University Operations During a Pandemic: A Flexible Decision Analysis Toolkit

Modeling infection spread during pandemics is not new, with models using past data to tune simulation parameters for predictions. These help in understanding of the healthcare burden posed by a pandemic and responding accordingly. However, the problem of how college/university campuses should function during a pandemic is new for the following reasons: (i) social contact in colleges are structured and can be engineered for chosen objectives; (ii) the last pandemic to cause such societal disruption was more than 100 years ago, when higher education was not a critical part of society; (iii) not much was known about causes of pandemics, and hence effective ways of safe operations were not known; and (iv) today with distance learning, remote operation of an academic institution is possible. As one of the first to address this problem, our approach is unique in presenting a flexible simulation system, containing a suite of model libraries, one for each major component. The system integrates agent-based modeling and the stochastic network approach, and models the interactions among individual entities (e.g., students, instructors, classrooms, residences) in great detail. For each decision to be made, the system can be used to predict the impact of various choices, and thus enables the administrator to make informed decisions. Although current approaches are good for infection modeling, they lack accuracy in social contact modeling. Our agent-based modeling approach, combined with ideas from Network Science, presents a novel approach to contact modeling. A detailed case study of the University of Minnesota’s Sunrise Plan is presented. For each decision made, its impact was assessed, and results were used to get a measure of confidence. We believe that this flexible tool can be a valuable asset for various kinds of organizations to assess their infection risks in pandemic-time operations, including middle and high schools, factories, warehouses, and small/medium-sized businesses.

Learning, translating, and applying the perceive, recall, plan, perform system of task analysis assessment to practice: Occupational therapists’ experiences

Introduction This study explored occupational therapists’ experiences of embedding the Perceive, Recall, Plan, Perform System of Task Analysis (PRPP) assessment in practice and gained understandings of the clinical utility. Methods Qualitative interpretive description research using data from semi-structured interviews with 11 New Zealand registered occupational therapists and a focus group with two Māori occupational therapists. Thematic data analysis was used to develop themes. Results Five themes emerged: Resonating with practice as it should be, Translating to practice, Communicating assessment findings using an occupational performance focus on cognition, Validating the practice fit and Cultural application. Participants described the PRPP assessment as a flexible tool, which was applied in different ways in varied practice settings, with clients performing a range of culturally specific occupations. Embedding the assessment in practice was an involved process that challenged many participants’ existing assessment procedures and included managing team expectations to perform cognitive-based assessments. Conclusions The PRPP assessment added value to occupational therapists’ practice. Effective implementation of the assessment required a secure occupation-focused perspective and client-centred culturally responsive communication skills. Commitment was needed to both adopt a new assessment process and to communicate occupation-based assessment results. Participants connected as social learners to address practice challenges post-training.

Mixture of Lindley and Lognormal Distributions: Properties, Estimation, and Application

Finite mixture models provide a flexible tool for handling heterogeneous data. This paper introduces a new mixture model which is the mixture of Lindley and lognormal distributions (MLLND). First, the model is formulated, and some of its statistical properties are studied. Next, maximum likelihood estimation of the parameters of the model is considered, and the performance of the estimators of the parameters of the proposed models is evaluated via simulation. Also, the flexibility of the proposed mixture distribution is demonstrated by showing its superiority to fit a well-known real data set of 128 bladder cancer patients compared to several mixture and nonmixture distributions. The Kolmogorov Smirnov test and some information criteria are used to compare the fitted models to the real dataset. Finally, the results are verified using several graphical methods.

Development of the Choices 5-Level Criteria to Support Multiple Food System Actions

In 2008, the Choices International Foundation developed its logo criteria, identifying best-in-class food products. More advanced, global and graded nutrient profiling systems (NPSs) are needed to substantiate different national nutrition policies. The objective of this work was to extend Choices NPS to identify five levels of the healthiness of food products, so that the Choices NPS can also be used to support other nutrition policies, next to front-of-pack labelling. Based on the same principles as the previous logo criteria, four sets of threshold criteria were determined using a combination of compliance levels, calculated from a large international food group-specific database, the Choices logo criteria, and WHO-NPSs developed to restrict marketing to children. Validation consisted of a comparison with indicator foods from food-based dietary guidelines from various countries. Some thresholds were adjusted after the validation, e.g., because intermediate thresholds were too lenient. This resulted in a new international NPS that can be applied to different contexts and to support a variety of health policies, to prevent both undernutrition and obesity. It can efficiently evaluate mixed food products and represents a flexible tool, applicable in various settings and populations.

Genetic Vaccination as a Flexible Tool to Overcome the Immunological Complexity of Invasive Fungal Infections

The COVID-19 pandemic has highlighted genetic vaccination as a powerful and cost-effective tool to counteract infectious diseases. Invasive fungal infections (IFI) remain a major challenge among immune compromised patients, particularly those undergoing allogeneic hematopoietic bone marrow transplantation (HSCT) or solid organ transplant (SOT) both presenting high morbidity and mortality rates. Candidiasis and Aspergillosis are the major fungal infections among these patients and the failure of current antifungal therapies call for new therapeutic aids. Vaccination represents a valid alternative, and proof of concept of the efficacy of this approach has been provided at clinical level. This review will analyze current understanding of antifungal immunology, with a particular focus on genetic vaccination as a suitable strategy to counteract these diseases.

Testing the Influence of Seascape Connectivity on Marine-Based Species Distribution Models

Species distribution models (SDMs) are commonly used in ecology to predict species occurrence probability and how species are geographically distributed. Here, we propose innovative predictive factors to efficiently integrate information on connectivity into SDMs, a key element of population dynamics strongly influencing how species are distributed across seascapes. We also quantify the influence of species-specific connectivity estimates (i.e., larval dispersal vs. adult movement) on the marine-based SDMs outcomes. For illustration, seascape connectivity was modeled for two common, yet contrasting, marine species occurring in southeast Australian waters, the purple sea urchin, Heliocidaris erythrogramma, and the Australasian snapper, Chrysophrys auratus. Our models illustrate how different species-specific larval dispersal and adult movement can be efficiently accommodated. We used network-based centrality metrics to compute patch-level importance values and include these metrics in the group of predictors of correlative SDMs. We employed boosted regression trees (BRT) to fit our models, calculating the predictive performance, comparing spatial predictions and evaluating the relative influence of connectivity-based metrics among other predictors. Network-based metrics provide a flexible tool to quantify seascape connectivity that can be efficiently incorporated into SDMs. Connectivity across larval and adult stages was found to contribute to SDMs predictions and model performance was not negatively influenced from including these connectivity measures. Degree centrality, quantifying incoming and outgoing connections with habitat patches, was the most influential centrality metric. Pairwise interactions between predictors revealed that the species were predominantly found around hubs of connectivity and in warm, high-oxygenated, shallow waters. Additional research is needed to quantify the complex role that habitat network structure and temporal dynamics may have on SDM spatial predictions and explanatory power.

genomicSimulation: fast R functions for stochastic simulation of breeding programs

Simulation tools are key to designing and optimising breeding programs that are many-year, high-effort endeavours. Tools that operate on real genotypes and integrate easily with other analysis software are needed for users to integrate simulated data into their analysis and decision-making processes. This paper presents genomicSimulation, a fast and flexible tool for the stochastic simulation of crossing and selection on real genotypes. It is fully written in C for high execution speeds, has minimal dependencies, and is available as an R package for integration with R's broad range of analysis and visualisation tools. Comparisons of a simulated recreation of a breeding program to the real data shows that the tool's simulated offspring correctly show key population features. Both versions of genomicSimulation are freely available on GitHub: The R package version at https://github.com/vllrs/genomicSimulation/ and the C library version at https://github.com/vllrs/genomicSimulationC

Beyond the mean: a flexible framework for studying causal effects using linear models

Graph-based causal models are a flexible tool for causal inference from observational data. In this paper, we develop a comprehensive framework to define, identify, and estimate a broad class of causal quantities in linearly parametrized graph-based models. The proposed method extends the literature, which mainly focuses on causal effects on the mean level and the variance of an outcome variable. For example, we show how to compute the probability that an outcome variable realizes within a target range of values given an intervention, a causal quantity we refer to as the probability of treatment success. We link graph-based causal quantities defined via the do-operator to parameters of the model implied distribution of the observed variables using so-called causal effect functions. Based on these causal effect functions, we propose estimators for causal quantities and show that these estimators are consistent and converge at a rate of $$N^{-1/2}$$ N - 1 / 2 under standard assumptions. Thus, causal quantities can be estimated based on sample sizes that are typically available in the social and behavioral sciences. In case of maximum likelihood estimation, the estimators are asymptotically efficient. We illustrate the proposed method with an example based on empirical data, placing special emphasis on the difference between the interventional and conditional distribution.