR conçue pour modéliser la propagation spatio-temporelle des phénomènes et a été spécialisée pour le biomédical grâce à l’intégration de UMLS et World-KG, menant à la création du graphe BioPropaPhenKG. Le cadre DDPF repose sur trois modules : la description, générant des ontologies spécifiques ; la détection, appliquant des techniques d'extraction de relations sur des textes hétérogènes ; et la prédiction, utilisant des méthodes avancées de clustering. Expérimenté sur des données du COVID-19 et de la variole du singe et validé avec les données de l’OMS, DDPF a démontré son efficacité dans la détection et la prédiction de clusters spatio-temporels. Son architecture modulaire assure son évolutivité et son adaptabilité à divers domaines, ouvrant des perspectives en santé publique, environnement et phénomènes sociauxEvent-Based Surveillance (EBS) systems are essential for detecting and tracking emerging health phenomena such as epidemics and public health crises. However, they face limitations, including strong dependence on human expertise, challenges processing heterogeneous textual data, and insufficient consideration of spatiotemporal dynamics. To overcome these issues, we propose a hybrid approach combining knowledge-driven and data-driven methodologies, anchored in the Propagation Phenomena Ontology (PropaPhen) and the Description-Detection-Prediction Framework (DDPF), to enhance the description, detection, and prediction of propagation phenomena. PropaPhen is a FAIR ontology designed to model the spatiotemporal spread of phenomena. It has been specialized in the biomedical domain through the integration of UMLS and World-KG, leading to the creation of the BioPropaPhenKG knowledge graph. The DDPF framework consists of three modules: description, which generates domain-specific ontologies; detection, which applies relation extraction techniques to heterogeneous textual sources; and prediction, which uses advanced clustering methods. Tested on COVID-19 and Monkeypox datasets and validated against WHO data, DDPF demonstrated its effectiveness in detecting and predicting spatiotemporal clusters. Its modular architecture ensures scalability and adaptability to various domains, opening perspectives in public health, environmental monitoring, and social phenomena
Book chapters on the topic "Event-Based Surveillance (EBS)"
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Okhmatovskaia, Anya, Yannan Shen, Iris Ganser, Nigel Collier, Nicholas B. King, Zaiqiao Meng, and David L. Buckeridge. "A Conceptual Framework for Representing Events Under Public Health Surveillance." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220480.
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Information integration across multiple event-based surveillance (EBS) systems has been shown to improve global disease surveillance in experimental settings. In practice, however, integration does not occur due to the lack of a common conceptual framework for encoding data within EBS systems. We aim to address this gap by proposing a candidate conceptual framework for representing events and related concepts in the domain of public health surveillance.
Conference papers on the topic "Event-Based Surveillance (EBS)"
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Tang, Catherine, Ye, Kok Liang Tan, Latief Riyanto, Fuziana Tusimin, Nik Fazril Sapian, and Noor Azima Sharim. "Lesson Learnt from First Application of Openhole Stand-Alone Screen OHSAS with Autonomous Inflow Control Devices AICD in Oilfield Offshore East Malaysia." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21223-ms.
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AbstractWell#1 was completed as horizontal oil producer with Openhole Stand-Alone Sandscreens (OHSAS) across a thin reservoir with average thickness of 20ft in Field B. The first Autonomous Inflow Control Device (AICD) in PETRONAS was installed to ensure balanced contribution across horizontal zones with permeability contrasts and to prevent early water and gas breakthrough. Integrated real-time reservoir mapping-while-drilling technology for well placement optimization combined with industry-leading inflow control simulator for AICD placement were opted. The early well tests post drilling showed promising results with production rate doubled the expected rate with no sand production, low water cut and lower Gas to Oil Ratio (GOR).Reservoir Management Plan (RMP) for this oil rim requires continuous gas injection into gas cap and water injection into aquifer. However, due to low gas injection uptime caused by prolonged injection facilities constraints, the well's watercut continued to increase steadily from 0% to 80% within a year of production despite prudent surveillance and controlling of production during injector's downtime. After the gas injection performance has improved, the well was beaned up as part of oil rim management for withdrawal balancing. Unfortunately, a month later, the production rate showed a sudden spike with significantly low wellhead pressure, followed by hairline leak on its choke valve and leak at Crude Oil Transfer Pump (COTP) recycle line. Sand analysis by particle size distribution (PSD) confirmed OHSAS failure, while the high gas rate from well test results confirmed AICD failure.A multidisciplinary investigation team was immediately formed to determine the root cause of the failure event. Root Cause Failure Analysis (RCFA) method was opted to determine the causes of failures, including the reanalyzing of the OHSAS and AICD completion design. The well operating strategy was also reviewed thoroughly by utilizing the well parameters trending provided in the Exceptional Based Surveillance (EBS) Process Information (PI) ProcessBook.Thorough RCFA concluded that frequent platform interruptions and improper well start-up practices have created abrupt pressure changes in the wellbore, which has likely destabilized the natural sand pack around the OHSAS and created frequent burst of sand influx across AICDs. The operating of a high gas-oil ratio (GOR) and high watercut sand prone well without pre-determined AICD sand erosion toleration envelope have also likely contributed to the failure of AICDs. The delay in detection of OHSAS failure in Well#1 due to ineffective sand monitoring method thus resulted in severe sand production which caused severe leak at its choke valve and COTP recycle line.