London, Ontario is a mid-sized Canadian city which appears to be experiencing a syndemic predominately amongst its marginalized populations. Since 2014, rates of HIV, hepatitis A (HAV), hepatitis C (HCV), and invasive group A streptococcal disease have climbed well above provincial rates amid increasing use of injection drugs. Rates of infective endocarditis have also been on the rise. Extensive public health and community-based efforts were taken in response to these concurrent outbreaks. These efforts included establishing improved client care pathways, creating specialized teams to engage underhoused clients, providing mass immunization, and developing new health promotion campaigns. Rates of HIV and HAV were subsequently controlled locally while rates of HCV, iGAS and infective endocarditis remain high within the community and throughout the province.

Clusters of Salmonella Enteritidis cases were identified by the Minnesota Department of Health using both pulsed-field gel electrophoresis (PFGE) and whole genome sequencing (WGS) single nucleotide polymorphism analysis from 1 January 2015 through 31 December 2017. The median turnaround time for obtaining WGS results was 11 days longer than for PFGE (12 vs. 1 day). WGS analysis more than doubled the number of clusters compared to PFGE analysis, but reduced the total number of cases included in clusters by 34%. The median cluster size was two cases for WGS compared to four for PFGE, and the median duration of WGS clusters was 27 days shorter than PFGE clusters. While the percentage of PFGE clusters with a confirmed source (46%) was higher than WGS clusters (32%), a higher percentage of cases in clusters that were confirmed as outbreaks reported the vehicle or exposure of interest for WGS (78%) than PFGE (46%). WGS cluster size was a significant predictor of an outbreak source being confirmed. WGS data have enhanced S. Enteritidis cluster investigations in Minnesota by improving the specificity of cluster case definitions and has become an integral part of the S. Enteritidis surveillance process.

The Malmquist index gives a measure of productivity in dynamic settings and has been widely applied in empirical work. The index is typically estimated using envelopment estimators, particularly data envelopment analysis (DEA) estimators. Until now, inference about productivity change measured by Malmquist indices has been problematic, including both inference regarding productivity change experienced by particular firms as well as mean productivity change. This paper establishes properties of a DEA-type estimator of distance to the conical hull of a variable returns to scale production frontier. In addition, properties of DEA estimators of Malmquist indices for individual producers are derived as well as properties of geometric means of these estimators. The latter requires new central limit theorem results, extending the work of Kneip, Simar, and Wilson (2015, Econometric Theory 31, 394–422). Simulation results are provided to give applied researchers an idea of how well inference may work in practice in finite samples. Our results extend easily to other productivity indices, including the Luenberger and Hicks–Moorsteen indices.

Datafied societies need informed public debate about the implications of data science technologies. At present, internet users are often unaware of the potential consequences of disclosing personal data online and few citizens have the knowledge to participate in such debates. This paper argues that critical big data literacy efforts are one way to address this lack of knowledge. It draws on findings from a small qualitative investigation and discusses the effectiveness of online critical big data literacy tools. Through pre and post use testing, the short- and longer-term influence of these tools on people’s privacy attitudes and behavior was investigated. The study’s findings suggested that the tools tested had a predominantly positive initial effect, leading to improved critical big data literacy among most participants, which resulted in more privacy-sensitive attitudes and internet usage. When analyzing the tools’ longer-term influence, results were more mixed, with evidence suggesting for some that literacy effects of the tools were short-lived, while for others they led to more persistent and growing literacy. The findings confirm previous research noting the complexity of privacy attitudes and also find that resignation toward privacy is multi-faceted. Overall, this study reaffirms the importance of critical big data literacy and produces new findings about the value of interactive data literacy tools. These tools have been under-researched to date. This research shows that these tools could provide a relevant means to work toward empowering internet users, promoting a critical internet usage and, ideally, enabling more citizens to engage in public debates about changing data systems.

At the present time, COVID-19 is spreading rapidly [1]. The global prevention and control of COVID-19 is focused on the estimation of the relevant incubation period, basic reproduction number (R0), effective reproduction number (Rt) and death risk. Although the prevention and control of COVID-19 requires a reliable estimation of the relevant incubation period, R0, Rt and death risk. Another key epidemiological parameter-asymptomatic ratio that provides strength and range for social alienation strategies of COVID-19, which is widely defined as the proportion of asymptomatic infections among all disease infections. In fact, the ratio of asymptomatic infection is a useful indicator of the burden of disease and a better measurement of the transmissibility of the virus. So far, people have not paid enough attention to asymptomatic carriers. The asymptomatic carriers discussed in this study are recessive infections, that is, those who have never shown symptoms after onset of infection. We will discuss three aspects: detection, infectivity and proportion of healthy carriers.

The class of distortion riskmetrics is defined through signed Choquet integrals, and it includes many classic risk measures, deviation measures, and other functionals in the literature of finance and actuarial science. We obtain characterization, finiteness, convexity, and continuity results on general model spaces, extending various results in the existing literature on distortion risk measures and signed Choquet integrals. This paper offers a comprehensive toolkit of theoretical results on distortion riskmetrics which are ready for use in applications.

The collection of data, its analysis, and the publication of insights from data promise a range of benefits, but can carry risks for individuals and organizations. This paper sets out considerations regarding the potential role for technologies in governance of data use, and some key limitations. The paper examines the potential of Privacy Enhancing Technologies (PETs) to support organizations and institutions that handle data in governing data use, and considers their role based on their current state of development and the trajectory of technological development. This involves consideration both of how these technologies can potentially enable governments and others to unlock the value of data, and also recognition of both contingent and in principle limitations on the role of PETs in ensuring well-governed use of data.

Dynamic spectrum access (DSA) systems, commonly known as spectrum sharing, are considered one of the most promising paths for more efficient spectrum allocation. When talking about DSA, the most discussed topics revolve around particular technologies such as cognitive radios or particular solutions such as the advanced wireless services-3 initiative. However, in this work, we explore a less discussed approach for spectrum sharing: the Federal Communications Commission (FCC)’s experimental radio service (ERS). The ERS grants licenses for experimentation, market trials, and product development in Federal and/or non-Federal bands. Frequencies in these licenses are assigned on a shared basis and not for the exclusive use of any one licensee. Using FCC’s scraped information in the period between 2007 and 2016, we were able to gain a deeper understanding of the ERS. We find that the processing time (i.e., time to get a license) has been reduced from 100 days to an average of 23 days in 2016. Moreover, the assignation process of experimental licenses is characterized great flexibility in terms of the authorized technical and nontechnical characteristics. We also explored what is behind these 10 years of information.

The aim of this study was to estimate the basic reproduction number (R0) of COVID-19 in the early stage of the epidemic and predict the expected number of new cases in Shahroud in Northeastern Iran. The R0 of COVID-19 was estimated using the serial interval distribution and the number of incidence cases. The 30-day probable incidence and cumulative incidence were predicted using the assumption that daily incidence follows a Poisson distribution determined by daily infectiousness. Data analysis was done using ‘earlyR’ and ‘projections’ packages in R software. The maximum-likelihood value of R0 was 2.7 (95% confidence interval (CI): 2.1−3.4) for the COVID-19 epidemic in the early 14 days and decreased to 1.13 (95% CI 1.03–1.25) by the end of day 42. The expected average number of new cases in Shahroud was 9.0 ± 3.8 cases/day, which means an estimated total of 271 (95% CI: 178–383) new cases for the period between 02 April to 03 May 2020. By day 67 (27 April), the effective reproduction number (Rt), which had a descending trend and was around 1, reduced to 0.70. Based on the Rt for the last 21 days (days 46–67 of the epidemic), the prediction for 27 April to 26 May is a mean daily cases of 2.9 ± 2.0 with 87 (48–136) new cases. In order to maintain R below 1, we strongly recommend enforcing and continuing the current preventive measures, restricting travel and providing screening tests for a larger proportion of the population.

Wild sheep and many primitive domesticated breeds have two coats: coarse hairs covering shorter, finer fibres. Both are shed annually. Exploitation of wool for apparel in the Bronze Age encouraged breeding for denser fleeces and continuously growing white fibres. The Merino is regarded as the culmination of this process. Archaeological discoveries, ancient images and parchment records portray this as an evolutionary progression, spanning millennia. However, examination of the fleeces from feral, two-coated and woolled sheep has revealed a ready facility of the follicle population to change from shedding to continuous growth and to revert from domesticated to primitive states. Modifications to coat structure, colour and composition have occurred in timeframes and to sheep population sizes that exclude the likelihood of variations arising from mutations and natural selection. The features are characteristic of the domestication phenotype: an assemblage of developmental, physiological, skeletal and hormonal modifications common to a wide variety of species under human control. The phenotypic similarities appeared to result from an accumulation of cryptic genetic changes early during vertebrate evolution. Because they did not affect fitness in the wild, the mutations were protected from adverse selection, becoming apparent only after exposure to a domestic environment. The neural crest, a transient embryonic cell population unique to vertebrates, has been implicated in the manifestations of the domesticated phenotype. This hypothesis is discussed with reference to the development of the wool follicle population and the particular roles of Notch pathway genes, culminating in the specific cell interactions that typify follicle initiation.

We present two complementary model-based methods for calculating the risk of international spread of the novel coronavirus SARS-CoV-2 from the outbreak epicentre. One model aims to calculate the number of cases that would be exported from an endemic country to disease-free regions by travellers. The second model calculates the probability that an infected traveller will generate at least one secondary autochthonous case in the visited country. Although this paper focuses on the data from China, our methods can be adapted to calculate the risk of importation and subsequent outbreaks. We found an average R0 = 5.31 (ranging from 4.08 to 7.91) and a risk of spreading of 0.75 latent individuals per 1000 travellers. In addition, one infective traveller would be able to generate at least one secondary autochthonous case in the visited country with a probability of 23%.

Mathematical modelling studies predicting the spread of the coronavirus disease 2019 (COVID-19) have been used worldwide, but precisions are limited. Thus, continuous evaluation of the modelling studies is crucial. We investigated situations of virus importation in sub-Saharan Africa (SSA) to assess effectiveness of a modelling study by Haider N et al. titled ‘Passengers’ destinations from China: low risk of novel coronavirus (2019-nCoV) transmission into Africa and South America’. We obtained epidemiological data of 2417 COVID-19 cases reported by 40 countries in SSA within 30 days of the first case confirmed in Nigeria on 27 February. Out of 442 cases which had travel history available, only one (0.2%) had a travel history to China. These findings underline the result of the model. However, the fact that there were numbers of imported cases from other regions shows the limits of the model. The limits could be attributed to the characteristics of the COVID-19 which is infectious even when the patients do not express any symptoms. Therefore, there is a profound need for all modelling researchers to take asymptomatic cases into account when they establish modelling studies.

We present a method for constructing and interpreting weighted premium principles. The method is based on modifying the underlying risk distribution in such a way that the risk-adjusted expected value (or premium) is greater than the expected value of some conveniently chosen function of claims, which defines the insurer’s perception of the risk. Under some assumptions on the function of claims, the method produces distortion premium principles. We provide several examples under different assumptions on the claim arrival process and different functions of claims, including record claims and kth record claims.

We study structural properties of graphs with bounded clique number and high minimum degree. In particular, we show that there exists a function L = L(r,ɛ) such that every Kr-free graph G on n vertices with minimum degree at least ((2r–5)/(2r–3)+ɛ)n is homomorphic to a Kr-free graph on at most L vertices. It is known that the required minimum degree condition is approximately best possible for this result.

For r = 3 this result was obtained by Łuczak (2006) and, more recently, Goddard and Lyle (2011) deduced the general case from Łuczak’s result. Łuczak’s proof was based on an application of Szemerédi’s regularity lemma and, as a consequence, it only gave rise to a tower-type bound on L(3, ɛ). The proof presented here replaces the application of the regularity lemma by a probabilistic argument, which yields a bound for L(r, ɛ) that is doubly exponential in poly(ɛ).

The current coronavirus (COVID-19) pandemic offers a unique opportunity to conduct an infodemiological study examining patterns in online searching activity about a specific disease and how this relates to news media within a specific country. Google Trends quantifies volumes of online activity. The relative search volume was obtained for ‘Coronavirus’, ‘handwashing’, ‘face mask’ and symptom related keywords, for the United Kingdom, from the date of the first confirmed case until numbers peaked in April. The relationship between online search traffic and confirmed case numbers was examined. Search volumes varied over time; peaks appear related to events in the progression of the epidemic which were reported in the media. Search activity on ‘Coronavirus’ correlated well against confirmed case number as did ‘face mask’ and symptom-related keywords. User-generated online data sources such as Google Trends may aid disease surveillance, being more responsive to changes in disease occurrence than traditional disease reporting. The relationship between media coverage and online searching activity is rarely examined, but may be driving online behavioural patterns.

The addition of a set of cohort parameters to a mortality model can generate complex identifiability issues due to the collinearity between the dimensions of age, period and cohort. These issues can lead to robustness problems and difficulties making projections of future mortality rates. Since many modern mortality models incorporate cohort parameters, we believe that a comprehensive analysis of the identifiability issues in age/period/cohort mortality models is needed. In this paper, we discuss the origin of identifiability issues in general models before applying these insights to simple but commonly used mortality models. We then discuss how to project mortality models so that our forecasts of the future are independent of any arbitrary choices we make when fitting a model to data in order to identify the historical parameters.

We consider a friendship model in which each member of a community has a latent value such that the probability that any two individuals are friends is a function of their latent values. We consider such questions as does information that i and j are both friends with k make it more likely that i and j are themselves friends. Among other things, we show that for fixed sets S and T, the more friends that i has in S, then the stochastically more friends i has in T. We consider how a variation of the friendship paradox applies to our model. We also study the distribution of the number of friendless individuals in the community and derive a bound on the total variation distance between it and a Poisson with the same mean.