scholarly journals Exploring the utility of Google(R) Mobility data during COVID-19 pandemic: A digital epidemiological analysis from India (Preprint)

2021 ◽  
Author(s):  
Kamal Kishore ◽  
Vidushi Jaswal ◽  
Madhur Verma ◽  
Vipin Kaushal
Keyword(s):  
Disease Severity ◽  
Disease Transmission ◽  
Human Mobility ◽  
National Level ◽  
Residential Areas ◽  
Mobility Data ◽  
Application Programming ◽  
Programming Software ◽  
Relative Change

BACKGROUND Association between human mobility and disease transmission for COVID-19 is established, but quantifying the levels of mobility over large geographical areas is difficult. Google released Community Mobility Report (CMR) data collated from mobile devices and gives an idea about the movement of people. OBJECTIVE Therefore, we attempt to explore the use of CMR to assess the role of mobility in spreading COVID-19 infection in India. METHODS An Ecological study analyzed CMR for human mobility. The data were compared for before, during, and after lockdown phases with the reference periods. Another dataset depicting the burden of COVID-19 after deriving various disease severity indicators was derived from a crowd-sourced Application Programming software. The relationship between the two datasets was investigated using Kendall’s tau correlation to depict the correlation between mobility and disease severity. RESULTS At the national level, mobility decreased everywhere except residential areas during the lockdown period, compared to the reference period. Mizoram (minimum cases) depicted a higher relative change in mobility than Maharashtra (maximum cases). Residential mobility negatively correlated with all other measures of mobility. The magnitude of correlations for intra-mobility indicators was comparatively low for the lockdown phase compared to other phases. A high correlation coefficient between epidemiological and mobility indicators is observed for the lockdown and unlock phases compared to the pre-lockdown. CONCLUSIONS We can use mobile-based open-source mobility data to provide the temporal anatomy of social distancing. CMR data depicted an association between mobility and disease severity, and we suggest that this technique supplement future COVID-19 surveillance. CLINICALTRIAL NA

scholarly journals Differential effects of intervention timing on COVID-19 spread in the United States

2020 ◽  
Vol 6 (49) ◽  
pp. eabd6370 ◽  
Author(s):  
Sen Pei ◽  
Sasikiran Kandula ◽  
Jeffrey Shaman
Keyword(s):  
United States ◽  
Disease Transmission ◽  
Human Mobility ◽  
The United States ◽  
Control Measures ◽  
Basic Reproductive Number ◽  
Transmission Model ◽  
Reproductive Number ◽  
Mobility Data ◽  
Nonpharmaceutical Interventions

Assessing the effects of early nonpharmaceutical interventions on coronavirus disease 2019 (COVID-19) spread is crucial for understanding and planning future control measures to combat the pandemic. We use observations of reported infections and deaths, human mobility data, and a metapopulation transmission model to quantify changes in disease transmission rates in U.S. counties from 15 March to 3 May 2020. We find that marked, asynchronous reductions of the basic reproductive number occurred throughout the United States in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same measures been implemented 1 to 2 weeks earlier, substantial cases and deaths could have been averted and that delayed responses to future increased incidence will facilitate a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive control in combatting the COVID-19 pandemic.

scholarly journals The effect of human mobility and control measures on the COVID-19 epidemic in China

2020 ◽  
Author(s):  
Moritz U.G. Kraemer ◽  
Chia-Hung Yang ◽  
Bernardo Gutierrez ◽  
Chieh-Hsi Wu ◽  
Brennan Klein ◽  
...  
Keyword(s):  
Human Mobility ◽  
Control Measures ◽  
Human Populations ◽  
Travel History ◽  
Mobility Data ◽  
Travel Restrictions ◽  
State Interventions ◽  
And Control ◽  
The Impact

AbstractThe ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19.

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

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.

scholarly journals Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data

2018 ◽  
Vol 7 (12) ◽  
pp. 459 ◽  
Author(s):  
Xiaoyi Zhang ◽  
Wenwen Li ◽  
Feng Zhang ◽  
Renyi Liu ◽  
Zhenhong Du
Keyword(s):  
Land Use ◽  
Travel Behavior ◽  
Visual Analytics ◽  
Human Mobility ◽  
Residential Areas ◽  
Trip Purpose ◽  
Mobility Data ◽  
Point Of Interest ◽  
Functional Zones ◽  
Urban Functions

Human mobility data have become an essential means to study travel behavior and trip purpose to identify urban functional zones, which portray land use at a finer granularity and offer insights for problems such as business site selection, urban design, and planning. However, very few works have leveraged public bicycle-sharing data, which provides a useful feature in depicting people’s short-trip transportation within a city, in the studies of urban functions and structure. Because of its convenience, bicycle usage tends to be close to point-of-interest (POI) features, the combination of which will no doubt enhance the understanding of the trip purpose for characterizing different functional zones. In our study, we propose a data-driven approach that uses station-based public bicycle rental records together with POI data in Hangzhou, China to identify urban functional zones. Topic modelling, unsupervised clustering, and visual analytics are employed to delineate the function matrix, aggregate functional zones, and present mixed land uses. Our result shows that business areas, industrial areas, and residential areas can be well detected, which validates the effectiveness of data generated from this new transportation mode. The word cloud of function labels reveals the mixed land use of different types of urban functions and improves the understanding of city structures.

scholarly journals Human mobility and COVID-19 transmission: a systematic review and future directions

2021 ◽  
Author(s):  
Mengxi Zhang ◽  
Siqin Wang ◽  
Tao Hu ◽  
Xiaokang Fu ◽  
Xiaoyue Wang ◽  
...  
Keyword(s):  
Systematic Review ◽  
Statistical Models ◽  
Human Mobility ◽  
Rapid Change ◽  
Mobility Control ◽  
Mobility Data ◽  
Prisma Statement ◽  
Meta Analyses ◽  
The Relationship

AbstractWithout a widely distributed vaccine, controlling human mobility has been identified and promoted as the primary strategy to mitigate the transmission of COVID-19. Many studies have reported the relationship between human mobility and COVID-19 transmission by utilizing the spatial-temporal information of mobility data from various sources. To better understand the role of human mobility in the pandemic, we conducted a systematic review of articles that measure the relationship between human mobility and COVID-19 in terms of their data sources, statistical models, and key findings. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we selected 47 articles from Web of Science Core Collection up to September 2020. Restricting human mobility reduced the transmission of COVID-19 spatially, although the effectiveness and stringency of policy implementation vary temporally and spatially across different stages of the pandemic. We call for prompt and sustainable measures to control the pandemic. We also recommend researchers 1) to enhance multi-disciplinary collaboration; 2) to adjust the implementation and stringency of mobility-control policies in corresponding to the rapid change of the pandemic; 3) to improve statistical models used in analyzing, simulating, and predicting the transmission of the disease; and 4) to enrich the source of mobility data to ensure data accuracy and suability.

scholarly journals Multiple drivers of the COVID-19 spread: role of climate, international mobility, and region-specific conditions

2020 ◽  
Author(s):  
Yasuhiro Kubota ◽  
Takayuki Shiono ◽  
Buntarou Kusumoto ◽  
Junichi Fujinuma
Keyword(s):  
Disease Transmission ◽  
Human Mobility ◽  
Explanatory Power ◽  
Host Susceptibility ◽  
Transmission Risk ◽  
International Mobility ◽  
Simple Regression Model ◽  
Host Mobility ◽  
Specific Susceptibility

AbstractThe novel Coronavirus Disease 2019 (COVID-19) has spread quickly across the globe. Here, we evaluated the role of climate (temperature and precipitation), region-specific susceptibility (BCG vaccination, malaria infection, and elderly population) and international traveller population (human mobility) in shaping the geographical patterns of COVID-19 cases across 1,055 countries/regions, and examined the sequential shift of multiple drivers of the accumulated cases from December, 2019 to April 12, 2020. The accumulated numbers of COVID-19 cases (per 1 million population) were well explained by a simple regression model. The explanatory power (R2) of the model increased up to > 70% in April 2020 as the COVID-19 spread progressed. Climate, host mobility, and host susceptibility largely explained the variance of the COVID-19 cases (per 1 million population), and their explanatory power improved as the pandemic progressed; the relative importance of host mobility and host susceptibility have been greater than that of climate. The number of days from outbreak onset showed greater explanatory power in the earlier stages of COVID-19 spread but rapidly lost its influence. Our findings demonstrate that the COVID-19 pandemic is deterministically driven by climate suitability, cross-border human mobility, and region-specific susceptibility. The present distribution of COVID-19 cases has not reached an equilibrium and is changing daily, especially in the Southern Hemisphere. Nevertheless, the present results, based on mapping the spread of COVID-19 and identifying multiple drivers of this outbreak trajectory, may contribute to a better understanding of the COVID-19 disease transmission risk and the measures against long-term epidemic.

scholarly journals Measles outbreak risk in Pakistan: exploring the potential of combining vaccination coverage and incidence data with novel data-streams to strengthen control

2018 ◽  
Vol 146 (12) ◽  
pp. 1575-1583 ◽  
Author(s):  
Amy Wesolowski ◽  
Amy Winter ◽  
Andrew J. Tatem ◽  
Taimur Qureshi ◽  
Kenth Engø-Monsen ◽  
...  
Keyword(s):  
Vaccination Coverage ◽  
Data Streams ◽  
Human Mobility ◽  
Time Frame ◽  
Control Programs ◽  
Incidence Data ◽  
Mobility Data ◽  
Measles Outbreak ◽  
Measles Outbreaks

AbstractAlthough measles incidence has reached historic lows in many parts of the world, the disease still causes substantial morbidity globally. Even where control programs have succeeded in driving measles locally extinct, unless vaccination coverage is maintained at extremely high levels, susceptible numbers may increase sufficiently to spark large outbreaks. Human mobility will drive potentially infectious contacts and interact with the landscape of susceptibility to determine the pattern of measles outbreaks. These interactions have proved difficult to characterise empirically. We explore the degree to which new sources of data combined with existing public health data can be used to evaluate the landscape of immunity and the role of spatial movement for measles introductions by retrospectively evaluating our ability to predict measles outbreaks in vaccinated populations. Using inferred spatial patterns of accumulation of susceptible individuals and travel data, we predicted the timing of epidemics in each district of Pakistan during a large measles outbreak in 2012–2013 with over 30 000 reported cases. We combined these data with mobility data extracted from over 40 million mobile phone subscribers during the same time frame in the country to quantify the role of connectivity in the spread of measles. We investigate how different approaches could contribute to targeting vaccination efforts to reach districts before outbreaks started. While some prediction was possible, accuracy was low and we discuss key uncertainties linked to existing data streams that impede such inference and detail what data might be necessary to robustly infer timing of epidemics.

scholarly journals Relationships between Local Green Space and Human Mobility Patterns during COVID-19 for Maryland and California, USA

2020 ◽  
Vol 12 (22) ◽  
pp. 9401
Author(s):  
Seulkee Heo ◽  
Chris C. Lim ◽  
Michelle L. Bell
Keyword(s):  
Disease Transmission ◽  
Vegetation Index ◽  
Green Space ◽  
Early Stage ◽  
Human Mobility ◽  
P Value ◽  
State Parks ◽  
Mobility Patterns ◽  
Mobility Data ◽  
State And Local

Human mobility is a significant factor for disease transmission. Little is known about how the environment influences mobility during a pandemic. The aim of this study was to investigate an effect of green space on mobility reductions during the early stage of the COVID-19 pandemic in Maryland and California, USA. For 230 minor civil divisions (MCD) in Maryland and 341 census county divisions (CCD) in California, we obtained mobility data from Facebook Data for Good aggregating information of people using the Facebook app on their mobile phones with location history active. The users’ movement between two locations was used to calculate the number of users that traveled into an MCD (or CCD) for each day in the daytime hours between 11 March and 26 April 2020. Each MCD’s (CCD’s) vegetation level was estimated as the average Enhanced Vegetation Index (EVI) level for 1 January through 31 March 2020. We calculated the number of state and local parks, food retail establishments, and hospitals for each MCD (CCD). Results showed that the daily percent changes in the number of travels declined during the study period. This mobility reduction was significantly lower in Maryland MCDs with state parks (p-value = 0.045), in California CCDs with local-scale parks (p-value = 0.048). EVI showed no association with mobility in both states. This finding has implications for the potential impacts of green space on mobility under an outbreak. Future studies are needed to explore these findings and to investigate changes in health effects of green space during a pandemic.

scholarly journals Insights into population behavior during the COVID-19 pandemic from cell phone mobility data and manifold learning

2021 ◽  
Vol 1 (9) ◽  
pp. 588-597 ◽  
Author(s):  
Roman Levin ◽  
Dennis L. Chao ◽  
Edward A. Wenger ◽  
Joshua L. Proctor
Keyword(s):  
Manifold Learning ◽  
Disease Transmission ◽  
Urban Areas ◽  
Cell Phone ◽  
Human Mobility ◽  
Measurement Instrument ◽  
Geographic Scale ◽  
Mobility Data ◽  
And Behavior ◽  
At Home

AbstractUnderstanding the complex interplay between human behavior, disease transmission and non-pharmaceutical interventions during the COVID-19 pandemic could provide valuable insights with which to focus future public health efforts. Cell phone mobility data offer a modern measurement instrument to investigate human mobility and behavior at an unprecedented scale. We investigate aggregated and anonymized mobility data, which measure how populations at the census-block-group geographic scale stayed at home in California, Georgia, Texas and Washington from the beginning of the pandemic. Using manifold learning techniques, we show that a low-dimensional embedding enables the identification of patterns of mobility behavior that align with stay-at-home orders, correlate with socioeconomic factors, cluster geographically, reveal subpopulations that probably migrated out of urban areas and, importantly, link to COVID-19 case counts. The analysis and approach provide local epidemiologists a framework for interpreting mobility data and behavior to inform policy makers’ decision-making aimed at curbing the spread of COVID-19.

scholarly journals Cell phone mobility data and manifold learning: Insights into population behavior during the COVID-19 pandemic

2020 ◽  
Author(s):  
Roman Levin ◽  
Dennis L. Chao ◽  
Edward A. Wenger ◽  
Joshua L. Proctor
Keyword(s):  
Manifold Learning ◽  
Disease Transmission ◽  
Urban Areas ◽  
Cell Phone ◽  
Human Mobility ◽  
The United States ◽  
Geographic Scale ◽  
Mobility Data ◽  
And Behavior ◽  
At Home

AbstractAs COVID-19 cases resurge in the United States, understanding the complex interplay between human behavior, disease transmission, and non-pharmaceutical interventions during the pandemic could provide valuable insights to focus future public health efforts. Cell-phone mobility data offers a modern measurement instrument to investigate human mobility and behavior at an unprecedented scale. We investigate mobility data collected, aggregated, and anonymized by SafeGraph Inc. which measures how populations at the census-block-group geographic scale stayed at home in California, Georgia, Texas, and Washington since the beginning of the pandemic. Using manifold learning techniques, we find patterns of mobility behavior that align with stay-at-home orders, correlate with socioeconomic factors, cluster geographically, and reveal sub-populations that likely migrated out of urban areas. The analysis and approach provides policy makers a framework for interpreting mobility data and behavior to inform actions aimed at curbing the spread of COVID-19.

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