This paper applies a scenario planning approach, to outline some current uncertainties related to COVID-19 and what they might mean for plausible futures for which we should prepare, and to identify factors that we as individual faculty members and university institutions should be considering now, when planning for the future under COVID-19. Although the contextual focus of this paper is Canada, the content is likely applicable to other places where the COVID-19 epidemic curve is in its initial rising stage, and where universities are predominantly publicly funded institutions.

The number of reported cases with Legionnaires' disease (LD) is increasing in Belgium. Previous studies have investigated the associations between LD incidence and meteorological factors, but the Belgian data remained unexplored. We investigated data collected between 2011 and 2019. Daily exposure data on temperature, relative humidity, precipitation and wind speed was obtained from the Royal Meteorological Institute for 29 weather stations. Case data were collected from the national reference centre and through mandatory notification. Daily case and exposure data were aggregated by province. We conducted a time-stratified case-crossover study. The ‘at risk’ period was defined as 10 to 2 days prior to disease onset. The corresponding days in the other study years were selected as referents. We fitted separate conditional Poisson models for each day in the ‘at risk’ period and a distributed lag non-linear model (DLNM) which fitted all data in one model. LD incidence showed a yearly peak in August and September. A total of 614 cases were included. Given seasonality, a sequence of precipitation, followed by high relative humidity and low wind speed showed a statistically significant association with the number of cases 6 to 4 days later. We discussed the advantages of DLNM in this context.

Since 2016, the European Region has experienced large-scale measles outbreaks. Several measles outbreaks in England during 2017/18 specifically affected Romanian and Romanian Roma communities. In this qualitative interview study, we looked at the effectiveness of outbreak responses and efforts to promote vaccination uptake amongst these underserved communities in three English cities: Birmingham, Leeds and Liverpool. Semi-structured in-depth interviews were conducted with 33 providers involved in vaccination delivery and outbreak management in these cities. Interviews were analysed thematically and factors that influenced the effectiveness of responses were categorised into five themes: (1) the ability to identify the communities, (2) provider knowledge and understanding of the communities, (3) the co-ordination of response efforts and partnership working, (4) links to communities and approaches to community engagement and (5) resource constraints. We found that effective partnership working and community engagement were key to the prevention and management of vaccine-preventable disease outbreaks in the communities. Effective engagement was found to be compromised by cuts to public health spending and services for underserved communities. To increase uptake in under-vaccinated communities, local knowledge and engagement are vital to build trust and relationships. Local partners must work proactively to identify, understand and build connections with communities.

Different mortality rates for different socio-economic groups within a population have been consistently reported throughout the years. In this study, we aim to exploit data from multiple public sources, including highly detailed cause-of-death data from the United States Centers for Disease Control and Prevention, to explore the mortality gap between the better and worse off in the US during the period 1989–2015, using education as a proxy.

This note derives analytic expressions for annuities based on a class of parametric mortality “laws” (the so-called Makeham–Beard family) that includes a logistic form that models a decelerating increase in mortality rates at the higher ages. Such models have been shown to provide a better fit to pensioner and annuitant mortality data than those that include an exponential increase. The expressions derived for evaluating single life and joint life annuities for the Makeham–Beard family of mortality laws use the Gauss hypergeometric function and Appell function of the first kind, respectively.

This article develops a Markov chain Monte Carlo (MCMC) method for a class of models that encompasses finite and countable mixtures of densities and mixtures of experts with a variable number of mixture components. The method is shown to maximize the expected probability of acceptance for cross-dimensional moves and to minimize the asymptotic variance of sample average estimators under certain restrictions. The method can be represented as a retrospective sampling algorithm with an optimal choice of auxiliary priors and as a reversible jump algorithm with optimal proposal distributions. The method is primarily motivated by and applied to a Bayesian nonparametric model for conditional densities based on mixtures of a variable number of experts. The mixture of experts model outperforms standard parametric and nonparametric alternatives in out of sample performance comparisons in an application to Engel curve estimation. The proposed MCMC algorithm makes estimation of this model practical.

We develop a method for long-run predictability testing in series Y by a persistent series X. We consider a class of tests based on the long-run behavior of these series that are robust to short-run dynamics and attempt to attain the highest possible power. The test is based on the Whittle approximation to the likelihood ratio that is adjusted to remain accurate across a range of persistence in X. We verify the properties of this test in small simulations and compare this test against a group of recently proposed methods.

In recent years, men who have sex with men (MSM) constitute a major group of HIV transmission in China. High primary drug-resistance (PDR) rate in MSM also represents a serious challenge for the Chinese antiretroviral therapy (ART) program. To assess the efficiency of ART in controlling HIV/AIDS infection among MSM, we developed a compartmental model for the annually reported HIV/AIDS MSM from 2007 to 2019 in the Zhejiang Province of China. R0 was 2.3946 (95% CI (2.2961–2.4881)). We predict that 90% of diagnosed HIV/AIDS individuals will have received treatment till 2020, while the proportion of the diagnosed remains as low as 40%. Even when the proportion of the diagnosed reaches 90%, R0 is still larger than the level of AIDS epidemic elimination. ART can effectively control the spread of HIV, even in the presence of drug resistance. The 90-90-90 strategy alone may not eliminate the HIV epidemic in Chinese MSM. Behavioural and biologic interventions are the most effective interventions to control the HIV/AIDS epidemic among MSM.

Genetic risk is particularly salient for families and testing for genetic conditions is necessarily a family-level process. Thus, risk for genetic disease represents a collective stressor shared by family members. According to communal coping theory, families may adapt to such risk vis-a-vis interpersonal exchange of support resources. We propose that communal coping is operationalized through the pattern of supportive relationships observed between family members. In this study, we take a social network perspective to map communal coping mechanisms to their underlying social interactions and include those who declined testing or were not at risk for Lynch Syndrome. Specifically, we examine the exchange of emotional support resources in families at risk of Lynch Syndrome, a dominantly inherited cancer susceptibility syndrome. Our results show that emotional support resources depend on the testing-status of individual family members and are not limited to the bounds of the family. Network members from within and outside the family system are an important coping resource in this patient population. This work illustrates how social network approaches can be used to test structural hypotheses related to communal coping within a broader system and identifies structural features that characterize coping processes in families affected by Lynch Syndrome.

Previous work on aesthetic experience suggests that aesthetic judgments are self-referential. The self-reference effect (SRE) is the tendency for individuals to show improved memory for items that are judged in relation to themselves. The current study sought to understand if the SRE exists for aesthetic judgments of music. Participants heard musical excerpts (classical, jazz, and electronic) and rated either a) how much they liked the music (Self condition), b) how much a close relative or friend would like the music (Other condition), or c) the genre of the music (Genre condition). After a retention interval, participants completed a recognition memory task for the musical excerpts. Participants did not show improved memory for musical excerpts encoded in the Self condition. These results extend the concept of the SRE into the domain of aesthetic judgments, but do not provide support for a memory advantage when making aesthetic judgments in relation to the self.