Survey-Based Calibration of the One-Community and Two-Community Social Network Models Used for Testing Singapore's Resilience to Pandemic Lockdown

Jon Spalding; Bertrand Jayles; Renate Schubert; Siew Ann Cheong; Hans Herrmann

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arXiv · 2503.20371

Survey-Based Calibration of the One-Community and Two-Community Social Network Models Used for Testing Singapore's Resilience to Pandemic Lockdown

Jon Spalding·Bertrand Jayles·Renate Schubert·Siew Ann Cheong·Hans Herrmann

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Abstract

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 $N = 2,057$ residents and non-residents in Singapore between Jul and Sep 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.

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Jon Spalding, Bertrand Jayles, Renate Schubert, Siew Ann Cheong, Hans Herrmann. 2025-03-26. Survey-Based Calibration of the One-Community and Two-Community Social Network Models Used for Testing Singapore's Resilience to Pandemic Lockdown. https://arxiv.org/abs/2503.20371

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