360 Quantified Self

Wearable devices with a wide range of sensors have contributed to the rise of the Quantified Self movement, where individuals log everything ranging from the number of steps they have taken, to their heart rate, to their sleeping patterns. Sensors do not, however, typically sense the social and ambient environment of the users, such as general life style attributes or information about their social network. This means that the users themselves, and the medical practitioners, privy to the wearable sensor data, only have a narrow view of the individual, limited mainly to certain aspects of their physical condition. In this paper we describe a number of use cases for how social media can be used to complement the check-up data and those from sensors to gain a more holistic view on individuals' health, a perspective we call the 360 Quantified Self. Health-related information can be obtained from sources as diverse as food photo sharing, location check-ins, or profile pictures. Additionally, information from a person's ego network can shed light on the social dimension of wellbeing which is widely acknowledged to be of utmost importance, even though they are currently rarely used for medical diagnosis. We articulate a long-term vision describing the desirable list of technical advances and variety of data to achieve an integrated system encompassing Electronic Health Records (EHR), data from wearable devices, alongside information derived from social media data.Comment: QCRI Technical Repor

Epidemiological Prediction using Deep Learning

Department of Mathematical SciencesAccurate and real-time epidemic disease prediction plays a significant role in the health system and is of great importance for policy making, vaccine distribution and disease control. From the SIR model by Mckendrick and Kermack in the early 1900s, researchers have developed a various mathematical model to forecast the spread of disease. With all attempt, however, the epidemic prediction has always been an ongoing scientific issue due to the limitation that the current model lacks flexibility or shows poor performance. Owing to the temporal and spatial aspect of epidemiological data, the problem fits into the category of time-series forecasting. To capture both aspects of the data, this paper proposes a combination of recent Deep Leaning models and applies the model to ILI (influenza like illness) data in the United States. Specifically, the graph convolutional network (GCN) model is used to capture the geographical feature of the U.S. regions and the gated recurrent unit (GRU) model is used to capture the temporal dynamics of ILI. The result was compared with the Deep Learning model proposed by other researchers, demonstrating the proposed model outperforms the previous methods.clos

Designing Effective Messages to Promote Future Zika Vaccine Uptake

The Zika virus is associated with the devastating birth defect microcephaly, and while a vaccine was not yet available in early-2017, several were under development. It is imperative to identify effective communication strategies to promote uptake of a new vaccine, particularly among women of reproductive age. Moreover, though the Zika outbreak has received much social media attention, little is known about these conversations on Instagram. The purpose of this dissertation, therefore, was to understand current Zika-focused communication on Instagram and to inform effective communication strategies to promote future Zika vaccine uptake intent. The study aims were: (1) explore Zika conversations on Instagram; (2) determine effective message characteristics to increase Zika vaccine uptake intent; and (3) explore salient demographic, healthcare, and psychosocial factors related to Zika vaccine uptake intent. A content analysis of 1,000 Zika-focused Instagram posts, found that these messages primarily focus on perceived threat constructs, yet they elicited little engagement. In addition, 10% of all Instagram posts mentioned conspiracy theories, and these messages elicited high engagement. A 2x2 online experiment tested the effect of message framing and visual type on Zika vaccine uptake intent. The 339 participants – all women of reproductive age – each were exposed to one of four messages (gain vs. loss-framed, and infographic vs. photo). There was no interaction effect of framing and visual type (p=.116), nor main effect of either framing (p=.185) or visual type (p=.724) on vaccine uptake intent. When testing the effect of these variables on those known to be predictors of behavioral intent, gain-framed messages were associated with higher subjective norms, perceived benefits, and self-efficacy. Data from the same online survey was used to examine whether demographics, healthcare-related variables, and psychosocial variables predict Zika vaccine uptake intent. Attitude (p\u3c.001), subjective norms (p=.002), perceived benefits (p=.001), self-efficacy (p=.031), perceived susceptibility (p=.030), and cues to action (p=.020) were predictive of higher Zika vaccine uptake intent, as was being African-American (p=.042). In summary, messages promoting the Zika vaccine should be designed to complement the high perceived threat of Zika while activating positive social norms and perceived benefits in order to allow the public to respond efficaciously

Infodemiology and Infoveillance: Scoping Review

Background: Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade. Objective: The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research. Methods: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment. Results: Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%). Conclusions: The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research