research-article

Airborne Disease Propagation on Large Scale Social Contact Networks

Authors:

Reza Arablouei,

Kanchana Thilakarathna,

Bernard MansAuthors Info & Claims

SocialSens'17: Proceedings of the 2nd International Workshop on Social Sensing

Pages 35 - 40

https://doi.org/10.1145/3055601.3055604

Published: 18 April 2017 Publication History

Abstract

Social sensing has received growing interest in a broad range of applications from business to health care. The potential benefits of modeling infectious disease spread through geo-tagged social sensing data has recently been demonstrated, yet it has not considered contagion events that can occur even when co-located individuals are no longer in physical contact, such as for capturing the dynamics of airborne diseases. In this study, we exploit the location updates made by 0.6 million users of the Momo social networking application to characterize airborne disease dynamics. Airborne diseases can transmit through infectious particles exhaled by the infected individuals. We introduce the concept of same-place different-time (SPDT) transmission to capture the persistent effect of airborne particles in their likelihood to spread a disease. Because the survival duration of these infectious particles is dependent on environmental conditions, we investigate through large-scale simulations the effects of three parameters on SPDT-based disease diffusion: the air exchange rate in the proximity of infected individuals, the infectivity decay rates of pathogen particles, and the infection probability of inhaled particles. Our results confirm a complex interplay between the underlying contact network dynamics and these parameters, and highlight the predictive potential of social sensing for epidemic outbreaks.

References

[1]

Charu C Aggarwal and Tarek Abdelzaher. 2013. Social sensing. In Managing and mining sensor data. Springer, 237--297.

[2]

Linus Bengtsson, Jean Gaudart, Xin Lu, Sandra Moore, Erik Wetter, Kankoe Sallah, Stanislas Rebaudet, and Renaud Piarroux. 2015. Using mobile phone data to predict the spatial spread of cholera. Scientific reports 5 (2015), 8923.

[3]

Todd Bodnar and Marcel Salathé. 2013. Validating models for disease detection using twitter. In Proceedings of the 22nd International Conference on World Wide Web. ACM, 699--702.

Digital Library

[4]

Terence Chen, Mohamed Ali Kaafar, and Roksana Boreli. 2013. The Where and When of Finding New Friends: Analysis of a Location based Social Discovery Network. In ICWSM.

[5]

Gerardo Chowell, Santiago Echevarría-Zuno, Cecile Viboud, Lone Simonsen, James Tamerius, Mark A Miller, and Víctor H Borja-Aburto. 2011. Characterizing the epidemiology of the 2009 influenza A/H1N1 pandemic in Mexico. PLoS Med 8, 5 (2011), e1000436.

[6]

Liuliu Du, Stuart Batterman, Christopher Godwin, Jo-Yu Chin, Edith Parker, Michael Breen, Wilma Brakefield, Thomas Robins, and Toby Lewis. 2012. Air change rates and interzonal flows in residences, and the need for multi-zone models for exposure and health analyses. International journal of environmental research and public health 9, 12 (2012), 4639--4661.

[7]

Aaron Fernstrom and Michael Goldblatt. 2013. Aerobiology and its role in the transmission of infectious diseases. Journal of pathogens 2013 (2013).

[8]

Lauren Gardner and Sahotra Sarkar. 2013. A global airport-based risk model for the spread of dengue infection via the air transport network. PloS one 8, 8 (2013), e72129.

[9]

Zhuyang Han, Wenguo Weng, Quanyi Huang, and Shaobo Zhong. 2014. A Risk Estimation Method for Airborne Infectious Diseases Based on Aerosol Transmission in Indoor Environment. In Proceedings of the World Congress on Engineering, Vol. 2.

[10]

Chunlin Huang, Xingwu Liu, Shiwei Sun, Shuai Cheng Li, Minghua Deng, Guangxue He, Haicang Zhang, Chao Wang, Yang Zhou, Yanlin Zhao, and others. 2016. Insights into the transmission of respiratory infectious diseases through empirical human contact networks. Scientific Reports 6 (2016).

[11]

Raja Jurdak, Kun Zhao, Jiajun Liu, Maurice AbouJaoude, Mark Cameron, and David Newth. 2015. Understanding human mobility from Twitter. PloS one 10, 7 (2015), e0131469.

[12]

Matt J Keeling and Pejman Rohani. 2008. Modeling infectious diseases in humans and animals. Princeton University Press.

[13]

Sheng Li, Joseph NS Eisenberg, Ian H Spicknall, and James S Koopman. 2009. Dynamics and control of infections transmitted from person to person through the environment. American journal of epidemiology 170, 2 (2009), 257--265.

[14]

William G Lindsley, Francoise M Blachere, Robert E Thewlis, Abhishek Vishnu, Kristina A Davis, Gang Cao, Jan E Palmer, Karen E Clark, Melanie A Fisher, Rashida Khakoo, and others. 2010. Measurements of airborne influenza virus in aerosol particles from human coughs. PloS one 5, 11 (2010), e15100.

[15]

Robert G Loudon and Linda C Brown. 1967. Cough Frequency in Patients with Respiratory Disease 1, 2. American Review of Respiratory Disease 96, 6 (1967), 1137--1143.

[16]

Anna Machens, Francesco Gesualdo, Caterina Rizzo, Alberto E Tozzi, Alain Barrat, and Ciro Cattuto. 2013. An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices. BMC infectious diseases 13, 1 (2013), 185.

[17]

Thomas O Richardson and Thomas E Gorochowski. 2015. Beyond contact-based transmission networks: the role of spatial coincidence. Journal of The Royal Society Interface 12, 111 (2015), 20150705.

[18]

Shanshan Shi, Chen Chen, and Bin Zhao. 2015. Air infiltration rate distributions of residences in Beijing. Building and Environment 92 (2015), 528--537.

[19]

Juliette Stehlé, Nicolas Voirin, Alain Barrat, Ciro Cattuto, Vittoria Colizza, Lorenzo Isella, Corinne Régis, Jean-François Pinton, Nagham Khanafer, Wouter Van den Broeck, and others. 2011. Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees. BMC medicine 9, 1 (2011), 87.

[20]

GN Sze To and CYH Chao. 2010. Review and comparison between the Wells-Riley and dose-response approaches to risk assessment of infectious respiratory diseases. Indoor Air 20, 1 (2010), 2--16.

[21]

GN Sze To, MP Wan, CYH Chao, F Wei, SCT Yu, and JKC Kwan. 2008. A methodology for estimating airborne virus exposures in indoor environments using the spatial distribution of expiratory aerosols and virus viability characteristics. Indoor air 18, 5 (2008), 425--438.

[22]

Andrew J Tatem, Zhuojie Huang, Clothilde Narib, Udayan Kumar, Deepika Kandula, Deepa K Pindolia, David L Smith, Justin M Cohen, Bonita Graupe, Petrina Uusiku, and others. 2014. Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning. Malaria journal 13, 1 (2014), 52.

[23]

Kanchana Thilakarathna, Suranga Seneviratne, Kamal Gupta, Mohamed Ali Kaafar, and Aruna Seneviratne. 2016. A deep dive into location-based communities in social discovery networks. Computer Communications (2016).

[24]

Jianjian Wei and Yuguo Li. 2015. Enhanced spread of expiratory droplets by turbulence in a cough jet. Building and Environment 93 (2015), 86--96.

[25]

S Yin, GN Sze-To, and Christopher YH Chao. 2012. Retrospective analysis of multi-drug resistant tuberculosis outbreak during a flight using computational fluid dynamics and infection risk assessment. Building and environment 47 (2012), 50--57.

Cited By

Shahzamal MMans BHoog FPaini DJurdak R(2020)Vaccination strategies on dynamic networks with indirect transmission links and limited contact informationPLOS ONE10.1371/journal.pone.024161215:11(e0241612)Online publication date: 12-Nov-2020
https://doi.org/10.1371/journal.pone.0241612
Agarwal RBanerjee A(2020)Infection Risk ScoreProceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-1910.1145/3423459.3430754(1-10)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3423459.3430754
Jeong SKuk SKim H(2019)A Smartphone Magnetometer-Based Diagnostic Test for Automatic Contact Tracing in Infectious Disease EpidemicsIEEE Access10.1109/ACCESS.2019.2895075(1-1)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2895075
Show More Cited By

Recommendations

Social network analysis for better understanding of influenza
Graphical abstract

Display Omitted
Highlights
- Heatmaps show spatial distribution of flu hospitalizations in NY state from 2003 to 2012.
Abstract Introduction
The objective of this study is to improve the understanding of spatial spreading of complicated cases of influenza that required hospitalizations, by creating heatmaps and social networks. They will allow to ...
Analysis of Disease Comorbidity Patterns in a Large-Scale China Population
Intelligent Computing Theories and Application
Abstract
Background: Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set.
Materials and ...
Extracting Signals from Social Media for Chronic Disease Surveillance
DH '16: Proceedings of the 6th International Conference on Digital Health Conference

Asthma is a chronic disease that affects people of all ages, and is a serious health and economic concern worldwide. However, accurate and timely surveillance and predicting hospital visits could allow for targeted interventions and reduce the societal ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SocialSens'17: Proceedings of the 2nd International Workshop on Social Sensing

April 2017

97 pages

ISBN:9781450349772

DOI:10.1145/3055601

Copyright © 2017 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]

Sponsors

IEEE Signal Processing Society
SIGBED: ACM Special Interest Group on Embedded Systems
IEEE CS

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

CPS Week '17

Sponsor:

SIGBED

CPS Week '17: Cyber Physical Systems Week 2017

April 18 - 21, 2017

PA, Pittsburgh, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
196
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Shahzamal MMans BHoog FPaini DJurdak R(2020)Vaccination strategies on dynamic networks with indirect transmission links and limited contact informationPLOS ONE10.1371/journal.pone.024161215:11(e0241612)Online publication date: 12-Nov-2020
https://doi.org/10.1371/journal.pone.0241612
Agarwal RBanerjee A(2020)Infection Risk ScoreProceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-1910.1145/3423459.3430754(1-10)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3423459.3430754
Jeong SKuk SKim H(2019)A Smartphone Magnetometer-Based Diagnostic Test for Automatic Contact Tracing in Infectious Disease EpidemicsIEEE Access10.1109/ACCESS.2019.2895075(1-1)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2895075
Shahzamal MJurdak RMans Bde Hoog F(2019)Indirect interactions influence contact network structure and diffusion dynamicsRoyal Society Open Science10.1098/rsos.1908456:8(190845)Online publication date: 28-Aug-2019
https://doi.org/10.1098/rsos.190845
Kuk SKim JPark YKim H(2018)Empirical Determination of Efficient Sensing Frequencies for Magnetometer-Based Continuous Human Contact MonitoringSensors10.3390/s1805135818:5(1358)Online publication date: 27-Apr-2018
https://doi.org/10.3390/s18051358
Abbas KShang MAbbasi ALuo XXu JZhang Y(2018)Popularity and Novelty Dynamics in Evolving NetworksScientific Reports10.1038/s41598-018-24456-28:1Online publication date: 20-Apr-2018
https://doi.org/10.1038/s41598-018-24456-2
Kim MPaini DJurdak R(2018)Real-world diffusion dynamics based on point process approaches: a reviewArtificial Intelligence Review10.1007/s10462-018-9656-9Online publication date: 4-Sep-2018
https://doi.org/10.1007/s10462-018-9656-9
Shahzamal MJurdak RMans BEl Shoghri ADe Hoog F(2018)Impact of Indirect Contacts in Emerging Infectious Disease on Social NetworksTrends and Applications in Knowledge Discovery and Data Mining10.1007/978-3-030-04503-6_5(53-65)Online publication date: 21-Nov-2018
https://doi.org/10.1007/978-3-030-04503-6_5
Kim MJurdak R(2017)Heterogeneous Social Signals Capturing Real-world Diffusion ProcessesProceedings of the 2nd International Workshop on Social Sensing10.1145/3055601.3055617(95-95)Online publication date: 18-Apr-2017
https://dl.acm.org/doi/10.1145/3055601.3055617

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents