Modeling partial lockdowns in multiplex networks using partition strategies

Area of Science:

• Epidemiology
• Network Science
• Public Health

Background:

• National stay-at-home orders (lockdowns) were implemented to curb SARS-CoV-2 transmission.
• These measures, while essential, incur significant economic costs.
• Modeling the impact of partial lockdowns is crucial for balancing public health and economic concerns.

Purpose of the Study:

• To develop a network model for analyzing partial lockdown strategies.
• To evaluate different partitioning methods for societal structures.
• To optimize lockdowns for both epidemic control and economic burden reduction.

Main Methods:

• Utilized a multiplex network model with Household, Work, and Social layers.
• Incorporated a Susceptible-Infected-Recovered (SIR) process to simulate epidemic spread.
• Compared various societal partitioning strategies to assess their effectiveness.

Main Results:

• Unrestricted social interactions significantly accelerate epidemic spreading.
• Partial lockdown strategies, particularly those restricting social interactions, can effectively reduce outbreaks.
• The network partition approach demonstrated potential for minimizing economic costs while controlling disease spread.

Conclusions:

• Network-based modeling offers a viable approach to designing effective partial lockdowns.
• Strategic restrictions on social interactions are more beneficial than broad lockdowns.
• Balancing public health and economic factors requires careful consideration of network structures and interaction patterns.