A popular validation procedure for Dynamic Stochastic General Equilibrium (DSGE) models consists in comparing the structural shocks and impulse-response functions obtained by estimation-calibration of the DSGE with those obtained in an Structural Vector Autoregressions (SVAR) identified by means of some of the DSGE restrictions. I show that this practice can be seriously misleading when the variables used in the SVAR contain measurement errors. If this is the case, for generic values of the parameters of the DSGE, the shocks estimated in the SVAR are not “made of” the corresponding structural shocks plus measurement error. Rather, each of the SVAR shocks is contaminated by noncorresponding structural shocks. We argue that High-Dimensional Dynamic Factor Models are free from this drawback and are the natural model to use in validation procedures for DSGEs.

Leafy green vegetables are a common source of Shiga toxin-producing Escherichia coli O157:H7 (STEC O157) foodborne illness outbreaks. Ruminant animals, primarily cattle, are the major reservoir of STEC O157. Epidemiological, traceback and field investigations were conducted to identify potential outbreak sources. Product and environmental samples were tested for STEC. A reoccurring strain of STEC O157 caused two multistate outbreaks linked to romaine lettuce in 2018 and 2019, resulting in 234 illnesses in 33 states. Over 80% of patients interviewed consumed romaine lettuce before illness. The romaine lettuce was sourced from two California growing regions: Santa Maria and Salinas Valley in 2018 and Salinas Valley in 2019. The outbreak strain was isolated from environmental samples collected at sites >90 miles apart across growing regions, as well as from romaine-containing products in 2019. Although the definitive route of romaine contamination was undetermined, use of a contaminated agricultural water reservoir in 2018 and contamination from cattle grazing on adjacent land in 2019 were suspected as possible factors. Preventing lettuce contamination from growth to consumption is imperative to preventing illness. These outbreaks highlight the need to further understand mechanisms of romaine contamination, including the role of environmental or animal reservoirs for STEC O157.

At present, there is scarce evidence about the burden associated with the isolation of COVID-19 patients. We aimed to assess the differences between COVID-19 and other influenza-like illnesses (ILIs) in disease burden brought by isolation. We conducted an online survey of 302 respondents who had COVID-19 or other ILIs and compared the burden of isolation due to sickness with one-to-one propensity score matching. The primary outcomes are the duration and productivity losses associated with isolation, the secondary outcome is the health-related quality of life (HRQoL) valuation on the day of the survey. Acute symptoms of outpatient COVID-19 and other ILIs lasted 17 (interquartile range (IQR) 9–32) and 7 (IQR 4–10) days, respectively. The length of isolation due to COVID-19 was 18 (IQR 10–33) days and that due to other ILIs was 7 (IQR 4–11) days, respectively. The monetary productivity loss of isolation due to COVID-19 was 1424.3 (IQR 825.6–2545.5) USD and that due to other ILIs was 606.1 (IQR 297.0–1090.9) USD, respectively. HRQoL at the time of the survey was lower in the COVID-19 group than in the ‘other ILIs’ group (0.89 and 0.96, P = 0.001). COVID-19 infection imposes a substantial disease burden, even in patients with non-severe disease. This burden is larger for COVID-19 than other ILIs, mainly because the required isolation period is longer.

It is often assumed that consumers’ willingness to pay (WTP) for eco-labeled products in research settings is not because of a desire for environmental protection, but rather that they are socially compelled to make decisions that reflects favorably on them, limiting the validity of findings. Using a second-price Vickrey experimental auction, this study found higher WTP for an eco-labeled product than a comparable good, but that social desirability bias, measured by the Marlowe–Crowne Social Desirability Scale, was not a significant predictor of WTP. Instead, environmental consciousness, environmental knowledge, education, and available information were stronger predictors of WTP for eco-labeled goods.

Previous research has demonstrated that unique names increased in Japan, which shows a rise in uniqueness-seeking and individualism. To increase the validity of the prior findings, it is important to confirm the robustness of their results. Therefore, this study examined another indicator of historical changes in names in Japan. Specifically, I investigated whether the rates of common names decreased in Japan between 2004 and 2018. The dataset used in the previous study was analyzed. The results consistently showed that the rates of common names decreased for both boys and girls for the period. These results were consistent with the previous research, which further increases the validity of the finding that Japanese culture became more individualistic.

The anthelmintic dinitroaniline oryzalin interferes with the formation of microtubules and inhibits meiosis and mitosis in nematodes. Exposure to oryzalin resulted in deterioration in morphology of the oocytes and loss of synaptonemal complexes at meiotic prophase I. The nuclear matrix and envelope were poorly formed, and the central rachis was diminished. These results provide the basis for the loss of fecundity after treatment with the oryzalin resulting in control of parasitic nematodes.

Numerous animal models and epidemiological and observational studies have demonstrated that enterovirus (EV) infection could be involved in the development of clinical type 1 diabetes mellitus (T1DM), but its aetiology is not fully understood. Therefore, we reviewed the association between EV infection and clinical T1DM. We searched PubMed and Embase from inception to April 2021 and reference lists of included studies without any language restrictions in only human studies. The correlation between EV infection and clinical T1DM was calculated as the pooled odds ratio (OR) and 95% confidence intervals (CIs), analysed using random-effects models. Subgroup and sensitivity analyses were performed to evaluate the robustness of the associations. A total of 25 articles (22 case–control studies and three nested case–control studies) met the inclusion criterion including 4854 participants (2948 cases and 1906 controls) with a high level of statistical heterogeneity (I2 = 80%, P < 0.001) mainly attributable to methods of EV detection, study type, age distribution, source of EV sample and control subjects. Meta-analysis showed a significant association between EV infection and clinical T1DM (OR 5.75, 95% CI 3.61–9.61). There is a clinically significant association between clinical T1DM and EV infection.

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.