Using a real-world network to model localized COVID-19 control strategies

Area of Science:

• Epidemiology
• Network Science
• Public Health

Background:

• Case isolation and contact tracing are key strategies for controlling COVID-19 outbreaks.
• The influence of real-world social networks on the effectiveness of these control measures is not fully understood.

Purpose of the Study:

• To simulate SARS-CoV-2 transmission control strategies within a real-world social network.
• To evaluate the impact of different contact tracing approaches and quarantine strategies on outbreak size and efficiency.

Main Methods:

• Utilized high-resolution GPS data from a citizen-science experiment to construct a real-world social network.
• Simulated various control strategies, including contact tracing, tracing of contacts of contacts, and testing/release from quarantine.
• Assessed the effectiveness of strategies based on outbreak size and the proportion of the population quarantined.

Main Results:

• Tracing contacts of contacts significantly reduced simulated outbreak size compared to tracing only contacts.
• Tracing contacts of contacts led to a substantial proportion of the population being quarantined.
• Testing and releasing non-infectious individuals from quarantine paradoxically increased outbreak size.
• Combining physical distancing with contact tracing showed potential for epidemic control with fewer quarantined individuals.

Conclusions:

• Contact tracing and quarantine can function as a 'local lockdown' during periods of high contact rates.
• Targeted tracing and quarantine strategies are most efficient when integrated with complementary measures like physical distancing.
• Understanding social network structures is crucial for optimizing infectious disease control strategies.