Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Cooperative innovation through a talent management pool: A qualitative study on coopetition in healthcare

In recent years, the Dutch healthcare sector has been confronted with increased competition. Not only are financial resources scarce, Dutch hospitals also need to compete with other hospitals in the same geographic area to attract and retain talented employees due to considerable labour shortages. However, four hospitals operating in the same region are cooperating to cope with these shortages by developing a joint Talent Management Pool. ‘Coopetiton’ is a concept used for simultaneous cooperation and competition. In this paper, a case study is performed in order to enhance our understanding of coopetition. Among other things, the findings suggest that perceptions of organizational actors on competition differ and might hinder cooperative innovation with competitors, while perceived shared problems and resource constraints stimulate coopetition. We reflect on the current coopetition literature in light of the research findings, which have implications for future research on this topic.     

Has Opposition to Immigration Increased in the United States after the Economic Crisis? An Experimental Approach

We employ two population‐level experiments to accurately measure opposition to immigration before and after the economic crisis of 2008. Our design explicitly addresses social desirability bias, which is the tendency to give responses that are seen favorably by others and can lead to substantial underreporting of opposition to immigration. We find that overt opposition to immigration, expressed as support for a closed border, increases slightly after the crisis. However, once we account for social desirability bias, no significant increase remains. We conclude that the observed increase in anti‐immigration sentiment in the post‐crisis United States is attributable to greater expression of opposition rather than any underlying change in attitudes.     

A new long-term survival model with dispersion induced by discrete frailty

Lifetime Data Analysis - Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there...     

Predicting the Risk of Biological Invasions Using Environmental Similarity and Transport Network Connectedness

Understanding the risk of biological invasions associated with particular transport pathways and source regions is critical for implementing effective biosecurity management. This may require both a model for physical connectedness between regions, and a measure of environmental similarity, so as to quantify the potential for a species to be transported from a given region and to survive at a destination region. We present an analysis of integrated biosecurity risk into Australia, based on flights and shipping data from each global geopolitical region, and an adaptation of the “range bagging” method to determine environmental matching between regions. Here, we describe global patterns of environmental matching and highlight those regions with many physical connections. We classify patterns of global invasion risk (high to low) into Australian states and territories. We validate our analysis by comparison with global presence data for 844 phytophagous insect pest species, and produce a list of high‐risk species not previously known to be present in Australia. We determined that, of the insect pest species used for validation, the species most likely to be present in Australia were those also present in geopolitical regions with high transport connectivity to Australia, and those regions that were geographically close, and had similar environments.     

Nested exposure case-control sampling: a sampling scheme to analyze rare time-dependent exposures

For large cohort studies with rare outcomes, the nested case-control design only requires data collection of small subsets of the individuals at risk. These are typically randomly sampled at the observed event times and a weighted, stratified analysis takes over the role of the full cohort analysis. Motivated by observational studies on the impact of hospital-acquired infection on hospital stay outcome, we are interested in situations, where not necessarily the outcome is rare, but time-dependent exposure such as the occurrence of an adverse event or disease progression is. Using the counting process formulation of general nested case-control designs, we propose three sampling schemes where not all commonly observed outcomes need to be included in the analysis. Rather, inclusion probabilities may be time-dependent and may even depend on the past sampling and exposure history. A bootstrap analysis of a full cohort data set from hospital epidemiology allows us to investigate the practical utility of the proposed sampling schemes in comparison to a full cohort analysis and a too simple application of the nested case-control design, if the outcome is not rare.

     

Aging in the Era of Regenerative Medicine: Analysis of Aging‐Related Representations among Canadian Researchers

Explicitly aimed at understanding and controlling molecular and cellular processes at the root of senescence and biological aging, regenerative medicine aspires to artificially reproduce the biological processes that enable the body to regenerate itself. This no longer involves conserving the body's state of balance by combating disease, as in clinical medicine, but rather fighting degeneration itself. From stem cell research to gene therapy to the production of replacement tissues, regenerative medicine perfectly corresponds to the logic of biomedicalization specific to postmodern society. Based on a series of 18 interviews conducted with Canadian researchers and clinicians in the field of regenerative medicine, this article seeks to understand representations of the aging body among researchers in this field. Seen from a strictly negative angle, aging is assimilated by researchers to an inevitable catastrophe that nevertheless must be combated. More closely observing the theoretical model of regenerative biology and the types of treatments developed, it can be observed, however, that this medicine of the future does not target the elderly, but rather promises youth the ability to regenerate themselves to avoid aging.