Exploring pig trade patterns to inform the design of risk-based disease surveillance and control strategies

An understanding of the patterns of animal contact networks provides essential information for the design of risk-based animal disease surveillance and control strategies. This study characterises pig movements throughout England and Wales between 2009 and 2013 with a view to characterising spatial and temporal patterns, network topology and trade communities. Data were extracted from the Animal and Plant Health Agency (APHA)’s RADAR (Rapid Analysis and Detection of Animal-related Risks) database, and analysed using descriptive and network approaches. A total of 61,937,855 pigs were moved through 872,493 movements of batches in England and Wales during the 5-year study period. Results show that the network exhibited scale-free and small-world topologies, indicating the potential for diseases to quickly spread within the pig industry. The findings also provide suggestions for how risk-based surveillance strategies could be optimised in the country by taking account of highly connected holdings, geographical regions and time periods with the greatest number of movements and pigs moved, as these are likely to be at higher risk for disease introduction. This study is also the first attempt to identify trade communities in the country, information which could be used to facilitate the pig trade and maintain disease-free status across the country in the event of an outbreak

Best practices in data analysis and sharing in neuroimaging using MRI

Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping, and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world’s neuroimaging data

Sharing riders: How bikesharing impacts bus ridership in New York City

The objective of this research is to quantify the impact that bikesharing systems have on bus ridership. We exploit a natural experiment of the phased implementation of a bikesharing system to different areas of New York City. This allows us to use a difference-in-differences identification strategy. We divide bus routes into control and treatment groups based on if they are located in areas that received bikesharing infrastructure or not. We find a significant decrease in bus ridership on treated routes compared to control routes that coincides with the implementation of the bikesharing system in New York City. The results from our preferred model indicate that every thousand bikesharing docks along a bus route is associated with a 2.42% fall in daily unlinked bus trips on routes in Manhattan and Brooklyn. A second model that also controls for the expansion of bike lanes during this time suggests that the decrease in bus ridership attributable to bikesharing infrastructure alone may be smaller (a 1.69% fall in daily unlinked bus trips). Although the magnitude of the reduction is a small proportion of total bus trips, these findings indicate that either a large proportion of overall bikeshare members are substituting bikesharing for bus trips or that bikesharing may have impacted the travel behavior of non-members, such as private bicyclists. Understanding how bikesharing and public transit systems are interrelated is vital for planning a mutually reinforcing sustainable transport network