Impact of Indirect Contacts in Emerging Infectious Disease on Social Networks

  • Md ShahzamalEmail author
  • Raja Jurdak
  • Bernard Mans
  • Ahmad El Shoghri
  • Frank De Hoog
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11154)


Interaction patterns among individuals play vital roles in spreading infectious diseases. Understanding these patterns and integrating their impact in modeling diffusion dynamics of infectious diseases are important for epidemiological studies. Current network-based diffusion models assume that diseases transmit through interactions where both infected and susceptible individuals are co-located at the same time. However, there are several infectious diseases that can transmit when a susceptible individual visits a location after an infected individual has left. Recently, we introduced a diffusion model called same place different time (SPDT) transmission to capture the indirect transmissions that happen when an infected individual leaves before a susceptible individual’s arrival along with direct transmissions. In this paper, we demonstrate how these indirect transmission links significantly enhance the emergence of infectious diseases simulating airborne disease spreading on a synthetic social contact network. We denote individuals having indirect links but no direct links during their infectious periods as hidden spreaders. Our simulation shows that indirect links play similar roles of direct links and a single hidden spreader can cause large outbreak in the SPDT model which causes no infection in the current model based on direct link. Our work opens new direction in modeling infectious diseases.


Social networks Dynamic networks Disease spreading 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Md Shahzamal
    • 1
    • 2
    Email author
  • Raja Jurdak
    • 1
    • 2
  • Bernard Mans
    • 1
  • Ahmad El Shoghri
    • 2
    • 3
  • Frank De Hoog
    • 4
  1. 1.Macquarie UniversitySydneyAustralia
  2. 2.Data61, CSIROBrisbaneAustralia
  3. 3.University of New South WalesSydneyAustralia
  4. 4.Data61, CSIROCanberraAustralia

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