Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19

Forecasting influenza in a timely manner aids health organizations and policymakers in adequate preparation and decision making. However, effective influenza forecasting still remains a challenge despite increasing research interest. It is even more challenging amidst the COVID pandemic, when the influenza-like illness (ILI) counts are affected by various factors such as symptomatic similarities with COVID-19 and shift in healthcare seeking patterns of the general population. Under the current pandemic, historical influenza models carry valuable expertise about the disease dynamics but face difficulties adapting. Therefore, we propose CALI-Net, a neural transfer learning architecture which allows us to 'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our framework enables this adaptation by automatically learning when it should emphasize learning from COVID-related signals and when it should learn from the historical model. Thus, we exploit representations learned from historical ILI data as well as the limited COVID-related signals. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic. In addition, we show that success in our primary goal, adaptation, does not sacrifice overall performance as compared with state-of-the-art influenza forecasting approaches.Comment: Appears in AAAI-2

Triaging Content Severity in Online Mental Health Forums

Mental health forums are online communities where people express their issues and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self-harm. Moderators need to respond to these severe posts in a timely manner to prevent potential self-harm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We present a framework for triaging user content into four severity categories which are defined based on indications of self-harm ideation. Our models are based on a feature-rich classification framework which includes lexical, psycholinguistic, contextual and topic modeling features. Our approaches improve the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F-1 scores). Using the proposed model, we analyze the mental state of users and we show that overall, long-term users of the forum demonstrate a decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need.Comment: Accepted for publication in Journal of the Association for Information Science and Technology (2017

Using large-scale syndromic datasets to support epidemiology and surveillance

Using large-scale syndromic datasets to support epidemiology and surveillance Healthcare and the healthcare industry have traditionally produced huge amounts of data and information; patient care necessitates accurate record keeping, records of attendances and often details of the reason for contact with healthcare and outcomes.7 During the past decade, there has been a dramatic shift to digitize healthcare related information, with a view to both increasing efficiencies in these areas, and to generate new insights.8 These rich, but often unstructured data sources can present both opportunities and challenges to data scientists and epidemiologists. Syndromic surveillance (SS) is the real-time (or near real-time) collection, analysis, interpretation, and dissemination of health-related data to enable the early identification of the impact (or absence of impact) of potential human or veterinary public-health threats which require effective public-health action.9 In England, Public Health England (PHE) coordinates a suite of national real-time syndromic surveillance systems. Underpinning their operation is the collation, analysis and interpretation of large-scale datasets (“big data”). This PhD by Published Works describes work which has evaluated, developed or utilised a number of these large healthcare datasets for both surveillance and epidemiology of public health events. The thesis is divided into four themes covering critical aspects of SS. Firstly, developing SS systems using novel data sources; something which is currently under-reported in the literature. Secondly, using syndromic data systems for non-infectious disease epidemiology; understanding how these systems can inform public health insight and action outside of their original remit. Thirdly, determining the utility in identifying outbreaks which was one of the original envisioned purposes of SS, using gastrointestinal illness (GI) as a case-study. The final theme is understanding how SS is used in the context of mass gatherings; again, a key original aspect of syndromic surveillance. The thesis collates a portfolio of indexed works, all of which use (combined with other data sources) large, health-related data collated and operated by the PHE Real-Time Syndromic Surveillance Team (ReSST) and employ a range of different methodologies to translate data into public health action. These include describing the development of a novel system, observational studies and time series analysis. Key findings from the papers include; learning how to develop these systems, demonstration of their utility in non-infectious disease epidemiology, leading to new insights into the socio-demographic distribution and causes of presentations to healthcare with Allergic Rhinitis, understanding the challenges and limitations of syndromic surveillance in identifying outbreaks of GI disease and how they can be used during mass gatherings. Using diverse methodologies and data as a collective, the papers have led to significant public health impacts; both in terms of how these systems are used in England currently and how they have influenced global development of this small but growing specialit

Comparative evaluation of methods that adjust for reporting biases in participatory surveillance systems

Over the past decade the widespread proliferation of mobile devices and wearable technology has significantly changed the landscape of epidemiological data gathering and evolved into a field known as Digital Epidemiology. One source of active digital data collection is online participatory syndromic surveillance systems. These systems actively engage the general public in reporting health-related information and provide timely information about disease trends within the community. This dissertation comprehensively addresses how researchers can effectively use this type of data to answer questions about Influenza-like Illness (ILI) disease burden in the general population. We assess the representativeness and reporting habits of volunteers for these systems and use this information to develop statistically rigorous methods that adjust for potential biases. Specifically, we evaluate how different missing data methods, such as complete case and multiple imputation models, affect estimates of ILI disease burden using both simulated data as well as data from the Australian system, Flutracking.net. We then extend these methods to data from the American system, Flu Near You, which has different patterns. Finally, we provide examples of how this data has been used to answer questions about ILI in the general community and promote better understanding of disease surveillance and data literacy among volunteers

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