High-resolution datasets of synthetic human contact network in 13 countries for infectious disease transmission

TL;DR

This paper creates detailed synthetic human contact networks for 13 countries to improve infectious disease transmission modeling.

Contribution

The study introduces a method to generate high-resolution, individual-level synthetic contact networks using nine key parameters for 13 countries.

Findings

Synthetic contact networks were generated for 13 countries, each simulating 10,000 individuals with demographic and contact attributes.

The networks were validated against census data for six socio-demographic metrics, showing alignment with real-world data.

The datasets can be used to simulate disease transmission and evaluate interventions in complex scenarios.

Abstract

The persistent escalation of global infectious disease threats, exemplified by COVID-19, has resulted in over 30 million deaths worldwide in the past five years. Agent-based models (ABMs) have emerged as powerful tools for simulating disease transmission and assessing intervention efficacy. High-resolution human contact networks are critical components in ABMs, particularly when simulating complex real-world scenarios. However, large-scale, high-quality, and fine-grained publicly accessible human contact network datasets remain scarce. To address this gap, we identified nine key parameters required to generate individual-level synthetic contact networks. Through online searches, we compiled the corresponding parameter values for 13 countries. Using these parameters, we constructed contact networks, each simulating 10,000 individuals. Each individual is characterized by demographic attributes (e.g., age and sex) and multi-layer contact patterns occurring in households, schools, workplaces, and community settings. We validated the generated contact networks by comparing six key socio-demographic metrics with relevant official census data, demonstrating their potential utility in simulating disease transmission dynamics.

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