How to maximise event-based surveillance web-systems: the example of ECDC/JRC collaboration to improve the performance of MedISys

The mission of the European Centre for Diseases prevention and Control (ECDC) is to identify, assess and communicate current and emerging infectious threats to human health within the European Union (EU). The identification of threats is based on the collection and analysis of information from established communicable disease surveillance networks and from unstructured information mostly originating from non-health care sources, e.g. online news sites. MedISys, an automatic real-time media monitoring and threat detection system developed by the Joint Research Centre (JRC) of the European Commission, is among the tools used by the ECDC for timely identification of potential public health threats from online information sources. In 2008, an ECDC internal analysis indicated that MedISys issued alerts faster than other human-mediated web-based systems. As timely detection is crucial to enable efficient response to public health threats, ECDC decided to further explore the potential of MedISys as an EU early warning tool. We analysed the functionality of the existing system in view of improving the usability of the web site, revising the sources and reducing the amount of irrelevant articles. We provided JRC with practical suggestions for the interface and asked public health experts at national level to assist in the revision of sources. To reduce the number of irrelevant articles, alternative search strategies for fifteen diseases were tested against the existing strategies using the positive predictive value (PPV) and the sensitivity to measure the performance of the system. Our intervention increased the PPV value (from 15.3% to 71.1%) and the sensitivity of the system. We conclude that the best search strategies use a limited number of keywords weighted as positive (with weights adjusted below the alert thresholds) and an extended list of keywords weighted as negative. We recommend a high number of epidemiological terms within the keyword combinations. The results indicate that user feedback is crucial to exploit the full potential of event-based surveillance systems such as MedISys. We will improve the detection of other infectious diseases and intend to cover all EU languages. Customized country versions will be set up in collaboration with JRC; ECDC will encourage the use of the system at national level in the EU member states.JRC.DG.G.2-Global security and crisis managemen

Event-based surveillance during EXPO Milan 2015. Rationale, tools, procedures, and initial results

More than 21 million participants attended EXPO Milan from May to October 2015, making it one of the largest protracted mass gathering events in Europe. Given the expected national and international population movement and health security issues associated with this event, Italy fully implemented, for the first time, an event-based surveillance (EBS) system focusing on naturally occurring infectious diseases and the monitoring of biological agents with potential for intentional release. The system started its pilot phase in March 2015 and was fully operational between April and November 2015. In order to set the specific objectives of the EBS system, and its complementary role to indicator-based surveillance, we defined a list of priority diseases and conditions. This list was designed on the basis of the probability and possible public health impact of infectious disease transmission, existing statutory surveillance systems in place, and any surveillance enhancements during the mass gathering event. This article reports the methodology used to design the EBS system for EXPO Milan and the results of 8 months of surveillance

Interaction Between Climatic, Environmental, and Demographic Factors on Cholera Outbreaks in Kenya

Background: Cholera remains an important public health concern in developing countries including Kenya where 11,769 cases and 274 deaths were reported in 2009 according to the World Health Organization (WHO). This ecological study investigates the impact of various climatic, environmental, and demographic variables on the spatial distribution of cholera cases in Kenya. Methods: District-level data was gathered from Kenya’s Division of Disease Surveillance and Response, the Meteorological Department, and the National Bureau of Statistics. The data included the entire population of Kenya from 1999 to 2009. Results: Multivariate analyses showed that districts had an increased risk of cholera outbreaks when a greater proportion of the population lived more than five kilometers from a health facility (RR: 1.025 per 1% increase; 95% CI: 1.010, 1.039), bordered a body of water (RR: 5.5; 95% CI: 2.472, 12.404), experienced increased rainfall from October to December (RR: 1.003 per 1 mm increase; 95% CI: 1.001, 1.005), and experienced decreased rainfall from April to June (RR: 0.996 per 1 mm increase; 95% CI: 0.992, 0.999). There was no detectable association between cholera and population density, poverty, availability of piped water, waste disposal methods, rainfall from January to March, or rainfall from July to September. Conclusion: Bordering a large body of water, lack of health facilities nearby, and changes in rainfall were significantly associated with an increased risk of cholera in Kenya

Sentiment analysis of COVID-19 cases in Greece using Twitter data.

Syndromic surveillance with the use of Internet data has been used to track and forecast epidemics for the last two decades, using different sources from social media to search engine records. More recently, studies have addressed how the World Wide Web could be used as a valuable source for analysing the reactions of the public to outbreaks and revealing emotions and sentiment impact from certain events, notably that of pandemics. Objective: The objective of this research is to evaluate the capability of Twitter messages (tweets) in estimating the sentiment impact of COVID-19 cases in Greece in real time as related to cases. Methods: 153,528 tweets were gathered from 18,730 Twitter users totalling 2,840,024 words for exactly one year and were examined towards two sentimental lexicons: one in English language translated into Greek (using the Vader library) and one in Greek. We then used the specific sentimental ranking included in these lexicons to track i) the positive and negative impact of COVID-19 and ii) six types of sentiments: Surprise, Disgust, Anger, Happiness, Fear and Sadness and iii) the correlations between real cases of COVID-19 and sentiments and correlations between sentiments and the volume of data. Results: Surprise (25.32%) mainly and secondly Disgust (19.88%) were found to be the prevailing sentiments of COVID-19. The correlation coefficient (R2 ) for the Vader lexicon is &#8722; 0.07454 related to cases and &#8722; 0.,70668 to the tweets, while the other lexicon had 0.167387 and &#8722; 0.93095 respectively, all measured at significance level of p < 0.01. Evidence shows that the sentiment does not correlate with the spread of COVID-19, possibly since the interest in COVID-19 declined after a certain time

Hepatitis B in the Greater San Francisco Bay Area: an integrated programme to respond to a diverse local epidemic

Although chronic hepatitis B (CHB) affects approximately 2 million United States residents, there is no systematic screening of at-risk individuals, and most remain unaware of their hepatitis B virus (HBV) infection. Unmonitored and untreated, CHB results in a 25–30% risk of death from liver cancer and/or cirrhosis, inflicting an increasing healthcare burden in high-prevalence regions. Despite high prevalence in immigrant Asians and Pacific Islanders, among whom CHB is a leading cause of death, community and healthcare provider awareness remains low. Because safe and effective vaccines and effective antiviral treatments exist, there is an urgent need for integrated programmes that identify, follow and treat people with existing CHB, while vaccinating the susceptible. We describe an extant San Francisco programme that integrates culturally targeted, population-based, HBV screening, vaccination or reassurance, management and research. After screening over 3000 at-risk individuals, we here review our operational and practical experience and describe a simple, rationally designed model that could be successfully used to greatly improve the current approach to hepatitis B while ultimately reducing the related healthcare costs, especially in the high-risk populations, which are currently underserved

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Epidemic Alert System: A Web-based Grassroots Model

Most web-based disease surveillance systems that give epidemic alerts are based on very large and unstructured data from various news sources, social media and online queries that are parsed by complex algorithms. This has the tendency to generate results that are so diverse and non-specific. When considered along with the fact that there are no existing standards for mining and analyzing data from the internet, the results or decisions reached based on internet sources have been classified as low-quality. This paper proposes a web-based grassroots epidemic alert system that is based on data collected specifically from primary health centers, hospitals and registered laboratories. It takes a more traditional approach to indicator-based disease surveillance as a step towards standardizing web-based disease surveillance. It makes use of a threshold value that is based on the third quartile (75th percentile) to determine the need to trigger the alarm for the onset of an epidemic. It also includes, for deeper analysis, demographic information

La Gran Manzana: The Road Ahead For New York Citys Latino Community

This report from Hispanic Federation is a policy blueprint with recommendations on how the next Mayor and City Council can improve the lives of nearly 2.5 million Latinos who call New York City home. Recommendations include, increasing New York City's Nonprofit Stabilization Fund to $50 million over a five-year period to support people of color-led nonprofit organizations and ensure that nonprofits can engage in long-term planning to meet operational infrastructure needs and technical assistance, establishing free full-day pre-kindergarten for all three- and four year olds, electing a Latino/a as the next Speaker of the City Council, and more

GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization

Text summarization (TS) is a natural language processing (NLP) subtask pertaining to the automatic formulation of a concise and coherent summary that covers the major concepts and topics from one or multiple documents. Recent advancements in deep learning have led to the development of abstractive summarization transformer-based models, which outperform classical approaches. In any case, research in this field focuses on high resource languages such as English, while the corresponding work for low resource languages is still underdeveloped. Taking the above into account, this paper proposes a series of novel TS models for Greek news articles. The proposed models were thoroughly evaluated on the same dataset against GreekBART, which is the state-of-the-art model in Greek abstractive news summarization. Our evaluation results reveal that most of the proposed models significantly outperform GreekBART on various evaluation metrics. We make our evaluation code public, aiming to increase the reproducibility of this work and facilitate future research in the field.Comment: 26 pages, 0 figure