Nontrivial effects of uncertainty on epidemic spreading under limited resources

While epidemic spreading often involves uncertainty (represented by noise), its control depends on the resource amount to be invested. It is unclear how the noise affects epidemic spreading under limited resources. Here we analyze an artificial model of epidemic spreading on a two-layer network, which considers three factors often neglected in other studies: noise, resource amount and time delay. We find that the noise can significantly impact epidemic spreading, depending on resource amount, sub-network topology, connection strength between sub-networks, and time delay. Main findings include: (1) for a given number of resources, a stronger noise correlates to a smaller fraction of the population infected. Moreover, if the more resources are invested, then the effect of the noise in reducing the fraction of the population infected becomes more apparent; (2) for a large range of time delay or topology degree, there is an optimal noise intensity such that the fraction of the population infected is smallest. These findings would provide useful guidelines for government decisions on the control of epidemic spreading.