Comparing Pregnancy and Childbirth-related Hospital Visits in Arizona Before and During COVID-19 Using Network Analysis

The COVID-19 pandemic has had a severe effect on all facets of human society, including healthcare. One of the primary concerns in healthcare is understanding and mitigating the impact of the pandemic on pregnancy and childbirth. While several studies have looked at challenges such as contract tracing of positive cases, predicting confirmed cases and deaths in individuals and communities, few studies have examined differences in hospitalization and treatment of pregnant mothers and infant care in large populations. In this study, the prevalence and co-occurrence of pregnancy and childbirth-related diagnoses reported in Arizona State hospitals for three sixth-month periods - before COVID-19 (second half of 2019), COVID-19 onset (first half of 2020), and COVID-19 (second half of 2020) are analyzed using network analysis. The results show that there are considerable differences in ego networks of few diagnoses during these time periods warranting further investigation into the causality of such population changes.

Collaboration enhances career progression in academic science, especially for female researchers

Collaboration and diversity are increasingly promoted in science. Yet how collaborations influence academic career progression, and whether this differs by gender, remains largely unknown. Here, we use co-authorship ego networks to quantify collaboration behaviour and career progression of a cohort of contributors to biennial International Society of Behavioral Ecology meetings (1992, 1994, 1996). Among this cohort, women were slower and less likely to become a principal investigator (PI; approximated by having at least three last-author publications) and published fewer papers over fewer years (i.e. had shorter academic careers) than men. After adjusting for publication number, women also had fewer collaborators (lower adjusted network size) and published fewer times with each co-author (lower adjusted tie strength), albeit more often with the same group of collaborators (higher adjusted clustering coefficient). Authors with stronger networks were more likely to become a PI, and those with less clustered networks did so more quickly. Women, however, showed a stronger positive relationship with adjusted network size (increased career length) and adjusted tie strength (increased likelihood to become a PI). Finally, early-career network characteristics correlated with career length. Our results suggest that large and varied collaboration networks are positively correlated with career progression, especially for women.

Effect of Infant Presence on Social Networks of Sterilized and Intact Wild Female Balinese Macaques (Macaca fascicularis)

Contraception is increasingly used to control wild animal populations. However, as reproductive condition influences social interactions in primates, the absence of new offspring could influence the females’ social integration. We studied two groups of wild macaques (Macaca fascicularis) including females recently sterilized in the Ubud Monkey Forest, Indonesia. We used social network analysis to examine female grooming and proximity networks and investigated the role of infant presence on social centrality and group connectivity, while controlling for the fertility status (sterilized N = 14, intact N = 34). We compared the ego networks of females experiencing different nursing conditions (young infant (YI) vs old infant (OI) vs non-nursing (NN) females). YI females were less central in the grooming network than other females while being more central in proximity networks, suggesting they could keep proximity within the group to protect their infant from hazards, while decreasing direct grooming interactions, involving potential risks such as kidnapping. The centrality of sterilized and intact females was similar, except for the proximity network where sterilized females had more partners and a better group connectivity. These results confirm the influence of nursing condition in female macaque social networks and did not show any negative short-term effects of sterilization on social integration.

Methods of Detecting Communities with Certain Relationship Typesin Graphs using Billing Information

Currently, one of the urgent tasks of graph analysis is community detection. A large number of algorithms have been developed for detecting communities in graphs. Meanwhile, these communities have nothing to do with groups of people, i.e., family, colleagues, friends, and are used to simplify the graph representation. For a large number of tasks, it is useful to detect a group of people who closely communicate with each other. Many algorithms for detecting communities do not take into account that one participant can belong to several communities, and this is a prerequisite for detecting social circles. The paper overviews the main approaches to community detection, and among these emphasizes the approaches based on functionality optimization, clique problem, cluster analysis and label distribution. The approaches based on the analysis of ego-networks, i.e., considering the subgraph formed by the connections of one participant, are considered separately. The study gives the basic algorithms that are applicable for the selection of communities with certain relationship types based on billing information. Findings of research are useful for community detection depending on the task and available input data