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Subgrid informed neural networks for high-resolution flood mapping
No full textPhysics-based hydrodynamic models are essential for accurate flood prediction but are computationally expensive, limiting their applicability for real-time forecasting and probabilistic analyses. Conversely, pure machine learning (ML) models offer both computational efficiency and accuracy but often lack interpretability. To address this gap, we propose SGUnet, a physics-informed ML model and a hybrid theory-guided data science approach, for rapid, high-resolution flood mapping. It utilizes a neural network with U-Net architecture and integrates subgrid-based coarse-grid hydrodynamic model predictions as initial estimates, upskilling them to achieve fine-grid model accuracy. Unlike traditional hydrodynamic models, the subgrid method embeds fine-scale topographic details within coarse-grid cells, enhancing both computational efficiency and predictive accuracy. SGUnet processes flood depth raster patches (512 × 512 pixels) and corresponding digital elevation models as inputs. It functions as a deep learning-based corrector, refining flood predictions from numerical simulators. Trained through supervised learning, SGUnet learns to correct deviations in coarse-grid predictions using fine-grid model outputs as target values. The model is evaluated across three large Australian watersheds—Wollombi, Chowilla, and Burnett River—using HEC-RAS flood simulations with subgrid formulation. SGUnet reduces root mean squared error by a factor of 4.5–5.3 compared to coarse-grid models, achieves a critical success index exceeding 0.9 for flood extent mapping, and delivers a 50x speed-up over fine-grid hydrodynamic models. Furthermore, SGUnet outperforms a state-of-the-art ML-based upskilling model in depth and extent predictions. By effectively correcting flood artifacts from coarse-grid models, SGUnet achieves near fine-grid accuracy with significantly reduced computational cost, demonstrating its potential for real-time flood risk assessment
Remote health: what are the problems and what can we do about them? Insights from Australia
No full textThis article analyses three broad questions: (i) How is 'remote' different from 'rural'?; (ii) How do these differences affect the provision of health care and health outcomes, positively and negatively?; and (iii) What is needed to address these issues and systematise solutions in order to deliver parity of health outcomes
Risk exchange under infinite-mean Pareto models
No full textWe study the optimal decisions and equilibria of agents who aim to minimize their risks by allocating their positions over extremely heavy-tailed (i.e., infinite-mean) and possibly dependent losses. The loss distributions of our focus are super-Pareto distributions, which include the class of extremely heavy-tailed Pareto distributions. Using a recent result on stochastic dominance, we show that for a portfolio of super-Pareto losses, non-diversification is preferred by decision makers equipped with well-defined and monotone risk measures. The phenomenon that diversification is not beneficial in the presence of super-Pareto losses is further illustrated by an equilibrium analysis in a risk exchange market. First, agents with super-Pareto losses will not share risks in a market equilibrium. Second, transferring losses from agents bearing super-Pareto losses to external parties without any losses may arrive at an equilibrium which benefits every party involved
Litigation risk and IPO underpricing: evidence from federal judge ideology
No full textAbstract
Using federal judge ideology as an exogenous measure of issuing firms’ litigation risk, we document that the initial public offerings (IPOs) of the firms headquartered in more liberal circuits are more underpriced. The effect is mitigated when plaintiffs’ pleading standards are more stringent and is amplified when judges have more discretion in their decisions. The effect is also amplified among deep pocketed issuing firms, while it is mitigated among issuing firms hiring reputable intermediaries in the IPO process. The results of additional analysis suggest that issuing firms located in more liberal circuits are more likely to become targets of lawsuits after their IPOs and that these lawsuits are less likely to be dismissed by the courts and result in larger settlements. Collectively, our findings underscore the salience of litigation risk stemming from the issuing firms’ legal environment in driving IPO underpricing
A workflow-optimized protocol for accelerated sample preparation and automated Sr separation from natural waters for 87Sr/86Sr determination
No full textAutomated HPIC vs manual IEC: streamlining Sr separation for efficient 87Sr/86Sr analysis
Evaluating malaria reactive surveillance and response strategies in northeast Cambodia: a mixed-methods study
No full textBACKGROUND: Cambodia aims to eliminate malaria latest by 2030 applying the 1-3-7 malaria reactive surveillance and response (RASR) strategy which involves malaria case notification, investigation and classification on the same day as diagnosis, reactive case detection within three days, and investigation and classification of new active focus within seven days of case notification. This study investigates the implementation of the RASR strategy in terms of its timeliness, facilitators and barriers, and acceptability for implementation, thereby providing recommendations to improve the strategy in the context of the national health system. METHODS: A mixed-methods study of secondary data analysis of aggregated routine malaria datasets, and cross-sectional survey, in-depth interviews and focus group discussions with malaria programme stakeholders, frontline health workers and mobile and migrant populations was conducted in Ratanakiri and Stung Treng provinces. Quantitative and qualitative data were analysed descriptively and thematically. RESULTS: In 2020 and 2022, 72% and 59% of malaria cases were notified and investigated within one day after diagnosis. Timeliness of reactive case detection was 89% and 45% in 2020 and 2022 respectively. Despite having challenges including minimal community participation in reactive case detection, poor mobile phone network coverage and road conditions, a heavy workload at the commune health centre level, inadequate surveillance technical knowledge among village malaria workers and insufficient budget to execute RASR, the existing RASR strategy was deemed acceptable among all levels of health personnels. CONCLUSION: The RASR strategy implemented in northeast Cambodia was generally functioning well despite some challenges. To improve the RASR strategy to achieve 100% timeliness and progress towards malaria elimination in Cambodia, allocating sufficient budget, capacity building to frontline health workers and better community engagement strategies are required
Re-presentations to the emergency department initial presentation with COVID-19: Insights from the omicron wave
No full textBackground Relapsing symptoms post-SARS-CoV-2 (COVID) infection, particularly with variants like Omicron, remain poorly understood and cumulative mortality rates are in the millions worldwide. Re-presentation rates to emergency departments (ED) post initial presentation are poorly defined. Objectives To identify the frequency and characteristics of ED re-presentations during the six months post initial COVID-19 admission. Methodology A retrospective chart review of patients with a positive COVID-19 PCR result and initial ED presentation at the Austin hospital in Victoria, Australia during January–February 2022 (wave one) and March–April 2022 (wave two). Subsequent ED re-presentations up to six months from initial admission were analyzed, concentrating on symptoms, diagnoses and mortalities. Results Of 926 wave one patients meeting the inclusion criteria, 162 (18 %) had subsequent ED presentations. For wave two, out of 556 patients, 129 (23 %) had re-presentations. The highest number of re-presentations for an individual were 24 and 11 for waves one and two respectively. Shortness of breath was the most common symptom for re-presentation during both waves (21 % and 19 % respectively), followed by cough. Additionally, 79 % of wave one patients and 29 % of wave two patients had respiratory-related comorbidities. Twelve percent of patients died within six months of the initial COVID-19 related presentation in wave one compared to 7 % in wave two. Conclusion Re-presentation rates were similar to previous COVID waves with the alpha and delta variants. Respiratory symptoms and related diagnoses were common. Strengthening public health strategies is vital to curb transmission, alleviate strain on hospitals, and prevent further morbidity and mortality
Identifying diabetes in hospital inpatients: A systematic review of diabetes digital phenotyping methods and applications
No full textBackground: Digital phenotyping for diabetes uses health data to identify the presence of diabetes, crucial for digital health applications to assist management of increasing inpatient burdens. Though multiple digital phenotyping methods for diabetes have been described, methodological heterogeneity limits implementation.
Objectives: To assess in inpatient contexts: 1) diabetes digital phenotyping applications, 2) methodologies and data types used, and 3) performance comparisons with established diagnostic methods
Including frameworks of public health ethics in computational modelling of infectious disease interventions
No full textDecisions on public health interventions to control infectious diseases are often informed by computational models. Interpreting the predicted outcomes of a public health decision requires not only high-quality modelling but also an ethical framework for assessing the benefits and harms associated with different options. The design and specification of ethical frameworks matured independently of computational modelling, so many values recognized as important for ethical decision-making are missing from computational models. We demonstrate a proof-of-concept approach to incorporate multiple public health values into the evaluation of a simple computational model for vaccination against a pathogen such as SARS-CoV-2. By examining a bounded space of alternative prioritizations of three values relevant to public health ethics (aggregate clinical burden, equity in clinical burden, equity in adverse effects from vaccination), we identify value trade-offs, where the outcomes of optimal strategies differ depending on the ethical framework. This work demonstrates an approach to incorporating diverse values into decision criteria used to evaluate outcomes of models of infectious disease interventions
Integration of metatranscriptomics data improves the predictive capacity of microbial community metabolic models
No full textMicrobial consortia play pivotal roles in nutrient cycling across diverse ecosystems, where the functionality and composition of microbial communities are shaped by metabolic interactions. Despite the critical importance of understanding these interactions, accurately mapping and manipulating microbial interaction networks to achieve specific outcomes remains challenging. Genome-scale metabolic models (GEMs) offer significant promise for predicting microbial metabolic functions from genomic data; however, traditional community GEMs typically rely on species abundance information, which may limit their predictive accuracy due to the absence of condition-specific gene expression or protein abundance data. Here, we introduce the Integration of Metatranscriptomes Into Community GEMs (IMIC) approach, which utilizes metatranscriptomic data to construct context-specific community models for predicting individual growth rates and metabolic interactions. By incorporating metatranscriptomic profiles, which reflect both gene expression activity and partially encode abundance information, IMIC could predict condition-specific flux distributions that enable the investigation of metabolite interactions among community members. Our results show that growth rates predicted by IMIC correlate strongly with relative as well as absolute abundance of species and offer a streamlined, automated procedure for estimating the single intrinsic parameter. Specifically, IMIC results in improved predictions of measured metabolite concentration changes compared with other approaches in our case study. We further demonstrate that this improvement is driven by the network-wide adjustment of flux bounds based on gene expression profiles. In conclusion, the IMIC approach enables the accurate prediction of individual growth rates and improves the model performance of predicting metabolite interactions, facilitating a deeper understanding of metabolic interdependencies within microbial communities