Complex Insight - Understanding our world
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Complex Insight  - Understanding our world
A few things the Symbol Research team are reading.  Complex Insight is curated by Phillip Trotter (www.linkedin.com/in/phillip-trotter) from Symbol Research
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Rescooped by Phillip Trotter from Papers
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A generalized model of social and biological contagion

We present a model of contagion that unifies and generalizes existing models of the spread of social influences and micro-organismal infections. Our model incorporates individual memory of exposure to a contagious entity (e.g., a rumor or disease), variable magnitudes of exposure (dose sizes), and heterogeneity in the susceptibility of individuals. Through analysis and simulation, we examine in detail the case where individuals may recover from an infection and then immediately become susceptible again (analogous to the so-called SIS model). We identify three basic classes of contagion models which we call \textit{epidemic threshold}, \textit{vanishing critical mass}, and \textit{critical mass} classes, where each class of models corresponds to different strategies for prevention or facilitation. We find that the conditions for a particular contagion model to belong to one of the these three classes depend only on memory length and the probabilities of being infected by one and two exposures respectively. These parameters are in principle measurable for real contagious influences or entities, thus yielding empirical implications for our model. We also study the case where individuals attain permanent immunity once recovered, finding that epidemics inevitably die out but may be surprisingly persistent when individuals possess memory.

 

A generalized model of social and biological contagion
Peter Sheridan Dodds, Duncan J. Watts


Via Complexity Digest
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Rescooped by Phillip Trotter from Papers
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The Hidden Geometry of Complex, Network-Driven Contagion Phenomena

The global spread of epidemics, rumors, opinions, and innovations are complex, network-driven dynamic processes. The combined multiscale nature and intrinsic heterogeneity of the underlying networks make it difficult to develop an intuitive understanding of these processes, to distinguish relevant from peripheral factors, to predict their time course, and to locate their origin. However, we show that complex spatiotemporal patterns can be reduced to surprisingly simple, homogeneous wave propagation patterns, if conventional geographic distance is replaced by a probabilistically motivated effective distance. In the context of global, air-traffic–mediated epidemics, we show that effective distance reliably predicts disease arrival times. Even if epidemiological parameters are unknown, the method can still deliver relative arrival times. The approach can also identify the spatial origin of spreading processes and successfully be applied to data of the worldwide 2009 H1N1 influenza pandemic and 2003 SARS epidemic.

 

The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
Dirk Brockmann, Dirk Helbing

Science 13 December 2013:
Vol. 342 no. 6164 pp. 1337-1342
http://dx.doi.org/10.1126/science.1245200


Via Complexity Digest
Phillip Trotter's insight:

This is an awesome insight that needs tested across other datasets to find out how universal it is. Good paper.

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