Abstract
To understand the contact patterns of a population—who is in contact with whom, and when the contacts happen—is crucial for modeling outbreaks of infectious disease. Traditional theoretical epidemiology assumes that any individual can meet any with equal probability. A more modern approach, network epidemiology, assumes people are connected into a static network over which the disease spreads. Newer yet, temporal network epidemiology, includes the time in the contact representations. In this paper, we investigate the effect of these successive inclusions of more information. Using empirical proximity data, we study both outbreak sizes from unknown sources, and from known states of ongoing outbreaks. In the first case, there are large differences going from a fully mixed simulation to a network, and from a network to a temporal network. In the second case, differences are smaller. We interpret these observations in terms of the temporal network structure of the data sets. For example, a fast overturn of nodes and links seem to make the temporal information more important.
Highlights
Background and problem statementEpidemics of infectious disease are complex phenomena involving processes at different scales
We do not try to show representative results, but those that help the discussion of the features of our data collection
We have studied how the level of information content in the representation of contacts patterns affects the SIR epidemic model
Summary
Background and problem statementEpidemics of infectious disease are complex phenomena involving processes at different scales. The largest and slowest processes involve the evolution (or rather co-evolution) of the pathogen and hosts and the development of new treatments. Intermediate to these are the movements of infected hosts and the response of society to an emergent outbreak. We assume that we have some understanding of the pathogen (and pathogenesis), the evolving outbreak, and the way people move and come in contact in such a way that the disease can spread. This is the type of challenge modelers face when there is an outbreak of a new pathogen, or a pathogen in a previously unaffected population[1,2]
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