Immunization strategies in networks with missing data

Abstract

Network-based intervention strategies can be effective and cost-efficient approaches to curtailing harmful contagions in myriad settings. As studied, these strategies are often impractical to implement, as they typically assume complete knowledge of the network structure, which is unusual in practice. In this paper, we investigate how different immunization strategies perform under realistic conditions-where the strategies are informed by partiallyobserved network data. Our results suggest that global immunization strategies, like degree immunization, are optimal in most cases; the exception is at very high levels of missing data, where stochastic strategies, like acquaintance immunization, begin to outstrip them in minimizing outbreaks. Stochastic strategies are more robust in some cases due to the different ways in which they can be affected by missing data. In fact, one of our proposed variants of acquaintance immunization leverages a logistically-realistic ongoing survey-intervention process as a form of targeted data-recovery to improve with increasing levels of missing data. These results support the effectiveness of targeted immunization as a general practice. They also highlight the risks of considering networks as idealized mathematical objects: overestimating the accuracy of network data and foregoing the rewards of additional inquiry.

A review and agenda for integrated disease models including social and behavioural factors

“…Interventions in this context can be any piece of information (for example, a vaccination campaign or information about transmission risks) that can reduce the risks of transmission. For top-down approaches, the objective of the model is to inform the best targeting strategies, which can be optimized to find highly connected individuals 58 , disconnect social groups 59,60 or be robust to data quality 61 . Bottom-up approaches tend to be more descriptive, often modelled as extensions of classic disease models, assuming random contact within populations and letting disease and information spread and interact 33,62 .…”

Detecting Portable Executable Malware by Binary Code Using an Artificial Evolutionary Fuzzy LSTM Immune System

“…Another important feature that AIS tries to model is the combination of innate and acquired immunization [20]. e innate immune system uses several molecular patterns to identify pathogens; it exists from birth and does not adapt during the life of living organisms.…”

Immunization of networks with limited knowledge and temporary immunity

“…Interestingly, it is revealed that with a relatively small value of n the immunization strategy may approximate the targeted immunization with full network information. Similar partiallyobserved networks have also been studied for problems like targeted immunization 11,12 , attack robustness 8,13 , and influence maximization 14 . These works, however, are from the perspective of blinding a portion of nodes through random or some specific sampling schemes, which is related but not equivalent to the limited knowledge notion studied in 10 .…”