Developing a Prototype System for Syndromic Surveillance and Visualization Using Social Media Data.

Syndromic surveillance of emerging diseases is crucial for timely planning and execution of epidemic response from both local and global authorities. Traditional sources of information employed by surveillance systems are not only slow but also impractical for developing countries. Internet and social media provide a free source of a large amount of data which can be utilized for Syndromic surveillance. We propose developing a prototype system for gathering, storing, filtering and presenting data collected from Twitter (a popular social media platform). Since social media data is inherently noisy we describe ways to preprocess the gathered data and utilize SVM (Support Vector Machine) to identify tweets relating to influenza like symptoms. The filtered data is presented in a web application, which allows the user to explore the underlying data in both spatial and temporal dimensions

Determining the Effects of Social Media Monitoring to Identify Potential Foodborne Illness in Southern Nevada

Foodborne illness, commonly referred to as food poisoning, affects an estimated 1 in 6 Americans every year, despite the fact that it is entirely preventable. Many cases of foodborne illness go unreported; however, better reporting leads to faster health department response and containment. Social media monitoring, using software to identify trends in social media posts, is a novel new tool that has been tested in a variety of public health fields with promising preliminary results. The Southern Nevada Health District (SNHD) has employed social media monitoring software to identify potential foodborne illness within Southern Nevada. The purpose of this study was to determine the extent to which this tactic was effective in identifying high risk facilities that could be the source of disease, and then characterizing those high risk facilities based on the Food and Drug Administration’s (FDA) five foodborne illness risk factors. This study revealed that restaurants flagged by the software performed worse on routine inspections than matched controls, both before and after adjusting the scores to account for every observation of risky food handling. Secondly, the data showed that in all inspections, contamination was the most frequently observed foodborne illness risk factor out of compliance. These findings show that social media monitoring can be a useful tool to guide inspectors to restaurants that may have an active lapse in food safety. Additionally, the fact that contamination was most frequently observed in both groups of restaurants shows that there is a need to educate food handlers and managers on effective contamination prevention techniques

The use and reporting of airline passenger data for infectious disease modelling:a systematic review

Background A variety of airline passenger data sources are used for modelling the international spread of infectious diseases. Questions exist regarding the suitability and validity of these sources. Aim We conducted a systematic review to identify the sources of airline passenger data used for these purposes and to assess validation of the data and reproducibility of the methodology. Methods Articles matching our search criteria and describing a model of the international spread of human infectious disease, parameterised with airline passenger data, were identified. Information regarding type and source of airline passenger data used was collated and the studies’ reproducibility assessed. Results We identified 136 articles. The majority (n = 96) sourced data primarily used by the airline industry. Governmental data sources were used in 30 studies and data published by individual airports in four studies. Validation of passenger data was conducted in only seven studies. No study was found to be fully reproducible, although eight were partially reproducible. Limitations By limiting the articles to international spread, articles focussed on within-country transmission even if they used relevant data sources were excluded. Authors were not contacted to clarify their methods. Searches were limited to articles in PubMed, Web of Science and Scopus. Conclusion We recommend greater efforts to assess validity and biases of airline passenger data used for modelling studies, particularly when model outputs are to inform national and international public health policies. We also recommend improving reporting standards and more detailed studies on biases in commercial and open-access data to assess their reproducibility

Towards Understanding Global Spread of Disease from Everyday Interpersonal Interactions

Monitoring and forecast of global spread of infectious diseases is difficult, mainly due to lack of finegrained and timely data. Previous work in computational epidemiology has shown that mining data from the web can improve the predictability of high-level aggregate patterns of epidemics. By contrast, this paper explores how individuals contribute to the global spread of disease. We consider the important task of predicting the prevalence of flulike illness in a given city based on interpersonal interactions of the city’s residents with the outside world. We use the geo-tagged status updates of traveling Twitter users to infer properties of the flow of individuals between cities. While previous research considered only the raw volume of passengers, we estimate a number of latent variables, including the number of sick (symptomatic) travelers and the number of sick individuals to whom each traveler was exposed. We show that AI techniques provide insights into the mechanisms of disease spread and significantly improve predictability of future flu outbreaks. Our experiments involve over 51,000 individuals traveling between 75 cities prior and during a severe ongoing flu epidemic (October 2012- January 2013). Our model leverages the text and interpersonal interactions recorded in over 6.5 million online status updates without any active user participation, enabling scalable public health applications

Analysis and Decision-Making with Social Media

abstract: The rapid advancements of technology have greatly extended the ubiquitous nature of smartphones acting as a gateway to numerous social media applications. This brings an immense convenience to the users of these applications wishing to stay connected to other individuals through sharing their statuses, posting their opinions, experiences, suggestions, etc on online social networks (OSNs). Exploring and analyzing this data has a great potential to enable deep and fine-grained insights into the behavior, emotions, and language of individuals in a society. This proposed dissertation focuses on utilizing these online social footprints to research two main threads – 1) Analysis: to study the behavior of individuals online (content analysis) and 2) Synthesis: to build models that influence the behavior of individuals offline (incomplete action models for decision-making). A large percentage of posts shared online are in an unrestricted natural language format that is meant for human consumption. One of the demanding problems in this context is to leverage and develop approaches to automatically extract important insights from this incessant massive data pool. Efforts in this direction emphasize mining or extracting the wealth of latent information in the data from multiple OSNs independently. The first thread of this dissertation focuses on analytics to investigate the differentiated content-sharing behavior of individuals. The second thread of this dissertation attempts to build decision-making systems using social media data. The results of the proposed dissertation emphasize the importance of considering multiple data types while interpreting the content shared on OSNs. They highlight the unique ways in which the data and the extracted patterns from text-based platforms or visual-based platforms complement and contrast in terms of their content. The proposed research demonstrated that, in many ways, the results obtained by focusing on either only text or only visual elements of content shared online could lead to biased insights. On the other hand, it also shows the power of a sequential set of patterns that have some sort of precedence relationships and collaboration between humans and automated planners.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Emergent Forms of Online Sociality in Disasters Arising from Natural Hazards

Disasters arising from natural hazards are associated with breakdown of existing structures, but they also result in creation of new social ties in the process of self-organization and problem solving by those affected. This dissertation focuses on emergent forms of sociality that arise in the context of crisis. Specifically, it considers collaborative work practices, social network structures, and organizational forms that emerge on social media during disasters arising from natural hazards. Social media platforms support highly-distributed social environments, and the forms of sociality that emerge in these contexts are affected by the affordances of their technical features, especially those that more or less successfully facilitate the creation of a shared information space. Thus, this dissertation is organized around two important aspects of social media spaces: the availability of an explicitly-shared site of work and the availability of a visible, legible record of activity.This dissertation investigates the forms of sociality that emerge during disasters in three social media activities: retweeting, crisis mapping in OpenStreetMap (OSM), and Twitter reply conversations. These three social media activities highlight various availability of an explicitly-shared site of work and visible record of activity. The studies of retweeting and reply conversations investigate the Twitter activity in response to the 2012 Hurricane Sandy—the second costliest hurricane in US history and the most tweeted about event to date at the time. Analysis of crisis mapping in OpenStreetMap—an open, editable, volunteer-based map of the world—focuses on the OSM activity after the 2010 Haiti earthquake, which was the first major disaster event supported by OpenStreetMap. For these investigations, the dissertation elaborates and develops human-centered data science methods—a set of methodological approaches that both harness the power of computational techniques and account for the highly-situated nature of the social activity in crisis. Finally, the dissertation positions the findings from the three studies within the larger context of high-tempo, high-volume social media activity and highlights how the framework of the two intersecting dimensions of the shared information space reveals larger patterns within the emergent forms of sociality across contexts