Policy and weather influences on mobility during the early US COVID-19 pandemic

Abstract

As the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to proliferate across the globe, it is a struggle to predict and prevent its spread. The successes of mobility interventions demonstrate how policies can help limit the person-to-person interactions that are essential to infection. With significant community spread, experts predict this virus will continue to be a threat until safe and effective vaccines have been developed and widely deployed. We aim to understand mobility changes during the first major quarantine period in the United States, measured via mobile device tracking, by assessing how people changed their behavior in response to policies and to weather. Here, we show that consistent national messaging was associated with consistent national behavioral change, regardless of local policy. Furthermore, although human behavior did vary with outdoor air temperature, these variations were not associated with variations in a proxy for the rate of encounters between people. The independence of encounters and temperatures suggests that weather-related behavioral changes will, in many cases, be of limited relevance for SARS-CoV-2 transmission dynamics. Both of these results are encouraging for the potential of clear national messaging to help contain any future pandemics, and possibly to help contain COVID-19.

Keywords: COVID-19; United States; mobility; policy; weather.

Fig. 1.

(A) Implementation of state policies and changes in mobility behavior, ordered by date of stay-at-home orders. Warm-colored circles are metrics based on mobility: peaks in grocery visitation (yellow), beginning of quarantine based on mobility (red), timing when mobility reaches 30% of prepandemic values (orange), and the end of quarantine based on mobility (pink). Cool-colored squares are policy implementations: date of implementation of stay-at-home for each state (blue), date of expiration of stay-at-home (cyan), and date of implementation of reopening plans (green). Black lines are national declarations: the announcement of a national state of emergency (dashed), and the start and end of national stay-at-home guidelines (solid). For details about mobility metrics, see Materials and Methods. (B) Bar graph of county-level changes in mobility behavior, using real quarantine time series (red, pink) and control time series (blue, cyan).

Fig. 2.

(A) Map of log₁₀ of leading coefficient a and (B) map of coefficient b on the state level. In A, the value for Washington, DC (log₁₀ a = 2.26) is not shown. Scatter plots of coefficients at the county level are shown against the log₁₀ of population density for log₁₀ a in C and for b in D.

Fig. 3.

(A) State-level observed correlation coefficients of temperature and park visitation. Both the temperature and the park visitation time series are high-pass filtered before computing the correlations, and the significance of the observed correlations is determined via comparisons to null distributions of correlations computed using prepandemic (1950−2019) weather data (see Materials and Methods for details). Single hatching is used to mark states for which the correlations are significant at the 90% level (P < 0.1), while no hatching indicates significance at the 95% level (P < 0.05). (Cross hatching marks states that are not significant at the 90% level [P > 0.1].) (B) Information about the number of states likely to exhibit spurious “significant” correlations in the absence of any causal effect of temperature on park visitation. Red star indicates the number of states with significant observed (2020) correlations. Each blue bar indicates the number of prepandemic years (1950−2019) in which the given number of states is (necessarily incorrectly) identified as having a significant correlation between the prepandemic year’s temperature and 2020 park visitation (see Materials and Methods for details). (C and D) As in A and B but for temperature−potential encounter rate correlations.