Testing and Estimation of Social Network Dependence With Time to Event Data

Admixture has obscured signals of historical hard sweeps in humans

The role of natural selection in shaping biological diversity is an area of intense interest in modern biology. To date, studies of positive selection have primarily relied on genomic datasets from contemporary populations, which are susceptible to confounding factors associated with complex and often unknown aspects of population history. In particular, admixture between diverged populations can distort or hide prior selection events in modern genomes, though this process is not explicitly accounted for in most selection studies despite its apparent ubiquity in humans and other species. Through analyses of ancient and modern human genomes, we show that previously reported Holocene-era admixture has masked more than 50 historic hard sweeps in modern European genomes. Our results imply that this canonical mode of selection has probably been underappreciated in the evolutionary history of humans and suggest that our current understanding of the tempo and mode of selection in natural populations may be inaccurate.

Recursive state estimation for a class of nonlinear uncertain coupled complex networks subject to random link failures and packet disorders

This paper is concerned with the recursive state estimation (RSE) problem under minimum mean-square error sense for a class of nonlinear complex networks (CNs) with uncertain inner coupling, random link failures and packet disorders. Firstly, a set of random variables obeying the Bernoulli distribution is adopted to characterize whether there are connections between different network units, i.e., there is no random link failure when the random variable is equal to 1, otherwise the random link failure occurs. In addition, the inner coupling strength is assumed to be varying within a given interval and the phenomenon of packet disorders caused by the random transmission delay (RTD) is also taken into account. In our study, the nonlinearity satisfies the continuously differentiable condition, which can be linearized by resorting to the Taylor expansion. The focus of the addressed RSE problem is on the design of an RSE approach in the mean-square error sense such that, for all uncertain inner coupling, random link failures and packet disorders, a suboptimal upper bound of the state estimation error covariance is obtained and minimized by parameterizing the state estimator gain with explicit expression form. Furthermore, a sufficient condition with respect to the uniform boundedness of state estimation error in mean-square sense is elaborated. Finally, a numerical experiment is introduced to demonstrate the validity of the presented RSE approach.

Synchronized nonpharmaceutical interventions for the control of COVID-19

The world is experiencing an ongoing pandemic of coronavirus disease-2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In attempts to control the pandemic, a range of nonpharmaceutical interventions (NPIs) has been implemented worldwide. However, the effect of synchronized NPIs for the control of COVID-19 at temporal and spatial scales has not been well studied. Therefore, a meta-population model that incorporates essential nonlinear processes was constructed to uncover the transmission characteristics of SARS-CoV-2 and then assess the effectiveness of synchronized NPIs on COVID-19 dynamics in China. Regional synchronization of NPIs was observed in China, and it was found that a combination of synchronized NPIs (the travel restrictions, the social distancing and the infection isolation) prevented 93.7% of SARS-CoV-2 infections. The use of synchronized NPIs at the time of the Wuhan lockdown may have prevented as much as 38% of SARS-CoV-2 infections, compared with the unsynchronized scenario. The interconnectivity of the epicenter, the implementation time of synchronized NPIs, and the number of regions considered all affected the performance of synchronized NPIs. The results highlight the importance of using synchronized NPIs in high-risk regions for the control of COVID-19 and shed light on effective strategies for future pandemic responses.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11071-021-06505-0.

Stepping up during a crisis: The unintended effects of a noncontributory pension program during the Covid-19 pandemic

We use a regression discontinuity design to study the impacts of a noncontributory pension program covering one-third of Bolivian households during the COVID-19 pandemic. Becoming eligible for the program during the crisis increased the probability that households had a week's worth of food stocked by 25% and decreased the probability of going hungry by 40%. Although the program was not designed to provide emergency assistance, it provided unintended positive impacts during the crisis. The program's effects on hunger were particularly large for households that lost their livelihoods during the crisis and for low-income households. The results suggest that, during a systemic crisis, a preexisting near-universal pension program can quickly deliver positive impacts in line with the primary goals of a social safety net composed of an income-targeted cash transfer and an unemployment insurance program.

Social Network Mediation Analysis: A Latent Space Approach

A social network comprises both actors and the social connections among them. Such connections reflect the dependence among social actors, which is essential for individuals' mental health and social development. In this article, we propose a mediation model with a social network as a mediator to investigate the potential mediation role of a social network. In the model, the dependence among actors is accounted for by a few mutually orthogonal latent dimensions which form a social space. The individuals' positions in such a latent social space are directly involved in the mediation process between an independent and dependent variable. After showing that all the latent dimensions are equivalent in terms of their relationship to the social network and the meaning of each dimension is arbitrary, we propose to measure the whole mediation effect of a network. Although individuals' positions in the latent space are not unique, we rigorously articulate that the proposed network mediation effect is still well defined. We use a Bayesian estimation method to estimate the model and evaluate its performance through an extensive simulation study under representative conditions. The usefulness of the network mediation model is demonstrated through an application to a college friendship network.