We examine salient trends of influenza pandemics in Australia, a rapidly urbanizing nation. To do so, we implement state-of-the-art influenza transmission and progression models within a large-scale stochastic computer simulation, generated using comprehensive Australian census datasets from 2006, 2011, and 2016. Our results offer the first simulation-based investigation of a population’s sensitivity to pandemics across multiple historical time points, and highlight three significant trends in pandemic patterns over the years: increased peak prevalence, faster spreading rates, and decreasing spatiotemporal bimodality. We attribute these pandemic trends to increases in two key quantities indicative of urbanization: population fraction residing in major cities, and international air traffic. In addition, we identify features of the pandemic’s geographic spread that can only be attributed to changes in the commuter mobility network. The generic nature of our model and the ubiquity of urbanization trends around the world make it likely for our results to be applicable in other rapidly urbanizing nations.
Vulnerability to pandemics in a rapidly urbanizing society
Cameron Zachreson, Kristopher M. Fair, Oliver M. Cliff, Nathan Harding, Mahendra Piraveenan, Mikhail Prokopenko
This text is about spreading of information and influence in complex networks. Although previously considered similar and modeled in parallel approaches, there is now experimental evidence that epidemic and social spreading work in subtly different ways. While previously explored through modeling, there is currently an explosion of work on revealing the mechanisms underlying complex contagion based on big data and data-driven approaches.
This volume consists of four parts. Part 1 is an Introduction, providing an accessible summary of the state-of-the-art. Part 2 provides an overview of the central theoretical developments in the field. Part 3 describes the empirical work on observing spreading processes in real-world networks. Finally, Part 4 goes into detail with recent and exciting new developments: dedicated studies designed to measure specific aspects of the spreading processes, often using randomized control trials to isolate the network effect from confounders, such as homophily.
Each contribution is authored by leading experts in the field. This volume, though based on technical selections of the most important results on complex spreading, remains quite accessible to the newly interested. The main benefit to the reader is that the topics are carefully structured to take the novice to the level of expert on the topic of social spreading processes. This book will be of great importance to a wide field: from researchers in physics, computer science, and sociology to professionals in public policy and public health.
Complex Spreading Phenomena in Social Systems
Influence and Contagion in Real-World Social Networks
Editors: Sune Lehmann, Yong-Yeol Ahn
Information theory provides various measures of correlations holding between the states of two systems, which are sometimes called measures of "syntactic information". At the same time, the concept of "semantic information" refers to information which is in some sense meaningful rather than merely correlational. Semantic information plays an important role in many fields — including biology, cognitive science, artificial intelligence — and there has been a long-standing interest in a quantitative theory of semantic information. In this work, we introduce such a theory, which defines semantic information as the syntactic information that a physical system has about its environment that is causally necessary for the system to maintain its own existence. We operationalize self-maintenance in terms of the ability of the system to maintain a low entropy state, which we use to make connections to results in nonequilibrium statistical physics. Our approach leads naturally to formal definitions of notions like "value of information", "semantic content", and "agency". Our approach is grounded purely in the intrinsic dynamics of a system coupled to some environment, and is applicable to any physical system.
Semantic information, agency, and nonequilibrium statistical physics
Artemy Kolchinsky, David H. Wolpert
Chemical evolution is essential in understanding the origins of life. We present a theory for the evolution of molecule masses and show that small molecules grow by random diffusion and large molecules by a preferential attachment process leading eventually to life’s molecules. It reproduces correctly the distribution of molecules found via mass spectroscopy for the Murchison meteorite and estimates the start of chemical evolution back to 12.8 billion years following the birth of stars and supernovae. From the Frontier mass between the random and preferential attachment dynamics the birth time of molecule families can be estimated. Amino acids emerge about 165 million years after the start of evolution. Using the scaling of reaction rates with the distance of the molecules in space we recover correctly the few days emergence time of amino acids in the Miller-Urey experiment. The distribution of interstellar and extragalactic molecules are both consistent with the evolutionary mass distribution, and their age is estimated to 108 and 65 million years after the start of evolution. From the model, we can determine the number of different molecule compositions at the time of the creation of Earth to be 1.6 million and the number of molecule compositions in interstellar space to a mere 719.
The clock of chemical evolution
Stuart A. Kauffman, David P. Jelenfi, Gabor Vattay