## 2 Materials and Methods  
We selected five human mobility indicators derived by SafeGraph: (1) median home-dwelling time; (2) the percentage of completely stay-at-home devices; (3) the percentage of frequently moving devices as would happen when, for example, making deliveries; (4) the percentage of stationary part-time (work) devices at locations other than at home; and (5) the percentage of stationary full-time (work) devices at locations other than home. They were calculated based on an aggregated anonymous mobile device by SafeGraph, which aggregated physical locations of non-identifiable individuals collected from third-party entities under licenses that complied with evolving laws regarding privacy to the census block group (CBG) level (Table 1). SafeGraph used the typical nighttime (i.e., from 6 p.m. in the evening to 7 a.m. the next morning) location over a 6-week period to assign \"homes\" to each of the participating devices. The initial assignments were made for squares measuring approximately 153 m on a side and then census block groups containing these squares. The daily median home dwelling time was calculated as the median number of minutes at home on a given day across all devices located in a given census block group. The percentage of devices completely staying at home was calculated by dividing the number of devices that did not leave their home squares located in a CBG with the number of devices seen during the same date range whose home was in that CBG. The full- and part-time work behavior devices were those that spent > 6 h and 3-6 h in places other than the designated \"home\" location from 8 a.m. to 6 p.m. each day. The mobile (delivery) behavior devices stopped for < 20 min at > 3 locations per day in some place other than the designated \"home\" location. We obtained these 5 indicators from 1 January 2019 to 3 October 2020 for the whole U.S. and extracted the devices located in CA for further analysis. The reason for choosing CA as our study location was due to the severe impact that the COVID-19 pandemic had on this area. This included state-implemented measures, such as lockdowns and gathering restrictions, to control the spread of the virus and to address the evolving situation. We excluded the census block groups (CBGs) in census tracts with \"98\" prefixes, which marked places of a large area with few or no residents, such as large parks or employment areas, and \"99\", which denotes large bodies of water. The final dataset included 23,142 census block groups, and the daily numbers of tracked mobile devices ranged from 1.74-2.58 million in 2019 to 1.37-1.93 million in 2020. Given the total number of 39.78 million residents, based on the 2020 U.S. Census, these metrics meant that we were able to track 4.5-6.5% and 3.5-5% of the total population in California in 2019 and 2020, respectively. To avoid the seasonality of human mobility caused by climatic drivers, school terms, religious festivals, and national holidays [21], we subtracted the daily values of 2020 from the five variables by their daily values in 2019 so that a positive value would mean an increase in the variable and vice versa. To match the day of the week, we calculated the changes in these five metrics by subtracting their daily values in 2020 from the daily values in 2019 using a 1-day lag before 29 February 2020 and a 2-day lag thereafter (since 2020 was a leap year). This approach meant that we could compare similar days across the two years. This was important because the human mobility metrics reveal strong weekly patterns. For example, median home dwelling times regularly peaked on weekends. As a consequence, the HMPs represented the 2019-2020 patterns of changes of the five metrics. We used time-series clustering methods to characterize temporal mobility patterns across geographic units [22]. We were able to produce HMP groups that were less sensitive to outliers and with lower clustering errors in our preliminary analyses using DTW-SOM: a