Social Communications Assisted Epidemic Disease Influence Minimization

This work explores the use of social communications for epidemic disease control. Since the most infectious diseases spread through human contacts, we focus on modeling the diffusion of diseases by analyzing the social relationship among individuals. In other words, we try to capture the interaction pattern among human beings using the social contact information, and investigate its impact on the spread of diseases. Particularly, we investigate the problem of minimizing the expected number of infected persons by treating a small fraction of the population with vaccines. We prove that this problem is NP-hard, and propose an approximate algorithm representing a preventive disease control strategy based on the social patterns. Simulation results confirm the superiority of our strategy over existing ones.

[1]  Tao Zhou,et al.  Erratum: Maximal planar networks with large clustering coefficient and power-law degree distribution [Phys. Rev. E 71, 046141 (2005)] , 2005 .

[2]  M. Newman.  Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Tao Zhou,et al.  Maximal planar networks with large clustering coefficient and power-law degree distribution. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Krishna P. Gummadi,et al.  A measurement-driven analysis of information propagation in the flickr social network , 2009, WWW '09.

[5]  Nedialko B. Dimitrov,et al.  Mathematical Approaches to Infectious Disease Prediction and Control , 2010 .

[6]  Jie Yang,et al.  Mobile Phone Enabled Social Community Extraction for Controlling of Disease Propagation in Healthcare , 2011, 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems.

[7]  P. Kaye.  Infectious diseases of humans: Dynamics and control , 1993 .

[8]  Xiuzhen Cheng,et al.  Mobile phone based social relationship identification for target vaccination in mobile healthcare , 2012, PhoneSense '12.

[9]  L. Meyers.  Contact network epidemiology: Bond percolation applied to infectious disease prediction and control , 2006 .

[10]  Xiaohui Liang,et al.  PEC: A privacy-preserving emergency call scheme for mobile healthcare social networks , 2011, Journal of Communications and Networks.

[11]  Ana Perisic,et al.  Social Contact Networks and Disease Eradicability under Voluntary Vaccination , 2009, PLoS Comput. Biol..

[12]  James Aspnes,et al.  Inoculation strategies for victims of viruses and the sum-of-squares partition problem , 2005, SODA '05.

[13]  Aravind Srinivasan,et al.  Mobile Data Offloading through Opportunistic Communications and Social Participation , 2012, IEEE Transactions on Mobile Computing.

[14]  Samuel Huang,et al.  Probabilistic Model Checking of Disease Spread and Prevention , 2009 .

[15]  Laks V. S. Lakshmanan,et al.  Learning influence probabilities in social networks , 2010, WSDM '10.

[16]  Joel C. Miller.  The spread of infectious diseases through clustered populations , 2008 .

[17]  Xiuzhen Cheng,et al.  A community based vaccination strategy over mobile phone records , 2012, mHealthSys '12.

[18]  Herbert W. Hethcote,et al.  The Mathematics of Infectious Diseases , 2000, SIAM Rev..

[19]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[20]  Joel C. Miller.  Spread of infectious disease through clustered populations , 2008, Journal of The Royal Society Interface.