Structure and geography of a hospital patient transfer network
| Autor: | Stivala, Alex, Byshkin, Maksym, Pallotti, Francesca, Lomi, Alessandro |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| DOI: | 10.5281/zenodo.7952184 |
| Popis: | We describe a secondary analysis of an interhospital patient transfer network, in which the nodes represent hospitals and the directed arcs between them represent transfers of critically ill patients. There are over 3000 nodes (hospitals) in this network. Each of the hospitals has a geographic location, and hence this is a spatially embedded network. We describe the power law distribution and "small world" properties of this network, and discuss the relationship between its network community structure and geography. We describe the difficulties and incremental progress in estimating an exponential random graph model (ERGM) for this network. Using currently available estimation methods we were only able to estimate models for subnetworks of the network (based on census divisions). With a recently published improved sampling algorithm, we were able to estimate a simple model of the entire network, when converted to an undirected network. However using the Equilibrium Expectation algorithm for estimating ERGMs for large directed networks, we were able to estimate models for the entire network relatively quickly. We present several models of the network, incorporating geographical information. Explicitly controlling for geographic distance between hospitals has a significant effect on certain other model parameters, in some cases reversing their sign. We discuss the implications of the models for the effect of hospital attributes and geography on interhospital patient transfer network tie formation. This research was supported by Melbourne Bioinformatics at the University of Melbourne, grant number VR0261. This work was partly funded by PASC project "Snowball sampling and conditional estimation for exponential random graph models for large networks in high performance computing" and was supported by a grant from the Swiss National Supercomputing Centre (CSCS) under project ID c09. This work was partly funded by Swiss National Science Foundation NRP 75 Big Data project 167326 "The Global Structure of Knowledge Networks: Data, Models and Empirical Results". |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|