Human mobility and COVID-19 transmission: a systematic review and future directions

Figures & data

Table 1. Inclusion and exclusion criteria of article selection.

Criteria	Definition
Inclusion	1) measure the association between human mobility and COVID-19 transmission, 2) measure the association between human mobility-related policies and COVID-19 transmission, and 3) apply quantitative methods.
Exclusion	1) measure the association between human mobility and other COVID-19 pandemic related issues, e.g. economic development, mental health, family relationship, 2) apply qualitative methods.

Figure 1. PRISMA flow chart on the identification and screening of studies on human mobility and COVID19 transmission.

Table 2. Summary of mobility data sources used in the selected articles.

Category	Data Source	Link	Public accessibility	Spatial coverage	Time coverage	Update	Advantages	Disadvantages	ID of selected articles *
Big data	Baidu Mobility Index	https://qianxi.baidu.com/	N	City/Province (China)	2020.1.1 ~  2020.5.8	Daily	Low costs; convenient in data gathering; available in indicating inter-city mobility	Limited region coverage (China mainland); difficult to collect data	1, 15, 21, 41, 3, 44, 17, 28, 29, 45
	SafeGraph	https://www.safegraph.com/covid-19-data-consortium	Y	Census Block Group (North America)		Daily	Comprehensive users check-in information with high resolution; reaggregated to any spatial scale, such as census tract, county, and state; estimate human mobility inter/intro regions	Difficult to process because of large data volume; data is available after applying; it only covers the North American region	10
	Google Mobility Report	https://www.google.com/covid19/mobility/	Y	County/State/Country (Global)	2020.1.1 ~ present	Daily	Wide region coverage; convenient in data gathering; low costs	Without inter-region mobility estimation	27
	Tencent Mobility Index	https://heat.qq.com/qianxi.php	N	city (China)	Daily	Daily	Real-time updates;	Difficult to collect data; only available at city level	7
Public Transit	Airline Flight	N/A	N	County/State/Country (Global)			Convenient in data gathering; wide region coverage	Partially covering global flight information; only available in historical data; not publicly available	35, 42
Census		N/A	Y	County/State/Country	Yearly	Yearly	Low costs; convenient data gathering; precise results	Low update frequency	38
Survey		N/A	N		2019	N/A	High representativeness; low costs; convenient data gathering; precise results	Low update frequency	6, 27

* ID of selected articles was listed in Supplementary Table 1.

Figure 2. Summary of mathematical models applied in the selected articles.