In this article, we explore the use of multivariate Archimedean copulas in modelling the mortality dependence between different countries and pricing mortality bonds. We study the fitting performance of multi-dimensional, fully nested, and partially nested Archimedean copulas and test 11 types of generators and two skewed distributions. To evaluate their practical usefulness, we adopt the fitted models to compute the market prices for some typical mortality bond structures. The results show that the copula assumption has a significant impact on the calculation of the prices of mortality-linked securities and the management of extreme mortality risks.

Despite SARS-CoV-19 infection has a stereotypical clinical picture, isolated cases with unusual manifestations have been reported, some of them being well-known to be triggered by viral infections. However, the real frequency in COVID-19 is unknown. Analysing data of 63 822 COVID patients attending 50 Spanish emergency department (ED) during the COVID outbreak, before hospitalisation, we report frequencies of (myo)pericarditis (0.71‰), meningoencephalitis (0.25‰), Guillain–Barré syndrome (0.13‰), acute pancreatitis (0.71‰) and spontaneous pneumothorax (0.57‰). Compared with general ED population, COVID patients developed more frequently Guillain–Barré syndrome (odds ratio (OR) 4.55, 95% confidence interval (CI) 2.09–9.90), spontaneous pneumothorax (OR 1.98, 95% CI 1.40–2.79) and (myo)pericarditis (OR 1.45, 95% CI 1.07–1.97), but less frequently pancreatitis (OR 0.44, 95% CI 0.33–0.60).

Rapid advances in artificial intelligence (AI) and machine learning are creating products and services with the potential not only to change the environment in which actuaries operate but also to provide new opportunities within actuarial science. These advances are based on a modern approach to designing, fitting and applying neural networks, generally referred to as “Deep Learning.” This paper investigates how actuarial science may adapt and evolve in the coming years to incorporate these new techniques and methodologies. Part 1 of this paper provides background on machine learning and deep learning, as well as an heuristic for where actuaries might benefit from applying these techniques. Part 2 of the paper then surveys emerging applications of AI in actuarial science, with examples from mortality modelling, claims reserving, non-life pricing and telematics. For some of the examples, code has been provided on GitHub so that the interested reader can experiment with these techniques for themselves. Part 2 concludes with an outlook on the potential for actuaries to integrate deep learning into their activities. Finally, a supplementary appendix discusses further resources providing more in-depth background on machine learning and deep learning.

In this paper, we discuss stochastic orderings of lifetimes of two heterogeneous parallel and series systems with heterogeneous dependent components having generalized Birnbaum–Saunders distributions. The comparisons presented here are based on the vector majorization of parameters. The ordering results are established in some special cases for the generalized Birnbaum–Saunders distribution based on the multivariate elliptical, normal, t, logistic, and skew-normal kernels. Further, we use these results by considering Archimedean copulas to model the dependence structure among systems with generalized Birnbaum–Saunders components. These results have been used to derive some upper and lower bounds for survival functions of lifetimes of parallel and series systems.

Sentinel surveillance system plays a key role in screening and monitoring emerging and acute infectious diseases in order to identify the suspected cases in time. During SARS period in 2003, fever clinics emerged in many cities in mainland China with the purpose to screen the suspected SARS patients and to transfer the confirmed cases to designated hospitals for professional management. Shanghai city has reserved the fever clinics and the designated hospitals since then. Hence, clinicians in the front line are able to respond quickly to the emerging COVID-19 outbreak with their accumulated knowledge and experiences from the past. One hundred seventeen fever clinics distributed in various district areas in Shanghai have played a vital ‘sentinel’ role to fight against the COVID-19 epidemic. Most of suspected patients were identified in fever clinics and thereafter among these suspected patients the COVID-19 cases were confirmed and were isolated quickly to avoid the spread. We would like to share the sentinel roadmap for screening and diagnosis of COVID-19 to medical healthcare workers around the world, especially countries who are facing great challenges to cope with COVID-19 and meanwhile with limited medical resources. These sentinel surveillance strategies will certainly provide insight into the early detection and timely isolation of suspected cases from the others.

This study aimed to analyse the trend and spatial–temporal clusters of risk of transmission of COVID-19 in northeastern Brazil. We conducted an ecological study using spatial and temporal trend analysis. All confirmed cases of COVID-19 in the Northeast region of Brazil were included, from 7 March to 22 May 2020. We used the segmented log-linear regression model to assess time trends, and the local empirical Bayesian estimator, the global and local Moran indexes for spatial analysis. The prospective space–time scan statistic was performed using the Poisson probability distribution model. There were 113 951 confirmed cases of COVID-19. The average incidence rate was 199.73 cases/100 000 inhabitants. We observed an increasing trend in the incidence rate in all states. Spatial autocorrelation was reported in metropolitan areas, and 178 municipalities were considered a priority, especially in the states of Ceará and Maranhão. We identified 11 spatiotemporal clusters of COVID-19 cases; the primary cluster included 70 municipalities from Ceará state. COVID-19 epidemic is increasing rapidly throughout the Northeast region of Brazil, with dispersion towards countryside. It was identified high risk clusters for COVID-19, especially in the coastal side.

The aim of this study was to apply a back-calculation model to Great Britain (GB) classical scrapie surveillance data, and use this model to estimate how many more cases might be expected, and over what time frame these cases might occur. A back-calculation model was applied to scrapie surveillance data between 2005 and 2019 to estimate the annual rate of decline of classical scrapie. This rate was then extrapolated to predict the number of future cases each year going forward. The model shows that there may be yet further cases of classical scrapie in GB. These will most likely occur in the fallen stock scheme, with approximately a 25% probability of at least 1 further scrapie positive, with a very low probability (~0.2%) of having up to three additional scrapie positives. This highlights the difficulty of completely eliminating all further cases, even in the presence of very effective control measures.

The aim of the paper is to derive a simple, implementable machine learning method for general insurance losses. An algorithm for learning a general insurance loss triangle is developed and justified. An argument is made for applying support vector regression (SVR) to this learning task (in order to facilitate transparency of the learning method as compared to more “black-box” methods such as deep neural networks), and SVR methodology derived is specifically applied to this learning task. A further argument for preserving the statistical features of the loss data in the SVR machine is made. A bespoke kernel function that preserves the statistical features of the loss data is derived from first principles and called the exponential dispersion family (EDF) kernel. Features of the EDF kernel are explored, and the kernel is applied to an insurance loss estimation exercise for homogeneous risk of three different insurers. Results of the cumulative losses and ultimate losses predicted by the EDF kernel are compared to losses predicted by the radial basis function kernel and the chain-ladder method. A backtest of the developed method is performed. A discussion of the results and their implications follows.

Understanding the effects of predicted rising sea levels, combined with changes in precipitation and freshwater inflow on key estuarine ecosystem engineers such as the eastern oyster would provide critical information to inform restoration design and predictive models. Using oyster ladders with shell bags placed at three heights to capture a range of inundation levels, oyster growth of naturally recruited spat was monitored over the course of 6 months. Oyster numbers and shell heights were consistently highest in bottom and mid bags experiencing greater than 50% inundation (mid: 63 ± 7%; bottom: 95 ± 3%). Identifying thresholds for optimal oyster growth and survival to enhance restoration engineering would require finer scale evaluation of inundation levels.

The President (Dr J. Taylor, F.F.A.): Good evening from Edinburgh. Today marks the first-ever presidential address of the merged Institute and Faculty of Actuaries (IFoA) to take place outside of London.

During the last months and following the implementation of containment measures in the context of coronavirus disease 2019 (COVID-19) pandemic, the number of new human immunodeficiency virus (HIV) diagnoses radically decreased in Liege AIDS Reference Center, Belgium. The number of HIV screening tests has also dramatically dropped down to an unprecedented level. This decline of HIV diagnosis is caused by missed diagnoses of individuals infected before the establishment of such measures and to the reduction of high-risk sexual behaviours during the COVID-19 pandemic.

Bordetella bronchiseptica is a potential zoonotic pathogen, which mainly causes respiratory diseases in humans and a variety of animal species. B. bronchiseptica is one of the important pathogens isolated from rabbits in Fujian Province. However, the knowledge of the epidemiology and characteristics of the B. bronchiseptica in rabbits in Fujian Province is largely unknown. In this study, 219 B. bronchiseptica isolates recovered from lung samples of dead rabbits with respiratory diseases in Fujian Province were characterised by multi-locus sequencing typing, screening virulence genes and testing antimicrobial susceptibility. The results showed that the 219 isolates were typed into 11 sequence types (STs) including five known STs (ST6, ST10, ST12, ST14 and ST33) and six new STs (ST88, ST89, ST90, ST91, ST92 and ST93) and the ST33 (30.14%, 66/219), ST14 (26.94%, 59/219) and ST12 (16.44%, 36/219) were the three most prevalent STs. Surprisingly, all the 219 isolates carried the five virulence genes (fhaB, prn, cyaA, dnt and bteA) in the polymerase chain reaction screening. Moreover, the isolates were resistant to cefixime, ceftizoxime, cefatriaxone and ampicillin at rates of 33.33%, 31.05%, 11.87% and 3.20%, respectively. This study showed the genetic diversity of B. bronchiseptica in rabbits in Fujian Province, and the colonisation of the human-associated ST12 strain in rabbits in Fujian Province. The results might be useful for monitoring the epidemic strains, developing preventive methods and preventing the transmission of epidemic strains from rabbits to humans.

In this paper, we mainly study a class of small deviation theorems for Markov chains indexed by an infinite tree with uniformly bounded degree in Markovian environment. Firstly, we give the definition of Markov chains indexed by a tree with uniformly bounded degree in random environment. Then, we introduce the some lemmas which are the basis of the results. Finally, a class of small deviation theorems for functionals of random fields on a tree with uniformly bounded degree in Markovian environment is established.

There is growing interest in quantifying attitudes towards autistic people, however there is relatively little research on psychometric properties of the only existing measure and its ability to predict engagement with people with autism. To begin addressing these issues, we compared three scales measuring attitudes towards autistic people following the development of two new measures. Exploratory factor analysis, across two datasets, revealed that the factor-structure of an established 16-item scale is unclear. Further, its predictive validity of intended engagement with autistic people was comparable to our novel and psychometrically robust 1- and 4-item measures of attitudes towards autistic people. We therefore conclude that a 1- or 4-item scale is sufficient to measure general attitudes towards autistic people in future research. Equally, we propose that additional research is required to develop measures that are grounded in theoretical models of attitude formation and therefore distinguish between different components of attitudes.

The ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to estimate a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coast population regions by considering the different phases of response implemented by the US as well as transmission dynamics of COVID-19 in countries that were most affected. The model is then fitted to current data and implemented using Runge-Kutta methods. Our computation results predict that the number of new cases would peak around mid-April 2020 and begin to abate by July provided that appropriate COVID-19 measures are promptly implemented and followed, and that the number of cases of COVID-19 might be significantly mitigated by having greater numbers of functional testing kits available for screening. The model is also sensitive to assigned parameter values and reflects the importance of healthcare preparedness during pandemics.

Purpose: The novel coronavirus (severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)) first appeared in Wuhan, China, in December 2019, and rapidly spread across the globe. Since most respiratory viruses are known to show a seasonal pattern of infection, it has been hypothesised that SARS-CoV-2 may be seasonally dependent as well. The present study looks at a possible effect of atmospheric temperature, which is one of the suspected factors influencing seasonality, on the evolution of the pandemic. Basic procedures: Since confirming a seasonal pattern would take several more months of observation, we conducted an innovative day-to-day micro-correlation analysis of nine outbreak locations, across four continents and both hemispheres, in order to examine a possible relationship between atmospheric temperature (used as a proxy for seasonality) and outbreak progression. Main findings: There was a negative correlation between atmospheric temperature variations and daily new cases growth rates, in all nine outbreaks, with a median lag of 10 days. Principal conclusions: The results presented here suggest that high temperatures might dampen SARS-CoV-2 propagation, while lower temperatures might increase its transmission. Our hypothesis is that this could support a potential effect of atmospheric temperature on coronavirus disease progression, and potentially a seasonal pattern for this virus, with a peak in the cold season and rarer occurrences in the summer. This could guide government policy in both the Northern and Southern hemispheres for the months to come.

Given extensive research underscoring the deleterious effects of bullying on youth adjustment, anti-bullying policies and programming are critical public health priorities. However, strategies that increase public support for anti-bullying causes are not well understood. This experiment assessed the influence of “bullying messaging” on support for anti-bullying policies. Specifically, I investigated whether learning about the health consequences of bullying, as opposed to its prevalence or educational impact, increased individuals’ support of anti-bullying policies. Participants (n = 329) were randomly assigned to one of four conditions where they read a brief summary about bullying research; conditions varied by whether the research documented the: a) prevalence of bullying b) mental health consequences of bullying c) physical health consequences of bullying or d) academic consequences of bullying. Results indicated that participants endorsed high levels of support for anti-bullying policies, regardless of experimental condition, and that policies aimed at increasing K-12 mental health resources were most supported.

Acute haemorrhagic conjunctivitis is a highly contagious eye disease, the prediction of acute haemorrhagic conjunctivitis is very important to prevent and grasp its development trend. We use the exponential smoothing model and the seasonal autoregressive integrated moving average (SARIMA) model to analyse and predict. The monthly incidence data from 2004 to 2017 were used to fit two models, the actual incidence of acute haemorrhagic conjunctivitis in 2018 was used to validate the model. Finally, the prediction effect of exponential smoothing is best, the mean square error and the mean absolute percentage error were 0.0152 and 0.1871, respectively. In addition, the incidence of acute haemorrhagic conjunctivitis in Chongqing had a seasonal trend characteristic, with the peak period from June to September each year.

Different countries have adopted strategies for the early detection of SARS-CoV-2 since the declaration of community transmission by the World Health Organization (WHO) and timely diagnosis has been considered one of the major obstacles for surveillance and healthcare. Here, we report the increase of the number of laboratories to COVID-19 diagnosis in Brazil. Our results demonstrate an increase and decentralisation of certified laboratories, which does not match the much higher increase in the number of COVID-19 cases. Also, it becomes clear that laboratories are irregularly distributed over the country, with a concentration in the most developed state, São Paulo.

We establish a fundamental property of bivariate Pareto records for independent observations uniformly distributed in the unit square. We prove that the asymptotic conditional distribution of the number of records broken by an observation given that the observation sets a record is Geometric with parameter 1/2.