Voluntary mobility clustering for epidemic control

In case of a future pandemic, the mobility dynamics of a city can be controlled by intervening in the mobility patterns of people. Instead of hard quarantine policies, incentives can be designed that are compatible with people's preferences. At first, we distinguish mobility from the different types of locations for which distance matters. We match these types of locations in a way that maximizes the natural preference of people to visit the locations. We investigate different approaches for matching locations, such as retail and educational services, while considering people's preferences. We show that satisfying the preferences of the entire city is a computationally hard problem. Approximation algorithms are proposed in which the penalty for preference violation is bounded. We propose a fast approximation algorithm that focuses on the penalty value of locations, and we propose a more computationally heavy approximation that focuses on user penalty with a specific scheme of user allocation to locations. Additionally, we investigated higher-order matching of locations and the complexity of urban partitioning. We tested our approach in Euclidean space and network space. Finally, we show that applying such mobility restrictions can reduce the transmission rate, and we extract cells whose people can be incentivized to fulfill their needs based on the proposed algorithms, slowing down a future pandemic and preventing potential superspreading events.