Social encounter networks : collective properties and disease transmission

A fundamental challenge of modern infectious disease epidemiology is to quantify the networks of social and physical contacts through which transmission can occur. Understanding the collective properties of these interactions is critical for both accurate prediction of the spread of infection and determining optimal control measures. However, even the basic properties of such networks are poorly quantified, forcing predictions to be made based on strong assumptions concerning network structure. Here, we report on the results of a large-scale survey of social encounters mainly conducted in Great Britain. First, we characterize the distribution of contacts, which possesses a lognormal body and a power-law tail with an exponent of −2.45; we provide a plausible mechanistic model that captures this form. Analysis of the high level of local clustering of contacts reveals additional structure within the network, implying that social contacts are degree assortative. Finally, we describe the epidemiological implications of this local network structure: these contradict the usual predictions from networks with heavy-tailed degree distributions and contain public-health messages about control. Our findings help us to determine the types of realistic network structure that should be assumed in future population level studies of infection transmission, leading to better interpretations of epidemiological data and more appropriate policy decisions

An overview of late-stage functionalization in today's drug discovery

Introduction: Late-stage functionalization (LSF) can introduce important chemical groups in the very last steps of the synthesis. LSF has the potential to speed up the preparation of novel chemical entities and diverse chemical libraries and have a major impact on drug discovery. Functional group tolerance and mild conditions allows access to new molecules not easily accessible by conventional approaches without the need for laborious de novo chemical synthesis. Areas Covered: A historical overview of late-stage functionalization and its applicability to drug discovery is provided. Pioneering methodologies that laid the foundations for the field are briefly covered and archetypal examples of their application to drug discovery are discussed. Novel methodologies reported in the past few years mainly stemming from the recent renaissances of photoredox catalysis and radical chemistry are reviewed and their application to drug discovery considered. Expert opinion: It is envisioned that late-stage functionalization will improve the efficiency and efficacy of drug discovery. There is evidence of the widespread uptake of LSF by the medicinal chemistry community and it is expected that the recent and continuing endeavors of many academic laboratories and pharmaceutical companies will soon have an impact on drug development.The authors are supported by the National Health and Medical Research Council of Australi

The Population Attributable Fraction (PAF) of cases due to gatherings and groups with relevance to COVID-19 mitigation strategies

SummaryBackgroundMany countries have banned groups and gatherings as part of their response to the pandemic caused by the coronavirus, SARS-CoV-2. Although there are outbreak reports involving mass gatherings, the contribution to overall transmission is unknown.MethodsWe used data from a survey of social contact behaviour that specifically asked about contact with groups to estimate the Population Attributable Fraction (PAF) due to groups as the relative change in the Basic Reproduction Number when groups are prevented.FindingsGroups of 50+ individuals accounted for 0.5% of reported contact events, and we estimate that the PAF due to groups of 50+ people is 5.4% (95%CI 1.4%, 11.5%). The PAF due to groups of 20+ people is 18.9% (12.7%, 25.7%) and the PAF due to groups of 10+ is 25.2% (19.4%, 31.4%)InterpretationLarge groups of individuals have a relatively small epidemiological impact; small and medium sized groups between 10 and 50 people have a larger impact on an epidemic.</jats:sec

Lead Slowing Down Spectrometer Status Report

This report documents the progress that has been completed in the first half of FY2012 in the MPACT-funded Lead Slowing Down Spectrometer project. Significant progress has been made on the algorithm development. We have an improve understanding of the experimental responses in LSDS for fuel-related material. The calibration of the ultra-depleted uranium foils was completed, but the results are inconsistent from measurement to measurement. Future work includes developing a conceptual model of an LSDS system to assay plutonium in used fuel, improving agreement between simulations and measurement, design of a thorium fission chamber, and evaluation of additional detector techniques

Lead Slowing-Down Spectrometry for Spent Fuel Assay: FY11 Status Report

Executive Summary Developing a method for the accurate, direct, and independent assay of the fissile isotopes in bulk materials (such as used fuel) from next-generation domestic nuclear fuel cycles is a goal of the Office of Nuclear Energy, Fuel Cycle R&D, Material Protection and Control Technology (MPACT) Campaign. To meet this goal, MPACT supports a multi-institutional collaboration to study the feasibility of Lead Slowing Down Spectroscopy (LSDS). This technique is an active nondestructive assay method that has the potential to provide independent, direct measurement of Pu and U isotopic masses in used fuel with an uncertainty considerably lower than the approximately 10% typical of today’s confirmatory assay methods. This document is a progress report for FY2011 collaboration activities. Progress made by the collaboration in FY2011 continues to indicate the promise of LSDS techniques applied to used fuel. PNNL developed an empirical model based on calibration of the LSDS to responses generated from well-characterized used fuel. The empirical model demonstrated the potential for the direct and independent assay of the sum of the masses of 239Pu and 241Pu to within approximately 3% over a wide used fuel parameter space. Similar results were obtained using a perturbation approach developed by LANL. Benchmark measurements have been successfully conducted at LANL and at RPI using their respective LSDS instruments. The ISU and UNLV collaborative effort is focused on the fabrication and testing of prototype fission chambers lined with ultra-depleted 238U and 232Th, and uranium deposition on a stainless steel disc using spiked U3O8 from room temperature ionic liquid was successful, with improving thickness obtained. In FY2012, the collaboration plans a broad array of activities. PNNL will focus on optimizing its empirical model and minimizing its reliance on calibration data, as well continuing efforts on developing an analytical model. Additional measurements are planned at LANL and RPI. LANL measurements will include a Pu sample, which is expected to provide more counts at longer slowing-down times to help identify discrepancies between experimental data and MCNPX simulations. RPI measurements will include the assay of an entire fresh fuel assembly for the study of self-shielding effects as well as the ability to detect diversion by detecting a missing fuel pin in the fuel assembly. The development of threshold neutron sensors will continue, and UNLV will calibrate existing ultra-depleted uranium deposits at ISU

A New Measure of Centrality for Brain Networks

Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centrality being three of the most popular measures. Degree identifies the most connected nodes, whereas betweenness centrality identifies those located on the most traveled paths. Eigenvector centrality considers nodes connected to other high degree nodes as highly central. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. Functional cartography was also used to identify neighborhood hubs (nodes with high degree within a network neighborhood). Provincial hubs provide structure within the local community, and connector hubs mediate connections between multiple communities. Leverage proved to yield information that was not captured by degree, betweenness, or eigenvector centrality and was more accurate at identifying neighborhood hubs. We propose that this metric may be able to identify critical nodes that are highly influential within the network

Testing the hypothesis of preferential attachment in social network formation

The hypothesis of preferential attachment (PA) - whereby better connected individuals make more connections - is hotly debated, particularly in the context of epidemiological networks. The simplest models of PA, for example, are incompatible with the eradication of any disease through population-level control measures such as random vaccination. Typically, evidence has been sought for the presence or absence of preferential attachment via asymptotic power-law behaviour. Here, we present a general statistical method to test directly for evidence of PA in count data and apply this to data for contacts relevant to the spread of respiratory diseases. We find that while standard methods for model selection prefer a form of PA, careful analysis of the best fitting PA models allows for a level of contact heterogeneity that in fact allows control of respiratory diseases. Our approach is based on a flexible but numerically cheap likelihood-based model that could in principle be applied to other integer data where the hypothesis of PA is of interest