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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.
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Mercy, Kyeng, Stephanie J. Salyer, Comfort Mankga, Calle Hedberg, Phumzile Zondo, and Yenew Kebede. "Establishing an early warning event management system at Africa CDC." PLOS Digital Health 3, no. 7 (July 8, 2024): e0000546. http://dx.doi.org/10.1371/journal.pdig.0000546.
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Africa is home to hotspots of disease emergence and re-emergence. To adequately detect and respond to these health threats, early warning systems inclusive of event-based surveillance (EBS) are needed. However, data systems to manage these events are not readily available. In 2020, Africa Centres for Disease Control and Prevention developed an event management system (EMS) to meet this need. The district health information software (DHIS2), which is free and open-source software was identified as the platform for the EMS because it can support data capture and analysis and monitor and report events. The EMS was created through a collaborative and iterative prototyping process that included modifying key DHIS2 applications like Tracker Capture. Africa CDC started piloting the EMS with both signal and event data entry in June 2020. By December 2022, 416 events were captured and over 140 weekly reports, including 19 COVID-19 specific reports, were generated and distributed to inform continental awareness and response efforts. Most events detected directly impacted humans (69%), were considered moderate (50%) to high (29%) risk level and reflected both emerging and endemic infectious disease outbreaks. Highly pathogenic avian influenza, specifically H5N1, was the most frequently detected animal event and storms and flooding were most frequently detected environmental events. Both data completeness and timeliness improved over time. Country-level interest and utility resulted in four African countries adapting the EMS in 2022 and two more in 2023. This system demonstrates how integrating digital technology into health systems and utilising existing digital platforms like DHIS2 can improve early warning at the continental and country level by improving EBS workflow.
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Chandrasekaran, Arvind. "Natural Language Processing." International Journal on Cybernetics & Informatics 12, no. 2 (March 11, 2023): 57–61. http://dx.doi.org/10.5121/ijci.2023.120205.
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Hints of the outbreak are detected through the modified circumstances favoring the outbreaks, like the warm weather contributing to epidermal outbreaks or the loss of sanitation leading to cholera outbreaks typically relying on the routine reports from the healthcare facilities, secondary data like attendance monitoring at workplaces and schools, the web, and the media play a significant informational source with more than 60% of the initial outbreak reporting to the informal sources. Through the application of natural language processing methods and machine learning technologies, a pipeline is developed which extracts the critical entities like country, confirmed case counts, disease, and case dates, which are mandatory entities from the epidemiological article and are saved in the database thereby facilitating the data entry easier. The advantages are the facilitation of relevant score articles shown first, thereby providing the web service results termed EventEpi integrated into the Event Based Surveillance (EBS) workflows.
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Pulnova, N. L., T. N. Rybalkina, N. V. Karazhas, R. E. Bosh’ian, M. N. Kornienko, O. F. Kabikova, N. I. Gabrielyan, I. E. Pashkova, and O. V. Silina. "The role of herpesviruses and pneumocysts in infectious complications in children during liver transplantation." CHILDREN INFECTIONS 21, no. 4 (November 23, 2022): 21–26. http://dx.doi.org/10.22627/2072-8107-2022-21-4-21-26.
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Infections occupy one of the central places among the complications of transplants. The frequency of fatal infectious complications during the first twelve months after transplantation ranges from 2.6 to 51.7%. Identifying markers of opportunistic infections before transplantation will help reduce the likelihood of developing these infections after induced immunosuppression. The aim of the study was to study the role of herpesviruses and pneumocysts in the occurrence of infectious complications in children before and after liver transplantation based on the detection of markers of a number of herpesvirus infections and pneumocystosis. The article presents the results of a comprehensive examination for markers of herpesvirus infections and pneumocystosis of 70 children who were treated at the Shumakov Transplantation Research Center. It should be noted that 55 patients (78.6%) were diagnosed with infectious complications, of which 46 people (65.7%) had pneumonia. To detect IgM and IgG antibodies to herpesviruses and pneumocysts, peripheral blood serum samples were examined by enzyme immunoassay (ELISA). Detection of common HBV antigens was carried out by indirect immunofluorescence reaction (NRIF). Early antigens and reproduction of herpesviruses were detected by rapid culture method (BCM) on Vero and M-19 cell cultures for CMVI. In the event of infectious complications (pneumonia) in children who underwent related liver transplantation, the number of patients with active EBI markers increased almost 7 times and active HCV-6 markers increased 3.5 times. The paper shows the need for a broader laboratory screening of opportunistic infections, which will serve to achieve better clinical results, contribute to the formation of more advanced diagnostic algorithms, as well as improve epidemiological surveillance of these infections.
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Chakraborty, Aditya, and Mohan D. Pant. "An Analytical Prior Selection Procedure for Empirical Bayesian Analysis Using Resampling Techniques: A Simulation-Based Approach Using the Pancreatic Adenocarcinoma Data from the SEER Database." Computation 13, no. 2 (February 12, 2025): 51. https://doi.org/10.3390/computation13020051.
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Introduction: In the field of medical research, empirical Bayesian analysis has emerged as an increasingly applicable approach. This statistical framework offers greater flexibility, enabling researchers to incorporate prior information and rigorously estimate parameters of interest. However, the selection of suitable prior distributions can be a challenging endeavor, with profound implications for the resulting inferences. To address this challenge, this study proposes a new analytical procedure that leverages resampling techniques to guide the choice of priors in Bayesian analysis. Subject and Methods: The study group consisted of patients who had been diagnosed and had died of pancreatic adenocarcinoma (cause-specific death) who had undergone both chemotherapy and radiation at stage IV of cancer. The data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Initially, the most suitable probabilistic behavior of the survival times of patients was identified parametrically via goodness-of-fit (GOF) tests, and afterward, empirical Bayesian analysis (EBA) was performed using resampling techniques (bootstrapping and the jackknife method). The Hamiltonian Monte Carlo (HMC) method was used to obtain the posterior distribution. Results: The most appropriate data distribution was found to be a two-parameter log-normal via GOF tests. A sensitivity analysis, followed by a simulation study, was performed to validate the analytical method. The performance of bootstrapped and jackknifed empirical Bayesian estimates was compared with maximum likelihood (ML) methods at each simulation stage. The empirical Bayesian estimates were found to be consistent with the ML estimates. Finally, a comparison was made among the parametric, Kaplan–Meier and empirical Bayesian survival estimates at different time points to illustrate the validity of the method. Conclusions: Determining the appropriate prior distribution is one of the crucial components in Bayesian analysis, as it can significantly influence the resulting inferences. The cautious selection of the prior information is essential, as it encapsulates the researcher’s beliefs or external prior knowledge about the parameters of interest. In the Bayesian framework, empirical resampling methods, such as bootstrapping and jackknifing, can offer valuable insights into the significance of prior selection, thus improving the consistency of statistical inferences. However, the analytical procedure is based on the time-to-event data, and the prior selection procedure can be extended to any real data, where Bayesian analysis is needed for decision-making and uncertainty quantification.
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Mercy, Kyeng, Arunmozhi Balajee, Tamuno-Wari Numbere, Philip Ngere, Davie Simwaba, and Yenew Kebede. "Africa CDC’s blueprint to enhance early warning surveillance: accelerating implementation of event-based surveillance in Africa." Journal of Public Health in Africa, August 11, 2023. http://dx.doi.org/10.4081/jphia.2023.2827.
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Event-based surveillance (EBS) is a core component of early warning surveillance. In 2018, Africa CDC developed the first edition of an event-based surveillance framework to guide African Union Member States in implementing EBS. Country experiences during the COVID-19 pandemic demonstrated the value of data from non-traditional sources for real time situational awareness; at the same time revealed the huge gaps in strengthening this arm of surveillance. Learning from these lessons and to begin to close those gaps, Africa CDC convened subject matter experts from African Union Member States and technical partners to develop the second edition of the EBS framework, 2023 and its training materials. The revised version includes additional sections such as, the multi-sectoral one health collaboration in EBS, monitoring and evaluation, cross border EBS, and use of event management systems. The current manuscript provides an overview of the 2023 Africa CDC EBS framework and highlights experience in two countries that have successfully employed this resource in their implementation efforts.
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Tahoun, Mohamed Mostafa, Mohammad Nadir Sahak, Muzhgan Habibi, Mohamad Jamaluddin Ahadi, Bahara Rasoly, Sabrina Shivji, Ahmed Taha Aboushady, Pierre Nabeth, Mahmoud Sadek, and Alaa Abouzeid. "Strengthening event-based surveillance (EBS): a case study from Afghanistan." Conflict and Health 18, no. 1 (April 30, 2024). http://dx.doi.org/10.1186/s13031-024-00598-1.
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SummaryThe sustained instability in Afghanistan, along with ongoing disease outbreaks and the impact of the COVID-19 pandemic, has significantly affected the country.During the COVID-19 pandemic, the country’s detection and response capacities faced challenges. Case identification was done in all health facilities from primary to tertiary levels but neglected cases at the community level, resulting in undetected and uncontrolled transmission from communities. This emphasizes a missed opportunity for early detection that Event-Based Surveillance (EBS) could have facilitated.Therefore, Afghanistan planned to strengthen the EBS component of the national public health surveillance system to enhance the capacity for the rapid detection and response to infectious disease outbreaks, including COVID-19 and other emerging diseases. This effort was undertaken to promptly mitigate the impact of such outbreaks.We conducted a landscape assessment of Afghanistan’s public health surveillance system to identify the best way to enhance EBS, and then we crafted an implementation work plan. The work plan included the following steps: establishing an EBS multisectoral coordination and working group, identifying EBS information sources, prioritizing public health events of importance, defining signals, establishing reporting mechanisms, and developing standard operating procedures and training guides.EBS is currently being piloted in seven provinces in Afghanistan. The lessons learned from the pilot phase will support its overall expansion throughout the country.
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Malik, Elfatih Mohamed, Ahmad Izzoddeen Abdullah, Sabir Ali Mohammed, Abdelgadir Ali Bashir, Rayyan Ibrahim, Abdalla Mohammed Abdalla, Muntasir Mohamed Osman, et al. "Structure, functions, performance and gaps of event-based surveillance (EBS) in Sudan, 2021: a cross-sectional review." Globalization and Health 18, no. 1 (December 1, 2022). http://dx.doi.org/10.1186/s12992-022-00886-6.
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Abstract Background Event-based surveillance (EBS) is an essential component of Early Warning Alert and Response (EWAR) as per the International Health Regulations (IHR), 2005. EBS was established in Sudan in 2016 as a complementary system for Indicator-based surveillance (IBS). This review will provide an overview of the current EBS structure, functions and performance in Sudan and identify the gaps and ways forward. Methods The review followed the WHO/EMRO guidelines and tools. Structured discussions, observation and review of records and guidelines were done at national and state levels. Community volunteers were interviewed through phone calls. Directors of Health Emergency and Epidemic Control, surveillance officers and focal persons for EBS at the state level were also interviewed. SPSS software was used to perform descriptive statistical analysis for quantitative data, while qualitative data was analysed manually using thematic analysis, paying particular attention to the health system level allowing for an exploration of how and why experiences differ across levels. Written and verbal consents were obtained from all participants as appropriate. Results Sudan has a functioning EBS; however, there is an underestimation of its contribution and importance at the national and states levels. The link between the national level and states is ad hoc or is driven by the need for reports. While community event-based surveillance (CEBS) is functioning, EBS from health facilities and from non-health sectors is not currently active. The integration of EBS into overall surveillance was not addressed, and the pathway from detection to action is not clear. The use of electronic databases and platforms is generally limited. Factors that would improve performance include training, presence of a trained focal person at state level, and regular follow-up from the national level. Factors such as staff turnover, income in relation to expenses and not having a high academic qualification (Diploma or MSc) were noticed as inhibiting factors. Conclusion The review recommended revisiting the surveillance structure at national and state levels to put EBS as an essential component and to update guidelines and standard operation procedures SOPs to foster the integration between EBS components and the overall surveillance system. The need for strengthening the link with states, capacity building and re-addressing the training modalities was highlighted.
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Gaulton, Tom, Charlotte Hague, David Cole, Eirian Thomas, and Raquel Duarte-Davidson. "Global event-based surveillance of chemical incidents." Journal of Exposure Science & Environmental Epidemiology, November 8, 2021. http://dx.doi.org/10.1038/s41370-021-00384-8.
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Abstract Background The number of chemicals in our society and in our daily lives continues to increase. Accompanying this is an increasing risk of human exposure to and injury from hazardous substances. Performing regular, structured surveillance of chemical incidents allows a greater awareness of the types of chemical hazards causing injury and the frequency of their occurrence, as well as providing a better understanding of exposures. Objective The objective of performing event-based surveillance (EBS) and capturing chemical incidents is to use this information to increase the situational awareness of chemical incidents, improve the management of these incidents and to inform measures to protect public health. Methods This paper describes a method for EBS for chemical incidents, including the sources used, storing the gathered information and subsequent analysis of potential trends in the data. Results We describe trends in the type of incidents that have been detected, the chemicals involved in these incidents and the health effects caused, in different geographic regions of the world. Significance The methodology presented here provides a rapid and simple means of identifying chemical incidents that can be set up rapidly and with minimal cost, the outputs of which can be used to identify emerging risks and inform preparedness planning, response and training for chemical incidents.
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"Implementation Level of Event-Based Surveillance (EBS) as Surveillance Core Capacity Under International Health Regulation in Sudan, 2020: Cross-Sectional Study." Current Research in Vaccines Vaccination 1, no. 1 (November 17, 2022). http://dx.doi.org/10.33140/crvv.01.01.09.
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Background: Surveillance system of infectious diseases and event is recognized as the cornerstone of public health decision-making and practice additionally, the international health regulation requested counties to implement other type of surveillance to support the routine surveillance system and to increase the detection rate and sensitivity in reporting the diseases, event, or any public health emergency with international concern (PHEIC). The aim of this study to assess the implementation level of event-based surveillance systems to ensuring that the system implemented efficiently and effectively. Methods: descriptive cross sectional institutional based study conducted for all 18 surveillance officer at states to assess the implementation level of event-based surveillance system as core capacity under the international health regulation 2005 (IHR), Data was collected using a per-prepared and pretested questionnaire followed WHO/EMRO tools for surveillance staff at state level felt through field visit and phone calls, data collection also cover the community based surveillance and surveillance system at point of entry as part of event based surveillance, interview done for surveillance focal person at federal level. Data were analyzed using Statistical Packages for Social Sciences (SPSS) (version 20). Written and verbal consents were obtained from all participants as appropriate. Results: Event based surveillance started in 2016 endorsement and approval of guidelines SOPs and training materials has been develop in 2017 so the study showed significant positive changes in implementation of this system for that the results showed the system implemented in all 18 states, availability of guidelines and SOPs at state level 72.2%, completeness, and timeliness of system data 94.4%, designated focal person in the surveillance structure at state level 94.4%. community Based Surveillance (CBS) implementation Results at states level -Sudan from 2017 – 2020 the result showed the system has been implemented in 17 states (94.4%), the percentage of assigned focal person for the system was 94.1 – trained volunteer at community level 94.4% and availability of system guidelines was 94.1%, the availability of system SOPs 88.2%. The study results also showed the percentage of 94.4 for definition of CBS syndromes, immediate response for event reported was 94.4%, Daily and weekly reports completed send by community volunteers was 94.4% and availability of reporting forms was 94.1% also percentage of 70.6% for system data base and shared the report with the partners. Point of entry Surveillance (PoE) implementation at the states level Results showed that the surveillance at points of entry has been fully implemented in 6 state 46.1% which it had point of entry and it has been designated by IHR, the system had focal person, the training done for all staff with availability of system guidelines and SOPs all this done by 100% 83.3% of point of entry reported Daily and weekly reports, the percentage of report completeness and timeliness was 66.7% with 83.3% for the zero report when no event of cases reported, availability of system data base and documentation for the events and cases reported through the system was 83.3%. Conclusion: The study showed significant positive changes in implementation of event-based surveillance system under the international health regulation, based on the finding the study recommended that, rapid and early response for the reported cases and rumors or any other event from the locality and state level, Regular refresh, and basic training for surveillance staff internally and external training and strengthen the data management mechanism.
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Keshavamurthy, Ravikiran, and Lauren E. Charles. "Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning." Scientific Reports 13, no. 1 (July 8, 2023). http://dx.doi.org/10.1038/s41598-023-38074-0.
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AbstractIn recent years, the reports of Kyasanur forest disease (KFD) breaking endemic barriers by spreading to new regions and crossing state boundaries is alarming. Effective disease surveillance and reporting systems are lacking for this emerging zoonosis, hence hindering control and prevention efforts. We compared time-series models using weather data with and without Event-Based Surveillance (EBS) information, i.e., news media reports and internet search trends, to predict monthly KFD cases in humans. We fitted Extreme Gradient Boosting (XGB) and Long Short Term Memory models at the national and regional levels. We utilized the rich epidemiological data from endemic regions by applying Transfer Learning (TL) techniques to predict KFD cases in new outbreak regions where disease surveillance information was scarce. Overall, the inclusion of EBS data, in addition to the weather data, substantially increased the prediction performance across all models. The XGB method produced the best predictions at the national and regional levels. The TL techniques outperformed baseline models in predicting KFD in new outbreak regions. Novel sources of data and advanced machine-learning approaches, e.g., EBS and TL, show great potential towards increasing disease prediction capabilities in data-scarce scenarios and/or resource-limited settings, for better-informed decisions in the face of emerging zoonotic threats.
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Dellanzo, Antonella, Viviana Cotik, Daniel Yunior Lozano Barriga, Jonathan Jimmy Mollapaza Apaza, Daniel Palomino, Fernando Schiaffino, Alexander Yanque Aliaga, and José Ochoa-Luna. "Digital surveillance in Latin American diseases outbreaks: information extraction from a novel Spanish corpus." BMC Bioinformatics 23, no. 1 (December 23, 2022). http://dx.doi.org/10.1186/s12859-022-05094-y.
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Abstract Background In order to detect threats to public health and to be well-prepared for endemic and pandemic illness outbreaks, countries usually rely on event-based surveillance (EBS) and indicator-based surveillance systems. Event-based surveillance systems are key components of early warning systems and focus on fast capturing of data to detect threat signals through channels other than traditional surveillance. In this study, we develop Natural Language Processing tools that can be used within EBS systems. In particular, we focus on information extraction techniques that enable digital surveillance to monitor Internet data and social media. Results We created an annotated Spanish corpus from ProMED-mail health reports regarding disease outbreaks in Latin America. The corpus has been used to train algorithms for two information extraction tasks: named entity recognition and relation extraction. The algorithms, based on deep learning and rules, have been applied to recognize diseases, hosts, and geographical locations where a disease is occurring, among other entities and relations. In addition, an in-depth analysis of micro-average F1 metrics shows the suitability of our approaches for both tasks. Conclusions The annotated corpus and algorithms presented could leverage the development of automated tools for extracting information from news and health reports written in Spanish. Moreover, this framework could be useful within EBS systems to support the early detection of Latin American disease outbreaks.
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McKnight, C. J., A. T. Aboushady, and C. R. Lane. "Beyond early warning: towards greater granularity in the use of event-based surveillance for public health emergencies." BMC Public Health 24, no. 1 (December 18, 2024). https://doi.org/10.1186/s12889-024-20963-2.
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Abstract Background The international health emergency caused by the emergence of the SARS-CoV-2 virus demonstrated the expanding usefulness of multi-country disease outbreak information gathered through event-based surveillance (EBS) as an extension beyond the main purposes of early warning, alert, and response (EWAR). In this article, previous events of multi-country outbreaks from 2010–2019 were reviewed for how EBS, within an expanded sphere of Epidemic Intelligence (EI), may help to enhance the understanding of outbreaks for a more timely and nuanced, multiple-point trigger approach to health emergencies. Methods The public, open-source database of ProMed reports were reviewed for the date of first notification on major outbreaks of infectious diseases and then compared for subsequent dates of any new, exceptional epidemiological findings (novel host, settings, transmission characteristics) as a determining factor for prolonged, multi-country events later acknowledged on the WHO disease outbreak news (DON) website, or by peer-reviewed journal publication if no related DON information became available. Results During the preceding decade, there was an ongoing occurrence of unexpected outbreaks requiring new information about previously unknown pathogens, such as MERS-CoV, and longstanding threats from multiple neglected tropical diseases. During these international outbreaks, key scientific insights about new host species, viral persistence, occurrence of human-to-human spread, and transmission setting, became known over the course of the response. Conclusion The timeliness between initial alerts of early outbreak detection and key epidemiological evidence about the emerging threat reached far beyond the first warning for the global community. To improve on the best knowledge available for an immediate response, it is recommended that further gathering and documentation from event-based surveillance is engaged to create a more complete assessment for uncontrollable infectious disease outbreaks and epidemics. Enhanced EBS (through modern tools, e.g., Epidemic Intelligence from Open Sources (EIOS) are critical for timely detection and response to such events.
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Dub, Timothee, Henna Mäkelä, Esther Van Kleef, Agnes Leblond, Alizé Mercier, Viviane Hénaux, Fanny Bouyer, et al. "Epidemic intelligence activities among national public and animal health agencies: a European cross-sectional study." BMC Public Health 23, no. 1 (August 4, 2023). http://dx.doi.org/10.1186/s12889-023-16396-y.
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AbstractEpidemic Intelligence (EI) encompasses all activities related to early identification, verification, analysis, assessment, and investigation of health threats. It integrates an indicator-based (IBS) component using systematically collected surveillance data, and an event-based component (EBS), using non-official, non-verified, non-structured data from multiple sources. We described current EI practices in Europe by conducting a survey of national Public Health (PH) and Animal Health (AH) agencies. We included generic questions on the structure, mandate and scope of the institute, on the existence and coordination of EI activities, followed by a section where respondents provided a description of EI activities for three diseases out of seven disease models. Out of 81 gatekeeper agencies from 41 countries contacted, 34 agencies (42%) from 26 (63%) different countries responded, out of which, 32 conducted EI activities. Less than half (15/32; 47%) had teams dedicated to EI activities and 56% (18/34) had Standard Operating Procedures (SOPs) in place. On a national level, a combination of IBS and EBS was the most common data source. Most respondents monitored the epidemiological situation in bordering countries, the rest of Europe and the world. EI systems were heterogeneous across countries and diseases. National IBS activities strongly relied on mandatory laboratory-based surveillance systems. The collection, analysis and interpretation of IBS information was performed manually for most disease models. Depending on the disease, some respondents did not have any EBS activity. Most respondents conducted signal assessment manually through expert review. Cross-sectoral collaboration was heterogeneous. More than half of the responding institutes collaborated on various levels (data sharing, communication, etc.) with neighbouring countries and/or international structures, across most disease models. Our findings emphasise a notable engagement in EI activities across PH and AH institutes of Europe, but opportunities exist for better integration, standardisation, and automatization of these efforts. A strong reliance on traditional IBS and laboratory-based surveillance systems, emphasises the key role of in-country laboratories networks. EI activities may benefit particularly from investments in cross-border collaboration, the development of methods that can automatise signal assessment in both IBS and EBS data, as well as further investments in the collection of EBS data beyond scientific literature and mainstream media.
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Briscoe, Erica, Scott Appling, Edward Clarkson, Nikolay Lipskiy, James Tyson, and Jacqueline Burkholder. "Semantic Analysis of Open Source Data for Syndromic Surveillance." Online Journal of Public Health Informatics 9, no. 1 (May 2, 2017). http://dx.doi.org/10.5210/ojphi.v9i1.7651.
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ObjectiveThe objective of this analysis is to leverage recent advances innatural language processing (NLP) to develop new methods andsystem capabilities for processing social media (Twitter messages)for situational awareness (SA), syndromic surveillance (SS), andevent-based surveillance (EBS). Specifically, we evaluated the useof human-in-the-loop semantic analysis to assist public health (PH)SA stakeholders in SS and EBS using massive amounts of publiclyavailable social media data.IntroductionSocial media messages are often short, informal, and ungrammatical.They frequently involve text, images, audio, or video, which makesthe identification of useful information difficult. This complexityreduces the efficacy of standard information extraction techniques1.However, recent advances in NLP, especially methods tailoredto social media2, have shown promise in improving real-time PHsurveillance and emergency response3. Surveillance data derived fromsemantic analysis combined with traditional surveillance processeshas potential to improve event detection and characterization. TheCDC Office of Public Health Preparedness and Response (OPHPR),Division of Emergency Operations (DEO) and the Georgia TechResearch Institute have collaborated on the advancement of PH SAthrough development of new approaches in using semantic analysisfor social media.MethodsTo understand how computational methods may benefit SS andEBS, we studied an iterative refinement process, in which the datauser actively cultivated text-based topics (“semantic culling”) in asemi-automated SS process. This ‘human-in-the-loop’ process wascritical for creating accurate and efficient extraction functions in large,dynamic volumes of data. The general process involved identifyinga set of expert-supplied keywords, which were used to collect aninitial set of social media messages. For purposes of this analysisresearchers applied topic modeling to categorize related messages intoclusters. Topic modeling uses statistical techniques to semanticallycluster and automatically determine salient aggregations. A user thensemantically culled messages according to their PH relevance.In June 2016, researchers collected 7,489 worldwide English-language Twitter messages (tweets) and compared three samplingmethods: a baseline random sample (C1, n=2700), a keyword-basedsample (C2, n=2689), and one gathered after semantically cullingC2 topics of irrelevant messages (C3, n=2100). Researchers utilizeda software tool, Luminoso Compass4, to sample and perform topicmodeling using its real-time modeling and Twitter integrationfeatures. For C2 and C3, researchers sampled tweets that theLuminoso service matched to both clinical and layman definitions ofRash, Gastro-Intestinal syndromes5, and Zika-like symptoms. Laymanterms were derived from clinical definitions from plain languagemedical thesauri. ANOVA statistics were calculated using SPSSsoftware, version. Post-hoc pairwise comparisons were completedusing ANOVA Turkey’s honest significant difference (HSD) test.ResultsAn ANOVA was conducted, finding the following mean relevancevalues: 3% (+/- 0.01%), 24% (+/- 6.6%) and 27% (+/- 9.4%)respectively for C1, C2, and C3. Post-hoc pairwise comparison testsshowed the percentages of discovered messages related to the eventtweets using C2 and C3 methods were significantly higher than forthe C1 method (random sampling) (p<0.05). This indicates that thehuman-in-the-loop approach provides benefits in filtering socialmedia data for SS and ESB; notably, this increase is on the basis ofa single iteration of semantic culling; subsequent iterations could beexpected to increase the benefits.ConclusionsThis work demonstrates the benefits of incorporating non-traditional data sources into SS and EBS. It was shown that an NLP-based extraction method in combination with human-in-the-loopsemantic analysis may enhance the potential value of social media(Twitter) for SS and EBS. It also supports the claim that advancedanalytical tools for processing non-traditional SA, SS, and EBSsources, including social media, have the potential to enhance diseasedetection, risk assessment, and decision support, by reducing the timeit takes to identify public health events.