Paper List
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for as...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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Hierarchical Molecular Language Models (HMLMs)
This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple ...
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium chan...
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
Household Bubbling Strategies for Epidemic Control and Social Connectivity
Departamento de Física, FCEyN, Universidad Nacional de Mar del Plata, Argentina | Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR), CONICET, Argentina
30秒速读
IN SHORT: This paper addresses the core challenge of designing household merging (social bubble) strategies that effectively control epidemic risk while maximizing social connectivity and psychological well-being during lockdowns.
核心创新
- Methodology Introduces a novel household merging criterion based on the number of economically active (working) members, moving beyond traditional criteria like household size or age composition.
- Methodology Develops a mathematical network model integrating real-world demographic data (from Argentina, China, Israel, Spain) with household structure, labor activity, and explicit SIR epidemic dynamics.
- Theory Derives analytical expressions for the epidemic threshold using generating functions, explicitly linking it to heterogeneity in worker connectivity (⟨k_E²⟩ - ⟨k_E⟩) and variability in workers per household (⟨w²⟩ - ⟨w⟩).
主要结论
- Merging strategies based on the number of working members can maintain epidemic risk at levels comparable to those based on household size, as shown by similar critical thresholds (β_c^E) in simulations.
- The worker-based approach enables a significantly larger portion of the population (exceeding 40% in some countries) to form larger social bubbles, directly addressing isolation and loneliness.
- The strategy of merging households with at most one worker (w* = 1) provides the optimal trade-off, maximizing social connectivity (increasing ⟨ℓ_I⟩) while keeping the epidemic risk effectively controlled across all studied countries.
摘要: During the COVID-19 crisis, policymakers have implemented "social bubble" merging strategies, which allowed people from different households to meet and interact. Although these measures can mitigate the negative effects of extreme isolation, they also introduce additional contacts that may facilitate disease spread. As a result, several modeling studies have explored the epidemiological impact of different household-merging strategies, in which the selection of households to be merged is guided by specific demographic criteria, such as household size or the age composition of their members. Here, we investigate an alternative pairing strategy in which households are merged according to the number of economically active (working) members. We develop a mathematical model of household networks using real demographic data from multiple regions around the world, and simulate a lockdown scenario in which only economically active individuals can leave their households, while the remaining non-working members stay indoors. By using numerical simulations and the generating function technique, we then estimate the epidemic risk for different household merging strategies. We found that merging strategies based on the number of working members can keep epidemic risk at similar levels as those based on household size. Moreover, the worker-based approach allows significantly more people to form larger social bubbles, exceeding 40% of the population in some countries. We found that merging households with at most one worker provides the best balance between controlling epidemic risk and addressing people’s need for social contact.