Paper List
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Translating Measures onto Mechanisms: The Cognitive Relevance of Higher-Order Information
This review addresses the core challenge of translating abstract higher-order information theory metrics (e.g., synergy, redundancy) into defensible, ...
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Emergent Bayesian Behaviour and Optimal Cue Combination in LLMs
This paper addresses the critical gap in understanding whether LLMs spontaneously develop human-like Bayesian strategies for processing uncertain info...
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Vessel Network Topology in Molecular Communication: Insights from Experiments and Theory
This work addresses the critical lack of experimentally validated channel models for molecular communication within complex vessel networks, which is ...
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Modulation of DNA rheology by a transcription factor that forms aging microgels
This work addresses the fundamental question of how the transcription factor NANOG, essential for embryonic stem cell pluripotency, physically regulat...
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Imperfect molecular detection renormalizes apparent kinetic rates in stochastic gene regulatory networks
This paper addresses the core challenge of distinguishing genuine stochastic dynamics of gene regulatory networks from artifacts introduced by imperfe...
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PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer
This paper addresses the dual challenge of achieving computational efficiency without sacrificing accuracy in whole-transcriptome single-cell represen...
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Beyond Bayesian Inference: The Correlation Integral Likelihood Framework and Gradient Flow Methods for Deterministic Sampling
This paper addresses the core challenge of calibrating complex biological models (e.g., PDEs, agent-based models) with incomplete, noisy, or heterogen...
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Contrastive Deep Learning for Variant Detection in Wastewater Genomic Sequencing
This paper addresses the core challenge of detecting viral variants in wastewater sequencing data without reference genomes or labeled annotations, ov...
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.