Leveraging Topological and Temporal Structure of Hospital Referral Networks for Epidemic Control
Antimicrobial-resistant pathogens constitute a major threat for health care systems worldwide. The hospital-related pathway is a key mechanism of their spread. Contrary to intra-hospital transmission data that requires sophisticated contact tracing technologies, data on inter-hospital transmission is collected on a regular basis. We investigate the dataset of patient referrals between hospitals in a large region of Germany. This dataset contains approximately one million patients over a 3-year period. The dataset is used to build a dynamic network of hospitals where nodes are hospitals and edges represent movements of patients between them. We consider the worst-case scenario of a highly contagious disease corresponding to deterministic infection dynamics. Furthermore, we investigate the impact on epidemic processes of the correction to the temporal network due to home (or community) visits of possibly contagious patients returning to hospitals. Moreover, we implement an extensive stochastic agent-based computational model of epidemics on this network. By leveraging the topological and temporal network structure for epidemic control, we propose intervention schemes able to hinder spread. Our approach can be used to design optimal control strategies for containment of nosocomial diseases in health-care networks.
All the authors acknowledge the courtesy of the AOK Niedersachsen for providing the anonymized data on patient referrals. VB and PH acknowledge funding by the Deutsche Forschungsgemeinschaft in the framework of Collaborative Research Center 910. At the early stage of this study VB was financially supported by the fellowship “Computational Sciences” of the VolkswagenStiftung.
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