tetano
Editor, Senior Moderator
J Transp Health
. 2021 Mar;20:101016.
doi: 10.1016/j.jth.2021.101016. Epub 2021 Jan 28.
Mobility and the effective reproduction rate of COVID-19
Robert B Noland[SUP] 1 [/SUP]
Affiliations
Abstract
Objectives: Due to the infectiousness of COVID-19, the mobility of individuals has sharply decreased, both in response to government policy and self-protection. This analysis seeks to understand how mobility reductions reduce the spread of the coronavirus (SAR-CoV-2), using readily available data sources.
Methods: Mobility data from Google is correlated with estimates of the effective reproduction rate, R [SUB]t[/SUB], which is a measure of viral infectiousness (Google, 2020). The Google mobility data provides estimates of reductions in mobility, for six types of trips and activities. R [SUB]t[/SUB] for US states are downloaded from an on-line platform that derives daily estimates based on data from the Covid Tracking Project (Wissel et al., 2020; Systrom et al., 2020). Fixed effects models are estimated relating mean R [SUB]t[/SUB] and 80% upper level credible interval estimates to changes in mobility and a time-trend value and with both 7-day and 14-day lags.
Results: All mobility variables are correlated with median R [SUB]t[/SUB] and the upper level credible interval of R [SUB]t[/SUB] . Staying at home is effective at reducing R [SUB]t,[/SUB] . Time spent at parks has a small positive effect, while other activities all have larger positive effects. The time trend is negative suggesting increases in self-protective behavior. Predictions suggest that returning to baseline levels of activity for retail, transit, and workplaces, will increase R [SUB]t[/SUB] above 1.0, but not for other activities. Mobility reductions of about 20-40% are needed to achieve an R [SUB]t[/SUB] below 1.0 (for the upper level 80% credible interval) and even larger reductions to achieve an R [SUB]t[/SUB] below 0.7.
Conclusions: Policy makers need to be cautious with encouraging return to normal mobility behavior, especially returns to workplaces, transit, and retail locations. Activity at parks appears to not increase R [SUB]t[/SUB] as much. This research also demonstrates the value of using on-line data sources to conduct rapid policy-relevant analysis of emerging issues.
Keywords: COVID-19; Mobility; Social-distancing.
. 2021 Mar;20:101016.
doi: 10.1016/j.jth.2021.101016. Epub 2021 Jan 28.
Mobility and the effective reproduction rate of COVID-19
Robert B Noland[SUP] 1 [/SUP]
Affiliations
- PMID: 33542894
- PMCID: PMC7843082
- DOI: 10.1016/j.jth.2021.101016
Abstract
Objectives: Due to the infectiousness of COVID-19, the mobility of individuals has sharply decreased, both in response to government policy and self-protection. This analysis seeks to understand how mobility reductions reduce the spread of the coronavirus (SAR-CoV-2), using readily available data sources.
Methods: Mobility data from Google is correlated with estimates of the effective reproduction rate, R [SUB]t[/SUB], which is a measure of viral infectiousness (Google, 2020). The Google mobility data provides estimates of reductions in mobility, for six types of trips and activities. R [SUB]t[/SUB] for US states are downloaded from an on-line platform that derives daily estimates based on data from the Covid Tracking Project (Wissel et al., 2020; Systrom et al., 2020). Fixed effects models are estimated relating mean R [SUB]t[/SUB] and 80% upper level credible interval estimates to changes in mobility and a time-trend value and with both 7-day and 14-day lags.
Results: All mobility variables are correlated with median R [SUB]t[/SUB] and the upper level credible interval of R [SUB]t[/SUB] . Staying at home is effective at reducing R [SUB]t,[/SUB] . Time spent at parks has a small positive effect, while other activities all have larger positive effects. The time trend is negative suggesting increases in self-protective behavior. Predictions suggest that returning to baseline levels of activity for retail, transit, and workplaces, will increase R [SUB]t[/SUB] above 1.0, but not for other activities. Mobility reductions of about 20-40% are needed to achieve an R [SUB]t[/SUB] below 1.0 (for the upper level 80% credible interval) and even larger reductions to achieve an R [SUB]t[/SUB] below 0.7.
Conclusions: Policy makers need to be cautious with encouraging return to normal mobility behavior, especially returns to workplaces, transit, and retail locations. Activity at parks appears to not increase R [SUB]t[/SUB] as much. This research also demonstrates the value of using on-line data sources to conduct rapid policy-relevant analysis of emerging issues.
Keywords: COVID-19; Mobility; Social-distancing.