Due to the high incidence of COVID-19 case numbers internationally, the World Health Organization (WHO) declared a Public Health Emergency of global relevance, advising countries to follow protocols to combat pandemic advance through actions that can reduce spread and consequently avoid a collapse in the local health system. This study aimed to evaluate the dynamics of the evolution of new community cases, and mortality records of COVID-19 in the State of Pará, which has a subtropical climate with temperatures between 20 and 35 °C, after the implementation of social distancing by quarantine and adoption of lockdown. The follow-up was carried out by the daily data from the technical bulletins provided by the State of Pará Public Health Secretary (SESPA). On 18 March 2020, Pará notified the first case of COVID-19. After 7 weeks, the number of confirmed cases reached 4756 with 375 deaths. The results show it took 49 days for 81% of the 144 states municipalities, distributed over an area of approximately 1 248 000 km2 to register COVID-19 cases. Temperature variations between 24.5 and 33.1 °C did not promote the decline in the new infections curve. The association between social isolation, quarantine and lockdown as an action to contain the infection was effective in reducing the region's new cases registration of COVID-19 in the short-term. However, short periods of lockdown may have promoted the virus spread among peripheral municipalities of the capital, as well as to inland regions.

The theory of optimal transportation has experienced a sharp increase in interest in many areas of economic research such as optimal matching theory and econometric identification. A particularly valuable tool, due to its convenient representation as the gradient of a convex function, has been the Brenier map: the matching obtained as the optimizer of the Monge–Kantorovich optimal transportation problem with the euclidean distance as the cost function. Despite its popularity, the statistical properties of the Brenier map have yet to be fully established, which impedes its practical use for estimation and inference. This article takes a first step in this direction by deriving a convergence rate for the simple plug-in estimator of the potential of the Brenier map via the semi-dual Monge–Kantorovich problem. Relying on classical results for the convergence of smoothed empirical processes, it is shown that this plug-in estimator converges in standard deviation to its population counterpart under the minimax rate of convergence of kernel density estimators if one of the probability measures satisfies the Poincaré inequality. Under a normalization of the potential, the result extends to convergence in the $L^2$ norm, while the Poincaré inequality is automatically satisfied. The main mathematical contribution of this article is an analysis of the second variation of the semi-dual Monge–Kantorovich problem, which is of independent interest.

Cats represent a potential source of Coxiella burnetii, the aetiological agent of Q fever in humans. The prevalence and risk factors of C. burnetii infection in farm, pet and feral cats were studied in Quebec, Canada, using a cross-sectional study. Serum samples were tested using a specific enzyme-linked immunosorbent assay (ELISA) for the presence of antibodies against C. burnetii, whereas rectal swabs were assayed using real-time quantitative polymerase chain reaction (qPCR) for the molecular detection of the bacteria. Potential risk factors for farm cats were investigated using clinical examinations, questionnaires and results from a concurrent study on C. burnetii farm status. A total of 184 cats were tested: 59 from ruminant farms, 73 pets and 52 feral cats. Among farm cats, 2/59 (3.4%) were ELISA-positive, 3/59 (5.1%) were ELISA-doubtful and 1/59 (1.7%) was qPCR-positive. All pets and feral cats were negative to C. burnetii ELISA and qPCR. Farm cat positivity was associated with a positive C. burnetii status on the ruminant farm (prevalence ratio = 7.6, P = 0.03). Our results suggest that although pet and feral cats do not seem to pose a great C. burnetii risk to public health, more active care should be taken when in contact with cats from ruminant farms.

The role of anthropometric status on dengue is uncertain. We investigated the relations between anthropometric characteristics (height, body mass index and waist circumference (WC)) and two dengue outcomes, seropositivity and hospitalisation, in a cross-sectional study of 2038 children (aged 2–15 years) and 408 adults (aged 18–72 years) from Bucaramanga, Colombia. Anthropometric variables were standardised by age and sex in children. Seropositivity was determined through immunoglobulin G antibodies; past hospitalisation for dengue was self-reported. We modelled the prevalence of each outcome by levels of anthropometric exposures using generalised estimating equations with restricted cubic splines. In children, dengue seropositivity was 60.8%; 9.9% of seropositive children reported prior hospitalisation for dengue. WC was positively associated with seropositivity in girls (90th vs. 10th percentile adjusted prevalence ratio (APR) = 1.19; 95% confidence interval (CI) 1.03–1.36). Among adults, dengue seropositivity was 95.1%; 8.1% of seropositive adults reported past hospitalisation. Height was inversely associated with seropositivity (APR = 0.90; 95% CI 0.83–0.99) and with hospitalisation history (APR = 0.19; 95% CI 0.04–0.79). WC was inversely associated with seropositivity (APR = 0.89; 95% CI 0.81–0.98). We conclude that anthropometry correlates with a history of dengue, but could not determine causation. Prospective studies are warranted to enhance causal inference on these questions.

Assessing conditional tail risk at very high or low levels is of great interest in numerous applications. Due to data sparsity in high tails, the widely used quantile regression method can suffer from high variability at the tails, especially for heavy-tailed distributions. As an alternative to quantile regression, expectile regression, which relies on the minimization of the asymmetric l2-norm and is more sensitive to the magnitudes of extreme losses than quantile regression, is considered. In this article, we develop a new estimation method for high conditional tail risk by first estimating the intermediate conditional expectiles in regression framework, and then estimating the underlying tail index via weighted combinations of the top order conditional expectiles. The resulting conditional tail index estimators are then used as the basis for extrapolating these intermediate conditional expectiles to high tails based on reasonable assumptions on tail behaviors. Finally, we use these high conditional tail expectiles to estimate alternative risk measures such as the Value at Risk (VaR) and Expected Shortfall (ES), both in high tails. The asymptotic properties of the proposed estimators are investigated. Simulation studies and real data analysis show that the proposed method outperforms alternative approaches.

Nakamoto doublespend strategy, described in Bitcoin foundational article, leads to total ruin with positive probability. The simplest strategy that avoids this risk incorporates a stopping threshold when success is unlikely. We compute the exact profitability and the minimal double spend that is profitable for this strategy. For a given amount of the transaction, we determine the minimal number of confirmations to be requested by the recipient that makes the double-spend strategy non-profitable. This number of confirmations is only 1 or 2 for average transactions and for a small relative hashrate of the attacker. This is substantially lower than the original Nakamoto number, which is about six confirmations and is widely used. Nakamoto analysis is only based on the success probability of the attack instead of on a profitability analysis that we carry out.

Coronavirus disease 2019 (COVID-19) has become a global pandemic. Previous studies showed that comorbidities in patients with COVID-19 are risk factors for adverse outcomes. This study aimed to clarify the association between nervous system diseases and severity or mortality in patients with COVID-19. We performed a systematic literature search of four electronic databases and included studies reporting the prevalence of nervous system diseases in COVID-19 patients with severe and non-severe disease or among survivors and non-survivors. The included studies were pooled into a meta-analysis to calculate the odds ratio (OR) with 95% confidence intervals (95%CI). We included 69 studies involving 17 879 patients. The nervous system diseases were associated with COVID-19 severity (OR = 3.19, 95%CI: 2.37 to 4.30, P < 0.001) and mortality (OR = 3.75, 95%CI: 2.68 to 5.25, P < 0.001). Specifically, compared with the patients without cerebrovascular disease, patients with cerebrovascular disease infected with COVID-19 had a higher risk of severity (OR = 3.10, 95%CI: 2.21 to 4.36, P < 0.001) and mortality (OR = 3.45, 95% CI: 2.46 to 4.84, P < 0.001). Stroke was associated with severe COVID-19 disease (OR = 1.95, 95%CI: 1.11 to 3.42, P = 0.020). No significant differences were found for the prevalence of epilepsy (OR = 1.00, 95%CI: 0.42 to 2.35, P = 0.994) and dementia (OR = 2.39, 95%CI: 0.55 to 10.48, P = 0.247) between non-severe and severe COVID-19 patients. There was no significant association between stroke (OR = 1.79, 95%CI: 0.76 to 4.23, P = 0.185), epilepsy (OR = 2.08, 95%CI: 0.08 to 50.91, P = 0.654) and COVID-19 mortality. In conclusion, nervous system diseases and cerebrovascular disease were associated with severity and mortality of patients with COVID-19. There might be confounding factors that influence the relationship between nervous system diseases and COVID-19 severity as well as mortality.

The pensions dashboard has been talked about across the industry for a long time. With the proposed implementation date of 2019 (although it has been questioned by some whether this is achievable or not), it is time to consider the actuarial aspects behind providing individuals with details of their pension benefits.

This paper outlines the perspective of the IFoA’s Future Pensions Landscape working party. The paper considers the objectives of the dashboard and the functionality that may be required to deliver on those. It also highlights the difficulties of the necessary consistency between different types of benefits and the need for alignment with other pensions communications. Lastly, it considers what is needed to enhance the dashboard to enable members to understand what their benefits might look like at retirement and the opportunities the dashboard delivers for further modelling and financial planning.

In Japan, respiratory syncytial virus (RSV) infection generally has occurred during autumn and winter. However, a possible change in the seasonal trend of RSV infection has been observed recently. The current study was conducted to determine whether the epidemic season of RSV infection in Japan has indeed changed significantly. We used expectation-based Poisson scan statistics to detect periods with high weekly reported RSV cases (epidemic cluster), and the epidemic clusters were detected between September and December in the 2012–2016 seasons while those were detected between July and October in the 2017–2019 seasons. Non-linear and linear ordinary least squares regression models were built to evaluate whether there is a difference in year trend in the epidemic seasonality, and the epidemic season was shifted to earlier in the year in 2017–2019 compared to that in 2012–2016. Although the reason for the shift is unclear, this information may help in clinical practice and public health.

Much of our current understanding about novel coronavirus disease 2019 (COVID-19) comes from hospitalised patients. However, the spectrum of mild and subclinical disease has implications for population-level screening and control. Forty-nine participants were recruited from a group of 99 adults repatriated from a cruise ship with a high incidence of COVID-19. Respiratory and rectal swabs were tested by polymerase chain reaction (PCR) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Sera were tested for anti-SARS-CoV-2 antibodies by enzyme-linked immunosorbent assay (ELISA) and microneutralisation assay. Symptoms, viral shedding and antibody response were examined. Forty-five participants (92%) were considered cases based on either positive PCR or positive ELISA for immunoglobulin G. Forty-two percent of cases were asymptomatic. Only 15% of symptomatic cases reported fever. Serial respiratory and rectal swabs were positive for 10% and 5% of participants respectively about 3 weeks after median symptom onset. Cycle threshold values were high (range 31–45). Attempts to isolate live virus were unsuccessful. The presence of symptoms was not associated with demographics, comorbidities or antibody response. In closed settings, incidence of COVID-19 could be almost double that suggested by symptom-based screening. Serology may be useful in diagnosis of mild disease and in aiding public health investigations.

RNA interference (RNAi) is a technique used in many insects to study gene function. However, prior research suggests possible off-target effects when using Green Fluorescent Protein (GFP) sequence as a non-target control. We used a transcriptomic approach to study the effect of GFP RNAi (GFP-i) in Nasonia vitripennis, a widely used parasitoid wasp model system. Our study identified 3.4% of total genes being differentially expressed in response to GFP-i. A subset of these genes appears involved in microtubule and sperm functions. In silico analysis identified 17 potential off-targets, of which only one was differentially expressed after GFP-i. We suggest the primary cause for differential expression after GFP-i is the non-specific activation of the RNAi machinery at the injection site, and a potentially disturbed spermatogenesis. Still, we advise that any RNAi study involving the genes deregulated in this study, exercises caution in drawing conclusions and uses a different non-target control.

Epidemic forecasting provides an opportunity to predict geographic disease spread and counts when an outbreak occurs and plays a key role in preventing or controlling their adverse impact. However, conventional prediction models based on complex mathematical modelling rely on the estimation of model parameters, which yields unreliable and unsustainable results. Herein, we proposed a simple model for predicting the epidemic transmission dynamics based on nonlinear regression of the epidemic growth rate and iterative methods, which is applicable to the progression of the COVID-19 outbreak under the strict control measures of the Chinese government. Our model yields reliable and accurate results as confirmed by the available data: we predicted that the total number of infections in mainland China would be 91 253, and the maximum number of beds required for hospitalised patients would be 62 794. We inferred that the inflection point (when the growth rate turns from positive to negative) of the epidemic across China would be mid-February, and the end of the epidemic would be in late March. This model is expected to contribute to resource allocation and planning in the health sector while providing a theoretical basis for governments to respond to future global health crises or epidemics.

To understand the characteristics and influencing factors related to cluster infections in Jiangsu Province, China, we investigated case reports to explore transmission dynamics and influencing factors of scales of cluster infection. The effectiveness of interventions was assessed by changes in the time-dependent reproductive number (Rt). From 25th January to 29th February, Jiangsu Province reported a total of 134 clusters involving 617 cases. Household clusters accounted for 79.85% of the total. The time interval from onset to report of index cases was 8 days, which was longer than that of secondary cases (4 days) (χ2 = 22.763, P < 0.001) and had a relationship with the number of secondary cases (the correlation coefficient (r) = 0.193, P = 0.040). The average interval from onset to report was different between family cluster cases (4 days) and community cluster cases (7 days) (χ2 = 28.072, P < 0.001). The average time interval from onset to isolation of patients with secondary infection (5 days) was longer than that of patients without secondary infection (3 days) (F = 9.761, P = 0.002). Asymptomatic patients and non-familial clusters had impacts on the size of the clusters. The average reduction in the Rt value in family clusters (26.00%, 0.26 ± 0.22) was lower than that in other clusters (37.00%, 0.37 ± 0.26) (F = 4.400, P = 0.039). Early detection of asymptomatic patients and early reports of non-family clusters can effectively weaken cluster infections.

Healthcare staff have been at the centre of the fight against the COVID-19 pandemic, facing diverse work-related stressors. Building upon studies from various countries, we aimed to investigate (1) the prevalence of various work-related stressors among healthcare professionals in Germany specific to the COVID-19 pandemic, (2) the psychological effects of these stressors in terms of clinical symptoms, and (3) the healthcare professionals' help-seeking behaviour. To this end, N = 300 healthcare professionals completed an online survey including the ICD-10 Symptom Rating checklist (ISR), event-sampling questions on pandemic-related stressors and self-formulated questions on help-seeking behaviour. Participants were recruited between 22 May and 22 July 2020. Findings were analysed using t tests, regressions and comparisons to large clinical and non-clinical samples assessed before and during the pandemic. Results show that healthcare professionals were most affected by protective measures at their workplace and changes in work procedures. Psychological symptoms, particularly anxiety and depression, were significantly more severe than in a non-clinical pre-pandemic sample and in the general population during the pandemic. At the same time, most professionals indicated that they would not seek help for psychological concerns. These findings indicate that healthcare employers need to pay greater attention to the mental health of their staff.

During a disease outbreak, healthcare workers (HCWs) are essential to treat infected individuals. However, these HCWs are themselves susceptible to contracting the disease. As more HCWs get infected, fewer are available to provide care for others, and the overall quality of care available to infected individuals declines. This depletion of HCWs may contribute to the epidemic's severity. To examine this issue, we explicitly model declining quality of care in four differential equation-based susceptible, infected and recovered-type models with vaccination. We assume that vaccination, recovery and survival rates are affected by quality of care delivered. We show that explicitly modelling HCWs and accounting for declining quality of care significantly alters model-predicted disease outcomes, specifically case counts and mortality. Models neglecting the decline of quality of care resulting from infection of HCWs may significantly under-estimate cases and mortality. These models may be useful to inform health policy that may differ for HCWs and the general population. Models accounting for declining quality of care may therefore improve the management interventions considered to mitigate the effects of a future outbreak.

Time variation is a fundamental problem in statistical and econometric analysis of macroeconomic and financial data. Recently, there has been considerable focus on developing econometric modelling that enables stochastic structural change in model parameters and on model estimation by Bayesian or nonparametric kernel methods. In the context of the estimation of covariance matrices of large dimensional panels, such data requires taking into account time variation, possible dependence and heavy-tailed distributions. In this paper, we introduce a nonparametric version of regularization techniques for sparse large covariance matrices, developed by Bickel and Levina (2008) and others. We focus on the robustness of such a procedure to time variation, dependence and heavy-tailedness of distributions. The paper includes a set of results on Bernstein type inequalities for dependent unbounded variables which are expected to be applicable in econometric analysis beyond estimation of large covariance matrices. We discuss the utility of the robust thresholding method, comparing it with other estimators in simulations and an empirical application on the design of minimum variance portfolios.

Laboratory data increasingly suggest that Salmonella infection may contribute to colon cancer (CC) development. Here, we examined epidemiologically the potential risk of CC associated with salmonellosis in the human population. We conducted a population-based cohort study using four health registries in Denmark. Person-level demographic data of all residents were linked to laboratory-confirmed non-typhoidal salmonellosis and to CC diagnoses in 1994–2016. Hazard ratios (HRs) for CC (overall/proximal/distal) associated with reported salmonellosis were estimated using Cox proportional hazard models. Potential effects of serovar, age, sex, inflammatory bowel disease and follow-up time post-infection were also assessed. We found no increased risk of CC ≥1 year post-infection (HR 0.99; 95% confidence interval (CI) 0.88–1.13). When stratifying by serovar, there was a significantly increased risk of proximal CC ≥1 year post-infection with serovars other than Enteritidis and Typhimurium (HR 1.40; 95% CI 1.03–1.90). CC risk was significantly increased in the first year post-infection (HR 2.08; 95% CI 1.48–2.93). The association between salmonellosis and CC in the first year post-infection can be explained by increased stool testing around the time of CC diagnosis. The association between proximal CC and non-Enteritidis/non-Typhimurium serovars is unclear and warrants further investigation. Overall, this study provides epidemiological evidence that notified Salmonella infections do not contribute significantly to CC risk in the studied population.

Reliability properties associated with the classic models of systems with age replacement have been a usual topic of research. Most previous works have checked the aging properties of the lifetime of the working units using stochastic comparisons among the systems with age replacement at different times. However, from a practical point of view, it would also be interesting to deduce to which aging classes the lifetime of the system belongs, making use of the aging properties of the lifetime of its working units. The first part of this article deals with this problem. Further along, stochastic orderings are established between the systems with replacement at the same time using several stochastic comparisons among the lifetimes of their working units. In addition, the lifetimes of two systems with age replacement are compared as well. This is performed assuming stochastic orderings between the number of replacement until failure, and the lifetimes of their working units conditioned to be less or equal than the replacement time. Similar comparisons are accomplished considering two systems with age replacement where the replacements occur at a random time. Illustrative examples are presented throughout the paper.