Modeling Individual-Level Infection Dynamics Using Social Network Information

Tuarob, Suppawong; Tucker, Conrad S.; Salathé, Marcel; Ram, Nilam

doi:10.1145/2806416.2806575

Cited by 15 publications

(3 citation statements)

References 39 publications

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“…Although personalized medicine that provides treatment specific to each individual genome has been implemented [108], psychological practitioners could use our algorithms along with other auxiliary information such as social networks [109] to generate a mental profile for each mental patient so that personalized psychotherapeutic treatment could be designed.…”

Section: Limitations and Potential Applicationsmentioning

confidence: 99%

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information

Tuarob

Tucker

Kumara

et al. 2017

Journal of Biomedical Informatics

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It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future time periods could prove critical to healthcare practitioners. Currently, the practical way to predict an individual’s mental state is through mental examinations that involve psychological experts performing the evaluations. However, such methods can be time and resource consuming, mitigating their broad applicability to a wide population. Furthermore, some individuals may also be unaware of their mental states or may feel uncomfortable to express themselves during the evaluations. Hence, their anomalous mental states could remain undetected for a prolonged period of time. The objective of this work is to demonstrate the ability of using advanced machine learning based approaches to generate mathematical models that predict current and future mental states of an individual. The problem of mental state prediction is transformed into the time series forecasting problem, where an individual is represented as a multivariate time series stream of monitored physical and behavioral attributes. A personalized mathematical model is then automatically generated to capture the dependencies among these attributes, which is used for prediction of mental states for each individual. In particular, we first illustrate the drawbacks of traditional multivariate time series forecasting methodologies such as vector autoregression. Then, we show that such issues could be mitigated by using machine learning regression techniques which are modified for capturing temporal dependencies in time series data. A case study using the data from 150 human participants illustrates that the proposed machine learning based forecasting methods are more suitable for high-dimensional psychological data than the traditional vector autoregressive model in terms of both magnitude of error and directional accuracy. These results not only present a successful usage of machine learning techniques in psychological studies, but also serve as a building block for multiple medical applications that could rely on an automated system to gauge individuals’ mental states.

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Section: Limitations and Potential Applicationsmentioning

confidence: 99%

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information

Tuarob

Tucker

Kumara

et al. 2017

Journal of Biomedical Informatics

Self Cite

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“…1) Data Collection and Information Distillation: One of the fundamental steps for tracing contacts in CovidTrak is the acquisition and filtration of raw signals from social data platforms. Several recent studies have proposed real-time data crawler engines for querying social media data (e.g., Tweets or Facebook posts) that impart experiences and encounters of people with other individuals (e.g., posts implying inperson meetings between a group of coworkers) [67], [68]. However, a few difficulties prevail in collecting the relevant posts that indicate in-person encounters between infected and uninfected individuals within appropriate time windows.…”

Section: B Architectural Overviewmentioning

confidence: 99%

CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact Tracing

Rashid

Wang

2023

IEEE Trans. Comput. Soc. Syst.

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With the proliferation of smart devices and widespread Internet connectivity, social sensing is advancing as a pervasive sensing paradigm where experiences shared by individuals on social platforms (e.g., Twitter and Facebook) are analyzed to interpret the physical world. In this article, we introduce CovidTrak, a vision of social intelligence-empowered contact tracing that aims to scrutinize the knowledge derived using social sensing to track Coronavirus Disease 2019 (COVID-19) infections among the general public. Contact tracing is known to be an effective technique for detecting and monitoring persons who may have been exposed to individuals infected with any communicable disease. While a good number of contact tracing schemes are existent today (e.g., in-person and phone interviews, paper forms, email and web-based questionnaires, and smartphone apps), they often require active user participation and might miss certain cases of social interactions that go off-the-records but still lead to COVID-19 transmission. By contrast, social sensing provides an alternative avenue for spontaneously determining such contacts by harnessing the rich experiences and information conveyed by people on social data platforms (e.g., a group photograph tweeted from a house party with a potential contact). As such, CovidTrak can form a powerful basis to combat the COVID-19 pandemic. The vision of CovidTrak intends to answer the following questions: 1) how to bolster the privacy and security of the online users while determining their contacts? 2) how to collect relevant social signals that indicate in-person encounters among people? 3) how to reliably process the vast amount of noisy data from social platforms to identify chains of transmission? 4) how to handle the scarcity of location metadata in the incoming data? 5) how to effectively communicate crucial contact information to concerned individuals? and 6) how to model and handle the responses of the common people toward contact information? We envision unexplored opportunities to leverage multidisciplinary techniques to address the above questions and develop effective future CovidTrak schemes.

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“…In this subsection, technologies used to process social media data that go beyond just keyword detection (which works only on explicit data) are reviewed. Multiple studies in the Information Retrieval field have agreed that it is necessary to develop special text processing techniques for social media messages, since they are different from traditional documents due to smaller textual content, heterogeneous language standards, and higher level of noise [25][26][27][28][29].…”

Section: Natural Language Technology For Semantic Interpretation In Smentioning

confidence: 99%

Automated Discovery of Product Feature Inferences Within Large-Scale Implicit Social Media Data

Tuarob¹,

Lim

Tucker

2018

Journal of Computing and Information Science in Engineering

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Recently, social media has emerged as an alternative, viable source to extract large-scale, heterogeneous product features in a time and cost-efficient manner. One of the challenges of utilizing social media data to inform product design decisions is the existence of implicit data such as sarcasm, which accounts for 22.75% of social media data, and can potentially create bias in the predictive models that learn from such data sources. For example, if a customer says “I just love waiting all day while this song downloads,” an automated product feature extraction model may incorrectly associate a positive sentiment of “love” to the cell phone's ability to download. While traditional text mining techniques are designed to handle well-formed text where product features are explicitly inferred from the combination of words, these tools would fail to process these social messages that include implicit product feature information. In this paper, we propose a method that enables designers to utilize implicit social media data by translating each implicit message into its equivalent explicit form, using the word concurrence network. A case study of Twitter messages that discuss smartphone features is used to validate the proposed method. The results from the experiment not only show that the proposed method improves the interpretability of implicit messages, but also sheds light on potential applications in the design domains where this work could be extended.

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Modeling Individual-Level Infection Dynamics Using Social Network Information

Cited by 15 publications

References 39 publications

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information

CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact Tracing

Automated Discovery of Product Feature Inferences Within Large-Scale Implicit Social Media Data

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