## Statistics of Epidemics in Networks by Passing Messages

Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/31743

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Title

Statistics of Epidemics in Networks by Passing Messages

Author(s)

Shrestha, Munik

Advisor(s)

Moore, Cris

Committee Member(s)

Moore, Cris

Caves, Carl

Roy, Mousumi

Dunlap, David

Caves, Carl

Roy, Mousumi

Dunlap, David

Department

University of New Mexico. Dept. of Physics & Astronomy

Degree Level

Doctoral

Abstract

Epidemic processes are common out-of-equilibrium phenomena of broad interdisciplinary interest. In this thesis, we show how message-passing approach can be a helpful tool for simulating epidemic models in disordered medium like networks, and in particular for estimating the probability that a given node will become infectious at a particular time. The sort of dynamics we consider are stochastic, where randomness can arise from the stochastic events or from the randomness of network structures.
As in belief propagation, variables or messages in message-passing approach are defined on the directed edges of a network. However, unlike belief propagation, where the posterior distributions are updated according to Bayes' rule, in message-passing approach we write differential equations for the messages over time. It takes correlations between neighboring nodes into account while preventing causal signals from backtracking to their immediate source, and thus avoids ``echo chamber effects" where a pair of adjacent nodes each amplify the probability that the other is infectious.
In our first results, we develop a message-passing approach to threshold models of behavior popular in sociology. These are models, first proposed by Granovetter, where individuals have to hear about a trend or behavior from some number of neighbors before adopting it themselves. In thermodynamic limit of large random networks, we provide an exact analytic scheme while calculating the time dependence of the probabilities and thus learning about the whole dynamics of bootstrap percolation, which is a simple model known in statistical physics for exhibiting discontinuous phase transition.
As an application, we apply a similar model to financial networks, studying when bankruptcies spread due to the sudden devaluation of shared assets in overlapping portfolios. We predict that although diversification may be good for individual institutions, it can create dangerous systemic effects, and as a result financial contagion gets worse with too much diversification. We also predict that financial system exhibits "robust yet fragile" behavior, with regions of the parameter space where contagion is rare but catastrophic whenever it occurs.
In further results, we develop a message-passing approach to recurrent state epidemics like susceptible-infectious-susceptible and susceptible-infectious-recovered-susceptible where nodes can return to previously inhabited states and multiple waves of infection can pass through the population. Given that message-passing has been applied exclusively to models with one-way state changes like susceptible-infectious and susceptible-infectious-recovered, we develop message-passing for recurrent epidemics based on a new class of differential equations and demonstrate that our approach is simple and efficiently approximates results obtained from Monte Carlo simulation, and that the accuracy of message-passing is often superior to the pair approximation (which also takes second-order correlations into account).

Date

December 2015

Subject(s)

Statistics, Epidemics, Networks, Message Passing

##### Collections

- Physics [69]