The effect of temporal aggregation level in social network monitoring

Co-roosting relationships are consistent across years in a bat maternity group

Long-lived, group living animals have the potential to form multiyear relationships. In some temperate bat species, maternity groups break apart and rejoin both daily, as females depart to forage and select day roosts to use, and annually, as bats leave for and return from hibernation. Here, we investigated whether bats have persistent social preferences by testing whether relationships between dyads in a focal year could be predicted by previous years. We also hypothesized that experience influences social preferences and predicted that an individual's age would influence its network position, while familiarity with bats of the same cohort would drive persistent social preferences. We quantified roost co-occurrence in little brown myotis (Myotis lucifugus) in Salmonier Nature Park, Newfoundland, Canada both within and among years. We found that roost co-occurrence patterns of previous years still had predictive value even when accounting for potential roost fidelity. However, we found no evidence that cohort familiarity or age explained any of the variation. Overall, we found long-term patterns of association in this temperate bat species that suggest levels of social complexity akin to other large mammal species.

A latent space model and Hotelling's T2 control chart to monitor the networks of Covid-19 symptoms

In the COVID-19 coronavirus pandemic, potential patients that suffer from different symptoms can be diagnosed with COVID-19. At the early stages of the pandemic, patients were mainly diagnosed with fever and respiratory symptoms. Recently, patients with new symptoms, such as gastrointestinal or loss of senses, are also diagnosed with COVID-19. Monitoring these symptoms can help the healthcare system to be aware of new symptoms that can be related to the COVID-19 coronavirus. This article focuses on monitoring the behavior of COVID-19 symptoms over time. In this regard, a Latent space model (LSM) and a Generalized linear model (GLM) are introduced to model the networks of symptoms. We apply Hotelling's T2 control chart to the estimated parameters of the LSM and GLM, to identify significant changes and detect anomalies in the networks. The performance of the proposed methods is evaluated using simulation and calculating average run length (ARL). Then, dynamic networks are generated from a COVID-19 epidemic survey dataset.

The Utilisation of Payment Models Across the HIV Continuum of Care: Systematic Review of Evidence

The increasing chronicity and multimorbidities associated with people living with HIV have posed important challenges to health systems across the world. In this context, payment models hold the potential to improve care across a spectrum of clinical conditions. This study aims to systematically review the evidence of HIV performance-based payments models. Literature searches were conducted in March 2020 using multiple databases and manual searches of relevant papers. Papers were limited to any study design that considers the real-world utilisation of performance-based payment models applied to the HIV domain. A total of 23 full-text papers were included. Due to the heterogeneity of study designs, the multiple types of interventions and its implementation across distinct areas of HIV care, direct comparisons between studies were deemed unsuitable. Most evidence focused on healthcare users (83%), seeking to directly affect patients' behaviour based on principles of behavioural economics. Despite the variability between interventions, the implementation of performance-based payment models led to either a neutral or positive impact throughout the HIV care continuum. Moreover, this improvement was likely to be cost-effective or, at least, did not compromise the healthcare system's financial sustainability. However, more research is needed to assess the durability of incentives and its appropriate relative magnitude.

Influence of number of individuals and observations per individual on a model of community structure

Social network analysis is increasingly applied to understand animal groups. However, it is rarely feasible to observe every interaction among all individuals in natural populations. Studies have assessed how missing information affects estimates of individual network positions, but less attention has been paid to metrics that characterize overall network structure such as modularity, clustering coefficient, and density. In cases such as groups displaying fission-fusion dynamics, where subgroups break apart and rejoin in changing conformations, missing information may affect estimates of global network structure differently than in groups with distinctly separated communities due to the influence single individuals can have on the connectivity of the network. Using a bat maternity group showing fission-fusion dynamics, we quantify the effect of missing data on global network measures including community detection. In our system, estimating the number of communities was less reliable than detecting community structure. Further, reliably assorting individual bats into communities required fewer individuals and fewer observations per individual than to estimate the number of communities. Specifically, our metrics of global network structure (i.e., graph density, clustering coefficient, Rcom) approached the 'real' values with increasing numbers of observations per individual and, as the number of individuals included increased, the variance in these estimates decreased. Similar to previous studies, we recommend that more observations per individual should be prioritized over including more individuals when resources are limited. We recommend caution when making conclusions about animal social networks when a substantial number of individuals or observations are missing, and when possible, suggest subsampling large datasets to observe how estimates are influenced by sampling intensity. Our study serves as an example of the reliability, or lack thereof, of global network measures with missing information, but further work is needed to determine how estimates will vary with different data collection methods, network structures, and sampling periods.

New dimensions for animal communication networks: space and time

Communication is a social process and usually occurs in a network of signalers and receivers. While social network analysis has received enormous recent attention from animal behaviorists, there have been relatively few attempts to apply these techniques to communication networks. Communication networks have the potential to offer novel insights into social network studies, and yet are especially challenging subjects, largely because of their unique spatiotemporal characteristics. Namely, signals propagate through the environment, often dissociating from the body of the signaler, to influence receiver behavior. The speed of signal propagation and the signal's active space will affect the congruence of communication networks and other types of social network; in extreme cases the signal may persist and only first be detected long after the signaler has left the area. Other signals move more rapidly and over greater distances than the signaler could possibly move to reach receivers. We discuss the spatial and temporal consequences of signaling in networks and highlight the distinction between the physical location of the signaler and the spread of influence of its signals, the effects of signal modality and receiver sensitivity on communication network properties, the potential for feedbacks between network layers, and approaches to analyzing spatial and temporal change in communication networks in conjunction with other network layers.