How well does societal mobility restriction help control the COVID-19 pandemic? Evidence from real-time evaluation

ABSTRACTObjectivesTo determine the impact of restrictions on mobility on reducing transmission of COVID-19.DesignDaily incidence rates lagged by 14 days were regressed on mobility changes using LOESS regression and logit regression between the day of the 100th case in each country to August 31, 2020.Setting34 OECD countries plus Singapore and Taiwan.ParticipantsGoogle mobility data were obtained from people who turned on mobile device-based global positioning system (GPS) and agreed to share their anonymized position information with Google.InterventionsWe examined the association of COVID-19 incidence rates with mobility changes, defined as changes in categories of domestic location, against a pre-pandemic baseline, using country-specific daily incidence data on newly confirmed COVID-19 cases and mobility data.ResultsIn two thirds of examined countries, reductions of up to 40% in commuting mobility (to workplaces, transit stations, retailers, and recreation) were associated with decreased COVID-19 incidence, more so early in the pandemic. However, these decreases plateaued as mobility remained low or decreased further. We found smaller or negligible associations between mobility restriction and incidence rates in the late phase in most countries.ConclusionMild to moderate degrees of mobility restriction in most countries were associated with reduced incidence rates of COVID-19 that appear to attenuate over time, while some countries exhibited no effect of such restrictions. More detailed research is needed to precisely understand the benefits and limitations of mobility restrictions as part of the public health response to the COVID-19 pandemic.WHAT IS ALREADY KNOWN ON THIS TOPICSince SARS-CoV-2 became a pandemic, restrictions on mobility such as limitations on travel and closure of offices, restaurants, and shops have been imposed in an unprecedented way in both scale and scope to prevent the spread of COVID-19 in the absence of effective treatment options or a vaccine. Although mobility restriction has also brought about tremendous costs such as negative economic growth and other collateral impacts on health such as increased morbidity and mortality from lack of access to other essential health services, little evidence exists on the effectiveness of mobility restriction for the prevention of disease transmission. A search of PUBMED and Google Scholar for publications on this topic through Sep 20, 2020 revealed that most of the evidence on the effectiveness of physical distancing comes from mathematical modeling studies using a variety of assumptions. One study investigated only the combined effect of several interventions, including physical distancing, among SARS-CoV-2 infected patients.WHAT THIS STUDY ADDSThis is the first study to investigate the association between change in mobility and incidence of COVID-19 globally using real-time measures of mobility at the population level. For this, we used Google Global Mobility data and the daily incidence of COVID-19 for 36 countries from the day of 100th case detection through August 31, 2020. Our findings from LOESS regression show that in two-thirds of countries, reductions of up to 40% in commuting mobility were associated with decreased COVID-19 incidence, more so early in the pandemic. This decrease, however, plateaued as mobility decreased further. We found that associations between mobility restriction and incidence became smaller or negligible in the late phase of the pandemic in most countries. The reduced incidence rate of COVID-19 cases with a mild to moderate degree of mobility restriction in most countries suggests some value to limited mobility restriction in early phases of epidemic mitigation. The lack of impact in some others, however, suggests further research is needed to confirm these findings and determine the distinguishing factors for when mobility restrictions are helpful in decreasing viral transmission. Governments should carefully consider the level and period of mobility restriction necessary to achieve the desired benefits and minimize harm.

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Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing

10.1101/2020.10.17.20214155 ◽

2020 ◽

Author(s):

Kathy Leung ◽

Joseph T Wu ◽

Gabriel M Leung

Keyword(s):

Real Time ◽

Disease Transmission ◽

Human Mobility ◽

Population Mobility ◽

Mobility Data ◽

Physical Mixing ◽

Real Time Tracking ◽

Case Data ◽

Transmission Models ◽

New Framework

AbstractDigital proxies of human mobility and physical mixing have been used to monitor viral transmissibility and effectiveness of social distancing interventions in the ongoing COVID-19 pandemic. We developed a new framework that parameterizes disease transmission models with age-specific digital mobility data. By fitting the model to case data in Hong Kong, we were able to accurately track the local effective reproduction number of COVID-19 in near real time (i.e. no longer constrained by the delay of around 9 days between infection and reporting of cases) which is essential for quick assessment of the effectiveness of interventions on reducing transmissibility. Our findings showed that accurate nowcast and forecast of COVID-19 epidemics can be obtained by integrating valid digital proxies of physical mixing into conventional epidemic models.

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Using Mobile, Wearable, Technology to Understand the Role of Built Environment Demand for Outdoor Mobility

Environment and Behavior ◽

10.1177/0013916517749256 ◽

2018 ◽

Vol 51 (6) ◽

pp. 671-688 ◽

Cited By ~ 5

Author(s):

Kate Duchowny ◽

Philippa Clarke ◽

Nancy Ambrose Gallagher ◽

Robert Adams ◽

Andrea L. Rosso ◽

...

Keyword(s):

Built Environment ◽

Real Time ◽

Real World ◽

Assessment Tool ◽

Community Dwelling ◽

Motion Sensors ◽

Convenience Sample ◽

Mobility Data ◽

Environmental Demand ◽

The Impact

Walking outdoors requires navigating a complex environment. However, no studies have evaluated how environmental barriers affect outdoor mobility in real time. We assessed the impact of the built environment on outdoor mobility, using mobile, wearable inertial measurement units. Data come from a convenience sample of 23 community-dwelling adults in Southeast Michigan. Participants walked a defined outdoor route where gait metrics were captured over a real-world urban environment with varying challenges. Street segments were classified as high versus low environmental demand using the Senior Walking Environmental Assessment Tool. Participants ranged in age from 22 to 74 years (mean age of 47 years). Outdoor gait speed was 0.3 m/s slower, and gait variability almost doubled, over the high- versus low-demand environments (coefficient of variability = 10.6% vs. 5.6%, respectively). This is the first study to demonstrate the feasibility of using wearable motion sensors to gather real-time mobility data in response to outdoor environmental demand. Findings contribute to the understanding of outdoor mobility by quantifying how real-world environmental challenges influence mobility in real time.

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The Role of Structural Inequality on COVID-19 Incidence Rates at the Neighborhood Scale in Urban Areas

COVID ◽

10.3390/covid1010016 ◽

2021 ◽

Vol 1 (1) ◽

pp. 186-202

Author(s):

Daniel L. Mendoza ◽

Tabitha M. Benney ◽

Rajive Ganguli ◽

Rambabu Pothina ◽

Cheryl S. Pirozzi ◽

...

Keyword(s):

Policy Implementation ◽

Disease Transmission ◽

Salt Lake ◽

Incidence Rates ◽

Urban Setting ◽

Structural Inequality ◽

Ongoing Research ◽

The Impact ◽

Zip Code ◽

At Home

The lockdown policies enacted in the spring of 2020, in response to the growing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, have remained a contentious policy tool due to the variability of outcomes they produced for some populations. While ongoing research has illustrated the unequal impact of Coronavirus disease (COVID-19) on minority populations, research in this area has been unable to fully explain the mechanisms that produce these findings. To understand why some groups have been at greater risk of contracting COVID-19, we employ structural inequality theory to better understand how inequality may impact disease transmission in a pandemic. We used a novel approach that enabled us to focus on the microprocesses of structural inequality at the zip code level to study the impact of stay-at-home pandemic policies on COVID-19 positive case rates in an urban setting across three periods of policy implementation. We then analyzed data on traffic volume, income, race, occupation, and instances of COVID-19 positive cases for each zip code in Salt Lake County, Utah (USA) between 17 February 2020 and 12 June 2020. We found that higher income, percent white, and white-collar zip codes had a greater response to the local stay-at-home order and reduced vehicular traffic by nearly 50% during lockdown. The least affluent zip codes only showed a 15% traffic decrease and had COVID-19 rates nearly 10 times higher. At this level of granularity, income and occupation were both associated with COVID-19 outcomes across all three stages of policy implementation, while race was only predictive of outcomes after the lockdown period. Our findings illuminate underlying mechanisms of structural inequality that may have facilitated unequal COVID-19 incidence rates. This study illustrates the need for more granular analyses in policy research and adds to the literature on how structural factors such as income, race, and occupation contribute to disease transmission in a pandemic.

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Lifting mobility restrictions and the induced short-term dynamics of COVID-19

10.1101/2020.07.23.20161026 ◽

2020 ◽

Cited By ~ 1

Author(s):

Mario Santana-Cibrian ◽

Manuel A. Acuna-Zegarra ◽

Jorge X. Velasco-Hernandez

Keyword(s):

Disease Transmission ◽

High Mobility ◽

Incidence Rates ◽

Mitigation Measures ◽

Epidemic Curve ◽

Effective Contact ◽

High Incidence ◽

The World ◽

And Control ◽

The Impact

SARS-CoV-2 has now infected 15 million people and produced more than six hundred thousand deaths around the world. Due to high transmission levels, many governments implemented social-distancing measures and confinement with different levels of required compliance to mitigate the COVID-19 epidemic. In several countries, these measures were effective, and it was possible to flatten the epidemic curve and control it. In others, this objective was not or has not been achieved. In far to many cities around the world rebounds of the epidemic are occurring or, in others, plateau-like states have appeared where high incidence rates remain constant for relatively long periods of time. Nonetheless, faced with the challenge of urgent social need to reactivate their economies, many countries have decided to lift mitigation measures at times of high incidence. In this paper, we use a mathematical model to characterize the impact of short duration transmission events within the confinement period previous but close to the epidemic peak. The model describes too, the possible consequences on the disease dynamics after mitigation measures are lifted. We use Mexico City as a case study. The results show that events of high mobility may produce either a later higher peak, a long plateau with relatively constant but high incidence or the same peak as in the original baseline epidemic curve, but with a post-peak interval of slower decay. Finally, we also show the importance of carefully timing the lifting of mitigation measures. If this occurs during a period of high incidence, then the disease transmission will rapidly increase, unless the effective contact rate keeps decreasing, which will be very difficult to achieve once the population is released.

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The exacerbation of Ebola outbreaks by conflict in the Democratic Republic of the Congo

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1913980116 ◽

2019 ◽

Vol 116 (48) ◽

pp. 24366-24372 ◽

Cited By ~ 16

Author(s):

Chad R. Wells ◽

Abhishek Pandey ◽

Martial L. Ndeffo Mbah ◽

Bernard-A. Gaüzère ◽

Denis Malvy ◽

...

Keyword(s):

Disease Transmission ◽

Democratic Republic ◽

Population Level ◽

Model Framework ◽

Public Health Response ◽

Local Conditions ◽

Health Response ◽

Republic Of The Congo ◽

And Control ◽

The Impact

The interplay between civil unrest and disease transmission is not well understood. Violence targeting healthcare workers and Ebola treatment centers in the Democratic Republic of the Congo (DRC) has been thwarting the case isolation, treatment, and vaccination efforts. The extent to which conflict impedes public health response and contributes to incidence has not previously been evaluated. We construct a timeline of conflict events throughout the course of the epidemic and provide an ethnographic appraisal of the local conditions that preceded and followed conflict events. Informed by temporal incidence and conflict data as well as the ethnographic evidence, we developed a model of Ebola transmission and control to assess the impact of conflict on the epidemic in the eastern DRC from April 30, 2018, to June 23, 2019. We found that both the rapidity of case isolation and the population-level effectiveness of vaccination varied notably as a result of preceding unrest and subsequent impact of conflict events. Furthermore, conflict events were found to reverse an otherwise declining phase of the epidemic trajectory. Our model framework can be extended to other infectious diseases in the same and other regions of the world experiencing conflict and violence.

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Real-time modelling of a pandemic influenza outbreak

Health Technology Assessment ◽

10.3310/hta21580 ◽

2017 ◽

Vol 21 (58) ◽

pp. 1-118 ◽

Cited By ~ 4

Author(s):

Paul J Birrell ◽

Richard G Pebody ◽

André Charlett ◽

Xu-Sheng Zhang ◽

Daniela De Angelis

Keyword(s):

Public Health ◽

Monte Carlo ◽

Real Time ◽

Pandemic Influenza ◽

Sequential Monte Carlo ◽

Health Technology ◽

School Closure ◽

Public Health Response ◽

The Uk ◽

The Impact

BackgroundReal-time modelling is an essential component of the public health response to an outbreak of pandemic influenza in the UK. A model for epidemic reconstruction based on realistic epidemic surveillance data has been developed, but this model needs enhancing to provide spatially disaggregated epidemic estimates while ensuring that real-time implementation is feasible.ObjectivesTo advance state-of-the-art real-time pandemic modelling by (1) developing an existing epidemic model to capture spatial variation in transmission, (2) devising efficient computational algorithms for the provision of timely statistical analysis and (3) incorporating the above into freely available software.MethodsMarkov chain Monte Carlo (MCMC) sampling was used to derive Bayesian statistical inference using 2009 pandemic data from two candidate modelling approaches: (1) a parallel-region (PR) approach, splitting the pandemic into non-interacting epidemics occurring in spatially disjoint regions; and (2) a meta-region (MR) approach, treating the country as a single meta-population with long-range contact rates informed by census data on commuting. Model discrimination is performed through posterior mean deviance statistics alongside more practical considerations. In a real-time context, the use of sequential Monte Carlo (SMC) algorithms to carry out real-time analyses is investigated as an alternative to MCMC using simulated data designed to sternly test both algorithms. SMC-derived analyses are compared with ‘gold-standard’ MCMC-derived inferences in terms of estimation quality and computational burden.ResultsThe PR approach provides a better and more timely fit to the epidemic data. Estimates of pandemic quantities of interest are consistent across approaches and, in the PR approach, across regions (e.g.R0is consistently estimated to be 1.76–1.80, dropping by 43–50% during an over-summer school holiday). A SMC approach was developed, which required some tailoring to tackle a sudden ‘shock’ in the data resulting from a pandemic intervention. This semi-automated SMC algorithm outperforms MCMC, in terms of both precision of estimates and their timely provision. Software implementing all findings has been developed and installed within Public Health England (PHE), with key staff trained in its use.LimitationsThe PR model lacks the predictive power to forecast the spread of infection in the early stages of a pandemic, whereas the MR model may be limited by its dependence on commuting data to describe transmission routes. As demand for resources increases in a severe pandemic, data from general practices and on hospitalisations may become unreliable or biased. The SMC algorithm developed is semi-automated; therefore, some statistical literacy is required to achieve optimal performance.ConclusionsFollowing the objectives, this study found that timely, spatially disaggregate, real-time pandemic inference is feasible, and a system that assumes data as per pandemic preparedness plans has been developed for rapid implementation.Future work recommendationsModelling studies investigating the impact of pandemic interventions (e.g. vaccination and school closure); the utility of alternative data sources (e.g. internet searches) to augment traditional surveillance; and the correct handling of test sensitivity and specificity in serological data, propagating this uncertainty into the real-time modelling.Trial registrationCurrent Controlled Trials ISRCTN40334843.FundingThis project was funded by the National Institute for Health Research (NIHR) Health Technology programme and will be published in full inHealth Technology Assessment; Vol. 21, No. 58. See the NIHR Journals Library website for further project information. Daniela De Angelis was supported by the UK Medical Research Council (Unit Programme Number U105260566) and by PHE. She received funding under the NIHR grant for 10% of her time. The rest of her salary was provided by the MRC and PHE jointly.

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Geolocated Twitter-based population mobility in Victoria, Australia, during the staged COVID-19 restrictions

10.51893/2020.4.sc1 ◽

2020 ◽

Author(s):

Thin Nguyen ◽

◽

Sunil Gupta ◽

Jaishankar Raman ◽

Rinaldo Bellomo ◽

...

Keyword(s):

Real Time ◽

Preliminary Evidence ◽

Population Mobility ◽

Mobility Index ◽

Mobility Data ◽

Twitter Data ◽

The Impact

Using geotagged Twitter data in Victoria, we created a mobility index and studied the changes during the staged restrictions during the coronavirus disease 2019 (COVID-19) pandemic. We describe preliminary evidence that geotagged Twitter data may be used to provide real-time population mobility data and information on the impact of restrictions on such mobility.

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Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing

Nature Communications ◽

10.1038/s41467-021-21776-2 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Kathy Leung ◽

Joseph T. Wu ◽

Gabriel M. Leung

Keyword(s):

Real Time ◽

Disease Transmission ◽

Human Mobility ◽

Population Mobility ◽

Mobility Data ◽

Physical Mixing ◽

Real Time Tracking ◽

Case Data ◽

Transmission Models ◽

New Framework

AbstractDigital proxies of human mobility and physical mixing have been used to monitor viral transmissibility and effectiveness of social distancing interventions in the ongoing COVID-19 pandemic. We develop a new framework that parameterizes disease transmission models with age-specific digital mobility data. By fitting the model to case data in Hong Kong, we are able to accurately track the local effective reproduction number of COVID-19 in near real time (i.e., no longer constrained by the delay of around 9 days between infection and reporting of cases) which is essential for quick assessment of the effectiveness of interventions on reducing transmissibility. Our findings show that accurate nowcast and forecast of COVID-19 epidemics can be obtained by integrating valid digital proxies of physical mixing into conventional epidemic models.

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