Estimating effects of physical distancing on the COVID-19 pandemic using an urban mobility index
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Abstract
Background
Governments have implemented population-wide physical distancing measures to control COVID-19, but metrics evaluating their effectiveness are not readily available.
Methods
We used a publicly available mobility index from a popular transit application to evaluate the effect of physical distancing on infection growth rates and reproductive numbers in 40 jurisdictions between March 23 and April 12, 2020.
Findings
A 10% decrease in mobility was associated with a 14.6% decrease (exp(β) = 0·854; 95% credible interval: 0·835, 0·873) in the average daily growth rate and a −0·061 (95% CI: −0·071, −0·052) change in the instantaneous reproductive number two weeks later.
Interpretation
Our analysis demonstrates that decreases in urban mobility were predictive of declines in epidemic growth. Mobility metrics offer an appealing method to calibrate population-level physical distancing policy and implementation, especially as jurisdictions relax restrictions and consider alternative physical distancing strategies.
Funding
No external funding was received for this study.
Research in Context
Evidence before this study
Widespread physical distancing interventions implemented in response to the COVID-19 pandemic led to sharp declines in global mobility throughout March 2020. Real-time metrics to evaluate the effects of these measures on future case growth rates will be useful for calibrating further interventions, especially as jurisdictions begin to relax restrictions. We searched PubMed on May 22, 2020 for studies reporting the use of aggregated mobility data to measure the effects of physical distancing on COVID-19 cases, using the keywords “COVID-19”, “2019-nCoV”, or “SARS-CoV-2” in combination with “mobility”, “movement”, “phone”, “Google”, or “Apple”. We scanned 252 published studies and found one that used mobility data to estimate the effects of physical distancing. This study evaluated temporal trends in reported cases in four U.S. metropolitan areas using a metric measuring the percentage of cell phone users leaving their homes. Many published papers examined how national and international travel predicted the spatial distribution of cases (particularly outflow from Wuhan, China), but very little has been published on metrics that could be used as prospective, proximal indicators of future case growth. We also identified a series of reports released by the Imperial College COVID-19 Response Team and several manuscripts deposited on preprint servers such as medRxiv addressing this topic, demonstrating this is an active area of research.
Added value of this study
We demonstrate that changes in a publicly available urban mobility index reported in over 40 global cities were associated with COVID-19 case growth rates and estimated reproductive numbers two to three weeks later. These cities, spread over 5 continents, include many regional epicenters of COVID-19 outbreaks. This is one of only a few studies using a mobility metric applicable to future growth rates that is both publicly available and international in scope.
Implications of all the available evidence
Restrictions on human mobility have proved effective for controlling COVID-19 in China and the rest of the world. However, such drastic public health measures cannot be sustained indefinitely and are currently being relaxed in many jurisdictions. Publicly available mobility metrics offer a method of estimating the effects of changes in mobility before they are reflected in the trajectory of COVID-19 case growth rates and thus merit further evaluation.
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Our take
This paper is available as a pre-print and therefore not yet peer-reviewed. Reduced movement was observed across cities in 23 countries across the world after the introduction of social distancing measures. Although authors estimated reductions in epidemic growth and transmissibility associated with reduced mobility patterns, strong assumptions were made about representativeness of the data and delays from infection to case report across 41 cities which may have biased the results.
Study design
modeling-simulation
Study population and setting
The Citymapper Mobility Index (CMI) which measures the relative frequency of planned trips (compared to an internal reference point from late 2019 or early 2020) using the Citymapper application was used as a proxy measure of adherence to social distancing …
Our take
This paper is available as a pre-print and therefore not yet peer-reviewed. Reduced movement was observed across cities in 23 countries across the world after the introduction of social distancing measures. Although authors estimated reductions in epidemic growth and transmissibility associated with reduced mobility patterns, strong assumptions were made about representativeness of the data and delays from infection to case report across 41 cities which may have biased the results.
Study design
modeling-simulation
Study population and setting
The Citymapper Mobility Index (CMI) which measures the relative frequency of planned trips (compared to an internal reference point from late 2019 or early 2020) using the Citymapper application was used as a proxy measure of adherence to social distancing measures implemented across 41 cities in 23 countries from March 2, 2020. Using reported cumulative case numbers over time, authors estimated how fast the total case numbers were growing in each of these 41 cities over 3 weeks beginning March 23 to April 6, 2020. Transmissibility as estimated by the instantaneous reproduction number was estimated for the weeks of March 23, March 30, and April 6, 2020 using the incidence of reported cases between March 8 to April 12, 2020. The authors then assumed a 14 day delay between infection and date of report to estimate the association between CMI and the mean daily growth rate and the reproduction number.
Summary of main findings
Authors found that declines in mobility (relative frequency of planned trips) corresponded with a decline in epidemic growth in 41 cities. The majority of cities saw a substantial reduction in mobility, as measured by the CMI, from a mean of 97.6% on March 2 to 12.7% on March 29, 2020. Similar patterns of reduced mobility were observed across cities in Europe, the Americas, and Australia corresponding to the implementation of national or subnational social distancing measures and mandatory closures e.g. of non-essential retail. A 10% reduction in mobility was associated with a decrease in the daily growth rate of 14.6% and a 0.061 reduction in the reproduction number 14 days later.
Study strengths
Authors used automatically collected app-based data to measure changes in mobility due to social distancing measures. Such data may be more accurate than self-reported behavioural changes. Authors checked whether their findings were affected by the timing of the epidemic and their assumption about the average delay between infection and case report.
Limitations
Whilst the mobility data are only available at the city-level, for some countries COVID-19 case counts were only available at national or sub-national level. Therefore the reduction in mobility in certain urban cities will not necessarily be representative of the whole country. The mobility data only applies to Citymapper app users which is not representative of the whole city, nor covers any journeys by car. The mobility metric also only captures the decline in the relative frequency of trips planned, and does not capture other indicators such as changes in the types of trips planned. Authors assumed a crude 14 day delay between infection and case report, which was the same for all countries considered. In reality this delay will differ significantly between settings due to healthcare capacity and testing policies, and may bias estimates. This bias may also apply to the reproduction number estimate which was based on cases by date of report rather than date of symptom onset. Finally, reduction in mobility does not necessarily equate to a reduction in social and physical contact that could lead to decreased transmission.
Value added
Findings are not novel, but similar to previous studies demonstrates the utility of mobility data to assess the potential impact of social distancing interventions.
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SciScore for 10.1101/2020.04.05.20054288: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study had other limitations. Our analysis does not confirm a causal pathway through mobility, but rather a strong association that warrants further evaluation. For example, it is possible that countries successfully enforcing physical distancing are also more successfully implementing interventions such as contact tracing or widespread testing, which may also contribute to the observed association. We also did not account for …
SciScore for 10.1101/2020.04.05.20054288: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study had other limitations. Our analysis does not confirm a causal pathway through mobility, but rather a strong association that warrants further evaluation. For example, it is possible that countries successfully enforcing physical distancing are also more successfully implementing interventions such as contact tracing or widespread testing, which may also contribute to the observed association. We also did not account for imported cases in the calculation of the instantaneous reproductive number; however, locally acquired cases were certainly undercounted during this period, and likely to a greater degree than imported cases due to the increased attention on international travelers. Further, imported cases are expected to account for an increasingly small proportion of total cases in the latter two weeks (March 30 and April 6) due to the implementation of travel restrictions in mid–late March. Re-running the models to exclude the first week (March 23) of case data produced relatively similar results to the full models (for both growth rate and reproductive number), suggesting that imported cases do not drive our results. Additional measures of human mobility and physical distancing are urgently needed in order to better understand the impacts of these policies on transmission dynamics. For example, Lasry and colleagues18 examined the relationship between the percentage of cell phone users leaving their homes and COVID-19 case trajectories in 4 metropolitan areas in th...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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