The incidence of scarlet fever has increased dramatically in recent years in Chongqing, China, but there has no effective method to forecast it. This study aimed to develop a forecasting model of the incidence of scarlet fever using a seasonal autoregressive integrated moving average (SARIMA) model. Monthly scarlet fever data between 2011 and 2019 in Chongqing, China were retrieved from the Notifiable Infectious Disease Surveillance System. From 2011 to 2019, a total of 5073 scarlet fever cases were reported in Chongqing, the male-to-female ratio was 1.44:1, children aged 3–9 years old accounted for 81.86% of the cases, while 42.70 and 42.58% of the reported cases were students and kindergarten children, respectively. The data from 2011 to 2018 were used to fit a SARIMA model and data in 2019 were used to validate the model. The normalised Bayesian information criterion (BIC), the coefficient of determination (R2) and the root mean squared error (RMSE) were used to evaluate the goodness-of-fit of the fitted model. The optimal SARIMA model was identified as (3, 1, 3) (3, 1, 0)12. The RMSE and mean absolute per cent error (MAPE) were used to assess the accuracy of the model. The RMSE and MAPE of the predicted values were 19.40 and 0.25 respectively, indicating that the predicted values matched the observed values reasonably well. Taken together, the SARIMA model could be employed to forecast scarlet fever incidence trend, providing support for scarlet fever control and prevention.

Inappropriate use of antibiotics is among the key drivers of antimicrobial resistance (AMR). Antibiotic use in Northern Ireland (NI) is the highest in the UK and approximately 80% is prescribed in primary care. Little information however exists about the patient and prescriber factors driving this. We described the trend in NI primary care total antibiotic prescribing 2010–2019 and conducted a cross-sectional study using a random sample of individuals registered with an NI GP on 1st January 2019. We used multilevel logistic regression to examine how sociodemographic factors and urinary catheter use was associated with the likelihood of being prescribed an antibiotic during 2019, adjusting for clustering at GP practice and GP federation levels. Finite mixture modelling (FMM) was conducted to determine the association between the aforementioned risk factors and quantity of antibiotic prescribed (defined daily doses). The association between age and antibiotic prescription differed by gender. Compared to males 41–50 years, adjusted odds of prescription were higher for males aged 0–10, 11–20 and 51 + years, and females of any age. Catheter use was strongly associated with antibiotic prescription (aOR = 6.82, 95% CI 2.50–18.64). Socioeconomic deprivation and urban/rural settlement were not associated in the multilevel logistic analysis. GP practices and federations accounted for 1.24% and 0.12% of the variation in antibiotic prescribing respectively. FMM showed associations between larger quantities of antibiotics and being older, male and having a catheter. This work described the profile of individuals most likely to receive an antibiotic prescription in NI primary care and identified GP practice as a source of variation; suggesting an opportunity for reduction from effective interventions targeted at both individuals and general practices.

Let $X_1, \ldots, X_n$ be mutually independent exponential random variables with distinct hazard rates $\lambda _1, \ldots, \lambda _n$ and let $Y_1, \ldots, Y_n$ be a random sample from the exponential distribution with hazard rate $\bar \lambda = \sum _{i=1}^{n} \lambda _i/n$. Also let $X_{1:n} \lt \cdots \lt X_{n:n}$ and $Y_{1:n} \lt \cdots \lt Y_{n:n}$ be their associated order statistics. It is proved that for $1\le i \lt j \le n$, the generalized spacing $X_{j:n} - X_{i:n}$ is more dispersed than $Y_{j:n} - Y_{i:n}$ according to dispersive ordering and for $2\le i \le n$, the dependence of $X_{i:n}$ on $X_{1:n}$ is less than that of $Y_{i:n}$ on $Y_{1 :n}$, in the sense of the more stochastically increasing ordering. This dependence result is also extended to the proportional hazard rates (PHR) model. This extends the earlier work of Genest et al. [(2009)]. On the range of heterogeneous samples. Journal of Multivariate Analysis 100: 1587–1592] who proved this result for $i =n$.

Despite the growing body of evidence suggesting that alcohol consumption is associated with an increased risk of and poorer treatment outcomes from pneumonia, little is known about the association between alcohol control policy and pneumonia mortality. As such, this study aimed to assess the impact of three alcohol control policies legislated in 2008, 2017 and 2018 in Lithuania on sex-specific pneumonia mortality rates among individuals 15+ years of age. An interrupted time-series analysis using a generalised additive mixed model was performed for each policy. Of the three policies, only the 2008 policy resulted in a significant slope change (i.e. decline) in pneumonia mortality rates among males; no significant slope change was observed among females. The low R2 values for all sex-specific models suggest that other external factors are likely also influencing the sex-specific pneumonia mortality rates in Lithuania. Overall, the findings from this study suggest alcohol control policy's targeting affordability may be an effective way to reduce pneumonia mortality rates, among males in particular. However, further research is needed to fully explore their impact.

We describe an outbreak of delta variant SARS-CoV-2 on a psychogeriatric ward of elderly patients. Retrospectively collected data was analysed using Fisher's exact test to assess the association between patients’ vaccination status and infection rates, severity of disease and mortality. Vaccination with two doses was shown to reduce severity of disease (5% vs. 75%, p < 0.001) and mortality (5% vs. 50%, p < 0.018) amongst an elderly inpatient population during an outbreak of delta variant SARS-CoV-2. Vaccination should be encouraged in elderly care institutions. Furthermore, adequate vaccination in elderly care institutions is an important consideration in current booster (third/fourth) dose schedules.

This prospective longitudinal epidemiological study was aimed at investigating the occupational SARS-CoV-2 infection risk of long distance train services in Germany. Three different employee groups (train attendants, train drivers and maintenance workers) within the workforce of the German railway carrier Deutsche Bahn Fernverkehr AG were studied based on their contact frequency with passengers and colleagues. Approximately 1100 employees were tested by PCR for acute infections and by antibody detection for past infections in June 2020, October 2020 and February 2021. Cumulative incidence (acute and past infections) after the third (final) test series in February 2021 was 8.5% (95% interval CI 6.8–10.4): 8.5% (95% CI 6.2–11.2) for train attendants, 5.5% (95% CI 2.9–9.5) for train drivers and 11.8% (95% CI 7.6–17.2) for maintenance workers. Between June 2020 and October 2020, the incidence was 1.2% (95% CI 0.6–2.3): 1.2% (95% CI 0.4–2.7) for train attendants, 1.1% (95% CI 0.1–3.9) for train drivers and 1.4% (95% CI 0.17–5.10) for maintenance workers. Between October 2020 and February 2021, it was 5.1% (95% CI 3.6–6.8): 5.2% (95% CI 3.3–7.8) for train attendants, 1.6% (95% CI 0.3–4.5) for train drivers and 8.8% (95% CI 4.9–14.3) for maintenance workers. Thus, contrary to expectation our exploratory data did not show train attendants to be at the highest risk of SARS-CoV-2 infections among the employee groups. In line with expectations, train drivers, representing the low contact group, seemed at lowest occupational risk.

To investigate the effect of maternal hepatitis B surface antigen (HBsAg) carrier status during pregnancy on pregnancy outcomes in a population of patients in Hangzhou, China. A retrospective cohort study was conducted to analyse data from 20 753 pregnant women who delivered at Hangzhou Women's Hospital between January 2015 and March 2020. Of these, 18 693 were normal pregnant women (the non-exposed group) and 735 were HBsAg carriers (the exposed group). We then analysed by binary multivariate logistic regression to determine the association between maternal HBsAg-positive and adverse pregnancy outcomes. The prevalence of HBsAg carriers was 3.78% and the odds ratio (OR) for maternal age in the exposed group was 1.081. Pregnant women who are HBsAg-positive in Hangzhou, China, are at higher risk of a range of adverse pregnancy outcomes, including intrahepatic cholestasis of pregnancy (ICP) (adjusted OR (aOR) 3.169), low birth weight (aOR 2.337), thrombocytopenia (aOR 2.226), fallopian cysts (aOR 1.610), caesarean scar pregnancy (aOR 1.283), foetal distress (aOR 1.414). Therefore, the obstetricians should pay particular attention to ICP, low birth weight, thrombocytopenia, fallopian cysts, caesarean scar, foetal distress in HBsAg-positive pregnant women.

We extend the notion of cointegration for time series taking values in a potentially infinite dimensional Banach space. Examples of such time series include stochastic processes in $C[0,1]$ equipped with the supremum distance and those in a finite dimensional vector space equipped with a non-Euclidean distance. We then develop versions of the Granger–Johansen representation theorems for I(1) and I(2) autoregressive (AR) processes taking values in such a space. To achieve this goal, we first note that an AR(p) law of motion can be characterized by a linear operator pencil (an operator-valued map with certain properties) via the companion form representation, and then study the spectral properties of a linear operator pencil to obtain a necessary and sufficient condition for a given AR(p) law of motion to admit I(1) or I(2) solutions. These operator-theoretic results form a fundamental basis for our representation theorems. Furthermore, it is shown that our operator-theoretic approach is in fact a closely related extension of the conventional approach taken in a Euclidean space setting. Our theoretical results may be especially relevant in a recently growing literature on functional time series analysis in Banach spaces.

Mathematical models are essential to analyze and understand the dynamics of complex systems. Recently, data-driven methodologies have gotten a lot of attention which is leveraged by advancements in sensor technology. However, the quality of obtained data plays a vital role in learning a good and reliable model. Therefore, in this paper, we propose an efficient heuristic methodology to collect data both in the frequency domain and the time domain, aiming at having more information gained from limited experimental data than equidistant points. In the frequency domain, the interpolation points are restricted to the imaginary axis as the transfer function can be estimated easily on the imaginary axis. The efficiency of the proposed methodology is illustrated by means of several examples, and its robustness in the presence of noisy data is shown.

It is well known that the conventional cumulative sum (CUSUM) test suffers from low power and large detection delay. In order to improve the power of the test, we propose two alternative statistics. The backward CUSUM detector considers the recursive residuals in reverse chronological order, whereas the stacked backward CUSUM detector sequentially cumulates a triangular array of backwardly cumulated residuals. A multivariate invariance principle for partial sums of recursive residuals is given, and the limiting distributions of the test statistics are derived under local alternatives. In the retrospective context, the local power of the tests is shown to be substantially higher than that of the conventional CUSUM test if a break occurs in the middle or at the end of the sample. When applied to monitoring schemes, the detection delay of the stacked backward CUSUM is found to be much shorter than that of the conventional monitoring CUSUM procedure. Furthermore, we propose an estimator of the break date based on the backward CUSUM detector and show that in monitoring exercises this estimator tends to outperform the usual maximum likelihood estimator. Finally, an application of the methodology to COVID-19 data is presented.

While the current pandemic is causing mortality shocks globally, the management of longevity risk remains a major challenge for both individuals and institutions. It is high time there be private market solutions designed for efficient longevity risk transfer among various stakeholders such as individuals, pension funds and annuity providers. From individuals’ point of view, appealing features of post-retirement solutions include stable and satisfactory benefit levels, flexibility, meeting bequest preferences and low fees. This paper proposes a dynamic target volatility strategy for group self-annuitization (GSA) schemes aimed at enhancing living benefits for pool participants. More specifically, we suggest investing GSA funds in a portfolio consisting of equity and cash, continuously rebalanced to maintain a target volatility level. The performance of a dynamic target volatility strategy is assessed against the static case which does not involve portfolio rebalancing. Benefit profiles are assessed by analysing quantiles and alternative strategies involving varying equity compositions. The case of death benefits is included, and the fund dynamics analysed by assessing resulting investment returns and the mortality credits. Overall, higher living benefit profiles are obtained under a dynamic target volatility strategy. From the analysis performed, a trade-off between the equity proportion and the impact on the lower quantile of the living benefit amount emerges, suggesting an optimal proportion of equity composition.

This commentary looks at the use of corporate social responsibility (CSR) mechanisms for implementing responsible data use. The commentary offers an overview of CSR theory and the discourse on a growing phenomenon known as corporate digital responsibility (CDR). The commentary links these theories to the historical debates on the nature of technology, ethics, and society. The aim is to reflect on CSR and CDR mechanisms and ignite the discussion on their adequacy considering the pursuit of data responsibility. Through our discussion and brief case studies, the paper reveals the gaps in relying on CSR and CDR and the need for a broader societal and comprehensive approach.

To evaluate the dynamic changes of antibody levels in different groups after inoculation with the coronavirus disease 2019 (COVID-19) vaccine. The 1493 subjects who were tested for IgM and IgG against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at Qionglai Medical Center Hospital from June to October in 2021 were accepted for analyses of geometric mean titre (GMT) of IgG and IgM. The overall GMT of IgM and IgG in the population of Qionglai reached at a peak value at 1.497 (+3.810, −3.810) S/CO and 4.048 (+2.059, −2.059) S/CO in the second week, and then gradually decreased to 0.114 (+2.707, −2.707) and 1.885 (+1.506, −1.506) S/CO in the 11th–25th weeks, respectively. IgG was positive within 1 day, after that GMT increased continuously and peaked on the 13th day. There was a significant difference between male and female groups for titre of IgM during the prior 2 weeks and among three age groups for titre of IgG during the 2nd–3rd week after vaccination. The GMT level of IgG in the population vaccinated with the COVID-19 vaccine remained at a high level within 25 weeks and peaked on the 13th day, indicating that IgG could exist for a longer period and exhibiting positive SARS-CoV-2- defending effect.