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

TL;DR

This study uses high-frequency mobility data from over 0.7 million individuals in a Chinese city to uncover silent COVID-19 transmission patterns, revealing infection dynamics across professions, locations, and age groups without intervention measures.

Contribution

It provides the first comprehensive analysis of silent COVID-19 transmission using detailed mobility data in China, highlighting transmission patterns and vulnerable groups.

Findings

Silent transmission can infect hundreds of thousands from a few initial cases.

Transmission peaks occur in mornings and afternoons daily.

Certain professions like retail and hospitality are more at risk.

Abstract

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence , especially for China where most population has not been infected and most Omicron transmissions are silent. This paper serves as the first study to reveal the complete silent transmission dynamics of COVID-19 overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, retailing, catering and hotel staff are more likely to get infected than other professions. Unlike all other age groups and professions, elderly and retirees are more likely to get infected at home than outside home.