EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

Crisis Analytics: Big Data Driven Crisis Response

Disasters have long been a scourge for humanity. With the advances in technology (in terms of computing, communications, and the ability to process and analyze big data), our ability to respond to disasters is at an inflection point. There is great optimism that big data tools can be leveraged to process the large amounts of crisis-related data (in the form of user generated data in addition to the traditional humanitarian data) to provide an insight into the fast-changing situation and help drive an effective disaster response. This article introduces the history and the future of big crisis data analytics, along with a discussion on its promise, challenges, and pitfalls

A Citizen Observatory Approach for Developing a Disease Outbreak Early Warning System

In health matters, early warning systems are timely surveillance systems that collect information on epidemic-prone diseases to trigger prompt public health interventions. However, these systems rarely apply statistical methods to detect changes in trends or sentinel events that would require intervention. Often, they rely on an in-depth review done by epidemiologists of the data coming in, which is rarely done systematically. This research introduced the use of ICT for collecting and analyzing citizen observations on disease trends and outbreaks. A citizen observatory ICT tool, which utilizes mobile and web features was developed. Data was collected on symptoms observed from diseases in four locations within Nairobi city. The system made use of mathematical models and outlier detection techniques to detect observations that deviated from the expected pattern in the dataset. New clusters were considered as outliers and the system flagged them as potential outbreaks. We clustered data using a K-Means algorithm and the Euclidean distance of each object from its corresponding cluster centre was obtained. From the results, the developed prototype was able to detect an outbreak of Flu and URTI diseases for the period of study. The proposed tool can therefore enhance the management of risks associated with disease outbreaks.  Keywords: Early Warning Systems, citizen observatory, health surveillance, Outlier Detection, Modeling disease outbreak

When Infodemic Meets Epidemic: a Systematic Literature Review

Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts. Social media offer significant amounts of data that can be leveraged for bio-surveillance. They also provide a platform to quickly and efficiently reach a sizeable percentage of the population, hence their potential impact on various aspects of epidemic mitigation. The general objective of this systematic literature review is to provide a methodical overview of the integration of social media in different epidemic-related contexts. Three research questions were conceptualized for this review, resulting in over 10000 publications collected in the first PRISMA stage, 129 of which were selected for inclusion. A thematic method-oriented synthesis was undertaken and identified 5 main themes related to social media enabled epidemic surveillance, misinformation management, and mental health. Findings uncover a need for more robust applications of the lessons learned from epidemic post-mortem documentation. A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. Harnessing the full potential of social media in epidemic related tasks requires streamlining the results of epidemic forecasting, public opinion understanding and misinformation propagation, all while keeping abreast of potential mental health implications. Pro-active prevention has thus become vital for epidemic curtailment and containment

Spatial and Temporal Analysis of SARS-CoV-2 in Sewer Network in Reno-Sparks Metropolitan Area

COVID-19 disease, caused by SARS-COV-2 virus, strained public health entities and communities, which faced unprecedented challenges in implementing clinical testing to monitor and inform public on the spread of the disease. In the early stages of the pandemic, researchers indicted that the virus is present in wastewater and infected individuals shed the genetic material of SARS-CoV-2 virus in their feces. Researchers all around the world implemented wastewater-based epidemiology (WBE) as a tool to monitor wastewater, which provides an efficient pooled community sample and may predict COVID-19 occurrence. Even though wastewater surveillance has been in practice for decades, the novel area of WBE research for COVID-19 is based on the exploration of the potential to provide an integrated, community-level indication of the presence of COVID-19. In this study, we implemented WBE with geospatial analysis using Geographic Information System (GIS). The study also identified statistically significant spatial patterns of SARS-CoV-2 in wastewater through spatial sampling strategy across neighborhood-scale sewershed catchments in the Truckee Meadows Water Reclamation Facility service area. Using GIS technology of local spatial autocorrelation and directional distribution methods, wastewater surveillance at a more granular level provided greater sensitivity for detecting clusters, outlier, hot spots, and cold spots through the sampling campaign of sewer network

Chinese social media reaction to the MERS-CoV and avian influenza A(H7N9) outbreaks

BACKGROUND: As internet and social media use have skyrocketed, epidemiologists have begun to use online data such as Google query data and Twitter trends to track the activity levels of influenza and other infectious diseases. In China, Weibo is an extremely popular microblogging site that is equivalent to Twitter. Capitalizing on the wealth of public opinion data contained in posts on Weibo, this study used Weibo as a measure of the Chinese people's reactions to two different outbreaks: the 2012 Middle East Respiratory Syndrome Coronavirus (MERS-CoV) outbreak, and the 2013 outbreak of human infection of avian influenza A(H7N9) in China. METHODS: Keyword searches were performed in Weibo data collected by The University of Hong Kong's Weiboscope project. Baseline values were determined for each keyword and reaction values per million posts in the days after outbreak information was released to the public. RESULTS: The results show that the Chinese people reacted significantly to both outbreaks online, where their social media reaction was two orders of magnitude stronger to the H7N9 influenza outbreak that happened in China than the MERS-CoV outbreak that was far away from China. CONCLUSIONS: These results demonstrate that social media could be a useful measure of public awareness and reaction to disease outbreak information released by health authorities.published_or_final_versio

The Promise and Peril of Big Data

The Promise and Peril of Big Data explores the implications of inferential technologies used to analyze massive amounts of data and the ways in which these techniques can positively affect business, medicine, and government. The report is the result of the Eighteenth Annual Roundtable on Information Technology

Measuring internet activity: a (selective) review of methods and metrics

Two Decades after the birth of the World Wide Web, more than two billion people around the world are Internet users. The digital landscape is littered with hints that the affordances of digital communications are being leveraged to transform life in profound and important ways. The reach and influence of digitally mediated activity grow by the day and touch upon all aspects of life, from health, education, and commerce to religion and governance. This trend demands that we seek answers to the biggest questions about how digitally mediated communication changes society and the role of different policies in helping or hindering the beneficial aspects of these changes. Yet despite the profusion of data the digital age has brought upon us—we now have access to a flood of information about the movements, relationships, purchasing decisions, interests, and intimate thoughts of people around the world—the distance between the great questions of the digital age and our understanding of the impact of digital communications on society remains large. A number of ongoing policy questions have emerged that beg for better empirical data and analyses upon which to base wider and more insightful perspectives on the mechanics of social, economic, and political life online. This paper seeks to describe the conceptual and practical impediments to measuring and understanding digital activity and highlights a sample of the many efforts to fill the gap between our incomplete understanding of digital life and the formidable policy questions related to developing a vibrant and healthy Internet that serves the public interest and contributes to human wellbeing. Our primary focus is on efforts to measure Internet activity, as we believe obtaining robust, accurate data is a necessary and valuable first step that will lead us closer to answering the vitally important questions of the digital realm. Even this step is challenging: the Internet is difficult to measure and monitor, and there is no simple aggregate measure of Internet activity—no GDP, no HDI. In the following section we present a framework for assessing efforts to document digital activity. The next three sections offer a summary and description of many of the ongoing projects that document digital activity, with two final sections devoted to discussion and conclusions