Survey-based calibration of social network models: Empirical evidence from Singapore

A resilient society is one capable of withstanding and thereafter recovering quickly from large shocks. Brought to the fore by the COVID-19 pandemic of 2020–2022, this social resilience is nevertheless difficult to quantify. In this paper, we measured how quickly the Singapore society recovered from the pandemic, by first modeling it as a dynamic social network governed by three processes: (1) random link addition between strangers, (2) social link addition between individuals with a friend in common and (3) random link deletion. To calibrate this model, we carried out a survey of a representative sample of [Formula: see text] residents and nonresidents in Singapore between July and September 2022 to measure the numbers of random and social contacts gained over a fixed duration, as well as the number of contacts lost over the same duration, using phone contacts as proxy for social contacts. Lockdown simulations using the model that fits the survey results best suggest that Singapore would recover from such a disruption after 1–2 months.