基于城市層面的每日疫情數據,利用空間可視化和統計分析模型,從人口流動視角考察中國新冠疫情擴散的時空動態規律,探究不同類型人口流動的影響。研究發現,人口流動帶來的城際傳播和家庭為主的本地傳播構成了中國疫情擴散的兩階段模式,塑造了疫情的時空格局。人口流動對疫情傳播的影響具有結構性差異和動態性特征,商務、旅游等短期人口流動是疫情暴發初期的主要傳播途徑,長期人口流動帶來的春節返鄉流則推動了疫情進入高峰期。醫療資源并未對疫情防控產生系統性約束,人口老齡化的提高也未導致城市感染人數增加。傳統數據和大數據對疫情傳播的解釋和預測具有同等效力,二者優勢的結合是深化定量社會研究、提升社會治理能力的有效途徑。

This study investigates the impact of human mobility on the spatio-temporal dynamics of COVID-19 spread by utilizing daily COVID-19 data of more than 300 cities in China. The spread of COVID-19 in China is characterized by a two-stage pattern, namely the inter-city transmission driven by human mobility in the first stage and the local transmission among family members in the second stage, which have further shaped the spatio-temporal patterns. The impacts of human mobility on the COVID-19 spread are featured by structural differences and dynamic patterns. Temporal movements including tourism and business travel are the main route of transmission at the beginning of COVID-19 outbreak, while internal migrants returning to their hometowns during the Chinese Spring Festival are mainly responsible for the peak outbreak in early February. Both survey data and big data have equally high statistical power in predicting and interpreting the spread of COVID-19, indicating that the combination of the two’s strengths would contribute substantially to advancing quantitative research and improving social governance capacity.