A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• Many countries are lifting non-pharmaceutical interventions for COVID-19, despite incomplete understanding of consequences.
• China's population has low infection rates, and Omicron transmissions are often silent.
• Existing studies lack the completeness and realism to fully understand silent transmission dynamics.

Purpose of the Study:

• To reveal the complete silent transmission dynamics of COVID-19.
• To analyze transmission patterns in a real-world scenario without intervention measures.
• To identify high-risk groups and locations for silent COVID-19 spread.

Main Methods:

• Agent-based simulations were employed.
• Over 0.7 million real individual mobility tracks from a Chinese city were utilized.
• Empirically inferred transmission rates of COVID-19 were incorporated.

Main Results:

• An initial 70 infected individuals led to 0.33 million silent infections.
• A daily periodic pattern in transmission dynamics was observed, with peaks in the morning and afternoon.
• Retailing, catering, and hotel staff showed higher infection likelihood; elderly and retirees were more vulnerable at home.

Conclusions:

• Silent COVID-19 transmission poses a significant threat, especially in populations with low prior infection rates.
• Understanding transmission dynamics, including daily patterns and high-risk groups, is crucial for public health strategies.
• Agent-based modeling with real mobility data provides a powerful tool for assessing infectious disease spread.