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Time evolution of predictability of epidemics on networks
Umeå University, Faculty of Science and Technology, Department of Physics. Department of Energy Science, Sungkyunkwan University, Suwon, Korea; Department of Sociology, Stockholm University.
2015 (English)In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 91, no 4, 042811Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2015. Vol. 91, no 4, 042811
National Category
Other Computer and Information Science
URN: urn:nbn:se:umu:diva-103211DOI: 10.1103/PhysRevE.91.042811ISI: 000353546300009OAI: diva2:813235
Available from: 2015-05-22 Created: 2015-05-18 Last updated: 2015-05-22Bibliographically approved

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