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

2020 ◽  
Author(s):  
Thin Nguyen ◽  
◽  
Sunil Gupta ◽  
Jaishankar Raman ◽  
Rinaldo Bellomo ◽  
...  

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.

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Kathleen R. Stevens ◽  
Robert L. Ferrer

Addressing microsystem problems from the frontline holds promise for quality enhancement. Frontline providers are urged to apply quality improvement; yet no systematic approach to problem detection has been tested. This study investigated a self-report approach to detecting operational failures encountered during patient care.Methods. Data were collected from 5 medical-surgical units over 4 weeks. Unit staff documented operational failures on a small distinctive Pocket Card. Frequency distributions for the operational failures in each category were calculated for each hospital overall and disaggregated by shift. Rate of operational failures on each unit was also calculated.Results. A total of 160 nurses participated in this study reporting a total of 2,391 operational failures over 429 shifts. Mean number of problems per shift varied from 4.0 to 8.5 problems with equipment/supply problems being the most commonly reported category.Conclusions. Operational failures are common on medical-surgical clinical units. It is feasible for unit staff to record these failures in real time. Many types of failures were recognized by frontline staff. This study provides preliminary evidence that the Pocket Card is a feasible approach to detecting operational failures in real time. Continued research on methodologies to investigate the impact of operational failures is warranted.


2020 ◽  
Author(s):  
Paiheng Xu ◽  
Mark Dredze ◽  
David A Broniatowski

BACKGROUND Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and “flattens the curve” so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues. OBJECTIVE The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week. METHODS We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic. RESULTS We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic. CONCLUSIONS We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning.


2020 ◽  
Author(s):  
Kathy Leung ◽  
Joseph T Wu ◽  
Gabriel M Leung

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.


PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253071
Author(s):  
Liana R. Woskie ◽  
Jonathan Hennessy ◽  
Valeria Espinosa ◽  
Thomas C. Tsai ◽  
Swapnil Vispute ◽  
...  

Background Social distancing have been widely used to mitigate community spread of SARS-CoV-2. We sought to quantify the impact of COVID-19 social distancing policies across 27 European counties in spring 2020 on population mobility and the subsequent trajectory of disease. Methods We obtained data on national social distancing policies from the Oxford COVID-19 Government Response Tracker and aggregated and anonymized mobility data from Google. We used a pre-post comparison and two linear mixed-effects models to first assess the relationship between implementation of national policies and observed changes in mobility, and then to assess the relationship between changes in mobility and rates of COVID-19 infections in subsequent weeks. Results Compared to a pre-COVID baseline, Spain saw the largest decrease in aggregate population mobility (~70%), as measured by the time spent away from residence, while Sweden saw the smallest decrease (~20%). The largest declines in mobility were associated with mandatory stay-at-home orders, followed by mandatory workplace closures, school closures, and non-mandatory workplace closures. While mandatory shelter-in-place orders were associated with 16.7% less mobility (95% CI: -23.7% to -9.7%), non-mandatory orders were only associated with an 8.4% decrease (95% CI: -14.9% to -1.8%). Large-gathering bans were associated with the smallest change in mobility compared with other policy types. Changes in mobility were in turn associated with changes in COVID-19 case growth. For example, a 10% decrease in time spent away from places of residence was associated with 11.8% (95% CI: 3.8%, 19.1%) fewer new COVID-19 cases. Discussion This comprehensive evaluation across Europe suggests that mandatory stay-at-home orders and workplace closures had the largest impacts on population mobility and subsequent COVID-19 cases at the onset of the pandemic. With a better understanding of policies’ relative performance, countries can more effectively invest in, and target, early nonpharmacological interventions.


2020 ◽  
Author(s):  
Lu Bai ◽  
Haonan Lu ◽  
Hailin Hu ◽  
M. Kumi Smith ◽  
Katherine Harripersaud ◽  
...  

Abstract BackgroundAs China is facing a potential second wave of the epidemic, we reviewed and evaluated the intervention measures implemented in a major metropolitan city, Shenzhen, during the early phase of Wuhan lockdown. MethodsBased on published epidemiological data on COVID-19 and population mobility data from Baidu Qianxi, we constructed a compartmental model to evaluate the impact of work and traffic resumption on the epidemic in Shenzhen in various scenarios.ResultsImported cases account for the majority (58.6%) of the early reported cases in Shenzhen. We demonstrated that with strict inflow population control and a high level of mask usage following work resumption, various resumption schemes resulted in only an insignificant difference in the number of cumulative infections. Shenzhen may experience this second wave of infections approximately two weeks after the traffic resumption if the incidence risk in Hubei is high at the moment of resumption.ConclusionControl of imported cases and extensive use of facial masks were the key for the prevention of the COVID-19 epidemic in Shenzhen during its reopening and work resumption.


2020 ◽  
Author(s):  
Juhwan Oh ◽  
Hwa-Young Lee ◽  
Khuong Quynh Long ◽  
Jeffrey F Markuns ◽  
Chris Bullen ◽  
...  

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.


2018 ◽  
Vol 51 (6) ◽  
pp. 671-688 ◽  
Author(s):  
Kate Duchowny ◽  
Philippa Clarke ◽  
Nancy Ambrose Gallagher ◽  
Robert Adams ◽  
Andrea L. Rosso ◽  
...  

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.


2020 ◽  
Author(s):  
Matia Vanoni ◽  
Martin McKee ◽  
Chris Bonell ◽  
Jan Semenza ◽  
David Stuckler

Abstract Background: Restricting mobility is a central aim for lowering contact rates and preventing COVID-19 transmission. Yet the impact on mobility of different policies of restriction is not well-understood.Methods: Trends were evaluated using Citymapper’s mobility index covering 41 cities worldwide between 2nd and 26th March 2020, expressed as percentages of typical usage periods from 0% as the lowest and 100% as normal. China and India were not covered. Multivariate fixed effects models were used to estimate the association of policies restricting movement on mobility before and after their introduction. Policy restrictions were assessed using the Oxford COVID-19 Government Response Stringency Index as well as measures coding the timing and degree of school and workplace closures, transport restrictions, and cancellation of mass gatherings. Results: Mobility declined in all major cities throughout March. Larger declines were seen in European than Asian cities. The COVID-19 Government Response Stringency Index was strongly associated with declines in mobility (r = -0.75, p<0.001). After adjusting for time-trends, we observed that implementing a mobility restriction to the recommended level was associated with a decline of mobility of 10.0% for school closures (95% CI: 4.36% to 15.7%), 15.0% for workplace closures (95% CI: 10.2% to 19.8%), 7.09% for cancelling public events (95% CI: 1.98% to 12.2%), 18.0% for closing public transport (95% CI: 6.74% to 29.2%), 13.3% for restricting internal movements (95% CI: 8.85% to 17.8%) and 5.30% for international travel controls (95% CI: 1.69 to 8.90). In contrast, as expected, there was no association between population mobility changes and fiscal or monetary measures or emergency healthcare investment.Conclusions: Understanding the effect of public policy on mobility is crucial to slowing and reducing COVID-19 transmission. By using Citymapper’s mobility index, this work provides the first evidence about trends in mobility and the impacts of different policy interventions, suggesting that closure of public transport, workplaces and schools are particularly impactful.


2020 ◽  
Author(s):  
Matia Vanoni ◽  
Martin McKee ◽  
Chris Bonell ◽  
Jan Semenza ◽  
David Stuckler

Abstract Objectives: Restricting mobility is a central aim for lowering contact rates and preventing COVID-19 transmission. Yet the impact on mobility of different non-pharmaceutical countermeasures in the earlier stages of the pandemic is not well-understood.Design: Trends were evaluated using Citymapper’s mobility index covering 2nd to 26th March 2020, expressed as percentages of typical usage periods from 0% as the lowest and 100% as normal. China and India were not covered. Multivariate fixed effects models were used to estimate the association of policies restricting movement on mobility before and after their introduction. Policy restrictions were assessed using the Oxford COVID-19 Government Response Stringency Index as well as measures coding the timing and degree of school and workplace closures, transport restrictions, and cancellation of mass gatherings.Setting: 41 cities worldwideMain outcome measures: Citymapper’s mobility indexResults: Mobility declined in all major cities throughout March. Larger declines were seen in European than Asian cities. The COVID-19 Government Response Stringency Index was strongly associated with declines in mobility (r = -0.75, p<0.001). After adjusting for time-trends, we observed that implementing non-pharmaceutical countermeasures was associated with a decline of mobility of 10.0% for school closures (95% CI: 4.36% to 15.7%), 15.0% for workplace closures (95% CI: 10.2% to 19.8%), 7.09% for cancelling public events (95% CI: 1.98% to 12.2%), 18.0% for closing public transport (95% CI: 6.74% to 29.2%), 13.3% for restricting internal movements (95% CI: 8.85% to 17.8%) and 5.30% for international travel controls (95% CI: 1.69 to 8.90). In contrast, as expected, there was no association between population mobility changes and fiscal or monetary measures or emergency healthcare investment.Conclusions: Understanding the effect of public policy on mobility in the early stages is crucial to slowing and reducing COVID-19 transmission. By using Citymapper’s mobility index, this work provides the first evidence about trends in mobility and the impacts of different policy interventions, suggesting that closure of public transport, workplaces and schools are particularly impactful.Summary boxWhat is already known on this topic?Governments across the global are experimenting with a range of policy interventions to restrict movement in populations. Yet their impact is not well understood. There is an urgent need to understand how alternative policy approaches to restricting movement can impact on population mobility trends. What this study addsOur study finds that policy restrictions markedly reduced population-wide mobility. Closing public transport, workplaces and schools have among the largest associations with mobility declines.


Author(s):  
Yi Xiao ◽  
Jian Peng ◽  
Yuan Chen ◽  
Zheming Yuan

The COVID-19 pandemic caused by SARS-CoV-2 poses a devastating threat to human society in terms of health, economy and lifestyle. Establishing accurate and real-time models to predict and assess the impact of the epidemic on the economy is instructive. We have designed a new model to quantitatively assess the impact of the COVID-19 on the economy of China&rsquo;s mainland. The nominal GDP in the Q1 of 2020 that we predicted for China&rsquo;s mainland with the Baidu Mi-gration Data is RMB 20,785.7 billion, which is less by 3.59% than that in 2019. The estimated val-ue is confirmed roughly by the official report released in April 17, 2020 (RMB 20,650 billion, 6.8% year-on-year declined). Strict control measures during the epidemic have greatly reduced Chi-na's economic activity and had a serious impact on the country's economy. Orderly promotion of population mobility plays a decisive role in economic recovery.


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