Human papillomavirus (HPV) has been confirmed as the causative agent for cervical cancer. In this study, a total of 301 880 women were recruited from four different regions of Western China, with 301 880 exfoliated cervical cell samples collected from women for DNA isolation and purification. The HPV genotype was tested by polymerase chain reaction. The overall HPV prevalence rate, high-risk (HR) HPV infection rate, low-risk (LR) HPV infection rate and mixed HPV infection rate was 18.24%, 79.14%, 12.56% and 8.30%, respectively. The four most common HR HPV subtypes were HPV-52, 16, 58 and 53, which accounted for 20.49%, 19.93%, 14.54% and 10.01%, respectively. In LR HPV genotype, HPV-6 ranked the highest (28.17%), followed by HPV-81 (9.09%) and HPV-11 (3.78%). HPV genotype subgroup analysis also showed that single-type infection was the most common (77.26%) among HPV-positive individuals. Among multi-infection genotypes, double infection was the most common with frequencies of 76.04%. The overall prevalence of HPV is high in Western China, whose distribution demonstrates different patterns across different ages and regions. Viral genotypes HPV 53, 6 were frequently detected in this population, which is worth of significant clinical attention.

Decentralized coordination is one of the fundamental challenges for societies and organizations. While extensively explored from a variety of perspectives, one issue that has received limited attention is human coordination in the presence of adversarial agents. We study this problem by situating human subjects as nodes on a network, and endowing each with a role, either regular (with the goal of achieving consensus among all regular players), or adversarial (aiming to prevent consensus among regular players). We show that adversarial nodes are, indeed, quite successful in preventing consensus. However, we demonstrate that having the ability to communicate among network neighbors can considerably improve coordination success, as well as resilience to adversarial nodes. Our analysis of communication suggests that adversarial nodes attempt to exploit this capability for their ends, but do so in a somewhat limited way, perhaps to prevent regular nodes from recognizing their intent. In addition, we show that the presence of trusted nodes generally has limited value, but does help when many adversarial nodes are present, and players can communicate. Finally, we use experimental data to develop computational models of human behavior and explore additional parametric variations: features of network topologies and densities, and placement, all using the resulting data-driven agent-based (DDAB) model.

We investigate joint modelling of longevity trends using the spatial statistical framework of Gaussian process (GP) regression. Our analysis is motivated by the Human Mortality Database (HMD) that provides unified raw mortality tables for nearly 40 countries. Yet few stochastic models exist for handling more than two populations at a time. To bridge this gap, we leverage a spatial covariance framework from machine learning that treats populations as distinct levels of a factor covariate, explicitly capturing the cross-population dependence. The proposed multi-output GP models straightforwardly scale up to a dozen populations and moreover intrinsically generate coherent joint longevity scenarios. In our numerous case studies, we investigate predictive gains from aggregating mortality experience across nations and genders, including by borrowing the most recently available “foreign” data. We show that in our approach, information fusion leads to more precise (and statistically more credible) forecasts. We implement our models in R, as well as a Bayesian version in Stan that provides further uncertainty quantification regarding the estimated mortality covariance structure. All examples utilise public HMD datasets.

Brucellosis remains one of the main zoonoses worldwide. Epidemiological data on human brucellosis in Spain are scarce. The objective of this study was to assess the epidemiological characteristics of inpatient brucellosis in Spain between 1997 and 2015. A retrospective longitudinal descriptive study was performed. Data were requested from the Health Information Institute of the Ministry of Health and Equality, which provided us with the Minimum Basic Data Set of patients admitted to the National Health System. We also obtained data published in the System of Obligatory Notifiable Diseases. A total of 5598 cases were registered. The period incidence rate was 0.67 (95% CI 0.65–0.68) cases per 100 000 person-years. We observed a progressive decrease in the number of cases and annual incidence rates. A total of 3187 cases (56.9%) came from urban areas. The group most at risk comprised men around the fifth decade of life. The average (±s.d.) hospital stay was 12.6 days (±13.1). The overall lethality rate of the cohort was 1.5%. The number of inpatients diagnosed with brucellosis decreased exponentially. The group of patients with the highest risk of brucellosis in our study was males under 45 years of age and of urban origin. The lethality rate has reduced to minimum values. It is probable that hospital discharge records could be a good database for the epidemiological analysis of the hospital management of brucellosis and offer a better information collection system than the notifiable diseases system (EDO in Spanish).

Epidemic intelligence activities are undertaken by the WHO Regional Office for Africa to support member states in early detection and response to outbreaks to prevent the international spread of diseases. We reviewed epidemic intelligence activities conducted by the organisation from 2017 to 2020, processes used, key results and how lessons learned can be used to strengthen preparedness, early detection and rapid response to outbreaks that may constitute a public health event of international concern. A total of 415 outbreaks were detected and notified to WHO, using both indicator-based and event-based surveillance. Media monitoring contributed to the initial detection of a quarter of all events reported. The most frequent outbreaks detected were vaccine-preventable diseases, followed by food-and-water-borne diseases, vector-borne diseases and viral haemorrhagic fevers. Rapid risk assessments generated evidence and provided the basis for WHO to trigger operational processes to provide rapid support to member states to respond to outbreaks with a potential for international spread. This is crucial in assisting member states in their obligations under the International Health Regulations (IHR) (2005). Member states in the region require scaled-up support, particularly in preventing recurrent outbreaks of infectious diseases and enhancing their event-based surveillance capacities with automated tools and processes.

The aim of this study was to systematically assess the association between smoking and cardiovascular disease (CVD) and disease progression among novel coronavirus pneumonia (coronavirus disease 2019 (COVID-19)) cases. PubMed database and Cochrane Library database were searched by computer to seek the epidemiological data of COVID-19 cases and literatures regarding CVDs from 1 Jan to 6 October 2020. Two researchers independently conducted literature screening, data collection and the assessment of the risk of bias of the studies included. RevMan 5.2 software was employed for meta-analysis. Funnel plot was adopted to assess the publication bias. On the whole, 21 studies comprising 7041 COVID-19 cases were included. As revealed from the meta-analysis, 14.0% (984/7027) of cases had a history of smoking, and 9.7% (675/6931) were subject to underlying CVDs. Cases with a history of smoking achieved a higher rate of COVID-19 disease progression as opposed to those having not smoked (OR 1.53, 95% CI 1.29–1.81, P < 0.00001), while no significant association could be found between smoking status and COVID-19 disease progression (OR 1.23, 95% CI 0.93–1.63, P = 0.15). Besides, smoking history elevated the mortality rate by 1.91-fold (OR 1.91, 95% CI 1.35–2.69, P = 0.0002). Moreover, underlying CVD elevated the incidence of severe disease by 2.87-fold (OR 2.87, 95% CI 2.29–3.61, P < 0.00001) and mortality by 3.05-fold (OR 3.05, 95% CI 1.82–5.11, P < 0.0001) in COVID-19 cases. As demonstrated from the current evidence, smoking displays a strong association with COVID-19 disease progression and mortality, and intensive tobacco control is imperative. Moreover, cases with CVD show a significantly elevated risk of disease progression and death when subject to COVID-19. However, the association between COVID-19 and CVD, and the potential effect exerted by smoking in the development of the two still require further verifications by larger and higher quality studies.

The Scenario Weights for Importance Measurement (SWIM) package implements a flexible sensitivity analysis framework, based primarily on results and tools developed by Pesenti et al. (2019). SWIM provides a stressed version of a stochastic model, subject to model components (random variables) fulfilling given probabilistic constraints (stresses). Possible stresses can be applied on moments, probabilities of given events, and risk measures such as Value-At-Risk and Expected Shortfall. SWIM operates upon a single set of simulated scenarios from a stochastic model, returning scenario weights, which encode the required stress and allow monitoring the impact of the stress on all model components. The scenario weights are calculated to minimise the relative entropy with respect to the baseline model, subject to the stress applied. As well as calculating scenario weights, the package provides tools for the analysis of stressed models, including plotting facilities and evaluation of sensitivity measures. SWIM does not require additional evaluations of the simulation model or explicit knowledge of its underlying statistical and functional relations; hence, it is suitable for the analysis of black box models. The capabilities of SWIM are demonstrated through a case study of a credit portfolio model.

We propose a new neighbouring prediction model for mortality forecasting. For each mortality rate at age x in year t, mx,t, we construct an image of neighbourhood mortality data around mx,t, that is, Ꜫmx,t (x1, x2, s), which includes mortality information for ages in [x-x1, x+x2], lagging k years (1 ≤ k ≤ s). Combined with the deep learning model – convolutional neural network, this framework is able to capture the intricate nonlinear structure in the mortality data: the neighbourhood effect, which can go beyond the directions of period, age, and cohort as in classic mortality models. By performing an extensive empirical analysis on all the 41 countries and regions in the Human Mortality Database, we find that the proposed models achieve superior forecasting performance. This framework can be further enhanced to capture the patterns and interactions between multiple populations.

We investigated likelihood to vaccinate and reasons for and against accepting a coronavirus disease 2019 (COVID-19) vaccine among adult residents of Finland. Vaccine acceptance declined from 70% in April to 64% in December 2020. Complacency and worry about side effects were main reasons against vaccination while concern about severe disease was a strong motive for vaccination. Convenience of vaccination and recommendations by healthcare workers were identified as enablers for vaccination among those aged under 50 years. Understanding barriers and enablers behind vaccine acceptance is decisive in ensuring a successful implementation of COVID-19 vaccination programmes, which will be key to ending the pandemic.

This article discusses the stochastic behavior and reliability properties for the inactivity times of failed components in coherent systems under double monitoring. A mixture representation of reliability function is obtained for the inactivity times of failed components, and some stochastic comparison results are also established. Furthermore, some sufficient conditions are developed in terms of the aging properties of the inactivity times of failed components. Finally, some numerical examples are presented to illustrate the theoretical results.

A hooking network is built by stringing together components randomly chosen from a set of building blocks (graphs with hooks). The vertices are endowed with “affinities” which dictate the attachment mechanism. We study the distance from the master hook to a node in the network chosen according to its affinity after many steps of growth. Such a distance is commonly called the depth of the chosen node. We present an exact average result and a rather general central limit theorem for the depth. The affinity model covers a wide range of attachment mechanisms, such as uniform attachment and preferential attachment, among others. Naturally, the limiting normal distribution is parametrized by the structure of the building blocks and their probabilities. We also take the point of view of a visitor uninformed about the affinity mechanism by which the network is built. To explore the network, such a visitor chooses the nodes uniformly at random. We show that the distance distribution under such a uniform choice is similar to the one under random choice according to affinities.

Understanding core statistical properties and data features in mortality data are fundamental to the development of machine learning methods for demographic and actuarial applications of mortality projection. The study of statistical features in such data forms the basis for classification, regression and forecasting tasks. In particular, the understanding of key statistical structure in such data can aid in improving accuracy in undertaking mortality projection and forecasting when constructing life tables. The ability to accurately forecast mortality is a critical aspect for the study of demography, life insurance product design and pricing, pension planning and insurance-based decision risk management. Though many stylised facts of mortality data have been discussed in the literature, we provide evidence for a novel statistical feature that is pervasive in mortality data at a national level that is as yet unexplored. In this regard, we demonstrate in this work a strong evidence for the existence of long memory features in mortality data, and second that such long memory structures display multifractality as a statistical feature that can act as a discriminator of mortality dynamics by age, gender and country. To achieve this, we first outline the way in which we choose to represent the persistence of long memory from an estimator perspective. We make a natural link between a class of long memory features and an attribute of stochastic processes based on fractional Brownian motion. This allows us to use well established estimators for the Hurst exponent to then robustly and accurately study the long memory features of mortality data. We then introduce to mortality analysis the notion from data science known as multifractality. This allows us to study the long memory persistence features of mortality data on different timescales. We demonstrate its accuracy for sample sizes commensurate with national-level age term structure historical mortality records. A series of synthetic studies as well a comprehensive analysis of real mortality death count data are studied in order to demonstrate the pervasiveness of long memory structures in mortality data, both mono-fractal and multifractal functional features are verified to be present as stylised facts of national-level mortality data for most countries and most age groups by gender. We conclude by demonstrating how such features can be used in kernel clustering and mortality model forecasting to improve these actuarial applications.

Successive waves of COVID-19 transmission have led to exponential increases in new infections globally. In this study, we have applied a decision-making tool to assess the risk of continuing transmission to inform decisions on tailored public health and social measures (PHSM) using data on cases and deaths reported by Member States to the WHO Regional Office for Africa as of 31 December 2020. Transmission classification and health system capacity were used to assess the risk level of each country to guide implementation and adjustments to PHSM. Two countries out of 46 assessed met the criteria for sporadic transmission, one for clusters of cases, and 43 (93.5%) for community transmission (CT) including three with uncontrolled disease incidence (Eswatini, Namibia and South Africa). Health system response's capacities were assessed as adequate in two countries (4.3%), moderate in 13 countries (28.3%) and limited in 31 countries (64.4%). The risk level, calculated as a combination of transmission classification and health system response's capacities, was assessed at level 0 in one country (2.1%), level 1 in two countries (4.3%), level 2 in 11 countries (23.9%) and level 3 in 32 (69.6%) countries. The scale of severity ranged from 0 to 4, with 0 the lowest. CT coupled with limited response capacity resulted in a level 3 risk assessment in most countries. Countries at level 3 should be considered as priority focus for additional assistance, in order to prevent the risk rising to level 4, which may necessitate enforcing hard and costly lockdown measures. The large number of countries at level 3 indicates the need for an effective risk management system to be used as a basis for adjusting PHSM at national and sub-national levels.

The role of the Eurasian badger (Meles meles) as a wildlife host has complicated the management of bovine tuberculosis (bTB) in cattle. Badger ranging behaviour has previously been found to be altered by culling of badgers and has been suggested to increase the transmission of bTB either among badgers or between badgers and cattle. In 2014, a five-year bTB intervention research project in a 100 km2 area in Northern Ireland was initiated involving selective removal of dual path platform (DPP) VetTB (immunoassay) test positive badgers and vaccination followed by release of DPP test negative badgers (‘Test and Vaccinate or Remove’). Home range sizes, based on position data obtained from global positioning system collared badgers, were compared between the first year of the project, where no DPP test positive badgers were removed, and follow-up years 2–4 when DPP test positive badgers were removed. A total of 105 individual badgers were followed over 21 200 collar tracking nights. Using multivariable analyses, neither annual nor monthly home ranges differed significantly in size between years, suggesting they were not significantly altered by the bTB intervention that was applied in the study area.

Varicella poses an occupational risk and a nosocomial risk for susceptible healthcare personnel and patients, respectively. Patients with varicella are thought to be infectious from 1 to 2 days before rash onset until all lesions are crusted, typically 4–7 days after onset of rash. We searched Medline, Embase, Cochrane Library and CINAHL databases to assess evidence of varicella-zoster virus (VZV) transmission before varicella rash onset. Few articles (7) contributed epidemiologic evidence; no formal studies were found. Published articles reported infectiousness at variable intervals before rash onset, between <1 day to 4 days prior to rash, with 1–2 patients for each interval. Laboratory assessment of transmission before rash was also limited (10 articles). No culture-positive results were reported. VZV DNA was identified by PCR before rash onset in only one study however, PCR does not indicate infectivity of the virus. Based on available medical literature, VZV transmission before rash onset seems unlikely, although the possibility of pre-rash, respiratory transmission cannot be entirely ruled out.

We explore the concept of parameter design applied to the production of glass beads in the manufacture of metal-encapsulated transistors. The main motivation is to complete the analysis hinted at in the original publication by Jim Morrison in 1957, which was an early example of discussing the idea of transmitted variation in engineering design, and an influential paper in the development of analytic parameter design as a data-centric engineering activity. Parameter design is a secondary design activity focused on selecting the nominals of the design variables to achieve the required target performance and to simultaneously reduce the variance around the target. Although the 1957 paper is not recent, its approach to engineering design is modern.

In August 2017, a cluster of four persons infected with genetically related strains of Shiga toxin-producing Escherichia coli (STEC) O157:H7 was identified. These strains possessed the Shiga toxin (stx) subtype stx2a, a toxin type known to be associated with severe clinical outcome. One person died after developing haemolytic uraemic syndrome. Interviews with cases revealed that three of the cases had been exposed to dogs fed on a raw meat-based diet (RMBD), specifically tripe. In two cases, the tripe had been purchased from the same supplier. Sampling and microbiological screening of raw pet food was undertaken and indicated the presence of STEC in the products. STEC was isolated from one sample of raw tripe but was different from the strain causing illness in humans. Nevertheless, the detection of STEC in the tripe provided evidence that raw pet food was a potential source of human STEC infection during this outbreak. This adds to the evidence of raw pet food as a risk factor for zoonotic transmission of gastrointestinal pathogens, which is widely accepted for Salmonella, Listeria and Campylobacter spp. Feeding RMBD to companion animals has recently increased in popularity due to the belief that they provide health benefits to animals. Although still rare, an increase in STEC cases reporting exposure to RMBDs was detected in 2017. There has also been an increased frequency of raw pet food incidents in 2017, suggesting an increasing trend in potential risk to humans from raw pet food. Recommendations to reduce the risk of infection included improved awareness of risk and promotion of good hygiene practices among the public when handling raw pet food.