Home for the Holidays? The Impact of US Holidays on Social Behaviors and Preventative Measures

Travel and Other Social Behaviors During US Holidays

Despite warnings from the CDC that the “safest way to celebrate is at home with people who live with you”, the TSA screened approximately 9.5 million airline passengers during the 2020 Thanksgiving holiday travel period (about 35-45% of 2019 volumes during the same time frame). Reports from airlines suggested that travel reached its highest levels since the start of the pandemic during the Christmas holiday with about 1% of the US population taking flights in just the few days surrounding the holiday.

library(covidcast)
library(ggplot2)
library(stringr)
library(dplyr)
library(grid)
library(gridExtra)

options(covidcast.auth = Sys.getenv("API_KEY")) # for more on API keys, see: https://cmu-delphi.github.io/delphi-epidata/api/api_keys.html

start_after_election <- "2020-11-10"
end_day <- "2021-01-05"

statelist <- c('AK', 'AZ', 'CA', 'CO', 'ID', 'MT', 'NV', 'NM', 'OR', 'UT',
               'WA', 'WY', 'HI', 'IL', 'IN', 'IA', 'KS', 'MI', 'MN', 'MO',
               'NE', 'ND', 'OH', 'SD', 'WI', 'CT', 'ME', 'MA', 'NH', 'NJ',
               'NY', 'PA', 'RI', 'VT', 'AL', 'AR', 'DE', 'FL', 'GA', 'KY',
               'LA', 'MD', 'MS', 'NC', 'OK', 'SC', 'TN', 'TX', 'WV', 'VA')
regions <- rep(c('West', 'Midwest', 'Northeast', 'South'),
               times = c(13, 12, 9, 16))

travel <- covidcast_signal("fb-survey",
                           "smoothed_wtravel_outside_state_5d",
                           start_day = start_after_election, end_day = end_day,
                           geo_type = "state")

travel_normed <- travel %>%
  rename(state = geo_value, date = time_value) %>%
  select(date, state, value, sample_size) %>%
  mutate(state = str_to_upper(state)) %>%
  filter(state %in% statelist) %>%
  mutate(Region = plyr::mapvalues(state, statelist, regions)) %>%
  group_by(state) %>%
  mutate(normed = value * 100 / first(value) - 100) %>%
  ungroup() %>%
  group_by(date, Region) %>%
  summarise_at("normed", list(mean = ~mean(.), sd = ~sd(.), se = ~sd(.)/sqrt(n()) ))

regionmap <- travel %>%
  filter(geo_value %in% str_to_lower(statelist)) %>%
  mutate(value = plyr::mapvalues(geo_value, str_to_lower(statelist), regions),
         value = as.integer(factor(value)))
attr(regionmap, "metadata") <- list(geo_type = "state")
grid_label <- textGrob("Data from Delphi COVIDcast, delphi.cmu.edu",
                       hjust = 1, x = 1, gp = gpar(fontsize = 9))

g1 <- travel_normed %>%
  ggplot() +
  geom_vline(xintercept = as.Date("2020-11-26"),
             linetype = "dashed", size = 1.5, color = "darkgrey") +
  geom_vline(xintercept = as.Date("2020-12-25"),
             linetype = "dashed", size = 1.5, color = "darkgrey") +
  geom_line(aes(x = date, y = mean, color = Region)) +
  geom_ribbon(aes(x = date, ymin = mean - se, ymax = mean + se, fill = Region),
              alpha = 0.2) +
  scale_x_date(date_breaks = "2 weeks",
               date_labels = "%b %d %Y",
               date_minor_breaks = "1 week") +
  labs(x = "Date", y = "% traveled out-of-state in last 5 days",
       title = "Relative out-of-state travel by US region",
       subtitle = "From Delphi's surveys, conducted through Facebook") +
  theme_bw() +
  theme(legend.position = c(0.095, 0.8)) +
  guides(color = FALSE)

g2 <- plot(regionmap,
           title = "US Regions",
           choro_col = c("#F8766D", "#7CAE00", "#00BFC4", "#C77CFF"),
           choro_params = list(breaks = c(1, 2, 3, 4))) +
    theme(legend.position = "none")

lay <- rbind(c(1,1,1,2),
             c(1,1,1,2))
grid.arrange(g1, g2, nrow = 1, bottom = grid_label, layout_matrix=lay)

In the COVID-19 survey, we see an increase in self-reported out-of-state travel in this time frame by surveyed adults. The figure above depicts the normalized percent of survey respondents who recently traveled out-of-state for 4 US regions (mean ± s.e. of states in region). Because different states have different baseline travel behaviors, values in the above figure are relative to out-of-state travel reported on November 11, 2020. While there was some variation between states, it appears that out-of-state travel around Thanksgiving and Christmas day (vertical gray dashed lines) rose steeply in all regions in the US. We note that since we are using a 7-day average, a travel peak on or very close to Thanksgiving day would show up 3 to 4 days later in our data, which is what we observe.

While the increase in out-of-state travel appears dramatic during Thanksgiving, the observed increase is less than the typical increase in travel around the holiday. The Bureau of Transportation Statistics notes that long distance trips (as defined by travel greater than 50 miles away) usually increase by 54% in the 6 days surrounding Thanksgiving. In contrast, the percentage increase in travel found in the survey was closer to 20%. For the Christmas through New Years time period, the increase in individuals reporting out-of-state travel in the survey is ~30%, which more closely matches prior year estimates from the Bureau of Transportation Statistics of 23%. Overall though, the out-of-state travel reported in the CSS is still down compared to earlier months in the pandemic.

spent_time <- covidcast_signal("fb-survey",
                               "smoothed_wspent_time_1d",
                               start_day = start_after_election, end_day = end_day,
                               geo_type = "state")

spent_time_stats <- spent_time %>%
  rename(state = geo_value, date = time_value) %>%
  select(date, state, value, sample_size) %>%
  mutate(state = str_to_upper(state)) %>%
  filter(state %in% statelist) %>%
  mutate(Region = plyr::mapvalues(state, statelist, regions)) %>%
  group_by(date, Region) %>%
  summarise_at("value", list(mean = ~mean(.), sd = ~sd(.), se = ~sd(.)/sqrt(n()) ))

spent_time_stats %>%
  ggplot() +
  geom_vline(xintercept = as.Date("2020-11-26"),
             linetype = "dashed", size = 1.5, color = "darkgrey") +
  geom_vline(xintercept = as.Date("2020-12-25"),
             linetype = "dashed", size = 1.5, color = "darkgrey") +
  geom_line(aes(x = date, y = mean, color = Region)) +
  geom_line(aes(x = date, y = mean, color = Region)) +
  geom_ribbon(aes(x = date, ymin = mean - se, ymax = mean + se, fill = Region),
              alpha = 0.2) +
  scale_x_date(date_breaks = "2 weeks",
               date_labels = "%b %d %Y",
               date_minor_breaks = "1 week") +
  labs(x = "Date", y = "% spent time with non-household member in past 24h",
       title = "Percent surveyed that spent time with a non-household member",
       subtitle = "7-day rolling average",
       caption = "Data from Delphi COVIDcast, delphi.cmu.edu") +
  theme_bw() +
  theme(legend.position = c(0.095, 0.2)) +
  guides(color = FALSE)

Through the survey, we also find that more people in many states spent time with people outside of their household in social gatherings for Thanksgiving and Christmas when compared to the preceding week. In the above figure, the percent of surveyed adults who recently spent time with non-household members clearly rises around the holidays for all 4 US regions (mean ± s.e. of states in region).

Experts had suggested quarantining before the holiday to lower the risk of spreading COVID-19 during family gatherings. We see a steady decrease in the number of people reporting contact outside of the household in the 14 days before Thanksgiving across all regions, possibly due to rising case counts across the nation. However this strong trend is not observed for the Christmas holiday. The Northeast and South show only very modest decreases in outside contact in the days just before Christmas, and rates of contact outside the household in the Midwest and West actually rose steadily during this time. Overall, we don’t see strong evidence in the data of people proactively quarantining between the Thanksgiving and Christmas holidays to reduce the potential for community spread of COVID-19.

shopped <- covidcast_signal("fb-survey",
                            "smoothed_wshop_1d",
                            start_day = start_after_election, end_day = end_day,
                            geo_type = "state")

shopped_stats <- shopped %>%
  rename(state = geo_value, date = time_value) %>%
  select(date, state, value, sample_size) %>%
  mutate(state = str_to_upper(state)) %>%
  filter(state %in% statelist) %>%
  mutate(Region = plyr::mapvalues(state, statelist, regions)) %>%
  group_by(date, Region) %>%
  summarise_at("value", list(mean = ~mean(.), sd = ~sd(.), se = ~sd(.)/sqrt(n()) ))

shopped_stats %>%
  ggplot() +
  geom_vline(xintercept = as.Date("2020-11-26"),
             linetype = "dashed", size = 1.5, color = "darkgrey") +
  geom_vline(xintercept = as.Date("2020-12-25"),
             linetype = "dashed", size = 1.5, color = "darkgrey") +
  geom_line(aes(x = date, y = mean, color = Region)) +
  geom_line(aes(x = date, y = mean, color = Region)) +
  geom_ribbon(aes(x = date, ymin = mean - se, ymax = mean + se, fill = Region),
              alpha = 0.2) +
  scale_x_date(date_breaks = "2 weeks",
               date_labels = "%b %d %Y",
               date_minor_breaks = "1 week") +
  labs(x = "Date", y = "% Went shopping in past 24h",
       title = "Percent surveyed that went shopping recently",
       subtitle = "7-day rolling average",
       caption = "Data from Delphi COVIDcast, delphi.cmu.edu") +
  theme_bw() +
  theme(legend.position = c(.095, 0.2)) +
  guides(color = FALSE)

Additionally, Thanksgiving and Christmas are major shopping holidays. When asked if they had gone to the “market, grocery store, or pharmacy” in the past 24 hours, a high percentage of survey respondents responded they had on Thanksgiving, Christmas Eve, and Christmas Day (mean ± s.e. of states in region). Notably, there were steep falls in shopping in the following weeks, perhaps as people instead chose to spend time indoors with their families. Typically the days after Thanksgiving and Christmas are among the 10 busiest in-store retail days of the year, though this trend does not necessarily extend to grocery stores, pharmacies and the like. As such, the lack of evidence for the typical post-holiday shopping binges likely reflects the specificity of the survey question.

Preventative Measures and Testing During the US Holidays

masked <- covidcast_signal("fb-survey",
                           "smoothed_wwearing_mask",
                           start_day = start_after_election, end_day = end_day,
                           geo_type = "state")

masked_stats <- masked %>%
  rename(state = geo_value, date = time_value) %>%
  select(date, state, value, sample_size) %>%
  mutate(state = str_to_upper(state)) %>%
  filter(state %in% statelist) %>%
  mutate(Region = plyr::mapvalues(state, statelist, regions)) %>%
  group_by(date, Region) %>%
  summarise_at("value", list(mean = ~mean(.), sd = ~sd(.), se = ~sd(.)/sqrt(n()) ))

masked_stats %>%
  ggplot() +
  geom_vline(xintercept = as.Date("2020-11-26"),
             linetype = "dashed", size = 1.5, color = "darkgrey") +
  geom_vline(xintercept = as.Date("2020-12-25"),
             linetype = "dashed", size = 1.5, color = "darkgrey") +
  geom_line(aes(x = date, y = mean, color = Region)) +
  geom_line(aes(x = date, y = mean, color = Region)) +
  geom_ribbon(aes(x = date, ymin = mean - se, ymax = mean + se, fill = Region),
              alpha = 0.2) +
  scale_x_date(date_breaks = "2 weeks",
               date_labels = "%b %d %Y",
               date_minor_breaks = "1 week") +
  labs(x = "Date", y = "% wore a mask in public in past 5 days",
       title = "Percent surveyed who reported wearing a mask in public",
       subtitle = "7-day rolling average",
       caption = "Data from Delphi COVIDcast, delphi.cmu.edu") +
  theme_bw() +
  theme(legend.position = c(0.9, 0.2)) +
  guides(color = FALSE)

While people in the US may have traveled more, shopped more, and spent more time with non-household members during the holidays, they also reported participating in preventative measures in public to a greater degree. For example, the percent of people who say they wore masks most or all of the time while in public in the past 5 days increased across the nation prior to Thanksgiving and held steady through the end of the year (mean ± s.e. of states in region).

Note, however, that this can’t be directly interpreted to mean that a certain percentage of people wear masks in public places—the survey question asks respondents if they wear masks most or all of the time in public, and since mask usage is self-reported, it might be biased. A recent Preventing Epidemics comparison found that this self-reported data tends to give higher rates of mask use when compared to direct observations of people in public places, such as through surveillance cameras at stores, but also found that these direct observations agree that mask use is increasing.

tested <- covidcast_signal("fb-survey",
                           "smoothed_wtested_14d",
                           start_day = start_after_election, end_day = end_day,
                           geo_type = "state")

tested_normed <- tested %>%
  rename(state = geo_value, date = time_value) %>%
  select(date, state, value, sample_size) %>%
  mutate(state = str_to_upper(state)) %>%
  filter(state %in% statelist) %>%
  mutate(Region = plyr::mapvalues(state, statelist, regions)) %>%
  group_by(state) %>%
  mutate(normed = value*100/first(value) - 100) %>% ungroup() %>%
  group_by(date, Region) %>%
  summarise_at("normed", list(mean = ~mean(.), sd = ~sd(.), se = ~sd(.)/sqrt(n()) ))

tested_normed %>%
  ggplot() +
  geom_vline(xintercept = as.Date("2020-11-26"),
             linetype = "dashed", size = 1.5, color = "darkgrey") +
  geom_vline(xintercept = as.Date("2020-12-25"),
             linetype = "dashed", size = 1.5, color = "darkgrey") +
  geom_line(aes(x = date, y = mean, color = Region)) +
  geom_line(aes(x = date, y = mean, color = Region)) +
  geom_ribbon(aes(x = date, ymin = mean - se, ymax = mean + se, fill = Region),
              alpha = 0.2) +
  scale_x_date(date_breaks = "2 weeks",
               date_labels = "%b %d %Y",
               date_minor_breaks = "1 week") +
  labs(x = "Date", y = "% tested in past 14 days",
       title = "Percent surveyed that were tested recently",
       subtitle = "7-day rolling average",
       caption = "Data from Delphi COVIDcast, delphi.cmu.edu") +
  theme_bw() +
  theme(legend.position = c(0.095, 0.2)) +
  guides(color = FALSE)

Several news agencies reported that the demand for COVID-19 tests surged before Thanksgiving. While this was in large part due to rising COVID-19 cases nationwide, it is also possible that many people chose to test before seeing friends or family during the holidays. The CDC had urged Americans not to travel for the holidays, but stated that for those “who decide to travel, testing can help you do so more safely.” In addition, several government agencies expanded testing prior to the holiday season to support testing for people who elected to travel and gather.

In the above figure, we do see an increase in survey respondents who had recently been tested for COVID-19 prior to Thanksgiving, and a slight increase before Christmas (mean ± s.e. of states in region). However, these effects are fairly weak, and dominated by longer-term trends that likely reflect case count trends in the region. For example, from mid-November through the end of the year we see a ~20% fall in self-reported testing in the Midwest, probably an effect of the ~50% decrease in COVID-19 cases in the region in the same time period. We therefore see only weak evidence of prophylactic COVID-19 testing before the holidays.

Altogether, it is clear that many Americans opted to travel out-of-state and gather with friends and family for the Thanksgiving and Christmas holidays. Public health experts provided a variety of recommendations to reduce the risks associated with holiday celebrations. Unfortunately, the survey data does not indicate widespread adherence to these guidelines, as we see only weak evidence of preemptive quarantining or testing for safer holiday gatherings. Overall, our analysis suggests conflicting pandemic behaviors during the 2020 holidays, where a vast majority of Americans used preventative measures like mask-wearing in public, but simultaneously increased the risk for disease spread by gathering with friends and family, shopping more, and traveling out-of-state.

See the Data Yourself!

Since April 2020, Carnegie Mellon University’s Delphi Group and Facebook’s Data for Good have partnered to conduct large-scale daily surveys to monitor the spread and impact of the COVID-19 pandemic in the United States. To date, the survey has collected more than 50 million responses. In this post, we show how the data from the COVID-19 survey can be used to provide a broader picture of how American’s risk behaviors and sentiments amidst a global pandemic.

All of the above plots were built using aggregate data for the United States that CMU/Delphi Group & Facebook provide to the public through the COVIDcast Epidata API, and visualize in the Delphi Group’s survey dashboard. Public, aggregate data for the global COVID-19 survey can be accessed via University of Maryland’s API. We encourage researchers, public health officials, and the public to use the survey data and tools for their own analyses. Microdata is also available upon request to academic and nonprofit researchers under data license agreements.