scholarly journals Practical geospatial and sociodemographic predictors of human mobility

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Corrine W. Ruktanonchai ◽  
Shengjie Lai ◽  
Chigozie E. Utazi ◽  
Alex D. Cunningham ◽  
Patrycja Koper ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Human Mobility ◽  
Human Movement ◽  
Spatiotemporal Analysis ◽  
International Travel ◽  
Sensitive Information ◽  
International Mobility ◽  
Mobility Patterns ◽  
Middle Income ◽  
And Control

AbstractUnderstanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

scholarly journals Practical geospatial and sociodemographic predictors of human mobility

2021 ◽  
Author(s):  
Corrine Ruktanonchai ◽  
Shengjie Lai ◽  
Chigozie Utazi ◽  
Alexander Cunningham ◽  
Patrycja Koper ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Human Mobility ◽  
Human Movement ◽  
Spatiotemporal Analysis ◽  
International Travel ◽  
Sensitive Information ◽  
International Mobility ◽  
Mobility Patterns ◽  
Middle Income ◽  
And Control

Abstract Background: Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe seasonal subnational mobility in Kenya, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings. Methods: To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates important to predicting human movement patterns, while accounting for spatial and temporal autocorrelations. Results: Mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, respectively, across both years. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly predicted mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. Conclusions: These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates and predictors of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

scholarly journals Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Meng-Chun Chang ◽  
Rebecca Kahn ◽  
Yu-An Li ◽  
Cheng-Sheng Lee ◽  
Caroline O. Buckee ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Initial Conditions ◽  
Human Mobility ◽  
Human Movement ◽  
Disease Spread ◽  
Spatial Spread ◽  
Movement Data ◽  
Intercity Travel ◽  
Travel Restrictions ◽  
The Impact

Abstract Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

scholarly journals Domestic and international mobility trends in the United Kingdom during the COVID-19 pandemic: an analysis of facebook data

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Harry E. R. Shepherd ◽  
Florence S. Atherden ◽  
Ho Man Theophilus Chan ◽  
Alexandra Loveridge ◽  
Andrew J. Tatem
Keyword(s):  
United Kingdom ◽  
Spatial Scales ◽  
International Travel ◽  
Population Mobility ◽  
International Mobility ◽  
Mobility Patterns ◽  
Social Distancing ◽  
The United Kingdom ◽  
The Uk ◽  
The Impact

Abstract Background Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which resulted in changes to mobility across different regions. An understanding of how these policies impacted travel patterns over time and at different spatial scales is important for designing effective strategies, future pandemic planning and in providing broader insights on the population geography of the country. Crowd level data on mobile phone usage can be used as a proxy for population mobility patterns and provide a way of quantifying in near-real time the impact of social distancing measures on changes in mobility. Methods Here we explore patterns of change in densities, domestic and international flows and co-location of Facebook users in the UK from March 2020 to March 2021. Results We find substantial heterogeneities across time and region, with large changes observed compared to pre-pademic patterns. The impacts of periods of lockdown on distances travelled and flow volumes are evident, with each showing variations, but some significant reductions in co-location rates. Clear differences in multiple metrics of mobility are seen in central London compared to the rest of the UK, with each of Scotland, Wales and Northern Ireland showing significant deviations from England at times. Moreover, the impacts of rapid changes in rules on international travel to and from the UK are seen in substantial fluctuations in traveller volumes by destination. Conclusions While questions remain about the representativeness of the Facebook data, previous studies have shown strong correspondence with census-based data and alternative mobility measures, suggesting that findings here are valuable for guiding strategies.

scholarly journals Human mobility and poverty as key drivers of COVID-19 transmission and control

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Matan Yechezkel ◽  
Amit Weiss ◽  
Idan Rejwan ◽  
Edan Shahmoon ◽  
Shachaf Ben-Gal ◽  
...  
Keyword(s):  
Human Mobility ◽  
The Elderly ◽  
School Closure ◽  
Transmission Model ◽  
Mobility Patterns ◽  
Mortality And Morbidity ◽  
Effective Strategies ◽  
Mobility Data ◽  
Key Drivers ◽  
And Control

Abstract Background Applying heavy nationwide restrictions is a powerful method to curtail COVID-19 transmission but poses a significant humanitarian and economic crisis. Thus, it is essential to improve our understanding of COVID-19 transmission, and develop more focused and effective strategies. As human mobility drives transmission, data from cellphone devices can be utilized to achieve these goals. Methods We analyzed aggregated and anonymized mobility data from the cell phone devices of> 3 million users between February 1, 2020, to May 16, 2020 — in which several movement restrictions were applied and lifted in Israel. We integrated these mobility patterns into age-, risk- and region-structured transmission model. Calibrated to coronavirus incidence in 250 regions covering Israel, we evaluated the efficacy and effectiveness in decreasing morbidity and mortality of applying localized and temporal lockdowns (stay-at-home order). Results Poorer regions exhibited lower and slower compliance with the restrictions. Our transmission model further indicated that individuals from impoverished areas were associated with high transmission rates. Considering a horizon of 1–3 years, we found that to reduce COVID-19 mortality, school closure has an adverse effect, while interventions focusing on the elderly are the most efficient. We also found that applying localized and temporal lockdowns during regional outbreaks reduces the overall mortality and morbidity compared to nationwide lockdowns. These trends were consistent across vast ranges of epidemiological parameters, and potential seasonal forcing. Conclusions More resources should be devoted to helping impoverished regions. Utilizing cellphone data despite being anonymized and aggregated can help policymakers worldwide identify hotspots and apply designated strategies against future COVID-19 outbreaks.

scholarly journals Understanding Human Mobility Within Metro Networks – Flow Distribution and Community Detection

2021 ◽  
Vol 33 (3) ◽  
pp. 413-423
Author(s):  
Zuoxian Gan ◽  
Jing Liang
Keyword(s):  
Community Detection ◽  
Dominant Role ◽  
Human Mobility ◽  
Flow Distribution ◽  
Human Movement ◽  
Mobility Patterns ◽  
Normalized Mutual Information ◽  
Smart Card Data ◽  
Potential Applications ◽  
Time Periods

In this paper, smart card data collected from the Nanjing Metro over 2-hour time periods are used to characterize within- and between-day human mobility patterns within the metro network. Results show that the OD (origin to destination) flows can be characterized well by shifted power law distributions with similar exponents around 2, which reflects the fact that a few OD pairs in the system play a dominant role and undertake disproportionately large OD flow distribution. The different exponents signify heterogeneous human movement in within- and between-day ranges. In addition, we analyze the metro community structures over different time periods based on the community detection method using random walks to visualize and understand passenger movement from a spatial perspective. Normalized mutual information is used to compare community partitions over different time-intervals. The results show that the properties of human mobility during different time periods have a similar rhythm, although some nuances exist, and the community structure is usually divided according to the line distribution. This empirical study provides spatiotemporal insights into understanding urban human mobility and some potential applications for transportation management.

Trace- and Social-Based Modeling of Human Mobility Patterns

2016 ◽  
pp. 318-354
Author(s):  
Ali Diab ◽  
Andreas Mitschele-Thiel
Keyword(s):  
Communication Networks ◽  
Mobile Communication ◽  
Human Mobility ◽  
Movement Patterns ◽  
Human Movement ◽  
Mobility Patterns ◽  
Community Based ◽  
Human Movements ◽  
Mobile Communication Networks ◽  
Synthetic Models

The 5th Generation (5G) of mobile communication networks is being developed to address the demands and business contexts of 2020 and beyond. Its vision is to enable a fully mobile and connected society and also to trigger socio-economic transformations in ways eventually unimagined today. This means that the physical world to be covered with planned 5G networks including communication networks, humans and objects is becoming a type of an information system. So as to improve the experience of individuals, communities, societies, etc. within such systems, a thorough comprehension of intelligence processes responsible of generating, handling and controlling those data is fundamental. One of the major aspects in this context and also the focus of this chapter is the development of novel methods to model human mobility patterns, which have crucial role in forthcoming communication technologies. Human mobility patterns models can be categorized into synthetic, trace-based and community-based models. Synthetic models are largely preferred and widely applied to simulate mobile communication networks. They try to capture the patterns of human movements by means of a set of equations. These models are traceable, however, not capable of generating realistic mobility models. The key idea of trace-based models is the exploitation of available measurements and traces achieved in deployed systems to reproduce synthetic traces characterized by the same statistical properties of real traces. A main drawback of trace-based modeling of human patterns is the tight coupling between the trace-based model and the traces collected, the network topology deployed and even the geographic location, where the traces were collected. This is why the results of various trace-based models deviate clearly from each other. Sure, this prohibits the generalization of trace-based models. When one also considers that the traces themselves are rarely available, one can understand why synthetic models are preferred over trace-based ones. Community-based modeling of human movements depends on the fact stating that mobile devices are usually carried by humans, which implies that movement patterns of such devices are necessarily related to human decisions and socialization behaviors. So, human movement routines heavily affect the overall movement patterns resulting. One of the major contributions in this context is the application of social networks theory to generate more realistic human movement patterns. The chapter highlights the state of art and provides a comprehensive investigation of current research efforts in the field of trace- and social-based modeling of human mobility patterns. It reviews well-known approaches going through their pros and cons. In addition, the chapter studies an aspect that heavily relates to human mobility patterns, namely the prediction of future locations of users.

scholarly journals Multisectoral Approach to Address Chikungunya Outbreaks Driven by Human Mobility: A Systematic Review and Meta-Analysis

2020 ◽  
Vol 222 (Supplement_8) ◽  
pp. S709-S716
Author(s):  
Rashad Abdul-Ghani ◽  
Florence Fouque ◽  
Mohammed A K Mahdy ◽  
Qingxia Zhong ◽  
Samira M A Al-Eryani ◽  
...  
Keyword(s):  
Systematic Review ◽  
Human Mobility ◽  
Meta Analysis ◽  
Health Sector ◽  
Mobility Patterns ◽  
Migrant Populations ◽  
Virus Genotypes ◽  
High Level ◽  
And Control

Abstract Background The role of human mobility in the epidemiology of emerging Aedes-transmitted viral diseases is recognized but not fully understood. The objective of this systematic review and meta-analysis was to examine how human mobility patterns are driving chikungunya outbreaks. Methods Literature was systematically reviewed for studies on chikungunya prevalence in countries/territories with high-level evidence of human mobility-driven outbreaks, based on: (1) emergence of chikungunya outbreaks with epidemic chikungunya virus genotypes among displaced/migrant populations and their hosting communities; and (2) identification of imported index case(s) with epidemic genotypes phylogenetically related to the genotypes circulating during emerging or subsequent outbreaks. Results The meta-analysis of extracted prevalence data revealed that a large proportion of the population in countries/territories afflicted by outbreaks is still at risk of infection during future outbreaks. On the other hand, approximately one-half of suspected chikungunya cases could be infected with other co-circulating acute febrile illnesses. Conclusions We discussed in this paper how human mobility-driven chikungunya outbreaks can be addressed, and how the involvement of several sectors in addition to the health sector in multisectoral approaches (MSAs) is important for prevention and control of chikungunya and other Aedes-transmitted arboviral outbreaks.

scholarly journals Monitoring the COVID-19 epidemic with nationwide telecommunication data

2021 ◽  
Vol 118 (26) ◽  
pp. e2100664118
Author(s):  
Joel Persson ◽  
Jurriaan F. Parie ◽  
Stefan Feuerriegel
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Human Movement ◽  
Spatiotemporal Analysis ◽  
Decision Makers ◽  
The Novel ◽  
School Closures ◽  
Time Period ◽  
Policy Measures ◽  
Novel Coronavirus

In response to the novel coronavirus disease (COVID-19), governments have introduced severe policy measures with substantial effects on human behavior. Here, we perform a large-scale, spatiotemporal analysis of human mobility during the COVID-19 epidemic. We derive human mobility from anonymized, aggregated telecommunication data in a nationwide setting (Switzerland; 10 February to 26 April 2020), consisting of ∼1.5 billion trips. In comparison to the same time period from 2019, human movement in Switzerland dropped by 49.1%. The strongest reduction is linked to bans on gatherings of more than five people, which are estimated to have decreased mobility by 24.9%, followed by venue closures (stores, restaurants, and bars) and school closures. As such, human mobility at a given day predicts reported cases 7 to 13 d ahead. A 1% reduction in human mobility predicts a 0.88 to 1.11% reduction in daily reported COVID-19 cases. When managing epidemics, monitoring human mobility via telecommunication data can support public decision makers in two ways. First, it helps in assessing policy impact; second, it provides a scalable tool for near real-time epidemic surveillance, thereby enabling evidence-based policies.

scholarly journals Intestinal Schistosomiasis and Giardiasis Co-Infection in Sub-Saharan Africa: Can a One Health Approach Improve Control of Each Waterborne Parasite Simultaneously?

2020 ◽  
Author(s):  
John Archer ◽  
Lisa O'Halloran ◽  
Hajri Al-Shehri ◽  
Shannan Summers ◽  
Tapan Bhattacharyya ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Large Scale ◽  
One Health ◽  
Sub Saharan Africa ◽  
Middle Income ◽  
Intestinal Schistosomiasis ◽  
Sub Saharan ◽  
The Many ◽  
And Control ◽  
Scale Control

Both intestinal schistosomiasis and giardiasis are co-endemic throughout many areas of sub-Saharan Africa, significantly impacting the health of millions of children within endemic areas. While giardiasis is not considered a neglected tropical disease, intestinal schistosomiasis is formally grouped within the NTD umbrella and, as such, receives significant advocacy and financial support for large-scale control, annually. Given the many epidemiological similarities between intestinal schistosomiasis and giardiasis, in this review, we critically discuss why disease surveillance and control activities for giardiasis are largely absent within low- and middle-income countries. With advances in new methods of parasite diagnostics and provision of existing anti-parasitic medications, better management of intestinal schistosomiasis and giardiasis co-infection could, not only be better understood but also, more effectively controlled. In this light, we appraise the suitability of a One Health approach for intestinal schistosomiasis, for if adopted more broadly, could also pave a way forward for more inclusive public health actions against giardiasis.

scholarly journals Characterizing human mobility patterns in rural settings of sub-Saharan Africa

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Hannah R Meredith ◽  
John R Giles ◽  
Javier Perez-Saez ◽  
Théophile Mande ◽  
Andrea Rinaldo ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Spatial Interaction ◽  
Human Mobility ◽  
Sub Saharan Africa ◽  
Mobile Phone Data ◽  
Mobility Patterns ◽  
African Countries ◽  
Middle Income ◽  
Rural Settings ◽  
Sub Saharan

Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.

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