Structural modeling of COVID-19 spread in relation to human mobility

Area of Science:

• Epidemiology
• Public Health
• Socioeconomics

Background:

• Human mobility is a key factor in pandemic control, with complex interactions with disease spread.
• The COVID-19 pandemic significantly altered mobility patterns, creating a need to understand these dynamics.

Purpose of the Study:

• To investigate the bidirectional relationship between human mobility and COVID-19 spread in U.S. counties.
• To analyze how socio-demographic factors, location, and state policies mediate this relationship.

Main Methods:

• Utilized Structural Regression (SR) model on U.S. county-level data.
• Integrated cross-sectional data on infection rates, human mobility, socio-demographics, and policy interventions.

Main Results:

• A significant bidirectional, opposing relationship was found between mobility and infection rates.
• Metropolitan counties showed higher infection and lower mobility; high-neighboring infection rates and external trips increased local infections.
• Stay-at-home orders and business closures reduced mobility, while mask mandates decreased infection rates.

Conclusions:

• Policy interventions have differential impacts on mobility and infection rates.
• Understanding the interplay between mobility and COVID-19 is crucial for effective public health policy.
• Findings inform targeted, mobility-driven strategies for pandemic management.