Message-passing approach for recurrent-state epidemic models on networks
Munik Shrestha1, Samuel V Scarpino2, Cristopher Moore2
1Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico 87131, USA and Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 19, 2015
Summary
We developed a new dynamic message-passing algorithm for simulating recurrent epidemic models on networks. This method accurately predicts disease spread and is computationally efficient for complex models.
Area of Science:
- Epidemiology
- Network Science
- Computational Biology
Background:
- Epidemic processes are critical out-of-equilibrium phenomena with broad interdisciplinary relevance.
- Dynamic message-passing (DMP) is an efficient simulation algorithm for epidemic models, particularly for estimating infection probabilities.
- Existing DMP methods are limited to one-way state change models, excluding recurrent dynamics seen in models like SIS and SIRS.
Purpose of the Study:
- To propose a novel dynamic message-passing (DMP) algorithm for complex, recurrent epidemic models on networks.
- To account for correlations between neighboring nodes while preventing signal backtracking and "echo chamber effects".
Main Methods:
- Developed a new DMP algorithm tailored for recurrent epidemic dynamics on networks.
- Incorporated mechanisms to manage correlations and prevent causal signal backtracking.
- Compared simulation results against Monte Carlo simulations and the pair approximation.
Main Results:
- The proposed DMP algorithm accurately approximates Monte Carlo simulation results.
- The new DMP approach often surpasses the accuracy of the pair approximation.
- Computational efficiency is improved, with variable growth of 2mk compared to mk^2 for the pair approximation.
Conclusions:
- The novel DMP algorithm provides an accurate and computationally efficient tool for modeling complex, recurrent epidemic dynamics on networks.
- Its conceptual simplicity and efficiency make it valuable for high-dimensional inference tasks in epidemic modeling.
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