In adaptive networks, the spreading process depends not only on the dynamics of a contact network, but vice versa, infection dynamics may alter risk behaviour and thus feed back onto contact dynamics. Detailed modelling approaches consider stochastic pathogen spreading on time-evolving contact networks. Modelling and simulating of infectious disease spreading supports public heath decisions, such as prevention and containment strategies and allows to perform cost-effectiveness calculations. A C++ implementation of the algorithm is available at. Moreover, it may serve to create benchmark data sets to validate novel numerical approaches for simulation, or for the data-driven analysis of the spreading dynamics on adaptive networks. We envision that SSATAN-X may extend the scope of analysis of pathogen spreading on adaptive networks. if contacts are short-lived and per-exposure infection risks are small, as applicable to most infectious diseases. The speed-up with SSATAN-X further increases when the contact dynamics are fast in relation to the spreading process, i.e.
The algorithm achieves up to 100 fold speed-up over the state-of-art stochastic simulation algorithm (SSA). We show that SSATAN-X captures the contact dynamics and consequently the spreading dynamics accurately. The key idea of SSATAN-X is to only capture the contact dynamics that are relevant to the spreading process. In this manuscript, we propose SSATAN-X, a new algorithm for the accurate stochastic simulation of pathogen spreading on adaptive networks. However, stochastic simulation of pathogen spreading processes on adaptive networks is currently computationally prohibitive. In adaptive networks, the spreading process depends not only on the dynamics of a contact network, but vice versa, infection dynamics may alter risk behaviour and thus feed back onto contact dynamics, leading to emergent complex dynamics. Pathogen spreading is often modelled as a stochastic process that is driven by pathogen exposure on time-evolving contact networks. Modelling and simulating the dynamics of pathogen spreading has been proven crucial to inform public heath decisions, containment strategies, as well as cost-effectiveness calculations.
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