research-article

Human network data collection in the wild: the epidemiological utility of micro-contact and location data

Authors:

Mohammad S. Hashemian,

Kevin G. Stanley,

Dylan L. Knowles,

Jonathan Calver,

Nathaniel D. OsgoodAuthors Info & Claims

IHI '12: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

Pages 255 - 264

https://doi.org/10.1145/2110363.2110394

Published: 28 January 2012 Publication History

Abstract

Contagions - either pathogens spread through contact networks or societal memes spread through social networks - impact the occurrence and character of both epidemic and endemic diseases. While computational models explore disease parameters in the context of a given contact network, these models are always subject to the caveat that reality may not be consistent with the simplified assumptions regarding contact, contagion or network structure. More - and more accurate - data on the contact dynamics between people and places could alleviate some uncertainties, and make models more robust tools for policy-makers and researchers. Properly applied, consumer electronics can serve as a valuable source of this data. Using smartphones as sensor platforms rather than personal communications devices, it is possible to record high fidelity information on a participant's location, activity level, and contacts between both people and places. This paper describes the design, architecture and a preliminary deployment of a general smartphone-based epidemiological data collection system. The dataset, gathered over one month, contains over 45 million records related to the behavioral patterns of 39 participants. We provide an initial analysis of aggregate level statistics to demonstrate the power and scope of the technique for capturing relevant data. Demonstrating the potential for such data to inform decision-making, we further perform an agent-based simulation of a flu-like illness that uses the dataset to capture aspects of both person-person and environmental (place-person) transmission. We demonstrate that the data collection is possible, valuable, and scalable and that the data can be leveraged to inform detailed models capturing more complex physical interactions than were previously feasible.

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https://doi.org/10.1371/journal.pcbi.1010917
Brodersen KHammami NKatapally T(2022)Smartphone Use and Mental Health among Youth: It Is Time to Develop Smartphone-Specific Screen Time GuidelinesYouth10.3390/youth20100032:1(23-38)Online publication date: 7-Feb-2022
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Index Terms

Human network data collection in the wild: the epidemiological utility of micro-contact and location data
1. Applied computing
  1. Life and medical sciences

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cover image ACM Conferences

IHI '12: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

January 2012

914 pages

ISBN:9781450307819

DOI:10.1145/2110363

General Chairs:
Gang Luo
IBM Research, USA
,
Jiming Liu
Hong Kong Baptist University
,
Program Chair:
Christopher C. Yang
Drexel University

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 28 January 2012

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IHI '12: ACM International Health Informatics Symposium

January 28 - 30, 2012

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https://doi.org/10.1371/journal.pcbi.1010917
Brodersen KHammami NKatapally T(2022)Smartphone Use and Mental Health among Youth: It Is Time to Develop Smartphone-Specific Screen Time GuidelinesYouth10.3390/youth20100032:1(23-38)Online publication date: 7-Feb-2022
https://doi.org/10.3390/youth2010003
Ou JBuskens Vvan de Rijt APanja D(2022)Influence maximization under limited network information: seeding high-degree neighborsJournal of Physics: Complexity10.1088/2632-072X/ac94443:4(045004)Online publication date: 28-Oct-2022
https://doi.org/10.1088/2632-072X/ac9444
Nguyen CSaputra YVan Huynh NNguyen NKhoa TTuan BNguyen DHoang DVu TDutkiewicz EChatzinotas SOttersten B(2020)A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open IssuesIEEE Access10.1109/ACCESS.2020.30181248(154209-154236)Online publication date: 2020
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Fragoso LPaul TVadan FStanley KBell SOsgood N(2019)Intrinsic dimensionality of human behavioral activity dataPLOS ONE10.1371/journal.pone.021896614:6(e0218966)Online publication date: 27-Jun-2019
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