Abstract

As the understanding of the importance of social contact networks in the spread of infectious diseases has increased, so has the interest in understanding the feedback process of the disease altering the social network. While many studies have explored the influence of individual epidemiological parameters and/or underlying network topologies on the resulting disease dynamics, we here provide a systematic overview of the interactions between these two influences on population-level disease outcomes. We show that the sensitivity of the population-level disease outcomes to the combination of epidemiological parameters that describe the disease are critically dependent on the topological structure of the population’s contact network. We introduce a new metric for assessing disease-driven structural damage to a network as a population-level outcome. Lastly, we discuss how the expected individual-level disease burden is influenced by the complete suite of epidemiological characteristics for the circulating disease and the ongoing process of network compromise. Our results have broad implications for prediction and mitigation of outbreaks in both natural and human populations.

Highlights

  • Effect of disease-induced mortality on structural network properties LAZAROS GALLOS, NINA FEFFERMAN, Department of Ecology, Rutgers University — We study epidemic processes on complex networks, where infected nodes are either removed permanently or they can potentially recover

  • The process influences the localization of the infection by creating buffered zones, which in turn isolate large parts of the network

  • We show that there is an interesting interplay between the percentage and location of the removed population with the network structural integrity, even before reaching the critical point of total network disruption

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Summary

Introduction

Effect of disease-induced mortality on structural network properties LAZAROS GALLOS, NINA FEFFERMAN, Department of Ecology, Rutgers University — We study epidemic processes on complex networks, where infected nodes are either removed permanently or they can potentially recover.

Results
Conclusion
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