Abstract Background: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. Objective: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. Methods: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. Results: The final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. Conclusions: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.
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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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Objective: To predict mortality by disability in a sample of 479 Dutch community-dwelling people aged 75 years or older. Methods: A longitudinal study was carried out using a follow-up of seven years. The Groningen Activity Restriction Scale (GARS), a self-reported questionnaire with good psychometric properties, was used for data collection about total disability, disability in activities in daily living (ADL) and disability in instrumental activities in daily living (IADL). The mortality dates were provided by the municipality of Roosendaal (a city in the Netherlands). For analyses of survival, we used Kaplan–Meier analyses and Cox regression analyses to calculate hazard ratios (HR) with 95% confidence intervals (CI). Results: All three disability variables (total, ADL and IADL) predicted mortality, unadjusted and adjusted for age and gender. The unadjusted HRs for total, ADL and IADL disability were 1.054 (95%-CI: [1.039;1.069]), 1.091 (95%-CI: [1.062;1.121]) and 1.106 (95%-CI: [1.077;1.135]) with p-values <0.001, respectively. The AUCs were <0.7, ranging from 0.630 (ADL) to 0.668 (IADL). Multivariate analyses including all 18 disability items revealed that only “Do the shopping” predicted mortality. In addition, multivariate analyses focusing on 11 ADL items and 7 IADL items separately showed that only the ADL item “Get around in the house” and the IADL item “Do the shopping” significantly predicted mortality. Conclusion: Disability predicted mortality in a seven years follow-up among Dutch community-dwelling older people. It is important that healthcare professionals are aware of disability at early stages, so they can intervene swiftly, efficiently and effectively, to maintain or enhance the quality of life of older people.
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Aims and objectives. The Forensic Early Warning Signs of Aggression Inventory (FESAI) was developed to assist nurses and patients in identifying early warning signs and constructing individual early detection plans (EDP) for the prevention of aggressive incidents. The aims of this research were as follows: First, to study the prevalence of early warning signs of aggression, measured with the FESAI, in a sample of forensic patients, and second, to explore whether there are any types of warning signs typical of diagnostic subgroups or offender subgroups. Background. Reconstructing patients’ changes in behaviour prior to aggressive incidents may contribute to identify early warning signs specific to the individual patient. The EDP comprises an early intervention strategy suggested by the patient and approved by the nurses. Implementation of EDP may enhance efficient risk assessment and management. Design. An explorative design was used to review existing records and to monitor frequencies of early warning signs. Methods. Early detection plans of 171 patients from two forensic hospital wards were examined. Frequency distributions were estimated by recording the early warning signs on the FESAI. Rank order correlation analyses were conducted to compare diagnostic subgroups and offender subgroups concerning types and frequencies of warning signs. Results. The FESAI categories with the highest frequency rank were the following: (1) anger, (2) social withdrawal, (3) superficial contact and (4) non-aggressive antisocial behaviour. There were no significant differences between subgroups of patients concerning the ranks of the four categories of early warning signs. Conclusion. The results suggest that the FESAI covers very well the wide variety of occurred warning signs reported in the EDPs. No group profiles of warning signs were found to be specific to diagnosis or offence type. Relevance to clinical practice. Applying the FESAI to develop individual EDPs appears to be a promising approach to enhance risk assessment and management.
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The concepts punitiveness and rehabilitation orientation in the general public are generally measured by rather broad attitude items that are not directly related to probation. In this study, two separate attitude scales were used that were tailor-made for the probation context and therefore have a high ecological validity. These ‘ecological scales’ were each analysed with the same predictor set. Cognitive emotive variables showed incremental prediction above demographics. Higher knowledge of probation and more satisfaction with society are related to a higher preference for rehabilitation. Less knowledge of probation and a higher feeling of victimization are related to a more punitive attitude.
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When it comes to hard to solve problems, the significance of situational knowledge construction and network coordination must not be underrated. Professional deliberation is directed toward understanding, acting and analysis. We need smart and flexible ways to direct systems information from practice to network reflection, and to guide results from network consultation to practice. This article presents a case study proposal, as follow-up to a recent dissertation about online simulation gaming for youth care network exchange (Van Haaster, 2014).
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Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.
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Abstract Purpose To determine the predictive value of quality of life for mortality at the domain and item levels. Methods This longitudinal study was carried out in a sample of 479 Dutch people aged 75 years or older living independently, using a follow-up of 7 years. Participants completed a self-report questionnaire. Quality of life was assessed with the WHOQOL-BREF, including four domains: physical health, psychological, social relationships, and environment. The municipality of Roosendaal (a town in the Netherlands) indicated the dates of death of the individuals. Results Based on mean, all quality of life domains predicted mortality adjusted for gender, age, marital status, education, and income. The hazard ratios ranged from 0.811 (psychological) to 0.933 (social relationships). The areas under the curve (AUCs) of the four domains were 0.730 (physical health), 0.723 (psychological), 0.693 (social relationships), and 0.700 (environment). In all quality of life domains, at least one item predicted mortality (adjusted). Conclusion Our study showed that all four quality of life domains belonging to the WHOQOL-BREF predict mortality in a sample of Dutch community-dwelling older people using a follow-up period of 7 years. Two AUCs were above threshold (psychological, physical health). The findings offer health care and welfare professionals evidence for conducting interventions to reduce the risk of premature death.
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Our ageing population is the result of two demographic trends: decreasing fertility levels and higher life expectancy. As a corollary to these demographic trends, the working population is ageing and shrinking at the same time. This development will affect the performance of organizations in the next decades. As today‟s economy and the performance of organizations is mainly based on knowledge, the ageing workforce will mainly affect the organizations ability to be knowledge productive. As current knowledge management (KM) and intellectual capital (IC) literature hardly addresses the issue of ageing, the aim of this paper is to explore this topic in order to formulate an agenda for further KM/IC research. Combining the temporary consequences of ageing (brain drain and talent gap) and the false assumptions about the capabilities of older workers (older workers contribute negatively to a firm‟s performance), the current ageing of the working population reveals two main risks for organizations and management: underutilization of older employees, and loss of knowledge. Based on the exploration of these two risks in this paper, several issues are proposed for further research. These issues focus on the specific competences of the older knowledge worker, the implications for talent development programs, the benefits of inter-generational learning, and effectiveness of knowledge retention strategies. Today, the main fear is that large scale retirement will lead to a shortage of skills, talents, knowledge. Although acknowlegding the risks and threats of this brain drain, the current temporary ageing of our workforce might also contribute to a structural better valuation of the potential of the older knowledge worker and its specific contribution to the process of knowledge creation. In an ageing knowledge economy, increased understanding about the abilities and distinct qualities of older workers will provide opportunities for organizations to enhance knowledge productivity and thus gain competitiveness.
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This study focuses on SME networks of design and high-tech companies in Southeast Netherland. By highlighting the personal networks of members across design and high-tech industries, the study attempts to identify the main brokers in this dynamic environment. In addition, we investigate whether specific characteristics are associated with these brokers. The main contribution of the paper lies in the fact that, in contrast to most other work, it is quantitative and that it focuses on brokers identified in an actual network (based on both suppliers and users of the knowledge infrastructure). Studying the phenomenon of brokerage provides us with clear insights into the concept of brokerage regarding SME networks in different fields. In particular we highlight how third parties contribute to the transfer and development of knowledge. Empirical results show, among others that the most influential brokers are found in the nonprofit and science sector and have a long track record in their branch.
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