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Efficient and accurate simulation of infectious diseases on adaptive networks

Fig 3

Comparison of computational speed between HAS and SSA.

The base process involves N = 100 agents, starting with an initial infection size of . Infection and diagnosis rates are homogeneous, set at and , respectively. The expected number of contacts per time step is drawn from an exponential distribution with parameter 1, ensuring no agents have zero contacts by shifting each value by 1. Each contact persists on average for one time step. We sampled 1,000 trajectories for each parameter and method up to an end time of T = 24. A. System size varied from 100 to 100,000 agents. B. The probability of edge existence, defined as + , was adjusted between 0 and 1 while keeping the edge deletion rate constant and modifying . C. The infection probability, calculated as + , was varied by holding the edge deletion rate constant and altering the infection rate . The SSA algorithm reaches completion once every agent is infected, benefiting from a reduction in running time when the infection probability is close to 1. Computations were conducted on a Xeon Skylake 6130 with 3GB of RAM [82]

doi: https://doi.org/10.1371/journal.pcsy.0000049.g003