Paper List

期刊: ArXiv Preprint
发布日期: 2026-03-16
EpidemiologyNetwork Science

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

L. D. Valdez, J. H. Peressutti

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.
研究空白： Existing models for social bubble strategies primarily rely on household size or age, which may not optimally balance epidemic control with the social needs of a heterogeneous population where infection risk is more closely tied to external contacts (workers) rather than total household members.

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.

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