Time evolution of predictability of epidemics on networks

Abstract

Epidemic outbreaks of new pathogens, or known pathogens in new populations, cause a great deal of fear because they are hard to predict. For theoretical models of disease spreading, on the other hand, quantities characterizing the outbreak converge to deterministic functions of time. Our goal in this paper is to shed some light on this apparent discrepancy. We measure the diversity of (and, thus, the predictability of) outbreak sizes and extinction times as functions of time given different scenarios of the amount of information available. Under the assumption of perfect information -- i.e., knowing the state of each individual with respect to the disease -- the predictability decreases exponentially, or faster, with time. The decay is slowest for intermediate values of the per-contact transmission probability. With a weaker assumption on the information available, assuming that we know only the fraction of currently infectious, recovered, or susceptible individuals, the predictability also decreases exponentially most of the time. There are, however, some peculiar regions in this scenario where the predictability decreases. In other words, to predict its final size with a given accuracy, we would need increasingly more information about the outbreak.

References in corpus (6)

• Epidemic spreading in scale-free networks
• Epidemic outbreaks in complex heterogeneous networks
• Inferring the origin of an epidemic with a dynamic message-passing algorithm
• Identifying the starting point of a spreading process in complex networks
• The contact network of patients in a regional healthcare system
• Extinction times of epidemic outbreaks in networks

Cited by in corpus (6)

• Exploring limits to prediction in complex social systems
• Interplay between the local information based behavioral responses and the epidemic spreading in complex networks
• Information content of contact-pattern representations and predictability of epidemic outbreaks
• Predicting epidemic evolution on contact networks from partial observations
• Temporal Dynamics of Connectivity and Epidemic Properties of Growing Networks
• The spreading ability of nodes towards localized targets in complex networks