Steps towards operationalizing One Health approaches.

One Health recognizes the health of humans, agriculture, wildlife, and the environment are interrelated. The concept has been embraced by international health and environmental authorities such as WHO, WOAH, FAO, and UNEP, but One Health approaches have been more practiced by researchers than national or international authorities. To identify priorities for operationalizing One Health beyond research contexts, we conducted 41 semi-structured interviews with professionals across One Health sectors (public health, environment, agriculture, wildlife) and institutional contexts, who focus on national-scale and international applications. We identify important challenges, solutions, and priorities for delivering the One Health agenda through government action. Participants said One Health has made progress with motivating stakeholders to attempt One Health approaches, but achieving implementation needs more guidance (action plans for how to leverage or change current government infrastructure to accommodate cross-sector policy and strategic mission planning) and facilitation (behavioral change, dedicated personnel, new training model).

Calculating incidence of Influenza-like and COVID-like symptoms from Flutracking participatory survey data.

This article describes a new method for estimating weekly incidence (new onset) of symptoms consistent with Influenza and COVID-19, using data from the Flutracking survey. The method mitigates some of the known self-selection and symptom-reporting biases present in existing approaches to this type of participatory longitudinal survey data. The key novel steps in the analysis are: 1) Identifying new onset of symptoms for three different Symptom Groupings: COVID-like illness (CLI1+, CLI2+), and Influenza-like illness (ILI), for responses reported in the Flutracking survey. 2) Adjusting for symptom reporting bias by restricting the analysis to a sub-set of responses from those participants who have consistently responded for a number of weeks prior to the analysis week. 3) Weighting responses by age to adjust for self-selection bias in order to account for the under- and over-representation of different age groups amongst the survey participants. This uses the survey package [22] in R [30]. 4) Constructing 95% point-wise confidence bands for incidence estimates using weighted logistic regression from the survey package [21] in R [28]. In addition to describing these steps, the article demonstrates an application of this method to Flutracking data for the 12 months from 27th April 2020 until 25th April 2021.

Structure of the Region-Technology Network as a Driver for Technological Innovation.

Agglomeration and spillovers are key phenomena of technological innovation, driving regional economic growth. Here, we investigate these phenomena through technological outputs of over 4,000 regions spanning 42 countries, by analyzing more than 30 years of patent data (approximately 2.7 million patents) from the European Patent Office. We construct a bipartite network-based on revealed comparative advantage-linking geographic regions with areas of technology and compare its properties to those of artificial networks using a series of randomization strategies, to uncover the patterns of regional diversity and technological ubiquity. Our results show that the technological outputs of regions create nested patterns similar to those of ecological networks. These patterns suggest that regions need to dominate various technologies first (those allegedly less sophisticated), creating a diverse knowledge base, before subsequently developing less ubiquitous (and perhaps more sophisticated) technologies as a consequence of complementary knowledge that facilitates innovation. Finally, we create a map-the Patent Space Network-showing the interactions between technologies according to their regional presence. This network reveals how technology across industries co-appear to form several explicit clusters, which may aid future works on predicting technological innovation due to agglomeration and spillovers.

Entropy of Co-Enrolment Networks Reveal Disparities in High School STEM Participation.

The current study uses a network analysis approach to explore the STEM pathways that students take through their final year of high school in Aotearoa New Zealand. By accessing individual-level microdata from New Zealand's Integrated Data Infrastructure, we are able to create a co-enrolment network comprised of all STEM assessment standards taken by students in New Zealand between 2010 and 2016. We explore the structure of this co-enrolment network though use of community detection and a novel measure of entropy. We then investigate how network structure differs across sub-populations based on students' sex, ethnicity, and the socio-economic-status (SES) of the high school they attended. Results show the structure of the STEM co-enrolment network differs across these sub-populations, and also changes over time. We find that, while female students were more likely to have been enrolled in life science standards, they were less well represented in physics, calculus, and vocational (e.g., agriculture, practical technology) standards. Our results also show that the enrollment patterns of Asian students had lower entropy, an observation that may be explained by increased enrolments in key science and mathematics standards. Through further investigation of differences in entropy across ethnic group and high school SES, we find that ethnic group differences in entropy are moderated by high school SES, such that sub-populations at higher SES schools had lower entropy. We also discuss these findings in the context of the New Zealand education system and policy changes that occurred between 2010 and 2016.

Transitivity and degree assortativity explained: The bipartite structure of social networks.

Dynamical processes, such as the diffusion of knowledge, opinions, pathogens, "fake news," innovation, and others, are highly dependent on the structure of the social network in which they occur. However, questions on why most social networks present some particular structural features, namely, high levels of transitivity and degree assortativity, when compared to other types of networks remain open. First, we argue that every one-mode network can be regarded as a projection of a bipartite network, and we show that this is the case using two simple examples solved with the generating functions formalism. Second, using synthetic and empirical data, we reveal how the combination of the degree distribution of both sets of nodes of the bipartite network-together with the presence of cycles of lengths four and six-explain the observed values of transitivity and degree assortativity coefficients in the one-mode projected network. Bipartite networks with top node degrees that display a more right-skewed distribution than the bottom nodes result in highly transitive and degree assortative projections, especially if a large number of small cycles are present in the bipartite structure.

Bourdieu, networks, and movements: Using the concepts of habitus, field and capital to understand a network analysis of gender differences in undergraduate physics.

Current trends suggest that significant gender disparities exist within Science, Technology, Engineering, and Mathematics (STEM) education at university, with female students being underrepresented in physics, but more equally represented in life sciences (e.g., biology, medicine). To understand these trends, it is important to consider the context in which students make decisions about which university courses to enrol in. The current study seeks to investigate gender differences in STEM through a unique approach that combines network analysis of student enrollment data with an interpretive lens based on the sociological theory of Pierre Bourdieu. We generate a network of courses taken by around 9000 undergraduate physics students (from 2009 to 2014) to quantify Bourdieu's concept of field. We identify the fields in which physics students participate by constructing a weighted co-enrollment network and finding communities within it. We then use odds ratios to report gender differences in transverse movements between different academic fields, and non-parametric tests to assess gender differences in vertical movements (changes in students' achievement rankings within a field). Odds ratios comparing the likelihood of progression from one field to another indicate that female students were more likely to make transverse movements into life science fields. We also found that university physics did a poor job in attracting high achieving students, and especially high achieving female students. Of the students who did choose to study physics at university, low and middle achieving female high school students were more likely to decrease their relative rank in their first year compared to their male counterparts. Low achieving female students were also less likely to continue with physics after their first year compared to their male counterparts. Results and implications are discussed in the context of Bourdieu's theory, and previous research. We argue that in order to remove constraints on female students' study choices, the field of physics needs to provide a culture in which all students feel like they belong.

Degree distributions of bipartite networks and their projections.

Bipartite (two-mode) networks are important in the analysis of social and economic systems as they explicitly show conceptual links between different types of entities. However, applications of such networks often work with a projected (one-mode) version of the original bipartite network. The topology of the projected network, and the dynamics that take place on it, are highly dependent on the degree distributions of the two different node types from the original bipartite structure. To date, the interaction between the degree distributions of bipartite networks and their one-mode projections is well understood for only a few cases, or for networks that satisfy a restrictive set of assumptions. Here we show a broader analysis in order to fill the gap left by previous studies. We use the formalism of generating functions to prove that the degree distributions of both node types in the original bipartite network affect the degree distribution in the projected version. To support our analysis, we simulate several types of synthetic bipartite networks using a configuration model where node degrees are assigned from specific probability distributions, ranging from peaked to heavy-tailed distributions. Our findings show that when projecting a bipartite network onto a particular set of nodes, the degree distribution for the resulting one-mode network follows the distribution of the nodes being projected on to, but only so long as the degree distribution for the opposite set of nodes does not have a heavier tail. Furthermore, we show that bipartite degree distributions are not the only feature driving topology formation of projected networks, in contrast to what is commonly described in the literature.

Power law distributions of patents as indicators of innovation.

The total number of patents produced by a country (or the number of patents produced per capita) is often used as an indicator for innovation. Here we present evidence that the distribution of patents amongst applicants within many countries is well-described by power laws with exponents that vary between 1.66 (Japan) and 2.37 (Poland). We suggest that this exponent is a useful new metric for studying innovation. Using simulations based on simple preferential attachment-type rules that generate power laws, we find we can explain some of the variation in exponents between countries, with countries that have larger numbers of patents per applicant generally exhibiting smaller exponents in both the simulated and actual data. Similarly we find that the exponents for most countries are inversely correlated with other indicators of innovation, such as research and development intensity or the ubiquity of export baskets. This suggests that in more advanced economies, which tend to have smaller values of the exponent, a greater proportion of the total number of patents are filed by large companies than in less advanced countries.