Nearly 1 year into the coronavirus disease 2019 pandemic, the first severe acute respiratory syndrome coronavirus 2 vaccines received emergency use authorisation and vaccination campaigns began. A number of factors can reduce the averted burden of cases and deaths due to vaccination. Here, we use a dynamic model, parametrised with Bayesian inference methods, to assess the effects of non-pharmaceutical interventions (NPIs) (such as social distancing, mask mandates, school and workplace closure), and vaccine administration and uptake rates on infections and deaths averted in the United States. We show that scenarios depicting higher compliance with NPIs avert more than 60% of infections and 70% of deaths during the period of vaccine administration, and that increasing the vaccination rate from 5 to 11 million people per week could increase the averted burden by more than one-third. These findings underscore the importance of maintaining NPIs and increasing vaccine administration rates.

This study aimed to investigate differences in the antimicrobial susceptibility of members of the Mycobacterium abscessus complex (MABC): subsp. massiliense and subsp. abscessus, and to identify associations between strain genotypes and antimicrobial resistance phenotypes. A total of 383 clinical MABC isolates (subsp. abscessus: n = 218, 56.9%; subsp. massiliense: n = 163, 42.6%; subsp. bolletii: n = 2, 0.5%) were characterised using multilocus variable number tandem repeat (VNTR) typing and drug susceptibility testing. Most isolates exhibited susceptibility to amikacin, clarithromycin and azithromycin but resistance to cefoxitin and minocycline was statistically more associated with isolates unclustered by VNTR type. The Simpson's diversity indexes of VNTR typing for M. abscessus and M. massiliense isolates were 0.999 and 0.997, respectively. Genotyping of M. abscessus and M. massiliense isolates by VNTR may provide valuable information for predicting resistance phenotype.

Our study was conducted to assess the sepsis-associated hospitalisations and antimicrobials prescribed for sepsis inpatients in Hong Kong. Demographic, diagnostic and antimicrobial prescription data were analysed for patients admitted to public hospitals with a diagnosis of septicaemia from 2000 to 2015. A total of 223 250 sepsis hospitalisations were recorded in Hong Kong from 2000 to 2015 during which the hospitalisation rate increased by 85.6%. The majority of the sepsis hospitalisations occurred in adults ≥65 years and children aged 0–4 years. Adults with a secondary diagnosis of sepsis were often admitted with a primary diagnosis of urological conditions or pneumonia; whereas diabetes mellitus was the most common secondary diagnosis among those with primary sepsis. Paediatric sepsis patients aged 0–4 years were often diagnosed with disorders relating to short gestation and low birthweight. Antimicrobial prescriptions increased by 51.1% and 34.4% for primary and secondary sepsis patients, respectively. β-Lactam and β-lactamase inhibitor combinations were the most used antibiotics whereas the usage of carbapenems increased more than 10 times over the study period. A substantial burden of hospitalisations was attributable to sepsis in Hong Kong, particularly in the extremes of age. Broad-spectrum and last-resort antibiotics had been increasingly dispensed for sepsis inpatients.

The Internet of Things (IoT) is currently developing fast and its potential as driver of innovative solutions is increasing, pushed by technologies, networks, communication, and computing power, and has the potential to drive the development of technological ecosystems, such as innovation clusters. Innovation clusters are agglomeration of enterprises and research organizations, which cooperate, interact and compete, generating innovation and driving the growth of ecosystems. The narrative around innovation clusters has been developing since many years and policy-makers seek to use such clusters as a policy instrument to support the growth of technology on the one hand and regional and sectoral development on the other hand. This policy paper expands an empirical study on IoT innovation clusters in Europe and places it within the current debate around clusters and innovation clusters to provide evidence-based advice to policy-makers on what may and may not work as public policy measures. The paper highlights the findings of the interaction with several hundred European IoT innovation clusters and points out their points of view on their own creation factors, operational characteristics, and success stories, as well as their expectations in respect to policy interventions for IoT and for clusters. Suggestions for IoT policy-making are provided. The paper has also undertaken an extensive review of up-to date research on innovation cluster creation and performance, thoroughly analyzing the real possibility to define causal relationships between clusters, productivity and economic growth, and business performance, and providing suggestions for policy-makers on the approach to cluster policy.

Effectiveness of corona virus disease-19 (COVID-19) vaccines used in India is unexplored and need to be substantiated. The present case-control study was planned to elicit the effectiveness of COVID-19 vaccines in preventing infection and disease severity in the general population of Bihar, India. This case-control study was conducted among people aged ≥45 years during April to June 2021. The cases were the COVID-19 patients admitted or visited All India Institute of Medical Sciences (AIIMS), Patna, Bihar, India, and were contacted directly. The controls were the individuals tested negative for severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) at the Virology laboratory, AIIMS-Patna and contacted telephonically for collection of relevant information. The vaccine effectiveness (VE) was calculated by using the formula (VE = 1 – odds ratio). The adjusted VE for partial and full vaccination were estimated to be 52.0% (95% confidence interval (CI) 39.0–63.0%) and 83.0% (95% CI 73.0–89.0%) respectively for preventing SARS CoV-2 infection. The sub-group analyses of the cases have shown that the length of hospital stays (LOS) (partially vaccinated: 9 days vs. unvaccinated: 12 days; P = 0.028) and the severity of the disease (fully vaccinated: 30.3% vs. partially vaccinated: 51.3% and unvaccinated: 54.1%; P = 0.035) were significantly low among vaccinated compared to unvaccinated individuals. To conclude, four out of every five fully vaccinated individuals are estimated to be protected from contracting SARS CoV-2 infection. Vaccination lowered LOS and chances of development of severe disease.

The use of antimicrobials in food-producing animals can lead to increased bacterial resistance. Important information to address this problem can be provided by monitoring antimicrobial resistance (AMR) in foodborne pathogens. As part of preliminary activities for the implementation of AMR surveillance in Brazil, a nationwide survey on AMR in Salmonella enterica isolates from poultry meat was conducted. The survey evaluated 146 Salmonella isolates from poultry meat in 2014, and 163 isolates obtained in 2017. Minimal inhibitory concentrations of 13 antimicrobials were determined by broth microdilution, and isolates were assigned to serotypes by automated ribotyping. High resistance rates were found in 2014 and 2017, in particular to nalidixic acid (84/146, 57.5% and 141/163, 86.5%, respectively), ampicillin (82/146, 56.2% and 125/163, 76.7%), cefotaxime (76/146, 52.1% and 124/163, 76.1%), ceftazidime (73/146, 50.0% and 124/163, 76.1%), ciprofloxacin (83/146, 56.9% and 145/163, 89.0%) and tetracycline (88/146, 60.3% and 135/163, 82.8%). There was a significant increase in resistance to these antibiotics in the second survey period. Salmonella ser. Heidelberg and Salmonella ser. Minnesota were the main serotypes expressing resistance to these antimicrobials. Multidrug resistance was found in 50.7% (74/146) of the isolates from 2014, and in 77.3% (126/163) of isolates from 2017 (P < 0.05). None of the isolates was resistant to azithromycin or meropenem. These findings indicate high and increasing rates of resistance among Salmonella from poultry meat in Brazil, mainly associated with Salmonella ser. Heidelberg and Salmonella ser. Minnesota, stressing the importance of continuous monitoring of AMR in the poultry chain.

We consider the component structure of the random digraph D(n,p) inside the critical window $p = n^{-1} + \lambda n^{-4/3}$. We show that the largest component $\mathcal{C}_1$ has size of order $n^{1/3}$ in this range. In particular we give explicit bounds on the tail probabilities of $|\mathcal{C}_1|n^{-1/3}$.

By now there is a solid theory for Polya urns. Finding the covariances is somewhat laborious. While these papers are “structural,” our purpose here is “computational.” We propose a practicable method for building the asymptotic covariance matrix in tenable balanced urn schemes, whereupon the asymptotic covariance matrix is obtained by solving a linear system of equations. We demonstrate the use of the method in growing tenable balanced irreducible schemes with a small index and in critical urns. In the critical case, the solution to the linear system of equations is explicit in terms of an eigenvector of the scheme.

We study identification in nonparametric regression models with a misclassified and endogenous binary regressor when an instrument is correlated with misclassification error. We show that the regression function is nonparametrically identified if one binary instrument variable and one binary covariate satisfy the following conditions. The instrumental variable corrects endogeneity; the instrumental variable must be correlated with the unobserved true underlying binary variable, must be uncorrelated with the error term in the outcome equation, but is allowed to be correlated with the misclassification error. The covariate corrects misclassification; this variable can be one of the regressors in the outcome equation, must be correlated with the unobserved true underlying binary variable, and must be uncorrelated with the misclassification error. We also propose a mixture-based framework for modeling unobserved heterogeneous treatment effects with a misclassified and endogenous binary regressor and show that treatment effects can be identified if the true treatment effect is related to an observed regressor and another observable variable.

We describe here a notion of diffusion similarity, a method for defining similarity between vertices in a given graph using the properties of random walks on the graph to model the relationships between vertices. Using the approach of graph vertex embedding, we characterize a vertex vi by considering two types of diffusion patterns: the ways in which random walks emanate from the vertex vi to the remaining graph and how they converge to the vertex vi from the graph. We define the similarity of two vertices vi and vj as the average of the cosine similarity of the vectors characterizing vi and vj. We obtain these vectors by modifying the solution to a differential equation describing a type of continuous time random walk.

This method can be applied to any dataset that can be assigned a graph structure that is weighted or unweighted, directed or undirected. It can be used to represent similarity of vertices within community structures of a network while at the same time representing similarity of vertices within layered substructures (e.g., bipartite subgraphs) of the network. To validate the performance of our method, we apply it to synthetic data as well as the neural connectome of the C. elegans worm and a connectome of neurons in the mouse retina. A tool developed to characterize the accuracy of the similarity values in detecting community structures, the uncertainty index, is introduced in this paper as a measure of the quality of similarity methods.

We consider the following question: given information on individual policyholder characteristics, how can we ensure that insurance prices do not discriminate with respect to protected characteristics, such as gender? We address the issues of direct and indirect discrimination, the latter resulting from implicit learning of protected characteristics from nonprotected ones. We provide rigorous mathematical definitions for direct and indirect discrimination, and we introduce a simple formula for discrimination-free pricing, that avoids both direct and indirect discrimination. Our formula works in any statistical model. We demonstrate its application on a health insurance example, using a state-of-the-art generalized linear model and a neural network regression model. An important conclusion is that discrimination-free pricing in general requires collection of policyholders’ discriminatory characteristics, posing potential challenges in relation to policyholder’s privacy concerns.

We define a new ribbon group action on ribbon graphs that uses a semidirect product of a permutation group and the original ribbon group of Ellis-Monaghan and Moffatt to take (partial) twists and duals, or twuals, of ribbon graphs. A ribbon graph is a fixed point of this new ribbon group action if and only if it is isomorphic to one of its (partial) twuals. This extends the original ribbon group action, which only used the canonical identification of edges, to the more natural setting of self-twuality up to isomorphism. We then show that every ribbon graph has in its orbit an orientable embedded bouquet and prove that the (partial) twuality properties of these bouquets propagate through their orbits. Thus, we can determine (partial) twualities via these one vertex graphs, for which checking isomorphism reduces simply to checking dihedral group symmetries. Finally, we apply the new ribbon group action to generate all self-trial ribbon graphs on up to seven edges, in contrast with the few, large, very high-genus, self-trial regular maps found by Wilson, and by Jones and Poultin. We also show how the automorphism group of a ribbon graph yields self-dual, -petrial or –trial graphs in its orbit, and produce an infinite family of self-trial graphs that do not arise as covers or parallel connections of regular maps, thus answering a question of Jones and Poulton.

Telematicsdevices installed in insured vehicles provide actuaries with new risk factors, such as the time of the day, average speeds, and other driving habits. This paper extends the multivariate mixed model describing the joint dynamics of telematics data and claim frequencies proposed by Denuit et al. (2019a) by allowing for signals with various formats, not necessarily integer-valued, and by replacing the estimation procedure with the Expected Conditional Maximization algorithm. A numerical study performed on a database related to Pay-How-You-Drive, or PHYD motor insurance illustrates the relevance of the proposed approach for practice.

This paper introduces a framework for understanding complex temporal interaction patterns in large-scale scientific collaboration networks. In particular, we investigate how two key concepts in science studies, scientific collaboration and scientific mobility, are related and possibly differ between fields. We do so by analyzing multilayer temporal motifs: small recurring configurations of nodes and edges.

Driven by the problem that many papers share the same publication year, we first provide a methodological contribution: an efficient counting algorithm for multilayer temporal motifs with concurrent edges. Next, we introduce a systematic categorization of the multilayer temporal motifs, such that each category reflects a pattern of behavior relevant to scientific collaboration and mobility. Here, a key question concerns the causal direction: does mobility lead to collaboration or vice versa? Applying this framework to scientific collaboration networks extracted from Web of Science (WoS) consisting of up to 7.7 million nodes (authors) and 94 million edges (collaborations), we find that international collaboration and international mobility reciprocally influence one another. Additionally, we find that Social sciences & Humanities (SSH) scholars co-author to a greater extent with authors at a distance, while Mathematics & Computer science (M&C) scholars tend to continue to collaborate within the established knowledge network and organization.