Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees.

Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a practical Bayesian framework for parameter estimation remains an outstanding challenge. We have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out cross-validation, and we further validated some of the predictions in the experimental scientific literature.

Incorporating social determinants of health into transmission modeling of COVID-19 vaccine in the US: a scoping review.

During COVID-19 in the US, social determinants of health (SDH) have driven health disparities. However, the use of SDH in COVID-19 vaccine modeling is unclear. This review aimed to summarize the current landscape of incorporating SDH into COVID-19 vaccine transmission modeling in the US. Medline and Embase were searched up to October 2022. We included studies that used transmission modeling to assess the effects of COVID-19 vaccine strategies in the US. Studies' characteristics, factors incorporated into models, and approaches to incorporate these factors were extracted. Ninety-two studies were included. Of these, 11 studies incorporated SDH factors (alone or combined with demographic factors). Various sets of SDH factors were integrated, with occupation being the most common (8 studies), followed by geographical location (5 studies). The results show that few studies incorporate SDHs into their models, highlighting the need for research on SDH impact and approaches to incorporating SDH into modeling. Funding: This research was funded by the Centers for Disease Control and Prevention (CDC).

Power and multicollinearity in small networks: A discussion of "Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks" by Krivitsky, Coletti & Hens.

The recent work by Krivitsky, Coletti & Hens [KCH] provides an important new contribution to the Exponential-Family Random Graph Models [ERGMs], a start-to-finish approach to dealing with multi-network ERGMs. Although multi-network ERGMs have been around for a while (mostly in the form of block-diagonal models and multi-level ERGMs, see Duxbury and Wertsching (2023), Wang et al. (2013), Slaughter and Koehly (2016)), not much care has been given to the estimation and post-estimation steps. In their paper, Krivitsky, Coletti & Hens give a detailed layout of how to build, estimate, and analyze multi-ERGMs with heterogeneous data sources. In this comment, I will focus on two issues the authors did not discuss, namely, sample size requirements and multicollinearity.

The formation of political discussion networks.

Dialogues among politicians provide a window into political landscapes and relations among parties and nations. Existing research has focused on the outcomes of such dialogues and on the structure of social networks on which they take place. Little is known, however, about how political discussion networks form and which are the main driving forces behind their formation. We study a collection of ego-networks from 30 randomly sampled Romanian politicians to reveal fundamental processes behind the formation of political discussion networks. We show that ties in such networks tend to be strong and balanced, and that their organization is not affected by sex, age or education homophily. We use the exponential family of random graph models for small networks to assess likely closure mechanisms and possible homophily effects, but we note that further research and additional data are needed to fully understand the impact of context and political affiliations on the generalization of our findings.

Diffusion/Contagion Processes on Social Networks.

This study models how new ideas, practices, or diseases spread within and between communities, the diffusion of innovations or contagion. Several factors affect diffusion such as the characteristics of the initial adopters, the seeds; the structure of the network over which diffusion occurs; and the shape of the threshold distribution, which is the proportion of prior adopting peers needed for the focal individual to adopt. In this study, seven seeding conditions are modeled: (1) three opinion leadership indicators, (2) two bridging measures, (3) marginally positioned seeds, and (4) randomly selected seeds for comparison. Three network structures are modeled: (1) random, (2) small-world, and (3) scale-free. Four threshold distributions are modeled: (1) normal; (2) uniform; (3) beta 7,14; and (4) beta 1,2; all of which have a mean threshold of 33%, with different variances. The results show that seeding with nodes high on in-degree centrality and/or inverse constraint has faster and more widespread diffusion. Random networks had faster and higher prevalence of diffusion than scale-free ones, but not different from small-world ones. Compared with the normal threshold distribution, the uniform one had faster diffusion and the beta 7,14 distribution had slower diffusion. Most significantly, the threshold distribution standard deviation was associated with rate and prevalence such that higher threshold standard deviations accelerated diffusion and increased prevalence. These results underscore factors that health educators and public health advocates should consider when developing interventions or trying to understand the potential for behavior change.

Smoking Diffusion through Networks of Diverse, Urban American Adolescents over the High School Period.

This study uses recent data to investigate if smoking initiation diffuses through friendship networks over the high school period and explores if diffusion processes differ across schools. One thousand four hundred and twenty-five racially and ethnically diverse youth from four high schools in Los Angeles were surveyed four times over the high school period from 2010 to 2013. Probit regression models and stochastic actor-based models for network dynamics tested for peer effects on smoking initiation. Friend smoking was found to predict adolescent smoking, and smoking initiation diffused through friendship networks in some but not all of the schools. School differences in smoking rates and the popularity of smokers may be linked to differences in the diffusion of smoking through peer networks. We conclude that there are differences in peer effects on smoking initiation across schools that will be important to account for in network-based smoking interventions.

Network influences on policy implementation: Evidence from a global health treaty.

This paper examines whether country implementation of a public health treaty is influenced by the implementation behaviors of other countries to which they have network ties. We examine implementation of the Framework Convention on Tobacco Control (FCTC) adopted by the World Health Organization in 2003 and ratified by approximately 94% of countries as of 2016. We constructed five networks: (1) geographic distance, (2) general trade, (3) tobacco trade, (4) GLOBALink referrals, and (5) GLOBALink co-subscriptions. Network exposure terms were constructed from these networks based on the implementation scores for six articles of the FCTC treaty. We estimate effects using a lagged Type 1 Tobit model. Results show that network effects were significant: (a) across all networks for article 6 (pricing and taxation), (b) distance, general trade, GL referrals, and GL co-subscriptions for article 8 (second hand smoke), (c) distance, general trade, and GL co-subscriptions for article 11 (packaging and labeling), and (d) distance and GL co-subscription for article 13 (promotion and advertising), (e) tobacco trade and GL co-subscriptions for article 14 (cessation). These results indicate that diffusion effects were more prevalent for pricing and taxation as well as restrictions on smoking in public places and packaging and labeling. These results suggest that network influences are possible in domains that are amenable to control by national governments but unlikely to occur in domains established by existing regulatory systems. Implications for future studies of policy implementation are discussed.

Sensing eating mimicry among family members.

Family relationships influence eating behavior and health outcomes (e.g., obesity). Because eating is often habitual (i.e., automatically driven by external cues), unconscious behavioral mimicry may be a key interpersonal influence mechanism for eating within families. This pilot study extends existing literature on eating mimicry by examining whether multiple family members mimicked each other's bites during natural meals. Thirty-three participants from 10 families were videotaped while eating an unstructured family meal in a kitchen lab setting. Videotapes were coded for participants' bite occurrences and times. We tested whether the likelihood of a participant taking a bite increased when s/he was externally cued by a family eating partner who had recently taken a bite (i.e., bite mimicry). A paired-sample t-test indicated that participants had a significantly faster eating rate within the 5 s following a bite by their eating partner, compared to their bite rate at other times (t = 7.32, p < .0001). Nonparametric permutation testing identified five of 78 dyads in which there was significant evidence of eating mimicry; and 19 of 78 dyads that had p values < .1. This pilot study provides preliminary evidence that suggests eating mimicry may occur among a subset of family members, and that there may be types of family ties more prone to this type of interpersonal influence during meals.