The United Nations recently released population projections based on data until 2012 and a Bayesian probabilistic methodology. Analysis of these data reveals that, contrary to previous literature, world population is unlikely to stop growing this century. There is an 80% probability that world population, now 7.2 billion, will increase to between 9.6 and 12.3 billion in 2100. This uncertainty is much smaller than the range from the traditional UN high and low variants. Much of the increase is expected to happen in Africa, in part due to higher fertility and a recent slowdown in the pace of fertility decline. Also, the ratio of working age people to older people is likely to decline substantially in all countries, even those that currently have young populations.The United Nations (UN) is the leading agency that projects world population into the future on a regular basis (2). Every two years it publishes revised data of the populations of all countries by age and sex, as well as fertility, mortality and migration rates, in a biennial publication called the World Population Prospects (WPP). In July 2014, probabilistic projections for individual countries to 2100 were released Unlike previous projections, they allow us to quantify our confidence in projected future trends using established methods of statistical inference. They are based on recent data, including the results of the 2010 round of censuses and recent surveys until 2012, as well as the most recent data on incidence, ‡

Random graph null models have found widespread application in diverse research communities analyzing network datasets, including social, information, and economic networks, as well as food webs, protein-protein interactions, and neuronal networks. The most popular family of random graph null models, called configuration models, are defined as uniform distributions over a space of graphs with a fixed degree sequence. Commonly, properties of an empirical network are compared to properties of an ensemble of graphs from a configuration model in order to quantify whether empirical network properties are meaningful or whether they are instead a common consequence of the particular degree sequence. In this work we study the subtle but important decisions underlying the specification of a configuration model, and investigate the role these choices play in graph sampling procedures and a suite of applications. We place particular emphasis on the importance of specifying the appropriate graph labeling-stub-labeled or vertex-labeled-under which to consider a null model, a choice that closely connects the study of random graphs to the study of random contingency tables. We show that the choice of graph labeling is inconsequential for studies of simple graphs, but can have a significant impact on analyses of multigraphs or graphs with self-loops. The importance of these choices is demonstrated through a series of three in-depth vignettes, analyzing three different network datasets under many different configuration models and observing substantial differences in study conclusions under different models. We argue that in each case, only one of the possible configuration models is appropriate. While our work focuses on undirected static networks, it aims to guide the study of directed networks, dynamic networks, and all other network contexts that are suitably studied through the lens of random graph null models. * All authors contributed equally to this work.

The substantial gender gap in the science, technology, engineering, and mathematics (STEM) workforce can be traced back to the underrepresentation of women at various milestones in the career pathway. Calculus is a necessary step in this pathway and has been shown to often dissuade people from pursuing STEM fields. We examine the characteristics of students who begin college interested in STEM and either persist or switch out of the calculus sequence after taking Calculus I, and hence either continue to pursue a STEM major or are dissuaded from STEM disciplines. The data come from a unique, national survey focused on mainstream college calculus. Our analyses show that, while controlling for academic preparedness, career intentions, and instruction, the odds of a woman being dissuaded from continuing in calculus is 1.5 times greater than that for a man. Furthermore, women report they do not understand the course material well enough to continue significantly more often than men. When comparing women and men with above-average mathematical abilities and preparedness, we find women start and end the term with significantly lower mathematical confidence than men. This suggests a lack of mathematical confidence, rather than a lack of mathematically ability, may be responsible for the high departure rate of women. While it would be ideal to increase interest and participation of women in STEM at all stages of their careers, our findings indicate that if women persisted in STEM at the same rate as men starting in Calculus I, the number of women entering the STEM workforce would increase by 75%.

[1] We conducted measurements of methane (CH 4 ) emission and ecosystem respiration on >200 points across the Arctic coastal tundra near Barrow, Alaska, United States, in July 2007 and August 2008. This site contains broad diversity in tundra microtopography, including polygonal tundra, thaw lakes, and drained lake basins. In 2007, we surveyed CH 4 emissions across this landscape, and found that soil water content was the strongest control of methane emission rate, such that emission rates rose exponentially with water content. However, there was considerable residual variation in CH 4 emission in the wettest soils (>80% volumetric water content) where CH 4 emissions were highest. A statistical analysis of possible soil and plant controls on CH 4 emission rates from these wet soils revealed that vegetation height (especially of Carex aquatilis) was the best predictor, with ecosystem respiration and permafrost depth as significant copredictors. To evaluate whether plant height served as a proxy for aboveground plant biomass, or gross primary production, we conducted a survey of CH 4 emission rates from wet, Carex-dominated sites in 2008, coincidently measuring these candidate predictors. Surprisingly, vegetation height remained the best predictor of CH 4 emission rates, with CH 4 emissions rising exponentially with vegetation height. We hypothesize that taller plants have more extensive root systems that both stimulate more methanogenesis and conduct more pore water CH 4 to the atmosphere. We anticipate that the magnitude of the climate change-CH 4 feedback in the Arctic Coastal Plain will strongly depend on how permafrost thaw alters the ecology of Carex aquatilis.

and ygrad@hsph.harvard.edu.Establishing how many people have already been infected by SARS-CoV-2 is an urgent priority for controlling the COVID-19 pandemic. Patchy virological testing has hampered interpretation of confirmed case counts, and unknown rates of asymptomatic and mild infections make it challenging to de-1 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

IntroductionThe infection fatality rate (IFR) of COVID-19 has been carefully measured and analysed in high-income countries, whereas there has been no systematic analysis of age-specific seroprevalence or IFR for developing countries.MethodsWe systematically reviewed the literature to identify all COVID-19 serology studies in developing countries that were conducted using representative samples collected by February 2021. For each of the antibody assays used in these serology studies, we identified data on assay characteristics, including the extent of seroreversion over time. We analysed the serology data using a Bayesian model that incorporates conventional sampling uncertainty as well as uncertainties about assay sensitivity and specificity. We then calculated IFRs using individual case reports or aggregated public health updates, including age-specific estimates whenever feasible.ResultsIn most locations in developing countries, seroprevalence among older adults was similar to that of younger age cohorts, underscoring the limited capacity that these nations have to protect older age groups.Age-specific IFRs were roughly 2 times higher than in high-income countries. The median value of the population IFR was about 0.5%, similar to that of high-income countries, because disparities in healthcare access were roughly offset by differences in population age structure.ConclusionThe burden of COVID-19 is far higher in developing countries than in high-income countries, reflecting a combination of elevated transmission to middle-aged and older adults as well as limited access to adequate healthcare. These results underscore the critical need to ensure medical equity to populations in developing countries through provision of vaccine doses and effective medications.

Theory predicts that social interactions are dynamically linked to phenotype. Yet because social interactions are difficult to quantify, little is known about the precise details on how interactivity is linked to phenotype. Here, we deployed proximity loggers on North American barn swallows (Hirundo rustica erythrogaster) to examine intercorrelations among social interactions, morphology and features of the phenotype that are sensitive to the social context: stress-induced corticosterone (CORT) and gut microbial diversity. We analysed relationships at two spatial scales of interaction: (i) body contact and (ii) social interactions occurring between 0.1 and 5 m. Network analysis revealed that relationships between social interactions, morphology, CORT and gut microbial diversity varied depending on the sexes of the individuals interacting and the spatial scale of interaction proximity. We found evidence that body contact interactions were related to diversity of socially transmitted microbes and that looser social interactions were related to signalling traits and CORT.

Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in applications involving animal social networks, collection of these data is often opportunistic and can be invasive. Frequently, the social network of interest for a given population is closely related to the way individuals move. Thus telemetry data, which are minimally-invasive and relatively inexpensive to collect, present an alternative source of information. We develop a framework for using telemetry data to infer social relationships among animals. To achieve this, we propose a Bayesian hierarchical model with an underlying dynamic social network controlling movement of individuals via two mechanisms: an attractive effect, and an aligning effect. We demonstrate the model and its ability to accurately identify complex social behavior in simulation, and apply our model to telemetry data arising from killer whales. Using auxiliary information about the study population, we investigate model validity and find the inferred dynamic social network is consistent with killer whale ecology and expert knowledge.