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AbstractDegree distribution is a fundamental property of networks. While mean degree provides a standard measure of scale, there are several commonly used shape measures. Widespread use of a single shape measure would enable comparisons between networks and facilitate investigations about the relationship between degree distribution properties and other network features. This paper describes five candidate measures of heterogeneity and recommends the Gini coefficient. It has theoretical advantages over many of the previously proposed measures, is meaningful for the broad range of distribution shapes seen in different types of networks, and has several accessible interpretations. While this paper focuses on degree, the distribution of other node-based network properties could also be described with Gini coefficients.

AbstractThis study uses simulation over real and artificial networks to compare the eventual adoption outcomes of network interventions, operationalized as idealized contagion processes with different sets of seeds. While the performance depends on the details of both the network and behaviour adoption mechanisms, interventions with seeds that are central to the network are more effective than random selection in the majority of simulations, with faster or more complete adoption throughout the network. These results provide additional theoretical justification for utilizing relevant network information in the design of public health behavior interventions.

Government communication is an important management tool during a public health crisis, but understanding its impact is difficult. Strategies may be adjusted in reaction to developments on the ground and it is challenging to evaluate the impact of communication separately from other crisis management activities. Agent-based modeling is a well-established research tool in social science to respond to similar challenges. However, there have been few such models in public health. We use the example of the TELL ME agent-based model to consider ways in which a non-predictive policy model can assist policy makers. This model concerns individuals' protective behaviors in response to an epidemic, and the communication that influences such behavior. Drawing on findings from stakeholder workshops and the results of the model itself, we suggest such a model can be useful: (i) as a teaching tool, (ii) to test theory, and (iii) to inform data collection. We also plot a path for development of similar models that could assist with communication planning for epidemics.

System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socioenvironmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socioenvironmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for the co-creation of knowledge and solutions to major socio-environmental problems. ; National Socio-Environmental Synthesis Center (SESYNC) under the National Science Foundation [DBI-1639145]; Australian Government Research Training Program (AGRTP) ScholarshipAustralian Government; ANU Hilda-John Endowment Fund; USDAUnited States Department of Agriculture (USDA); ARSUnited States Department of Agriculture (USDA)USDA Agricultural Research Service [58-3091-6-035]; Texas A&M AgriLife Research; Key Program of NSF of China [41930648]; NSFNational Science Foundation (NSF) [EEC 1937012] ; Published version ; This work was supported by the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the National Science Foundation DBI-1639145. The primary author (Takuya Iwanaga) is supported through an Australian Government Research Training Program (AGRTP) Scholarship and a top-up scholarship from the ANU Hilda-John Endowment Fund. Hsiao-Hsuan Wang and Tomasz E. Koralewski acknowledge partial support from USDA, ARS Agreement No. 58-3091-6-035 with Texas A&M AgriLife Research, titled `Areawide pest management of the invasive sugarcane aphid in grain sorghum, regional population monitoring and forecasting.' Min Chen is supported by the Key Program of NSF of China (No. 41930648). John Little acknowledges partial support from NSF Award EEC 1937012. The authors would like to thank the three anonymous reviewers and Prof. Randall Hunt (USGS) for their constructive feedback and comments. The authors additionally thank Faye Duchin and Adrian Hindes for comments provided on an earlier draft. ; Public domain authored by a U.S. government employee