Event-based Surveillance Research Articles

Mass gathering events and COVID-19; lessons learnt from the 2020 European football championship.

Evidence about the impact of mass gatherings during the coronavirus disease 2019 (COVID-19) pandemic on the number of disease cases and on the health-care systems of host countries is limited. Additionally, there have been few publications on the lessons identified from the adaptation of mass gatherings held during the pandemic, including the implementation of comprehensive public health and social measures aimed at reducing viral transmission. This article describes preparations made for the 2020 Union of European Football Associations (UEFA) European Football Championship (UEFA Euro 2020) by the World Health Organization's (WHO) Regional Office for Europe, UEFA and other stakeholders after the championship had been rescheduled because of the COVID-19 pandemic. Technical guidance on preparations for the football tournament and risk assessment tools were provided by WHO. A task force established by the WHO Regional Office for Europe conducted traditional and event-based disease surveillance before and during UEFA Euro 2020, monitored public health and social measures in the 11 host countries, and developed a risk communication and community engagement strategy that involved multimedia campaigns targeting news and social media, fans, athletes, event organizers and other stakeholders. The lessons and good practices identified during UEFA Euro 2020 are described to help guide preparations for future mass gatherings in health emergencies. Sharing data and recommendations on best practice from previous mass gatherings with the organizers and countries involved in planning for a major event is particularly important.

Utility of syndromic surveillance for the surveillance of healthcare-associated infections in resource-limited settings: a narrative review.

Globally, Healthcare-associated infections (HCAIs) pose a significant threat to patient safety and healthcare systems. In low- and middle-income countries (LMICs), the lack of adequate resources to manage HCAIs, as well as the weak healthcare system, further exacerbate the burden of these infections. Traditional surveillance methods that rely on laboratory tests are cost-intensive and impractical in these settings, leading to ineffective monitoring and delayed management of HCAIs. The rates of HCAIs in resource-limited settings have not been well established for most LMICs, despite their negative consequences. This is partly due to costs associated with surveillance systems. Syndromic surveillance, a part of active surveillance, focuses on clinical observations and symptoms rather than laboratory confirmation for HCAI detection. Its cost-effectiveness and efficiency make it a beneficial approach for monitoring HCAIs in LMICs. It provides for early warning capabilities, enabling timely identification and response to potential HCAI outbreaks. Syndromic surveillance is highly sensitive and this helps balance the challenge of low sensitivity of laboratory-based surveillance systems. If syndromic surveillance is used hand-in-hand with laboratory-based surveillance systems, it will greatly contribute to establishing the true burden of HAIs in resource-limited settings. Additionally, its flexibility allows for adaptation to different healthcare settings and integration into existing health information systems, facilitating data-driven decision-making and resource allocation. Such a system would augment the event-based surveillance system that is based on alerts and rumours for early detection of events of outbreak potential. If well streamlined and targeted, to monitor priority HCAIs such as surgical site infections, hospital-acquired pneumonia, diarrheal illnesses, the cost and burden of the effects from these infections could be reduced. This approach would offer early detection capabilities and could be expanded into nationwide HCAI surveillance networks with standardised data collection, healthcare worker training, real-time reporting mechanisms, stakeholder collaboration, and continuous monitoring and evaluation. Syndromic surveillance offers a promising strategy for combating HCAIs in LMICs. It provides early warning capabilities, conserves resources, and enhances patient safety. Effective implementation depends on strategic interventions, stakeholder collaboration, and ongoing monitoring and evaluation to ensure sustained effectiveness in HCAI detection and response.

Integrating indicator-based and event-based surveillance data for risk mapping of West Nile virus, Europe, 2006 to 2021.

BackgroundWest Nile virus (WNV) has an enzootic cycle between birds and mosquitoes, humans being incidental dead-end hosts. Circulation of WNV is an increasing public health threat in Europe. While detection of WNV is notifiable in humans and animals in the European Union, surveillance based on human case numbers presents some limitations, including reporting delays.AimWe aimed to perform risk mapping of WNV circulation leading to human infections in Europe by integrating two types of surveillance systems: indicator-based and event-based surveillance.MethodsFor indicator-based surveillance, we used data on human case numbers reported to the European Centre for Disease Prevention and Control (ECDC), and for event-based data, we retrieved information from news articles collected through an automated biosurveillance platform. In addition to these data sources, we also used environmental data to train ecological niche models to map the risk of local WNV circulation leading to human infections.ResultsThe ecological niche models based on both types of surveillance data highlighted new areas potentially at risk of WNV infection in humans, particularly in Spain, Italy, France and Greece.ConclusionAlthough event-based surveillance data do not constitute confirmed occurrence records, integrating both indicator-based and event-based surveillance data proved useful. These results underscore the potential for a more proactive and comprehensive strategy in managing the threat of WNV in Europe by combining indicator- and event-based and environmental data for effective surveillance and public health response.

Enhancement of Communicable Disease Surveillance During FIFA World Cup Qatar 2022™: A Legacy

Background Communicable disease surveillance in Qatar was expanded and improved to adapt to the demanding requirements during the Fédération Internationale de Football Association (FIFA) World Cup Qatar 2022™. This article documents Qatar’s surveillance enhancements to support future mass-gathering preparedness strategies. Methods The notification enhancement project (NEP) and the mass gathering – early warning, alert, and response system (MG-EWARS) project were executed to intensify communicable disease surveillance. Global public-health intelligence was monitored using Epidemic Intelligence from Open Sources (EIOS). A unified command oversaw all surveillance activities, assessed risks, and made decisions. Results The surveillance system detected and responded to public health threats effectively. The NEP increased disease notifications from healthcare workers by 214%. The EIOS screened ~300,000 articles, identifying three major health events. The MG-EWARS Cell provided daily situational analysis to stakeholders, including insights from indicator-based, event-based, and sentinel-syndromic surveillance. Conclusion The Ministry of Public Health in Qatar successfully implemented novel projects and activities to enhance surveillance of infectious diseases during the FIFA World Cup Qatar 2022™. Early and research-driven planning, use of the latest technology, appropriate training programs, and prudent allocation of resources were the key strategies employed.

Epidemiological analysis of event-based surveillance data of Crimean-Congo Hemorrhagic Fever (CCHF) outbreaks across Balochistan Province, Pakistan, from 2000 to August 2021

Background Crimean-Congo hemorrhagic fever (CCHF) is endemic in Balochistan, caused by the Bunyaviridae family’s tick-borne virus (Nairovirus). The CCHF virus leads to severe viral hemorrhagic fever outbreaks with a fatality rate of 10–40%. This study aims to describe the epidemiological trend of CCHF in Balochistan and provide recommendations for controlling current and future outbreaks. Methods A descriptive approach was adopted for data analysis, utilizing the standard case definition of the Vector-Borne Diseases (VBD) program. “Any person of any age and gender residing in Balochistan from 2000 until August 10, 2021, presenting with the acute onset of illness, featuring a high-grade fever (38.5°C) persisting for more than 3 days but less than 10 days. Individuals should also exhibit any two of the following symptoms: hemorrhagic or purpuric rash, nosebleed, blood in vomit/sputum/stool, or other hemorrhagic symptoms. Moreover, there should be no known predisposing factors for hemorrhagic manifestations, and individuals must have had contact with a confirmed patient or engaged in handling animals and raw animal products.” The study covered cases identified from 2000 to August 10, 2021, and the descriptive study was conducted at the Provincial Disease Surveillance and Response Unit (PDSRU) in Quetta. Frequencies were calculated, and Excel 2016 was used to generate tables and graphs. Results Based on the case definition, 1418 laboratory-confirmed cases of CCHF were identified out of 2542 reported cases from 2000 to August 10, 2021. Most cases (89%, n = 1262) were found to be males. The case fatality rate increased from 5% to 13% over the last decade. The highest number of CCHF cases occurred in 2017 (n = 172), followed by 2005 (n = 108) and 2004 (n = 107). Direct animal contact was reported in 61% of cases, with 22% being butchers and farmers each. CCHF poses a significant public health issue in Balochistan. Conclusion This study provides a detailed overview of CCHF in Balochistan over the last 21 years, recommending the declaration of CCHF as a public health emergency. Establishing a comprehensive tick surveillance system, ecological studies, and health education sessions in collaboration with the livestock department is crucial to prevent future outbreaks.

Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar

BackgroundPublic health threats can significantly impact mass gatherings and enhancing surveillance systems would thus be crucial. Epidemic Intelligence from Open Sources (EIOS) was introduced to Qatar to complement the existing surveillance measures in preparation to the FIFA World Cup Qatar 2022 (FWC22). This study estimated the empirical probability of EIOS detecting signals of public health relevance. It also looked at the factors responsible for discerning a moderate-high risk signal during a mass gathering event. MethodsThis cross-sectional descriptive study used data collected between November 8th and December 25th, 2022, through an EIOS dashboard that filtered open-source articles using specific keywords. Triage criteria and scoring scheme were developed to capture signals and these were maintained in MS Excel. EIOS’ contribution to epidemic intelligence was assessed by the empirical probability estimation of relevant public health signals. Chi-squared tests of independence were performed to check for associations between various hazard categories and other independent variables. A multivariate logistic regression evaluated the predictors of moderate-high risk signals that required prompt action. ResultsThe probability of EIOS capturing a signal relevant to public health was estimated at 0.85 % (95 % confidence interval (CI) [0.82 %−0.88 %]) with three signals requiring a national response. The hazard category of the signal had significant association to the region of occurrence (χ2 (5, N = 2543) = 1021.6, p < .001). The hazard category also showed significant association to its detection during matchdays of the tournament (χ2 (5, N = 2543) = 11.2, p < .05). The triage criteria developed was able to discern between low and moderate-high risk signals with an acceptable discrimination (Area Under the Curve=0.79). ConclusionEIOS proved useful in the early warning of public health threats.

Establishing an early warning event management system at Africa CDC.

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.

Strengthening event-based surveillance (EBS): a case study from Afghanistan

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.

Infectious risk events and their novelty in event-based surveillance: new definitions and annotated corpus

Infectious risk events and their novelty in event-based surveillance: new definitions and annotated corpus

Strengthening global health security through health early warning systems: A literature review and case study

Disease transmission is dependent on a variety of factors, including the characteristics of an event, such as crowding and shared accommodations, the potential of participants having prolonged exposure and close contact with infectious individuals, the type of activities, and the characteristics of the participants, such as their age and immunity to infectious agents [1–3]. Effective control of outbreaks of infectious diseases requires rapid diagnosis and intervention in high-risk settings. As a result, syndromic and event-based surveillance may be used to enhance the responsiveness of the surveillance system [1]. In public health, surveillance is collecting, analyzing, and interpreting data across time to inform decision-making and aid policy implementation [1]. In this review article we aimed to provide an overview of the principles, types, uses, advantages, and limitations of surveillance systems and to highlight the importance of early warning systems in response to the information received by disease surveillance. The study conducted a comprehensive literature search using several databases, selecting, and reviewing 78 articles that covered different types of surveillance systems, their applications, and their impact on controlling infectious diseases. The article also presents a case study from the Hajj gathering, which highlighted the development, evaluation, and impact of early warning systems on response to the information received by disease surveillance. The study concludes that ongoing disease surveillance should be accompanied by well-designed early warning and response systems, and continuous efforts should be invested in evaluating and validating these systems to minimize the risk of reporting delays and reducing the risk of outbreaks.

Evaluation of a multi-component early warning system for pastoralist populations in Doolo zone, Ethiopia: mixed-methods study

BackgroundThis study evaluated an early warning, alert and response system for a crisis-affected population in Doolo zone, Somali Region, Ethiopia, in 2019–2021, with a history of epidemics of outbreak-prone diseases. To adequately cover an area populated by a semi-nomadic pastoralist, or livestock herding, population with sparse access to healthcare facilities, the surveillance system included four components: health facility indicator-based surveillance, community indicator- and event-based surveillance, and alerts from other actors in the area. This evaluation described the usefulness, acceptability, completeness, timeliness, positive predictive value, and representativeness of these components.MethodsWe carried out a mixed-methods study retrospectively analysing data from the surveillance system February 2019–January 2021 along with key informant interviews with system implementers, and focus group discussions with local communities. Transcripts were analyzed using a mixed deductive and inductive approach. Surveillance quality indicators assessed included completeness, timeliness, and positive predictive value, among others.Results1010 signals were analysed; these resulted in 168 verified events, 58 alerts, and 29 responses. Most of the alerts (46/58) and responses (22/29) were initiated through the community event-based branch of the surveillance system. In comparison, one alert and one response was initiated via the community indicator-based branch. Positive predictive value of signals received was about 6%. About 80% of signals were verified within 24 h of reports, and 40% were risk assessed within 48 h. System responses included new mobile clinic sites, measles vaccination catch-ups, and water and sanitation-related interventions. Focus group discussions emphasized that responses generated were an expected return by participant communities for their role in data collection and reporting. Participant communities found the system acceptable when it led to the responses they expected. Some event types, such as those around animal health, led to the community’s response expectations not being met.ConclusionsEvent-based surveillance can produce useful data for localized public health action for pastoralist populations. Improvements could include greater community involvement in the system design and potentially incorporating One Health approaches.

Twenty-first-century demographic and social inequalities of heat-related deaths in Brazilian urban areas.

Population exposure to heat waves (HWs) is increasing worldwide due to climate change, significantly affecting society, including public health. Despite its significant vulnerabilities and limited adaptation resources to rising temperatures, South America, particularly Brazil, lacks research on the health impacts of temperature extremes, especially on the role played by socioeconomic factors in the risk of heat-related illness. Here, we present a comprehensive analysis of the effects of HWs on mortality rates in the 14 most populous urban areas, comprising approximately 35% of the country's population. Excess mortality during HWs was estimated through the observed-to-expected ratio (O/E) for total deaths during the events identified. Moreover, the interplay of intersectionality and vulnerability to heat considering demographics and socioeconomic heterogeneities, using gender, age, race, and educational level as proxies, as well as the leading causes of heat-related excess death, were assessed. A significant increase in the frequency was observed from the 1970s (0-3 HWs year-1) to the 2010s (3-11 HWs year-1), with higher tendencies in the northern, northeastern, and central-western regions. Over the 2000-2018 period, 48,075 (40,448-55,279) excessive deaths were attributed to the growing number of HWs (>20 times the number of landslides-related deaths for the same period). Nevertheless, our event-based surveillance analysis did not detect the HW-mortality nexus, reinforcing that extreme heat events are a neglected disaster in Brazil. Among the leading causes of death, diseases of the circulatory and respiratory systems and neoplasms were the most frequent. Critical regional differences were observed, which can be linked to the sharp North-South inequalities in terms of socioeconomic and health indicators, such as life expectancy. Higher heat-related excess mortality was observed for low-educational level people, blacks and browns, older adults, and females. Such findings highlight that the strengthening of primary health care combined with reducing socioeconomic, racial, and gender inequalities represents a crucial step to reducing heat-related deaths.

Multicenter study to describe viral etiologies, clinical profiles, and outcomes of hospitalized children with severe acute respiratory infections, Egypt 2022

In late 2022, severe acute respiratory infections (SARI) surveillance reported an abrupt increase in non-COVID-19 infections among children after three years of drastic reductions. Signals of increased absenteeism due to respiratory symptoms among primary and preparatory school children were detected by Event-Based Surveillance. We conducted a hospital-based survey of children who were admitted with SARI to identify the causative pathogen(s) and estimate the burden of infection. A survey was conducted among children < 16 years in 21 referral hospitals in the three governorates with the highest SARI rates. Patients’ demographics, clinical symptoms, and severity were collected from medical records using a line list. Patients were swabbed and tested for a panel of 33 respiratory pathogens by RT-PCR at the Central Laboratory in Cairo. Descriptive data analysis was performed for demographic data. Patients’ characteristics were compared by causative agents’ clinical picture and severity using Chi2 with a p < 0.05 significance. Overall, 317 patients were enrolled, 58.3% were ≤ 1 year of age, 61.5% were males. Of 229 (72.7%) of positively tested patients, viruses caused 92.1% including RSV 63.8%, Rhinovirus 10.0%, Influenza 9.2%, Adenovirus 5.2%, and 1.3% co-infected with two viruses. Bacteria caused 3.5% of cases and 4.4% had mixed viral-bacterial infections. Rhinovirus was the most common cause of death among children with SARI, followed by RSV (8.7% and 1.4%), whereas influenza and Adenovirus did not result in any deaths. Patients with viral-bacterial infections are more likely to be admitted to ICU and die at the hospital than bacterial or viral infections (60% and 20% vs. 31.8% and 1.9% vs. 12.5% and 12.5%, p < 0.001). Viruses particularly RSV are the leading cause of SARI causing significant health problem among children < 16 years in Egypt. Bacterial on top of viral infection can worsen disease courses and outcomes. Studies are required to estimate the SARI burden accurately among Egyptian children and a comprehensive approach tailored to Egypt is necessary to reduce its burden.

Evaluating event-based surveillance capacity in Africa: Use of the Africa CDC scorecard, 2022–2023

IntroductionEvent-based surveillance (EBS) is a critical component of Early Warning, Alert and Response (EWAR) capacity needed for outbreak prevention and control. To better understand existing EBS and monitor the progress of capacity-building efforts over time, Africa CDC developed an EBS scorecard as part of a revision to the EBS Framework. MethodsWe distributed the scorecard to African Union (AU) Member States (MSs). Survey responses from the MSs’ human health sector were aggregated, cleaned, and analysed. MS, regional, and continental EBS capacity was assessed. ResultsBetween 21 July 2022 and 4 April 2023, a total of 63 respondents representing 49 (89%) of 55 MSs completed the survey. Given Africa CDC’s public health mandate, we acknowledged the importance of One Health collaboration in MSs but focused on and analysed only the human health sector responses. Thirty-four (71%) MSs stated having EBS in place; hotline was the most common type of EBS implemented (76%). Seventeen (50%) MSs reported multisectoral, One Health collaboration as part of EBS implementation. Scorecard outcomes showed a minimal (score of <60%) to average (score between 60–80%) level of EBS capacity in 29 and five (5) MSs respectively. DiscussionCurrent EBS capacity levels need to be strengthened in Africa to ensure the continent remains prepared for future public health threats. The Africa CDC EBS scorecard provides a useful way to measure and track this capacity over time. Results can be used to advocate for and target resources for capacity building to foster public health emergency preparedness efforts.

Dissemination of information in event-based surveillance, a case study of Avian Influenza.

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.

Lessons learned from identifying clusters of severe acute respiratory infections with influenza sentinel surveillance, Bangladesh, 2009-2020.

We explored whether hospital-based surveillance is useful in detecting severe acute respiratory infection (SARI) clusters and how often these events result in outbreak investigation and community mitigation. During May 2009-December 2020, physicians at 14 sentinel hospitals prospectively identified SARI clusters (i.e., ≥2 SARI cases who developed symptoms ≤10 days of each other and lived <30 min walk or <3 km from each other). Oropharyngeal and nasopharyngeal swabs were tested for influenza and other respiratory viruses by real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). We describe the demographic of persons within clusters, laboratory results, and outbreak investigations. Field staff identified 464 clusters comprising 1427 SARI cases (range 0-13 clusters per month). Sixty percent of clusters had three, 23% had two, and 17% had ≥4 cases. Their median age was 2 years (inter-quartile range [IQR] 0.4-25) and 63% were male. Laboratory results were available for the 464 clusters with a median of 9 days (IQR = 6-13 days) after cluster identification. Less than one in five clusters had cases that tested positive for the same virus: respiratory syncytial virus (RSV) in 58 (13%), influenza viruses in 24 (5%), human metapneumovirus (HMPV) in five (1%), human parainfluenza virus (HPIV) in three (0.6%), adenovirus in two (0.4%). While 102/464 (22%) had poultry exposure, none tested positive for influenza A (H5N1) or A (H7N9). None of the 464 clusters led to field deployments for outbreak response. For 11 years, none of the hundreds of identified clusters led to an emergency response. The value of this event-based surveillance might be improved by seeking larger clusters, with stronger epidemiologic ties or decedents.

Africa CDC's blueprint to enhance early warning surveillance: accelerating implementation of event-based surveillance in Africa.

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.

West Nile Virus Lineage 2 in Free-Living Corvus cornix Birds in Poland.

The summer temperatures recorded in Poland in 2022 were among the highest in over 30 years and, combined with higher-than-expected rainfall, gave the impression of an almost tropical climate. Such climatic conditions were ideal for the transmission of vector-borne zoonotic diseases such as West Nile fever. In northeastern Poland, in the Mazowieckie region, the Polish event-based surveillance network reported increased fatalities of free-living hooded crows (Corvus corone cornix). West Nile virus (WNV) lineage 2 was identified for the first time as the etiological agent responsible for the death of the birds. WNV was detected in 17 out of the 99 (17.17%) free-living birds tested in this study. All the WNV-infected dead birds were collected in the same area and were diagnosed in September by the NVRI and confirmed by the EURL for equine diseases, ANSES, in October 2022. Unnaturally high temperatures recorded in Poland in 2022 likely favored the infection and spread of the virus in the avian population. A nationwide alert and awareness raising of blood transfusion centers and hospitals was carried out to prevent human infections by WNV.

Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning

In 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.

SARS-CoV-2 Case Detection Using Community Event-Based Surveillance System – February to September 2020: Lessons Learned from Senegal

SARS-CoV-2 Case Detection Using Community Event-Based Surveillance System – February to September 2020: Lessons Learned from Senegal