Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups.

Background: Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data.

Methods: We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007.

Sensing Enhancement on Social Networks: The Role of Network Topology.

Sensing and processing information from dynamically changing environments is essential for the survival of animal collectives and the functioning of human society. In this context, previous work has shown that communication between networked agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement.

Shadowing and shielding: Effective heuristics for continuous influence maximisation in the voting dynamics.

Influence maximisation, or how to affect the intrinsic opinion dynamics of a social group, is relevant for many applications, such as information campaigns, political competition, or marketing. Previous literature on influence maximisation has mostly explored discrete allocations of influence, i.e.