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Background The International Health Regulations state that early detection and immediate reporting of unusual health events is important for early warning and response systems. Objective To describe a pilot surveillance program established in health facilities in Yaoundé, Cameroon in 2017 which aimed to enable detection and reporting of public health events. Methods Cameroon’s Ministry of Health, in partnership with the US Centers for Disease Control and Prevention, Cameroon Pasteur Center, and National Public Health Laboratory, implemented event‐based surveillance (EBS) in nine Yaoundé health facilities. Four signals were defined that could indicate possible public health events, and a reporting, triage, and verification system was established among partner organizations. A pre‐defined laboratory algorithm was defined, and a series of workshops trained health facilities, laboratory, and public health staff for surveillance implementation. Results From May 2017 to January 2018, 30 signals were detected, corresponding to 15 unusual respiratory events. All health facilities reported a signal at least once, and more than three‐quarters of health facilities reported ≥2 times. Among specimens tested, the pathogens detected included Klebsiella pneumoniae, Moraxella catarrhalis, Streptococcus pneumoniae, Haemophilus influenza, Staphylococcus aureus, Pneumocystis jiroveci, influenza A (H1N1) virus, rhinovirus, and adenovirus. Conclusions The events detected in this pilot were caused by routine respiratory bacteria and viruses, and no novel influenza viruses or other emerging respiratory threats were identified. The surveillance system, however, strengthened relationships and communication linkages between health facilities and public health authorities. Astute clinicians can play a critical role in early detection and EBS is one approach that may enable reporting of emerging outbreaks and public health events.

Event-based surveillance (EBS) is the organised approach to the detection and reporting of 'signals,' defined as information that may represent events of public health importance, often through channels outside of routine surveillance systems. Signals can be designed to detect patterns of disease, such as clusters of similar illness in a community, or clusters of disease or death in animals. Signals can also include single cases of suspected high-priority events such a patient with viral haemorrhagic fever. EBS can be a key component of an effective early warning system, which enables countries to be better prepared for endemic and pandemic illness outbreaks. EBS uses an all-hazards approach that includes the principles of One Health. This review covers the concept and process of EBS, different sources for EBS data, and methods to obtain information from these sources. This overview will aid countries in implementing this important form of surveillance.

Background Public health intelligence (PHI) allows for timely detection of public health threats. Exchange and close cooperation between PHI teams are crucial for early threat detection and standards harmonization, yet a comprehensive overview documenting their activities is lacking. We aimed to enhance mutual awareness and collaboration possibilities by mapping and characterizing PHI teams. Methods We developed and distributed an online survey through network sampling (June-November 2023) via regional Centres for Disease Prevention and Control (Africa CDC, ECDC, US CDC), the International Association of National Public Health Institutes (IANPHI), the Robert Koch-Institute, and the World Health Organization (WHO). We identified and described PHI teams and their activities, including implementation of PHI processes, workforce, capacity building, priorities and perspectives on challenges and opportunities, by WHO regions and type of institution. Results We identified 132 PHI teams from 87 countries in all regions, primarily in public health institutions (40%, 53/132) and ministries (27%, 35/132). Potential public health threats are monitored at national (39%, 51/132), international (20%, 26/132), and both levels (42%, 55/132). Most teams (89%, 114/128) integrate indicator-based and event-based surveillance. Teams focus mainly on human communicable diseases (95%, 125/132), healthcare-associated infections/antimicrobial resistance (66%, 87/132) and health in natural disasters (64%, 85/132). Nearly half of the teams (43%, 50/117) are not part of early event detection networks. All teams based in regional public health institutions (7/7) and United Nations (UN) institutions (10/10) are able to scale up their activities if needed. More than half of the teams (59%, 71/120) provide training activities on early detection. There is a strong interest in capacity building and networking. Conclusion PHI teams worldwide perform partially overlapping tasks, suggesting benefits from broader exchange. The interest in trainings and networking underscores the need for platforms supporting exchange, peer-to-peer cooperation and capacity building. International partner institutions are key in fostering global development of PHI.

Nigeria Centres for Disease Control and Prevention established an event-based surveillance (EBS) system in 2016 to supplement traditional surveillance structures. The EBS system is comprised of an internet-based data mining tool and a call center. To evaluate the EBS system for usefulness, simplicity, acceptability, timeliness, and data quality, we performed a descriptive analysis of signals received during September 2017-June 2018. We used questionnaires, semistructured interviews, and direct observation to collect information from EBS staff. Amongst 43,631 raw signals detected, 138 (0.3%) were escalated; 63 (46%) of those were verified as events, including 25 Lassa fever outbreaks and 13 cholera outbreaks. Interviewees provided multiple examples of earlier outbreak detections but suggested notifications and logging could be improved to ensure action. EBS proved effective in detecting outbreaks, but we noted clear opportunities for efficiency gains. We recommend improving signal logging, standardizing processes, and revising outputs to ensure appropriate public health action.

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.

Epidemic 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.

Background: Event Based Surveillance (EBS) is the organized and rapid captures of information about events that are a potential risk to public health. Information can be about events related to occurrence of disease in humans or a potential exposure to a risk e.g. contaminated food,-chemicals or radio nuclear events. In Pakistan it is a recent phenomenon and is a compliment to Indicator Based Surveillance (IBS) which is traditionally going on in public sector health care facilities. Literature on effectiveness and contribution of EBS is scarce. Methodology: A cross sectional study was conducted based on retrospective record review of events reporting in Pakistan. For this purpose, Disease Surveillance and Response Units (DSRU) were identified and descriptive analysis of their reporting per epidemiological week was done. Results: Public sector health facilities were found the most frequent 75% (n=88) in reported events. Vector borne diseases 49% (n=58) were the most frequently reported event. In 85% (n=101) of events, WHO and/or CDC standard case definitions were used followed by operational cases definition 10% (n=12). System was found operational in all provinces and regions and was linked to laboratory based surveillance in all DSRUs. Median delay was found high in Vaccine Preventable Diseases (Measles and Diphtheria), eight days (range: 0-10 days) and Vector borne diseases (Dengue, Chikungunya and CL) six days (range: 0-8 days). Among all, 90% (n=105) response activities were establishment and/ or strengthening of surveillance system by training of local staff. All investigations were linked with public health laboratory investigation except for food/water borne outbreak 3% (n=4), where no laboratory confirmation of causative organism was found. Conclusion: Event based surveillance (EBS) should be strengthened as a compliment of IBS and its network should be expanded to regional and district levels. Frequent trainings of health care providers at health facilities and informal sources like media should be done to gather credible and timely information of events’ time place and persons involved. However, monitoring and evaluation should be integral part to improve event recording and hence targeted response.

Background: Event Based Surveillance (EBS) is the organized and rapid captures of information about events that are a potential risk to public health. Information can be about events related to occurrence of disease in humans or a potential exposure to a risk e.g. contaminated food,-chemicals or radio nuclear events. In Pakistan it is a recent phenomenon and is a compliment to Indicator Based Surveillance (IBS) which is traditionally going on in public sector health care facilities. Literature on effectiveness and contribution of EBS is scarce. Methodology: A cross sectional study was conducted based on retrospective record review of events reporting in Pakistan. For this purpose, Disease Surveillance and Response Units (DSRU) were identified and descriptive analysis of their reporting per epidemiological week was done. Results: Public sector health facilities were found the most frequent 75% (n=88) in reported events. Vector borne diseases 49% (n=58) were the most frequently reported event. In 85% (n=101) of events, WHO and/or CDC standard case definitions were used followed by operational cases definition 10% (n=12). System was found operational in all provinces and regions and was linked to laboratory based surveillance in all DSRUs. Median delay was found high in Vaccine Preventable Diseases (Measles and Diphtheria), eight days (range: 0-10 days) and Vector borne diseases (Dengue, Chikungunya and CL) six days (range: 0-8 days). Among all, 90% (n=105) response activities were establishment and/ or strengthening of surveillance system by training of local staff. All investigations were linked with public health laboratory investigation except for food/water borne outbreak 3% (n=4), where no laboratory confirmation of causative organism was found. Conclusion: Event based surveillance (EBS) should be strengthened as a compliment of IBS and its network should be expanded to regional and district levels. Frequent trainings of health care providers at health facilities and informal sources like media should be done to gather credible and timely information of events’ time place and persons involved. However, monitoring and evaluation should be integral part to improve event recording and hence targeted response.

Event-based surveillance is crucial for the early detection and rapid response to potential public health risks. In recent years, social networking services (SNS) have been recognized for their potential role in this domain. Previous studies have demonstrated the capacity of SNS posts for the early detection of health crises and affected individuals, including those related to infectious diseases. However, the reliability of such posts, being subjective and not clinically diagnosed, remains a challenge. In this study, we address this issue by assessing the classification performance of transformer-based pretrained language models to accurately classify Japanese tweets related to heat stroke, a significant health effect of climate change, as true or false. We also evaluated the efficacy of combining SNS and artificial intelligence for event-based public health surveillance by visualizing the data on correctly classified tweets and heat stroke emergency medical evacuees in time–space and animated video, respectively. The transformer-based pretrained language models exhibited good performance in classifying the tweets. Spatiotemporal and animated video visualizations revealed a reasonable correlation. This study demonstrates the potential of using Japanese tweets and deep learning algorithms based on transformer networks for event-based surveillance at high spatiotemporal levels to enable early detection of heat stroke risks.

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.