Household Bubbling Strategies for Epidemic Control and Social Connectivity

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 find 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 find that merging households with at most one worker provides the best balance between controlling epidemic risk and addressing people's need for social contact.