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Faculty hiring and retention determine the composition of the US academic workforce and directly shape educational outcomes1, careers2, the development and spread of ideas3 and research priorities4,5. However, hiring and retention are dynamic, reflecting societal and academic priorities, generational turnover and efforts to diversify the professoriate along gender6–8, racial9 and socioeconomic10 lines. A comprehensive study of the structure and dynamics of the US professoriate would elucidate the effects of these efforts and the processes that shape scholarship more broadly. Here we analyse the academic employment and doctoral education of tenure-track faculty at all PhD-granting US universities over the decade 2011–2020, quantifying stark inequalities in faculty production, prestige, retention and gender. Our analyses show universal inequalities in which a small minority of universities supply a large majority of faculty across fields, exacerbated by patterns of attrition and reflecting steep hierarchies of prestige. We identify markedly higher attrition rates among faculty trained outside the United States or employed by their doctoral university. Our results indicate that gains in women’s representation over this decade result from demographic turnover and earlier changes made to hiring, and are unlikely to lead to long-term gender parity in most fields. These analyses quantify the dynamics of US faculty hiring and retention, and will support efforts to improve the organization, composition and scholarship of the US academic workforce.

Background The coronavirus disease 2019 pandemic spread to >200 countries in Methods Given high residence hall student density, the University of Colorado Boulder established a mandatory weekly screening test program. We analyzed longitudinal data from 6408 students and identified 116 likely transmission events in which a second roommate tested positive within 14 days of the index roommate. Results Although the infection rate was lower in single-occupancy rooms (10%) than in multiple-occupancy rooms (19%), interroommate transmission occurred only about 20% of the time. Cases were usually asymptomatic at the time of detection. Notably, individuals who likely transmitted had an average viral load approximately 6.5-fold higher than individuals who did not (mean quantification cycle [Cq], 26.2 vs 28.9). Although students with diagnosed SARS-CoV-2 infection moved to isolation rooms, there was no difference in time to isolation between cases with or without interroommate transmission. Conclusions This analysis argues that interroommate transmission occurs infrequently in residence halls and provides strong correlative evidence that viral load is proportional to transmission probability.

Networks are ubiquitous in life, structuring options available for choice and influencing their relative attractiveness. In this article, we propose an integration of network science and choice theory beyond merely incorporating metrics from one area into models of the other. We posit a typology and framework for “network-choice models” that highlight the distinct ways choices occur in and influence networked environments, as well as two specific feedback processes that guide their mutual interaction, emergent valuation and contingent options. In so doing, we discuss examples, data sources, methodological challenges, anticipated benefits, and research pathways to fully interweave network and choice models.

Faculty hiring and retention determine the composition of the US academic workforce and directly shape educational outcomes

Analyzing social networks is challenging. Key features of relational data require the use of non-standard statistical methods such as developing system-specific null, or reference, models that randomize one or more components of the observed data. Here we review a variety of randomization procedures that generate reference models for social network analysis. Reference models provide an expectation for hypothesis-testing when analyzing network data. We outline the key stages in producing an effective reference model and detail four approaches for generating reference distributions: permutation, resampling, sampling from a distribution, and generative models. We highlight when each type of approach would be appropriate and note potential pitfalls for researchers to avoid. Throughout, we illustrate our points with examples from a simulated social system. Our aim is to provide social network researchers with a deeper understanding of analytical approaches to enhance their confidence when tailoring reference models to specific research questions.

Community testing programs focused on the unvaccinated population are being enacted in populations with mixed vaccination status to mitigate SARS-CoV-2 spread. Presumably, these policies assume that the unvaccinated are driving transmission, though it is not well understood how viral spread occurs in mixed-status populations. Here, we analyze a model of transmission in which a variable fraction of the population is vaccinated, with unvaccinated individuals proactively screened for infection. By exploring a range of transmission rates, vaccine effectiveness (VE) scenarios, and rates of prior infection, this analysis reveals principles of viral spread in communities of mixed vaccination status, with implications for screening policies. As vaccination rates increase, the proportion of transmission driven by the unvaccinated population decreases, such that most community spread is driven by breakthrough infections once vaccine coverage exceeds 55% (omicron) or 80% (delta), with additional variation dependent on waning or boosted VE. More broadly, the potential impacts of unvaccinated-only screening fall into three distinct parameter regions: (I) “flattening the curve” with little impact on cumulative infections, (II) effectively suppressing transmission, and (III) negligible impact because herd immunity is reached without screening. By evaluating a wide range of scenarios, this work finds broadly that effective mitigation of SARS-CoV-2 transmission by unvaccinated-only screening is highly dependent on vaccination rate, population-level immunity, screening compliance, and vaccine effectiveness against the current variant.

Faculty hiring networks—who hires whose graduates as faculty—exhibit steep hierarchies, which can reinforce both social and epistemic inequalities in academia. Understanding the mechanisms driving these patterns would inform efforts to diversify the academy and shed new light on the role of hiring in shaping which scientific discoveries are made. Here, we investigate the degree to which structural mechanisms can explain hierarchy and other network characteristics observed in empirical faculty hiring networks. We study a family of adaptive rewiring network models, which reinforce institutional prestige within the hierarchy in five distinct ways. Each mechanism determines the probability that a new hire comes from a particular institution according to that institution’s prestige score, which is inferred from the hiring network’s existing structure. We find that structural inequalities and centrality patterns in real hiring networks are best reproduced by a mechanism of global placement power, in which a new hire is drawn from a particular institution in proportion to the number of previously drawn hires anywhere. On the other hand, network measures of biased visibility are better recapitulated by a mechanism of local placement power, in which a new hire is drawn from a particular institution in proportion to the number of its previous hires already present at the hiring institution. These contrasting results suggest that the underlying structural mechanism reinforcing hierarchies in faculty hiring networks is a mixture of global and local preference for institutional prestige. Under these dynamics, we show that each institution’s position in the hierarchy is remarkably stable, due to a dynamic competition that overwhelmingly favors more prestigious institutions. These results highlight the reinforcing effects of a prestige-based faculty hiring system, and the importance of understanding its ramifications on diversity and innovation in academia.

Counting the number of species, items, or genes that are shared between two groups, sets, or communities is a simple calculation when sampling is complete. However, when only partial samples are available, quantifying the overlap between two communities becomes an estimation problem. Furthermore, to calculate normalized measures of β-diversity, such as the Jaccard and Sorenson-Dice indices, one must also estimate the total sizes of the communities being compared. Previous efforts to address these problems have assumed knowledge of total community sizes and then used Bayesian methods to produce unbiased estimates with quantified uncertainty. Here, we address communities of unknown size and show that this produces systematically better estimates—both in terms of central estimates and quantification of uncertainty in those estimates. We further show how to use species, item, or gene count data to refine estimates of community size in a Bayesian joint model of community size and overlap.

Counting the number of species, items, or genes that are shared between two sets is a simple calculation when sampling is complete. However, when only partial samples are available, quantifying the overlap between two sets becomes an estimation problem. Furthermore, to calculate normalized measures of β-diversity, such as the Jaccard and Sorenson-Dice indices, one must also estimate the total sizes of the sets being compared. Previous efforts to address these problems have assumed knowledge of total population sizes and then used Bayesian methods to produce unbiased estimates with quantified uncertainty. Here, we address populations of unknown size and show that this produces systematically better estimates—both in terms of central estimates and quantification of uncertainty in those estimates. We further show how to use species count data to refine estimates of population size in a Bayesian joint model of populations and overlap.

Test-negative designs (TNDs) can be used to estimate vaccine effectiveness by comparing the relative rates of the target disease and symptomatically similar diseases among vaccinated and unvaccinated populations. However, the diagnostic tests used to identify the target disease typically suffer from imperfect sensitivity and specificity, leading to biased vaccine effectiveness estimates. Here we present a solution to this problem via a Bayesian statistical model which can either incorporate point estimates of test sensitivity and specificity, or can jointly infer them directly from laboratory validation data. This approach enables uncertainties in the performance characteristics of the diagnostic test to be correctly propagated to estimates, avoiding both bias and false precision in vaccine effectiveness. By further incorporating individual covariates of study participants, and by allowing data streams from multiple diagnostic test types to be rigorously combined, our approach provides a flexible model for the analysis of TNDs with explicitly stated assumptions.

A central problem in the COVID-19 pandemic is that there is not enough testing to prevent infectious spread of SARS-CoV-2, causing surges and lockdowns with human and economic toll. Molecular tests that detect viral RNAs or antigens will be unable to rise to this challenge unless testing capacity increases by at least an order of magnitude while decreasing turnaround times. Here, we evaluate an alternative strategy based on the monitoring of olfactory dysfunction, a symptom identified in 76–83% of SARS-CoV-2 infections—including those with no other symptoms—when a standardized olfaction test is used. We model how screening for olfactory dysfunction, with reflexive molecular tests, could be beneficial in reducing community spread of SARS-CoV-2 by varying testing frequency and the prevalence, duration, and onset time of olfactory dysfunction. We find that monitoring olfactory dysfunction could reduce spread via regular screening, and could reduce risk when used at point-of-entry for single-day events. In light of these estimated impacts, and because olfactory tests can be mass produced at low cost and self-administered, we suggest that screening for olfactory dysfunction could be a high impact and cost-effective method for broad COVID-19 screening and surveillance., A central problem in the COVID-19 pandemic is that there is not enough testing to prevent infectious spread of SARS-CoV-2, causing surges and lockdowns with human and economic toll. Here, the authors evaluate an alternative strategy based on the monitoring of olfactory dysfunction with a mathematical model.

Pathogens can spread epidemically through populations. Beneficial contagions, such as viruses that enhance host survival or technological innovations that improve quality of life, also have the potential to spread epidemically. How do the dynamics of beneficial biological and social epidemics differ from those of detrimental epidemics? We investigate this question using three theoretical approaches. First, in the context of population genetics, we show that a horizontally-transmissible element that increases fitness, such as viral DNA, spreads superexponentially through a population, more quickly than a beneficial mutation. Second, in the context of behavioral epidemiology, we show that infections that cause increased connectivity lead to superexponential fixation in the population. Third, in the context of dynamic social networks, we find that preferences for increased global infection accelerate spread and produce superexponential fixation, but preferences for local assortativity halt epidemics by disconnecting the infected from the susceptible. We conclude that the dynamics of beneficial biological and social epidemics are characterized by the rapid spread of beneficial elements, which is facilitated in biological systems by horizontal transmission and in social systems by active spreading behavior of infected individuals., Comment: The original version of this paper [v1] was produced, from conception of idea, to execution, to writing, by a team in just 72 hours (see Appendix of [v1]). This is a revised version

Faculty at prestigious institutions produce more scientific papers, receive more citations and scholarly awards, and are typically trained at more-prestigious institutions than faculty with less prestigious appointments. This imbalance is often attributed to a meritocratic system that sorts individuals into more-prestigious positions according to their reputation, past achievements, and potential for future scholarly impact. Here, we investigate the determinants of scholarly productivity and measure their dependence on past training and current work environments. To distinguish the effects of these environments, we apply a matched-pairs experimental design to career and productivity trajectories of 2,453 early-career faculty at all 205 PhD-granting computer science departments in the United States and Canada, who together account for over 200,000 publications and 7.4 million citations. Our results show that the prestige of faculty’s current work environment, not their training environment, drives their future scientific productivity, while current and past locations drive prominence. Furthermore, the characteristics of a work environment are more predictive of faculty productivity and impact than mechanisms representing preferential selection or retention of more-productive scholars by more-prestigious departments. These results identify an environmental mechanism for cumulative advantage, in which an individual’s past successes are “locked in” via placement into a more prestigious environment, which directly facilitates future success. The scientific productivity of early-career faculty is thus driven by where they work, rather than where they trained for their doctorate, indicating a limited role for doctoral prestige in predicting scientific contributions.

To better understand the factors underlying the continued incidence of clinical episodes of falciparum malaria in E-2020 countries targeting elimination, we have characterised Plasmodium falciparum disease transmission dynamics after a clonal outbreak on the northwest coast of Ecuador over a period of two years. We apply a novel, high-resolution genotyping method, the “varcode” based on a single PCR to fingerprint the DBLα region of the 40-60 members of the variant surface antigen-encoding var multigene family. Var genes are highly polymorphic within and between genomes, with var repertoires rapidly evolving by outcrossing during the obligatory sexual phase of P. falciparum in the mosquito. The continued incidence of clinical malaria after the outbreak in Ecuador provided a unique opportunity to use varcodes to document parasite microevolution and explore signatures of local disease transmission on the time scale of months to two years post-outbreak. We identified nine genetic varcodes circulating locally with spatiotemporal parasite genetic relatedness networks revealing that diversification of the clonal outbreak parasites by sexual recombination was associated with increased incidence of clinical episodes of malaria. Whether this was due to chance, immune selection or sexual recombination per se is discussed. Comparative analyses to other South American parasite populations where P. falciparum transmission remains endemic elucidated the possible origins of Ecuadorian varcodes. This analysis demonstrated that the majority of clinical cases were due to local transmission and not importation. Nonetheless, some of the varcodes that were unrelated to the outbreak varcode were found to be genetically related to other South American parasites. Our findings demonstrate the utility of the varcode as a high-resolution surveillance tool to spatiotemporally track disease outbreaks using variant surface antigen genes and resolve signatures of recombination in an E-2020 setting nearing elimination.

Many social and biological systems are characterized by enduring hierarchies, including those organized around prestige in academia, dominance in animal groups, and desirability in online dating. Despite their ubiquity, the general mechanisms that explain the creation and endurance of such hierarchies are not well understood. We introduce a generative model for the dynamics of hierarchies using time-varying networks in which new links are formed based on the preferences of nodes in the current network and old links are forgotten over time. The model produces a range of hierarchical structures, ranging from egalitarianism to bistable hierarchies, and we derive critical points that separate these regimes in the limit of long system memory. Importantly, our model supports statistical inference, allowing for a principled comparison of generative mechanisms using data. We apply the model to study hierarchical structures in empirical data on hiring patterns among mathematicians, dominance relations among parakeets, and friendships among members of a fraternity, observing several persistent patterns as well as interpretable differences in the generative mechanisms favored by each. Our work contributes to the growing literature on statistically grounded models of time-varying networks., Updates corresponding to final published version

BackgroundRobust community-level SARS-CoV-2 prevalence estimates have been difficult to obtain in the American South and outside of major metropolitan areas. Furthermore, though some previous studies have investigated the association of demographic factors such as race with SARS-CoV-2 exposure risk, fewer have correlated exposure risk to surrogates for socioeconomic status such as health insurance coverage.MethodsWe used a highly specific serological assay utilizing the receptor binding domain of the SARS-CoV-2 spike-protein to identify SARS-CoV-2 antibodies in remnant blood samples collected by the University of North Carolina Health system. We estimated the prevalence of SARS-CoV-2 in this cohort with Bayesian regression, as well as the association of critical demographic factors with higher prevalence odds.FindingsBetween April 21st and October 3rd of 2020, a total of 9,624 unique samples were collected from clinical sites in central NC and we observed a seroprevalence increase from 2·9 (1·7, 4·3) to 9·1 (7·2, 11·1) over the study period. Individuals who identified as Latinx were associated with the highest odds ratio of SARS-CoV-2 exposure at 7·77 overall (5·20, 12·10). Increased odds were also observed among Black individuals and individuals without public or private health insurance.InterpretationOur data suggests that for this care-accessing cohort, SARS-CoV-2 seroprevalence was significantly higher than cumulative total cases reported for the study geographical area six months into the COVID-19 pandemic in North Carolina. The increased odds of seropositivity by ethnoracial grouping as well as health insurance highlights the urgent and ongoing need to address underlying health and social disparities in these populations.RESEARCH IN CONTEXTEvidence before this studyWe searched PubMed for studies published through March 21st, 2021. We used search terms that included “COVID-19”, “SARS-CoV-2”, “prevalence” and “seroprevalence”. Our search resulted in 399 papers, from which we identified 58 relevant studies describing SARS-CoV-2 seroprevalence at sites around the United States from March 1 to December 9, 2020, 12 of which utilized remnant clinical samples and three of which overlapped with our study area. Most notably, one study of 4,422 asymptomatic inpatients and outpatients in central NC from April 28-June 19, 2020 found an estimated seroprevalence of 0·7 −0·8%, and another study of 177,919 inpatients and outpatients (3,817 from NC) from July 27-September 24, 2020 found an estimated seroprevalence of 2·5 −6·8%.Added value of this studyThis is the largest SARS-CoV-2 seroprevalence cohort published to date in NC. Importantly, we used a Bayesian framework to account for uncertainty in antibody assay sensitivity and specificity and investigated seropositivity by important demographic variables that have not yet been studied in this context in NC. This study corroborates other reports that specific demographic factors including race, ethnicity and the lack of public or private insurance are associated with elevated risk of SARS-CoV-2 infection. Furthermore, in a subset of serum samples, we identify other SARS-CoV-2 antibodies elicited by these individuals, including functionally neutralizing antibodies.Implications of all the available evidenceIt is difficult to say the exact seroprevalence in the central North Carolina area, but a greater proportion of the population accessing healthcare has been infected by SARS-CoV-2 than is reflected by infection cases confirmed by molecular testing. Furthermore, local governments need to prioritize addressing the many forms of systemic racism and socioeconomic disadvantage that drive SARS-CoV-2 exposure risk, such as residential and occupational risk, and an urgent need to provide access to SARS-CoV-2 testing and vaccination to these groups.