Understanding National Nonprofit Data Environments.

We develop the concept of the nonprofit data environment as all data collected and reported in a country resulting from law implemented into practice. We map data environments across 20 countries and propose explanations for differences between the information nongovernmental organizations report (collected) and what is made publicly available (reported). Domestic factors including regime type, civil society autonomy, and regulatory quality increase the amount of information collected and released publicly. Exposure to international political forces, including aid flows and globalization, increases the gap, which runs counter to expectations of greater openness with global engagement. Our findings point to the need for a better understanding of patterns in non-profit organizations (NPOs) data environments; while all governments collect information, countries with similar legal codes have widely varying data environments. This matters for NPOs as their ability to learn and improve depends on access to quality data and coincides with a feared global political backlash.

Epidemic Wave Dynamics Attributable to Urban Community Structure: A Theoretical Characterization of Disease Transmission in a Large Network.

BACKGROUND: Multiple waves of transmission during infectious disease epidemics represent a major public health challenge, but the ecological and behavioral drivers of epidemic resurgence are poorly understood. In theory, community structure­aggregation into highly intraconnected and loosely interconnected social groups­within human populations may lead to punctuated outbreaks as diseases progress from one community to the next. However, this explanation has been largely overlooked in favor of temporal shifts in environmental conditions and human behavior and because of the difficulties associated with estimating large-scale contact patterns. OBJECTIVE: The aim was to characterize naturally arising patterns of human contact that are capable of producing simulated epidemics with multiple wave structures. METHODS: We used an extensive dataset of proximal physical contacts between users of a public Wi-Fi Internet system to evaluate the epidemiological implications of an empirical urban contact network. We characterized the modularity (community structure) of the network and then estimated epidemic dynamics under a percolation-based model of infectious disease spread on the network. We classified simulated epidemics as multiwave using a novel metric and we identified network structures that were critical to the network's ability to produce multiwave epidemics. RESULTS: We identified robust community structure in a large, empirical urban contact network from which multiwave epidemics may emerge naturally. This pattern was fueled by a special kind of insularity in which locally popular individuals were not the ones forging contacts with more distant social groups. CONCLUSIONS: Our results suggest that ordinary contact patterns can produce multiwave epidemics at the scale of a single urban area without the temporal shifts that are usually assumed to be responsible. Understanding the role of community structure in epidemic dynamics allows officials to anticipate epidemic resurgence without having to forecast future changes in hosts, pathogens, or the environment.