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With outbreaks of airborne diseases such as measles occurring with growing frequency, modeling how the diffusion process works in dynamic contact networks is an increasingly important research area for epidemiology. A team including Macquarie University researchers Mohammad Shahzamal, Raja Jurdak, and Bernard Mans has developed a computational diffusion model that overcomes previous limitations in capturing an accurate view of the possible spread of infection. The research has been published in Royal Society Open Science.
Most diffusion models assume both infected and susceptible individuals are present in the same physical space and time for an inter-node transmission, or individual-level transmission, to occur. However, when a person who is either incubating or showing active symptoms of an airborne disease releases infectious particles, (through sneezing or coughing for example), these can linger in the air for some time, continuing to infect others even after that person has left the area.
Diffusion - Models - Transmissions - Interactions - Result
Current diffusion models cannot capture transmissions that occur through indirect interactions. As a result, the epidemiological models available until now have not factored in transmissions by this indirect interaction, reducing both their accuracy and effectiveness.
The researchers developed a computational diffusion model—same place different time transmission (SPDT)-based diffusion—that considers transmission links for these indirect interactions.
Model - Network - Dynamics - Connectivity - Individuals
This model changes the network dynamics where the connectivity among individuals varies as the link—such as an airborne illness—becomes less concentrated in the area. To work this out, the researchers...
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