Paper List
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
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.