Cyclospora cayetanensis is an emerging food- and waterborne pathogen that causes cyclosporiasis, a gastrointestinal disease in humans. The parasite is endemic in tropical and subtropical regions; however, its prevalence is largely dependent on environmental factors, such as climate and rainfall patterns. The objective of this paper was to conduct a systematic review and meta-analysis to determine the prevalence of C. cayetanensis in water and to determine if geography, water source and other variables influence this prevalence. A literature search was performed using search terms relating to water and C. cayetanensis in MEDLINE®, CAB Direct, Food Science and Technology Abstracts, Agricola databases and Environmental Science Index. Observational studies published in English after 1979 were eligible. Screening, data extraction and risk-of-bias assessment were performed independently by two reviewers. A multi-level random-effects meta-analysis was completed to determine the prevalence of C. cayetanensis in water and subgroup meta-analyses were performed to explore between-study heterogeneity. The search identified 828 unique articles, and after the screening, 33 articles were included in the review. The pooled prevalence of C. cayetanensis in water was 6.90% [95% confidence interval (CI) 2.25%–13.05%, I2 = 84.38%]. Subgroup meta-analyses revealed significant differences in the prevalence between continents. Additionally, laboratory methods between studies were highly variable and these findings highlight the need for further environmental research on C. cayetanensis in water using detection methods that include PCR and sequencing to accurately identify the organism. The results of this study can be used to help assess the risk of waterborne cyclosporiasis.

In March 2020, rapidly spreading across the world, the severe acute respiratory syndrome coronavirus 2 reached Poland. Since then, many efforts have been made to develop methods to forecast the coronavirus disease-2019 (COVID-19) pandemic spread and to prevent its negative consequences. In this paper, we presented one of such methods, a simplified way of building a data-driven model for predicting the daily number of new coronavirus infections.

Our method is based on parameter selection of the exponentially modified Gaussian cumulative curve, where the obtained curve should describe the curve of a total of COVID-19 cases in Poland with the best possible fit.

We showed that a simplified modelling approach can give good correlations between model values and actual COVID-19 cases data. By forecasting during the COVID-19 epidemic in Poland, we obtained a high enough accuracy for our model to be considered a valuable and helpful tool for making health policy.

Risky sexual behaviour (RSB) is defined as behaviours leading to sexually transmitted diseases and unintended pregnancies. According to the Joint United Nations Program on HIV/AIDS, HIV infection was very high among adolescents and youths living in sub-Saharan Africa including Ethiopia. This study was aimed to assess the prevalence of RSB and associated factors among undergraduate students at the University of Gondar.

An institution-based cross-sectional study was conducted from June to July 2019 and a simple random sampling technique was employed to select 420 students. Data were collected using a structured self-administered questionnaire, entered into Epi-info version 7.0 and exported to Statistical Package for Social Sciences (SPSS) version 25 for analysis, and presented in frequencies, percentages and tables. Bivariable and multivariable logistic regression analysis were carried out to identify variables having significant association with RSB.

The prevalence of RSB among undergraduate students at the University of Gondar was 44.0%. Age [adjusted odds ratio (AOR): 2.12; 95% confidence interval (CI) (1.19–3.79)], residence [AOR: 2.14; 95% CI (1.22–3.75)], living arrangement [AOR: 9.79; 95% CI (5.34–17.9)], daily religious attendance[AOR: 0.57; 95% CI (0.33–0.99)], drink alcohol [AOR: 9.19; 95% CI (3.74–22.59)] and having information about reproductive health and sexually transmitted diseases [AOR:3.05; 95% CI (1.00–9.27)] were factors significantly associated with RSB.

Nearly half of the respondents engaged in risky sexual activity. This prevalence is high and the students are at high risk of exposure to sexually transmitted diseases that need reproductive health intervention like counselling and discussion. Creating awareness is needed for the students regarding reproductive health and the risk of sexually transmitted diseases. In addition, giving special attention is required for students who use alcohol, who did not live with family and who have urban residence.

The early identification and prediction of hand-foot-and-mouth disease (HFMD) play an important role in the disease prevention and control. However, suitable models are different in regions due to the differences in geography, social economy factors. We collected data associated with daily reported HFMD cases and weather factors of Zibo city in 2010~2019 and used the generalised additive model (GAM) to evaluate the effects of weather factors on HFMD cases. Then, GAM, support vectors regression (SVR) and random forest regression (RFR) models are used to compare predictive results. The annual average incidence was 129.72/100 000 from 2010 to 2019. Its distribution showed a unimodal trend, with incidence increasing from March, peaking from May to September. Our study revealed the nonlinear relationship between temperature, rainfall and relative humidity and HFMD cases and based on the predictive result, the performances of three models constructed ranked in descending order are: SVR > GAM> RFR, and SVR has the smallest prediction errors. These findings provide quantitative evidence for the prediction of HFMD for special high-risk regions and can help public health agencies implement prevention and control measures in advance.

In this study, we define the cardinal temperatures and thermal time for germination and emergence of pigeonpea genotypes. Seeds of six genotypes were subjected to constant temperatures ranging between 5 and 50°C in petri dishes with filter paper (germination) and with media (emergence) were placed in a thermal gradient plate. A nonlinear bent-stick model fitted to the rate of development to germination and emergence resulted in parameters predicting cardinal temperatures including base (Tb), optimum (To), maximum (Tm), and thermal time. Estimated Tb for 50% germination and emergence were 8.4 and 10.8°C, respectively, with no significant differences between genotypes. Optimum temperatures were 33.8 and 37.9°C for germination and emergence, respectively, with genotypes differing significantly. Thermal time for 50% germination and emergence varied significantly among genotypes. The results suggest that genotypic responses to the temperature are typical for their tropical origin and hence their suitability for cropping in summer dominant rainfall regions insubtropical Australia.

Coronavirus disease-2019 (COVID-19) elicits a range of different responses in patients and can manifest into mild to very severe cases in different individuals, depending on many factors. We aimed to establish a prediction model of severe risk in COVID-19 patients, to help clinicians achieve early prevention, intervention and aid them in choosing effective therapeutic strategy. We selected confirmed COVID-19 patients who were admitted to First Hospital of Changsha city between 29 January and 15 February 2020 and collected their clinical data. Multivariate logical regression was used to identify the factors associated with severe risk. These factors were incorporated into the nomogram to establish the model. The ROC curve, calibration plot and decision curve were used to assess the performance of the model. A total of 228 patients were enrolled and 33 (14.47%) patients developed severe pneumonia. Univariate and multivariate analysis showed that shortness of breath, fatigue, creatine kinase, lymphocytes and h CRP were independent factors for severe risk in COVID-19 patients. Incorporating age, chronic obstructive pulmonary disease (COPD) and these factors, the nomogram achieved good concordance indexes of 0.89 [95% confidence interval (CI) 0.832–0.949] and well-fitted calibration plot curves (Hosmer–Lemeshow test: P = 0.97). The model provided superior net benefit when clinical decision thresholds were between 15% and 85% predicted risk. Using the model, clinicians can intervene early, improve therapeutic effects and reduce the severity of COVID-19, thus ensuring more targeted and efficient use of medical resources.

Electronic linking of public records and predictive analytics to identify families for preventive early intervention increasingly is promoted by governments. We use the concept of social license to address questions of social legitimacy, agreement, and trust in data linkage and analytics for parents of dependent children, who are the focus of early intervention initiatives in the UK. We review data-steered family policy and early intervention operational service practices. We draw on a consensus baseline analysis of data from a probability-based panel survey of parents, to show that informed consent to data linkage and use is important to all parents, but there are social divisions of knowledge, agreement, and trust. There is more social license for data linkage by services among parents in higher occupation, qualification, and income groups, than among Black parents, lone parents, younger parents, and parents in larger households. These marginalized groups of parents, collectively, are more likely to be the focus of identification for early intervention. We argue that government awareness-raising exercises about the merits of data linkage are likely to bolster existing social license among advantaged parents while running the risk of further disengagement among disadvantaged groups. This is especially where inequalities and forecasting inaccuracies are encoded into early intervention data gathering, linking, and predictive practices, with consequences for a cohesive and equal society.

To achieve the elimination of the hepatitis C virus (HCV), sustained and sufficient levels of HCV testing is critical. The purpose of this study was to assess trends in testing and evaluate the effectiveness of strategies to diagnose people living with HCV. Data were from 12 primary care clinics in Victoria, Australia, that provide targeted services to people who inject drugs (PWID), alongside general health care. This ecological study spanned 2009–2019 and included analyses of trends in annual numbers of HCV antibody tests among individuals with no previous positive HCV antibody test recorded and annual test yield (positive HCV antibody tests/all HCV antibody tests). Generalised linear models estimated the association between count outcomes (HCV antibody tests and positive HCV antibody tests) and time, and χ2 test assessed the trend in test yield. A total of 44 889 HCV antibody tests were conducted 2009–2019; test numbers increased 6% annually on average [95% confidence interval (CI) 4–9]. Test yield declined from 2009 (21%) to 2019 (9%) (χ2P = <0.01). In more recent years (2013–2019) annual test yield remained relatively stable. Modest increases in HCV antibody testing and stable but high test yield within clinics delivering services to PWID highlights testing strategies are resulting in people are being diagnosed however further increases in the testing of people at risk of HCV or living with HCV may be needed to reach Australia's HCV elimination goals.

Helicobacter pylori eradication therapy was included with insurance coverage from 1999 onwards in Japan, with the incidence of peptic ulcer expected to decrease as a consequence. This study investigated the temporal dynamics of peptic ulcer in Japan and identified underlying contributory factors using mathematical models. We investigated the seroprevalence of H. pylori and analysed a snapshot of peptic ulcer cases. Ten statistical models that incorporated important events – H. pylori infection, the cohort effect, eradication therapy and the natural trend for reduction – were fitted to the case data. The hazard of infection with H. pylori was extracted from published estimates. Models were compared using the Akaike information criterion (AIC), and factor contributions were quantified using the coefficient of determination. The best-fit model indicated that 88.1% of the observed snapshot of cases (AIC = 289.2) included the effects of (i) H. pylori infection, (ii) the cohort effect and (iii) eradication therapy, as explanatory variables, the contributions of which were 80.8%, 4.0% and 3.2%, respectively. Among inpatients, a simpler model with (i) H. pylori infection only was favoured (AIC = 107.7). The time-dependent epidemiological dynamics of peptic ulcers were captured and H. pylori infection and eradication therapy explained ⩾84% of the dramatic decline in peptic ulcer occurrence.

Current approaches to fair valuation in insurance often follow a two-step approach, combining quadratic hedging with application of a risk measure on the residual liability, to obtain a cost-of-capital margin. In such approaches, the preferences represented by the regulatory risk measure are not reflected in the hedging process. We address this issue by an alternative two-step hedging procedure, based on generalised regression arguments, which leads to portfolios that are neutral with respect to a risk measure, such as Value-at-Risk or the expectile. First, a portfolio of traded assets aimed at replicating the liability is determined by local quadratic hedging. Second, the residual liability is hedged using an alternative objective function. The risk margin is then defined as the cost of the capital required to hedge the residual liability. In the case quantile regression is used in the second step, yearly solvency constraints are naturally satisfied; furthermore, the portfolio is a risk minimiser among all hedging portfolios that satisfy such constraints. We present a neural network algorithm for the valuation and hedging of insurance liabilities based on a backward iterations scheme. The algorithm is fairly general and easily applicable, as it only requires simulated paths of risk drivers.

The Lee–Carter model has become a benchmark in stochastic mortality modeling. However, its forecasting performance can be significantly improved upon by modern machine learning techniques. We propose a convolutional neural network (NN) architecture for mortality rate forecasting, empirically compare this model as well as other NN models to the Lee–Carter model and find that lower forecast errors are achievable for many countries in the Human Mortality Database. We provide details on the errors and forecasts of our model to make it more understandable and, thus, more trustworthy. As NN by default only yield point estimates, previous works applying them to mortality modeling have not investigated prediction uncertainty. We address this gap in the literature by implementing a bootstrapping-based technique and demonstrate that it yields highly reliable prediction intervals for our NN model.

The present paper introduces a simple aggregated reserving model based on claim count and payment dynamics, which allows for claim closings and re-openings. The modelling starts off from individual Poisson process claim dynamics in discrete time, keeping track of accident year, reporting year and payment delay. This modelling approach is closely related to the one underpinning the so-called double chain-ladder model, and it allows for producing separate reported but not settled and incurred but not reported reserves. Even though the introduction of claim closings and re-openings will produce new types of dependencies, it is possible to use flexible parametrisations in terms of, for example, generalised linear models (GLM) whose parameters can be estimated based on aggregated data using quasi-likelihood theory. Moreover, it is possible to obtain interpretable and explicit moment calculations, as well as having consistency of normalised reserves when the number of contracts tend to infinity. Further, by having access to simple analytic expressions for moments, it is computationally cheap to bootstrap the mean squared error of prediction for reserves. The performance of the model is illustrated using a flexible GLM parametrisation evaluated on non-trivial simulated claims data. This numerical illustration indicates a clear improvement compared with models not taking claim closings and re-openings into account. The results are also seen to be of comparable quality with machine learning models for aggregated data not taking claim openness into account.

China and the US are two contrasting countries in terms of functional disability and long-term care. China is experiencing declining family support for long-term care and developing private long-term care insurance. The US has a more developed public aged care system and private long-term care insurance market than China. Changes in the demand for long-term care are driven by the levels, trends and uncertainty in mortality and functional disability. To understand the future potential demand for long-term care, we compare mortality and functional disability experiences in China and the US, using a multi-state latent factor intensity model with time trends and systematic uncertainty in transition rates. We estimate the model with the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and the US Health and Retirement Study (HRS) data. The estimation results show that if trends continue, both countries will experience longevity improvement with morbidity compression and a declining proportion of the older population with functional disability. Although the elderly Chinese have a shorter estimated life expectancy, they are expected to spend a smaller proportion of their future lifetime functionally disabled than the elderly Americans. Systematic uncertainty is shown to be significant in future trends in disability rates and our model estimates higher uncertainty in trends for the Chinese elderly, especially for urban residents.

The public health measures implemented to control coronavirus disease 2019 (COVID-19) may influence also other infectious diseases. Using national laboratory surveillance data, we assessed the impact of the COVID-19 pandemic on human salmonellosis in the Netherlands until March 2021. Salmonellosis incidence decreased significantly after March 2020: in the second, third and fourth quarters of 2020, and in the first quarter of 2021, the incidence decreased by 55%, 57%, 47% and 37%, respectively, compared to the same quarters of 2016–2019. The decrease was strongest among travel-related cases (94%, 84%, 79% and 93% in the aforementioned quarters, respectively). Other significant changes were: increased proportion of cases among older adults and increased proportion of invasive infections, decreased proportion of trimethoprim resistance and increased proportion of serovar Typhimurium monophasic variant vs. Enteritidis. This led to decreased contributions of laying hens and increased contributions of pigs and cattle as sources of human infections. The observed changes probably reflect a combination of reduced exposure to Salmonella due to restrictions on international travels and gatherings, closure of dine-in restaurants, catering and hospitality sectors at large and changes in healthcare-seeking and diagnostic behaviours.

We quantified the potential impact of different social distancing and self-isolation scenarios on the coronavirus disease 2019 (COVID-19) pandemic trajectory in Saudi Arabia and compared the modelling results to the confirmed epidemic trajectory. Using the susceptible, exposed, infected, quarantined and self-isolated, requiring hospitalisation, recovered/immune individuals, fatalities model, we assessed the impact of a non-pharmacological interventions’ subset. An unmitigated scenario (baseline), mitigation scenarios (25% reduction in social contact/twofold increase in self-isolation) and enhanced mitigation scenarios (50% reduction in social contact/twofold increase in self-isolation) were assessed and compared to the actual epidemic trajectory. For the unmitigated scenario, mitigation scenarios, enhanced mitigation scenarios and actual observed epidemic, the peak daily incidence rates (per 10 000 population) were 77.00, 16.00, 9.00 and 1.14 on days 71, 54, 35 and 136, respectively. The peak fatality rates were 35.00, 13.00, 5.00 and 0.016 on days 150, 125, 60 and 155, respectively. The R0 was 1.15, 1.14, 1.22 and 2.50, respectively. Aggressive implementation of social distancing and self-isolation contributed to the downward trend of the disease. We recommend using extensive models that comprehensively consider the natural history of COVID-19, social and behavioural patterns, age-specific data, actual network topology and population to elucidate the epidemic's magnitude and trajectory.

This study aimed to describe the incidence of Streptococcus bovis/Streptococcus equinus complex (SBSEC) bacteremia, distribution of the SBSEC subspecies, and their respective association with colorectal cancer (CRC). A population-based retrospective cohort study of all episodes of SBSEC-bacteremia from 2003 to 2018 in Skåne Region, Sweden. Subspecies was determined by whole-genome sequencing. Medical charts were reviewed. The association between subspecies and CRC were analysed using logistic regression. In total 266 episodes of SBSEC-bacteremia were identified and the average annual incidence was 2.0 per 100 000 inhabitants. Of the 236 isolates available for typing, the most common subspecies was S. gallolyticus subsp. pasteurianus 88/236 (37%) followed by S. gallolyticus subsp. gallolyticus 58/236 (25%). In order to determine the risk of cancer following bacteremia, an incidence cohort of 174 episodes without a prior diagnosis of CRC or metastasised cancer was followed for 560 person-years. CRC was found in 13/174 (7%), of which 9 (69%) had S. gallolyticus subsp. gallolyticus-bacteremia. In contrast to other European studies, S. gallolyticus subsp. pasteurianus was the most common cause of SBSEC-bacteremia. CRC diagnosis after bacteremia was strongly associated with S. gallolyticus subsp. gallolyticus-bacteremia. Identification of SBSEC subspecies can guide clinical decision-making regarding CRC work-up following bacteremia.

Let $\mathrm{AP}_k=\{a,a+d,\ldots,a+(k-1)d\}$ be an arithmetic progression. For $\varepsilon>0$ we call a set $\mathrm{AP}_k(\varepsilon)=\{x_0,\ldots,x_{k-1}\}$ an $\varepsilon$-approximate arithmetic progression if for some a and d, $|x_i-(a+id)|<\varepsilon d$ holds for all $i\in\{0,1\ldots,k-1\}$. Complementing earlier results of Dumitrescu (2011, J. Comput. Geom. 2(1) 16–29), in this paper we study numerical aspects of Van der Waerden, Szemerédi and Furstenberg–Katznelson like results in which arithmetic progressions and their higher dimensional extensions are replaced by their $\varepsilon$-approximation.

Due to the importance of generalized order statistics (GOS) in many branches of Statistics, a wide interest has been shown in investigating stochastic comparisons of GOS. In this article, we study the likelihood ratio ordering of $p$-spacings of GOS, establishing some flexible and applicable results. We also settle certain unresolved related problems by providing some useful lemmas. Since we do not impose restrictions on the model parameters (as previous studies did), our findings yield new results for comparison of various useful models of ordered random variables including order statistics, sequential order statistics, $k$-record values, Pfeifer's record values, and progressive Type-II censored order statistics with arbitrary censoring plans. Some results on preservation of logconvexity properties among spacings are provided as well.