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ZENODO
Dataset . 2019
License: CC 0
Data sources: ZENODO
DRYAD
Dataset . 2019
License: CC 0
Data sources: Datacite
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Data from: Indirect interactions influence contact network structure and diffusion dynamics

Authors: Shahzamal, Md.; Jurdak, Raja; Mans, Bernard; Hoog, Frank;

Data from: Indirect interactions influence contact network structure and diffusion dynamics

Abstract

Interaction patterns at the individual level influence the behaviour of diffusion over contact networks. Most of the current diffusion models only consider direct interactions, capable of transferring infectious items among individuals, to build transmission networks of diffusion. However, delayed indirect interactions, where a susceptible individual interacts with infectious items after the infected individual has left the interaction space, can also cause transmission events. We define a diffusion model called the same place different time transmission (SPDT) based diffusion that considers transmission links for these indirect interactions. Our SPDT model changes the network dynamics where the connectivity among individuals varies with the decay rates of link infectivity. We investigate SPDT diffusion behaviours by simulating airborne disease spreading on data-driven contact networks. The SPDT model significantly increases diffusion dynamics with a high rate of disease transmission. By making the underlying connectivity denser and stronger due to the inclusion of indirect transmissions, SPDT models are more realistic than SPST models for the study of various airborne diseases outbreaks. Importantly, we also find that the diffusion dynamics including indirect links are not reproducible by the current SPST models based on direct links, even if both SPDT and SPST networks assume the same underlying connectivity. This is because the transmission dynamics of indirect links are different from those of direct links. These outcomes highlight the importance of the indirect links for predicting outbreaks of airborne diseases.

SPDT_NETThis is the constructed SPDT contact network among 364K nodes over 32 days using GPS locations of Momo users. This network has both direct and indirect links. In this network, the links have the following format: Host-ID, Neighbor-ID, Host-Arrival-Time, Host-Depart-Time, Neb-Arrival-Time, Neb-Depart-Time. Each link has been created for visiting a common location within 3 hours of host node departure. The node ID is anonymised and spatial location information is also removed. This is only the links over time.

Keywords

Mathematical model, influenza, diffusion process, contact network, Influenza

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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