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Belligoni S, Stevens KA, Hasan S, Yu H. Privacy and security concerns with passively collected location data for digital contact tracing among U.S. college students. PLoS One 2023; 18:e0294419. [PMID: 37992048 PMCID: PMC10664924 DOI: 10.1371/journal.pone.0294419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 10/31/2023] [Indexed: 11/24/2023] Open
Abstract
People continue to use technology in new ways, and how governments harness digital information should consider privacy and security concerns. During COVID19, numerous countries deployed digital contact tracing that collect location data from user's smartphones. However, these apps had low adoption rates and faced opposition. We launched an interdisciplinary study to evaluate smartphone location data concerns among college students in the US. Using interviews and a large survey, we find that college students have higher concerns regarding privacy, and place greater trust in local government with their location data. We discuss policy recommendations for implementing improved contact tracing efforts.
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Affiliation(s)
- Sara Belligoni
- Department of Human Ecology, Rutgers University, New Brunswick, New Jersey, United States of America
| | - Kelly A. Stevens
- School of Public Administration, University of Central Florida, Orlando, Florida, United States of America
| | - Samiul Hasan
- Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, Florida, United States of America
| | - Haofei Yu
- Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, Florida, United States of America
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Gligorić K, Kamath C, Weiss DJ, Bavadekar S, Liu Y, Shekel T, Schulman K, Gabrilovich E. Revealed versus potential spatial accessibility of healthcare and changing patterns during the COVID-19 pandemic. COMMUNICATIONS MEDICINE 2023; 3:157. [PMID: 37923904 PMCID: PMC10624905 DOI: 10.1038/s43856-023-00384-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 10/12/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND Timely access to healthcare is essential but measuring access is challenging. Prior research focused on analyzing potential travel times to healthcare under optimal mobility scenarios that do not incorporate direct observations of human mobility, potentially underestimating the barriers to receiving care for many populations. METHODS We introduce an approach for measuring accessibility by utilizing travel times to healthcare facilities from aggregated and anonymized smartphone Location History data. We measure these revealed travel times to healthcare facilities in over 100 countries and juxtapose our findings with potential (optimal) travel times estimated using Google Maps directions. We then quantify changes in revealed accessibility associated with the COVID-19 pandemic. RESULTS We find that revealed travel time differs substantially from potential travel time; in all but 4 countries this difference exceeds 30 minutes, and in 49 countries it exceeds 60 minutes. Substantial variation in revealed healthcare accessibility is observed and correlates with life expectancy (⍴=-0.70) and infant mortality (⍴=0.59), with this association remaining significant after adjusting for potential accessibility and wealth. The COVID-19 pandemic altered the patterns of healthcare access, especially for populations dependent on public transportation. CONCLUSIONS Our metrics based on empirical data indicate that revealed travel times exceed potential travel times in many regions. During COVID-19, inequitable accessibility was exacerbated. In conjunction with other relevant data, these findings provide a resource to help public health policymakers identify underserved populations and promote health equity by formulating policies and directing resources towards areas and populations most in need.
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Affiliation(s)
- Kristina Gligorić
- Google Research, Mountain View, CA, USA
- Computer Science Department, Stanford University, Stanford, CA, USA
| | | | - Daniel J Weiss
- Telethon Kids Institute, Perth Children's Hospital, Nedlands, WA, Australia
- Faculty of Health Sciences, Curtin University, Bentley, WA, Australia
| | | | - Yun Liu
- Google Research, Mountain View, CA, USA
| | | | - Kevin Schulman
- Clinical Excellence Research Center, School of Medicine and Graduate School of Business, Stanford University, Stanford, CA, USA
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Minora U, Iacus SM, Batista e Silva F, Sermi F, Spyratos S. Nowcasting tourist nights spent using innovative human mobility data. PLoS One 2023; 18:e0287063. [PMID: 37831658 PMCID: PMC10575538 DOI: 10.1371/journal.pone.0287063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 05/26/2023] [Indexed: 10/15/2023] Open
Abstract
The publication of tourism statistics often does not keep up with the highly dynamic tourism demand trends, especially critical during crises. Alternative data sources such as digital traces and web searches represent an important source to potentially fill this gap, since they are generally timely, and available at detailed spatial scale. In this study we explore the potential of human mobility data from the Google Community Mobility Reports to nowcast the number of monthly nights spent at sub-national scale across 11 European countries in 2020, 2021, and the first half of 2022. Using a machine learning implementation, we found that this novel data source is able to predict the tourism demand with high accuracy, and we compare its potential in the tourism domain to web search and mobile phone data. This result paves the way for a more frequent and timely production of tourism statistics by researchers and statistical entities, and their usage to support tourism monitoring and management, although privacy and surveillance concerns still hinder an actual data innovation transition.
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Affiliation(s)
- Umberto Minora
- European Commission, Joint Research Centre, Ispra, Italy
| | - Stefano Maria Iacus
- Institute for Quantitative Social Sciences, Harvard University, Cambridge, MA, United States of America
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Ellis-Soto D, Oliver RY, Brum-Bastos V, Demšar U, Jesmer B, Long JA, Cagnacci F, Ossi F, Queiroz N, Hindell M, Kays R, Loretto MC, Mueller T, Patchett R, Sims DW, Tucker MA, Ropert-Coudert Y, Rutz C, Jetz W. A vision for incorporating human mobility in the study of human-wildlife interactions. Nat Ecol Evol 2023; 7:1362-1372. [PMID: 37550509 DOI: 10.1038/s41559-023-02125-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 06/19/2023] [Indexed: 08/09/2023]
Abstract
As human activities increasingly shape land- and seascapes, understanding human-wildlife interactions is imperative for preserving biodiversity. Habitats are impacted not only by static modifications, such as roads, buildings and other infrastructure, but also by the dynamic movement of people and their vehicles occurring over shorter time scales. Although there is increasing realization that both components of human activity substantially affect wildlife, capturing more dynamic processes in ecological studies has proved challenging. Here we propose a conceptual framework for developing a 'dynamic human footprint' that explicitly incorporates human mobility, providing a key link between anthropogenic stressors and ecological impacts across spatiotemporal scales. Specifically, the dynamic human footprint integrates a range of metrics to fully acknowledge the time-varying nature of human activities and to enable scale-appropriate assessments of their impacts on wildlife behaviour, demography and distributions. We review existing terrestrial and marine human-mobility data products and provide a roadmap for how these could be integrated and extended to enable more comprehensive analyses of human impacts on biodiversity in the Anthropocene.
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Affiliation(s)
- Diego Ellis-Soto
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA.
- Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA.
| | - Ruth Y Oliver
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA.
- Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA.
- Bren School of Environmental Science and Management, University of California, Santa Barbara, CA, USA.
| | - Vanessa Brum-Bastos
- School of Geography and Sustainable Development, University of St Andrews, St Andrews, UK
- Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental Sciences, Wroclaw, Poland
- School of Earth and Environment, University of Canterbury, Christchurch, New Zealand
| | - Urška Demšar
- School of Geography and Sustainable Development, University of St Andrews, St Andrews, UK
| | - Brett Jesmer
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
- Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA
- Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, USA
| | - Jed A Long
- Department of Geography & Environment, Centre for Animals on the Move, Western University, London, Ontario, Canada
| | - Francesca Cagnacci
- Animal Ecology Unit, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy
- National Biodiversity Future Center S.C.A.R.L., Palermo, Italy
| | - Federico Ossi
- Animal Ecology Unit, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy
| | - Nuno Queiroz
- CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado/BIOPOLIS Program in Genomics, Biodiversity and Land Planning, Universidade do Porto, Vairão, Portugal
- Marine Biological Association, Plymouth, UK
| | - Mark Hindell
- Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia
- Antarctic Climate and Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, Tasmania, Australia
| | - Roland Kays
- North Carolina Museum of Natural Sciences, Raleigh, NC, USA
- Dept Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA
| | - Matthias-Claudio Loretto
- Ecosystem Dynamics and Forest Management Group, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- Berchtesgaden National Park, Berchtesgaden, Germany
- Department of Migration, Max-Planck Institute of Animal Behavior, Radolfzell, Germany
| | - Thomas Mueller
- Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt (Main), Germany
- Department of Biological Sciences, Goethe University, Frankfurt (Main), Germany
| | - Robert Patchett
- Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, UK
| | - David W Sims
- Marine Biological Association, Plymouth, UK
- Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, UK
- Centre for Biological Sciences, University of Southampton, Southampton, UK
| | - Marlee A Tucker
- Department of Environmental Science, Radboud Institute for Biological and Environmental Sciences, Radboud University, Nijmegen, The Netherlands
| | - Yan Ropert-Coudert
- Centre d'Etudes Biologiques de Chizé, La Rochelle Université - CNRS, Villiers en Bois, France
| | - Christian Rutz
- Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, UK
| | - Walter Jetz
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
- Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA
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Fofana AM, Moultrie H, Scott L, Jacobson KR, Shapiro AN, Dor G, Crankshaw B, Silva PD, Jenkins HE, Bor J, Stevens WS. Cross-municipality migration and spread of tuberculosis in South Africa. Sci Rep 2023; 13:2674. [PMID: 36792792 PMCID: PMC9930008 DOI: 10.1038/s41598-023-29804-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
Human migration facilitates the spread of infectious disease. However, little is known about the contribution of migration to the spread of tuberculosis in South Africa. We analyzed longitudinal data on all tuberculosis test results recorded by South Africa's National Health Laboratory Service (NHLS), January 2011-July 2017, alongside municipality-level migration flows estimated from the 2016 South African Community Survey. We first assessed migration patterns in people with laboratory-diagnosed tuberculosis and analyzed demographic predictors. We then quantified the impact of cross-municipality migration on tuberculosis incidence in municipality-level regression models. The NHLS database included 921,888 patients with multiple clinic visits with TB tests. Of these, 147,513 (16%) had tests in different municipalities. The median (IQR) distance travelled was 304 (163 to 536) km. Migration was most common at ages 20-39 years and rates were similar for men and women. In municipality-level regression models, each 1% increase in migration-adjusted tuberculosis prevalence was associated with a 0.47% (95% CI: 0.03% to 0.90%) increase in the incidence of drug-susceptible tuberculosis two years later, even after controlling for baseline prevalence. Similar results were found for rifampicin-resistant tuberculosis. Accounting for migration improved our ability to predict future incidence of tuberculosis.
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Affiliation(s)
- Abdou M Fofana
- Institute for Health System Innovation & Policy, Boston University, Questrom School of Business, Boston, USA.
- Boston University School of Public Health, Boston, USA.
| | - Harry Moultrie
- Centre for Tuberculosis, National Institute for Communicable Diseases, a division of the National Health Laboratory Services, Johannesburg, South Africa
| | - Lesley Scott
- Wits Diagnostic Innovation Hub, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Karen R Jacobson
- Section of Infectious Diseases, Boston University School of Medicine and Boston Medical Center, Boston, USA
| | | | - Graeme Dor
- Wits Diagnostic Innovation Hub, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Beth Crankshaw
- Centre for Tuberculosis, National Institute for Communicable Diseases, a division of the National Health Laboratory Services, Johannesburg, South Africa
| | - Pedro Da Silva
- National Health Laboratory Service, Johannesburg, South Africa
| | | | - Jacob Bor
- Health Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Boston University School of Public Health, Boston, USA
| | - Wendy S Stevens
- Wits Diagnostic Innovation Hub, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- National Health Laboratory Service, Johannesburg, South Africa
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Hystad P, Amram O, Oje F, Larkin A, Boakye K, Avery A, Gebremedhin A, Duncan G. Bring Your Own Location Data: Use of Google Smartphone Location History Data for Environmental Health Research. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:117005. [PMID: 36356208 PMCID: PMC9648904 DOI: 10.1289/ehp10829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
BACKGROUND Environmental exposures are commonly estimated using spatial methods, with most epidemiological studies relying on home addresses. Passively collected smartphone location data, like Google Location History (GLH) data, may present an opportunity to integrate existing long-term time-activity data. OBJECTIVES We aimed to evaluate the potential use of GLH data for capturing long-term retrospective time-activity data for environmental health research. METHODS We included 378 individuals who participated in previous Global Positioning System (GPS) studies within the Washington State Twin Registry. GLH data consists of location information that has been routinely collected since 2010 when location sharing was enabled within android operating systems or Google apps. We created instructions for participants to download their GLH data and provide it through secure data transfer. We summarized the GLH data provided, compared it to available GPS data, and conducted an exposure assessment for nitrogen dioxide (NO2) air pollution. RESULTS Of 378 individuals contacted, we received GLH data from 61 individuals (16.1%) and 53 (14.0%) indicated interest but did not have historical GLH data available. The provided GLH data spanned 2010-2021 and included 34 million locations, capturing 66,677 participant days. The median number of days with GLH data per participant was 752, capturing 442 unique locations. When we compared GLH data to 2-wk GPS data (∼1.8 million points), 95% of GPS time-activity points were within 100m of GLH locations. We observed important differences between NO2 exposures assigned at home locations compared with GLH locations, highlighting the importance of GLH data to environmental exposure assessment. DISCUSSION We believe collecting GLH data is a feasible and cost-effective method for capturing retrospective time-activity patterns for large populations that presents new opportunities for environmental epidemiology. Cohort studies should consider adding GLH data collection to capture historical time-activity patterns of participants, employing a "bring-your-own-location-data" citizen science approach. Privacy remains a concern that needs to be carefully managed when using GLH data. https://doi.org/10.1289/EHP10829.
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Affiliation(s)
- Perry Hystad
- College of Public Health and Human Sciences, Oregon State University, Corvallis, Oregon, USA
| | - Ofer Amram
- Department of Nutrition and Exercise Physiology, Elson S. Floyd College of Medicine, Washington State University (WSU), Spokane, Washington, USA
- Paul G. Allen School for Global Animal Health, WSU, Pullman, Washington, USA
| | - Funso Oje
- School of Electrical Engineering and Computer Science, WSU, Pullman, Washington, USA
| | - Andrew Larkin
- College of Public Health and Human Sciences, Oregon State University, Corvallis, Oregon, USA
| | - Kwadwo Boakye
- College of Public Health and Human Sciences, Oregon State University, Corvallis, Oregon, USA
| | - Ally Avery
- Department of Nutrition and Exercise Physiology, Elson S. Floyd College of Medicine, Washington State University (WSU), Spokane, Washington, USA
| | - Assefaw Gebremedhin
- School of Electrical Engineering and Computer Science, WSU, Pullman, Washington, USA
| | - Glen Duncan
- Department of Nutrition and Exercise Physiology, Elson S. Floyd College of Medicine, Washington State University (WSU), Spokane, Washington, USA
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Breslawski J, Ives B. Movement under state and non-state authorities during COVID-19: Evidence from Lebanon. SSM Popul Health 2022; 19:101157. [PMID: 35814188 PMCID: PMC9249666 DOI: 10.1016/j.ssmph.2022.101157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/27/2022] [Accepted: 06/25/2022] [Indexed: 11/24/2022] Open
Abstract
COVID-19 has presented governing authorities with challenging decisions that have required them to consider the tradeoffs between movement restriction and economic activity. We propose that non-state armed groups may make different decisions than state governments in response to these challenges. Drawing upon the case of Hezbollah in Lebanon, we investigate whether movement levels differed between areas with Hezbollah private governance in comparison to other areas of Lebanon. Using Google COVID-19 mobility data and a difference in differences model, we show that following the first COVID-related death in Lebanon, movement in districts with private Hezbollah governance decreased significantly less than in other districts. We present a number of potential reasons for this disparity, arguing that the most probable explanation is the relatively high level of economic assistance that Hezbollah provided to people living in areas under Hezbollah's authority, which led to comparatively lesser rates of movement decline.
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Affiliation(s)
- Jori Breslawski
- Political Science Department, Naftali Building Tel Aviv University, 6997801, Israel
| | - Brandon Ives
- Department of Political Science and International Relations, College of Social Sciences, Seoul National University Gwanak-ro, Gwanak-gu 1, Seoul, 151-746, South Korea
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8
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Google Mobility Data as a Predictor for Tourism in Romania during the COVID-19 Pandemic—A Structural Equation Modeling Approach for Big Data. ELECTRONICS 2022. [DOI: 10.3390/electronics11152317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Our exploratory research focuses on the possible relations between tourism and the mobility of people, using short longitudinal data for mobility dimensions during the COVID-19 pandemic. One of these is real-time, exhaustive type data, published by Google, about the mobility of people in six different dimensions, (retail, parks, residential, workplace, grocery, and transit). The aim is to analyze the directional, intensity, causal, and complex interplay between the statistical data of tourism and mobility data for Romanian counties. The main objective is to determine if real-world big data can be linked with tourism arrivals in the first 14 months of the pandemic. We have found, using correlations, factorial analysis (PCA), regression models, and SEM, that there are strong and/or medium relationships between retail and parks and overnights, and weak or no relations between other mobility dimensions (workplace, transit). By applying factorial analysis (PCA), we have regrouped the six Google Mobility dimensions into two new factors that are good predictors for Romanian tourism at the county location. These findings can help provide a better understanding of the relationship between the real movement of people in different urban areas and the tourism phenomenon: the GM parks dimension best predicts tourism indicators (overnights), the GM residential dimension correlates inversely with the tourism indicator, and the rest of the GM indices are generally weak predictors for tourism. A more complex analysis could signal the potential and the character of tourism in different destinations, by territorially and chronologically determining the GM indices that are better linked with the tourism statistical indicators. Further research is required to establish forecasting models using Google Mobility data.
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Internet of Things-based smart helmet to detect possible COVID-19 infections. CYBER-PHYSICAL SYSTEMS 2022. [PMCID: PMC9261912 DOI: 10.1016/b978-0-12-824557-6.00004-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
At the beginning of 2020, while the world was celebrating New Year’s Eve, China’s headquarter of the World Health Organization came across a case of pneumonia in the city of Wuhan, China and was termed as coronavirus. Initially the symptoms were fever, cold, and cough; so thermal screening was done that could cause infection to the medical staff. In this chapter we discuss the design of the system known as smart helmet that has the capability to detect coronavirus automatically by using thermal imaging, which is used to capture the image with less human interaction. The thermal camera technology is integrated with smart helmets and combined with Internet of Things technology for monitoring of the screening process to get the real-time data. It is equipped with facial recognition technology; it can also display personal information of the infectee, which can automatically take temperature and can detect more infectee than normal thermal screening.
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Shepherd HER, Atherden FS, Chan HMT, Loveridge A, Tatem AJ. Domestic and international mobility trends in the United Kingdom during the COVID-19 pandemic: an analysis of facebook data. Int J Health Geogr 2021; 20:46. [PMID: 34863206 PMCID: PMC8643186 DOI: 10.1186/s12942-021-00299-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 11/08/2021] [Indexed: 11/23/2022] Open
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.
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Affiliation(s)
- Harry E R Shepherd
- School of Biological Sciences, University of Southampton, Southampton, UK
| | - Florence S Atherden
- Ocean and Earth Science, National Oceanography Centre, University of Southampton, Southampton, UK
| | | | - Alexandra Loveridge
- Ocean and Earth Science, National Oceanography Centre, University of Southampton, Southampton, UK
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
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Periyasamy AG, Venkatesh U. Population Mobility, Lockdowns, and COVID-19 Control: An Analysis Based on Google Location Data and Doubling Time from India. Healthc Inform Res 2021; 27:325-334. [PMID: 34788913 PMCID: PMC8654337 DOI: 10.4258/hir.2021.27.4.325] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 08/23/2021] [Indexed: 12/24/2022] Open
Abstract
Objectives Physical distancing is a control measure against coronavirus disease 2019 (COVID-19). Lockdowns are a strategy to enforce physical distancing in urban areas, but they are drastic measures. Therefore, we assessed the effectiveness of the lockdown measures taken in the world’s second-most populous country, India, by exploring their relationship with community mobility patterns and the doubling time of COVID-19. Methods We conducted a retrospective analysis based on community mobility patterns, the stringency index of lockdown measures, and the doubling time of COVID-19 cases in India between February 15 and April 26, 2020. Pearson correlation coefficients were calculated between the stringency index, community mobility patterns, and the doubling time of COVID-19 cases. Multiple linear regression was applied to predict the doubling time of COVID-19. Results Community mobility drastically fell after the lockdown was instituted. The doubling time of COVID-19 cases was negatively correlated with population mobility patterns in outdoor areas (r = −0.45 to −0.58). The stringency index and outdoor mobility patterns were also negatively correlated (r = −0.89 to −0.95). Population mobility patterns (R2 = 0.67) were found to predict the doubling time of COVID-19, and the model’s predictive power increased when the stringency index was also added (R2 = 0.73). Conclusions Lockdown measures could effectively ensure physical distancing and reduce short-term case spikes in India. Therefore, lockdown measures may be considered for tailored implementation on an intermittent basis, whenever COVID-19 cases are predicted to exceed the health care system’s capacity to manage.
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Affiliation(s)
- Aravind Gandhi Periyasamy
- Department of Community Medicine, School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - U Venkatesh
- Department of Community Medicine, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
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Giles JR, Cummings DAT, Grenfell BT, Tatem AJ, zu Erbach-Schoenberg E, Metcalf CJE, Wesolowski A. Trip duration drives shift in travel network structure with implications for the predictability of spatial disease spread. PLoS Comput Biol 2021; 17:e1009127. [PMID: 34375331 PMCID: PMC8378725 DOI: 10.1371/journal.pcbi.1009127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 08/20/2021] [Accepted: 05/28/2021] [Indexed: 11/19/2022] Open
Abstract
Human travel is one of the primary drivers of infectious disease spread. Models of travel are often used that assume the amount of travel to a specific destination decreases as cost of travel increases with higher travel volumes to more populated destinations. Trip duration, the length of time spent in a destination, can also impact travel patterns. We investigated the spatial patterns of travel conditioned on trip duration and find distinct differences between short and long duration trips. In short-trip duration travel networks, trips are skewed towards urban destinations, compared with long-trip duration networks where travel is more evenly spread among locations. Using gravity models to inform connectivity patterns in simulations of disease transmission, we show that pathogens with shorter generation times exhibit initial patterns of spatial propagation that are more predictable among urban locations. Further, pathogens with a longer generation time have more diffusive patterns of spatial spread reflecting more unpredictable disease dynamics. During an epidemic of an infectious pathogen, cases of disease can be imported to new locations when people travel. The amount of time that an infected person spends in a destination (trip duration) determines how likely they are to infect others while travelling. In this study, we analyzed travel data and found specific spatial patterns in trip duration, where short-duration trips are more common between urban destinations and long-duration trips are evenly spread out among locations. To show how this spatial pattern impacts the spread of infectious diseases, we used data-driven models and simulations to show that pathogens with shorter generation times have patterns of spatial spread that are more predictable among urban locations. However, pathogens with longer generation times tend to spread along the long-duration travel networks that are more evenly distributed among locations giving them more unpredictable disease dynamics.
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Affiliation(s)
- John R. Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- * E-mail:
| | - Derek A. T. Cummings
- Department of Biology and the Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology and the Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey, United States of America
| | - Andrew J. Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | | | - CJE Metcalf
- Department of Ecology and Evolutionary Biology and the Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey, United States of America
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
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Gluck S, Andrawos A, Summers MJ, Lange J, Chapman MJ, Finnis ME, Deane AM. The use of smartphone-derived location data to evaluate participation following critical illness: A pilot observational cohort study. Aust Crit Care 2021; 35:225-232. [PMID: 34373172 DOI: 10.1016/j.aucc.2021.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 05/03/2021] [Accepted: 05/23/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Disability is common following critical illness, impacting the quality of life of survivors, and is difficult to measure. 'Participation' can be quantified as involvement in life outside of their home requiring movement from their home to other locations. Participation restriction is a key element of disability, and following critical illness, participation may be diminished. It may be possible to quantify this change using pre-existing smartphone data. OBJECTIVES The feasibility of extracting location data from smartphones of survivors of intensive care unit (ICU) admission and assessing participation, using location-based outcomes, during recovery from critical illness was evaluated. METHODS Fifty consecutively admitted, consenting adult survivors of non-elective admission to ICU of greater than 48-h duration were recruited to a prospective observational cohort study where they were followed up at 3 and 6 months following discharge. The feasibility of extracting location data from survivors' smartphones and creating location-derived outcomes assessing participation was investigated over three 28-d study periods: pre-ICU admission and at 3 and 6 months following discharge. The following were calculated: time spent at home; the number of destinations visited; linear distance travelled; and two 'activity spaces', a minimum convex polygon and standard deviation ellipse. RESULTS Results are median [interquartile range] or n (%). The number of successful extractions was 9/50 (18%), 12/39 (31%), and 13/33 (39%); the percentage of time spent at home was 61 [56-68]%, 77 [66-87]%, and 67 [58-77]% (P = 0.16); the number of destinations visited was 34 [18-64], 38 [22-63], and 65 [46-88] (P = 0.02); linear distance travelled was 367 [56-788], 251 [114-323], and 747 [326-933] km over 28 d (P = 0.02), pre-ICU admission and at 3 and 6 months following ICU discharge, respectively. Activity spaces were successfully created. CONCLUSION Limited smartphone ownership, missing data, and time-consuming data extraction limit current implementation of mass extraction of location data from patients' smartphones to aid prognostication or measure outcomes. The number of journeys taken and the linear distance travelled increased between 3 and 6 months, suggesting participation may improve over time.
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Affiliation(s)
- Samuel Gluck
- Discipline of Acute Care Medicine, The University of Adelaide, AHMS, North Terrace, Adelaide, SA 5000, Australia; 4G751 Intensive Care Unit Research Department, The Royal Adelaide Hospital, Port Rd, Adelaide, SA 5000, Australia.
| | - Alice Andrawos
- Discipline of Acute Care Medicine, The University of Adelaide, AHMS, North Terrace, Adelaide, SA 5000, Australia; 4G751 Intensive Care Unit Research Department, The Royal Adelaide Hospital, Port Rd, Adelaide, SA 5000, Australia.
| | - Matthew J Summers
- Discipline of Acute Care Medicine, The University of Adelaide, AHMS, North Terrace, Adelaide, SA 5000, Australia.
| | - Jarrod Lange
- Hugo Centre for Population and Housing, University of Adelaide, Napier Building, North Terrace, Adelaide, SA 5000, Australia.
| | - Marianne J Chapman
- Discipline of Acute Care Medicine, The University of Adelaide, AHMS, North Terrace, Adelaide, SA 5000, Australia; 4G751 Intensive Care Unit Research Department, The Royal Adelaide Hospital, Port Rd, Adelaide, SA 5000, Australia.
| | - Mark E Finnis
- Discipline of Acute Care Medicine, The University of Adelaide, AHMS, North Terrace, Adelaide, SA 5000, Australia; 4G751 Intensive Care Unit Research Department, The Royal Adelaide Hospital, Port Rd, Adelaide, SA 5000, Australia.
| | - Adam M Deane
- Intensive Care Unit, The Royal Melbourne Hospital, 300 Grattan St, Parkville, Melbourne, VIC 3010, Australia; The University of Melbourne, Melbourne Medical School, Department of Medicine and Radiology, Royal Melbourne Hospital, Parkville, Australia, VIC 3050.
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14
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Yakubenko S. Home alone? Effect of weather-induced behaviour on spread of SARS-CoV-2 in Germany. ECONOMICS AND HUMAN BIOLOGY 2021; 42:100998. [PMID: 33838616 PMCID: PMC8012168 DOI: 10.1016/j.ehb.2021.100998] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 02/26/2021] [Accepted: 03/17/2021] [Indexed: 06/12/2023]
Abstract
In early 2020 the world was struck by the epidemic of novel SARS-CoV-2 virus. Like many others, German government has introduced severe contact restrictions to limit the spread of infection. This paper analyses effects of weather on the spread of the disease under the described circumstances. We demonstrate that regions reported lower growth rates of the number of the infection cases after days with higher temperatures, no rain and low humidity. We argue that this effect is channelled through human behaviour. The evidence suggests that "good" weather attracts individuals to outdoor (safer) environments, thus, deterring people from indoor (less safe) environments. Understanding this relationship is important for improving the measures aiming at combating the spread of the virus.
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15
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Ruktanonchai CW, Lai S, Utazi CE, Cunningham AD, Koper P, Rogers GE, Ruktanonchai NW, Sadilek A, Woods D, Tatem AJ, Steele JE, Sorichetta A. Practical geospatial and sociodemographic predictors of human mobility. Sci Rep 2021; 11:15389. [PMID: 34321509 PMCID: PMC8319369 DOI: 10.1038/s41598-021-94683-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 07/13/2021] [Indexed: 11/08/2022] Open
Abstract
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 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.
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Affiliation(s)
- Corrine W Ruktanonchai
- Population Health Sciences, College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, USA.
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Chigozie E Utazi
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Alex D Cunningham
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Patrycja Koper
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Grant E Rogers
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Nick W Ruktanonchai
- Population Health Sciences, College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, USA
| | | | - Dorothea Woods
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Jessica E Steele
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Alessandro Sorichetta
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
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16
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Lai S, Ruktanonchai NW, Carioli A, Ruktanonchai CW, Floyd JR, Prosper O, Zhang C, Du X, Yang W, Tatem AJ. Assessing the Effect of Global Travel and Contact Restrictions on Mitigating the COVID-19 Pandemic. ENGINEERING (BEIJING, CHINA) 2021; 7:914-923. [PMID: 33972889 PMCID: PMC8099556 DOI: 10.1016/j.eng.2021.03.017] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 02/01/2021] [Accepted: 03/23/2021] [Indexed: 05/04/2023]
Abstract
Travel restrictions and physical distancing have been implemented across the world to mitigate the coronavirus disease 2019 (COVID-19) pandemic, but studies are needed to understand their effectiveness across regions and time. Based on the population mobility metrics derived from mobile phone geolocation data across 135 countries or territories during the first wave of the pandemic in 2020, we built a metapopulation epidemiological model to measure the effect of travel and contact restrictions on containing COVID-19 outbreaks across regions. We found that if these interventions had not been deployed, the cumulative number of cases could have shown a 97-fold (interquartile range 79-116) increase, as of May 31, 2020. However, their effectiveness depended upon the timing, duration, and intensity of the interventions, with variations in case severity seen across populations, regions, and seasons. Additionally, before effective vaccines are widely available and herd immunity is achieved, our results emphasize that a certain degree of physical distancing at the relaxation of the intervention stage will likely be needed to avoid rapid resurgences and subsequent lockdowns.
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Affiliation(s)
- Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Nick W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
- Population Health Sciences, Virginia Tech, Blacksburg, VA 24061, USA
| | - Alessandra Carioli
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Corrine W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Jessica R Floyd
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Olivia Prosper
- Department of Mathematics, University of Tennessee, Knoxville, TN 37996, USA
| | - Chi Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510275, China
| | - Xiangjun Du
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510275, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
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17
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Basellini U, Alburez-Gutierrez D, Del Fava E, Perrotta D, Bonetti M, Camarda CG, Zagheni E. Linking excess mortality to mobility data during the first wave of COVID-19 in England and Wales. SSM Popul Health 2021; 14:100799. [PMID: 33898726 PMCID: PMC8058100 DOI: 10.1016/j.ssmph.2021.100799] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/09/2021] [Accepted: 04/11/2021] [Indexed: 02/08/2023] Open
Abstract
Non-pharmaceutical interventions have been implemented worldwide to curb the spread of COVID-19. However, the effectiveness of such governmental measures in reducing the mortality burden remains a key question of scientific interest and public debate. In this study, we leverage digital mobility data to assess the effects of reduced human mobility on excess mortality, focusing on regional data in England and Wales between February and August 2020. We estimate a robust association between mobility reductions and lower excess mortality, after adjusting for time trends and regional differences in a mixed-effects regression framework and considering a five-week lag between the two measures. We predict that, in the absence of mobility reductions, the number of excess deaths could have more than doubled in England and Wales during this period, especially in the London area. The study is one of the first attempts to quantify the effects of mobility reductions on excess mortality during the COVID-19 pandemic.
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Affiliation(s)
- Ugofilippo Basellini
- Max Planck Institute for Demographic Research (MPIDR), Rostock, Germany
- Institut National D’études Démographiques (INED), Aubervilliers, France
| | | | - Emanuele Del Fava
- Max Planck Institute for Demographic Research (MPIDR), Rostock, Germany
| | - Daniela Perrotta
- Max Planck Institute for Demographic Research (MPIDR), Rostock, Germany
| | - Marco Bonetti
- Carlo F. Dondena Centre & Covid Crisis Lab, Bocconi University, Milan, Italy
| | - Carlo G. Camarda
- Institut National D’études Démographiques (INED), Aubervilliers, France
| | - Emilio Zagheni
- Max Planck Institute for Demographic Research (MPIDR), Rostock, Germany
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18
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Bargain O, Aminjonov U. Poverty and COVID-19 in Africa and Latin America. WORLD DEVELOPMENT 2021; 142:105422. [PMID: 33612919 PMCID: PMC7885669 DOI: 10.1016/j.worlddev.2021.105422] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/27/2021] [Indexed: 05/22/2023]
Abstract
Since March 2020, governments have recommended or enacted lockdown policies to curb the spread of COVID-19. Yet, poorer segments of the population cannot afford to stay at home and must continue to work. In this paper, we test whether work-related mobility is effectively influenced by the local intensity of poverty. To do so, we exploit poverty data and Google mobility data for 242 regions of nine Latin American and African countries. We find that the drop in work-related mobility during the first lockdown period was indeed significantly lower in high-poverty regions compared to other regions. We also illustrate how higher poverty has induced a faster spread of the virus. The policy implication is that social protection measures in the form of food or cash trasfers must be complementary to physical distancing measures. Further research must evaluate how such transfers, when implemented, have attenuated the difference between poor and non-poor regions in terms of exposure to the virus.
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Affiliation(s)
- Olivier Bargain
- Bordeaux University and Institut Universitaire de France (France) and IZA (Germany)
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19
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Cortés U, Cortés A, Garcia-Gasulla D, Pérez-Arnal R, Álvarez-Napagao S, Àlvarez E. The ethical use of high-performance computing and artificial intelligence: fighting COVID-19 at Barcelona Supercomputing Center. AI AND ETHICS 2021; 2:325-340. [PMID: 34790948 PMCID: PMC8101339 DOI: 10.1007/s43681-021-00056-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 04/15/2021] [Indexed: 10/24/2022]
Abstract
The COVID-19 pandemic has created an extraordinary medical, economic and humanitarian emergency. Artificial intelligence, in combination with other digital technologies, is being used as a tool to support the fight against the viral pandemic that has affected the entire world since the beginning of 2020. Barcelona Supercomputing Center collaborates in the battle against the coronavirus in different areas: the application of bioinformatics for the research on the virus and its possible treatments, the use of artificial intelligence, natural language processing and big data techniques to analyse the spread and impact of the pandemic, and the use of the MareNostrum 4 supercomputer to enable massive analysis on COVID-19 data. Many of these activities have included the use of personal and sensitive data of citizens, which, even during a pandemic, should be treated and handled with care. In this work we discuss our approach based on an ethical, transparent and fair use of this information, an approach aligned with the guidelines proposed by the European Union.
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Affiliation(s)
- Ulises Cortés
- Universitat Politècnica de Catalunya, Edifici Omega 205, Jordi Girona 29, 08034 Barcelona, Spain
- Barcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain
| | - Atia Cortés
- Barcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain
| | - Dario Garcia-Gasulla
- Barcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain
| | - Raquel Pérez-Arnal
- Barcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain
| | - Sergio Álvarez-Napagao
- Barcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain
| | - Enric Àlvarez
- Universitat Politècnica de Catalunya, Edifici Omega 205, Jordi Girona 29, 08034 Barcelona, Spain
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20
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Zimmerman FJ, Anderson NW. Association of the Timing of School Closings and Behavioral Changes With the Evolution of the Coronavirus Disease 2019 Pandemic in the US. JAMA Pediatr 2021; 175:501-509. [PMID: 33616635 PMCID: PMC7900933 DOI: 10.1001/jamapediatrics.2020.6371] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 11/18/2020] [Indexed: 11/14/2022]
Abstract
Importance The consequences of school closures for children's health are profound, but existing evidence on their effectiveness in limiting severe acute respiratory syndrome coronavirus 2 transmission is unsettled. Objective To determine the independent associations of voluntary behavioral change, school closures, and bans on large gatherings with the incidence and mortality due to coronavirus disease 2019 (COVID-19). Design, Setting, and Participants This population-based, interrupted-time-series analysis of lagged independent variables used publicly available observational data from US states during a 60-day period from March 8 to May 18, 2020. The behavioral measures were collected from anonymized cell phone or internet data for individuals in the US and compared with a baseline of January 3 to February 6, 2020. Estimates were also controlled for several state-level characteristics. Exposures Days since school closure, days since a ban on gatherings of 10 or more people, and days since residents voluntarily conducted a 15% or more decline in time spent at work via Google Mobility data. Main Outcomes and Measures The natural log of 7-day mean COVID-19 incidence and mortality. Results During the study period, the rate of restaurant dining declined from 1 year earlier by a mean (SD) of 98.3% (5.2%) during the study period. Time at work declined by a mean (SD) of 40.0% (7.9%); time at home increased by a mean (SD) of 15.4% (3.7%). In fully adjusted models, an advance of 1 day in implementing mandatory school closures was associated with a 3.5% reduction (incidence rate ratio [IRR], 0.965; 95% CI, 0.946-0.984) in incidence, whereas each day earlier that behavioral change occurred was associated with a 9.3% reduction (IRR, 0.907; 95% CI, 0.890-0.925) in incidence. For mortality, each day earlier that school closures occurred was associated with a subsequent 3.8% reduction (IRR, 0.962; 95% CI, 0.926-0.998), and each day of advance in behavioral change was associated with a 9.8% reduction (IRR, 0.902; 95% CI, 0.869-0.936). Simulations suggest that a 2-week delay in school closures alone would have been associated with an additional 23 000 (95% CI, 2000-62 000) deaths, whereas a 2-week delay in voluntary behavioral change with school closures remaining the same would have been associated with an additional 140 000 (95% CI, 65 000-294 000) deaths. Conclusions and Relevance In light of the harm to children of closing schools, these findings suggest that policy makers should consider better leveraging the public's willingness to protect itself through voluntary behavioral change.
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Affiliation(s)
- Frederick J. Zimmerman
- Center for Health Advancement, Department of Health Policy and Management, Fielding School of Public Health at University of California, Los Angeles
| | - Nathaniel W. Anderson
- Center for Health Advancement, Department of Health Policy and Management, Fielding School of Public Health at University of California, Los Angeles
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21
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Changes of Spatiotemporal Pattern and Network Characteristic in Population Flow under COVID-19 Epidemic. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2021. [DOI: 10.3390/ijgi10030145] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The COVID-19 pandemic is a major problem facing humanity throughout the world. The rapid and accurate tracking of population flows may therefore be epidemiologically informative. This paper adopts a massive amount of daily population flow data (from January 10 to March 15, 2020) for China obtained from the Baidu Migration platform to analyze the changes of the spatiotemporal patterns and network characteristics in population flow during the pre-outbreak period, outbreak period, and post-peak period. The results show that (1) for temporal characteristics of population flow, the total population flow varies greatly between the three periods, with an overall trend of the pre-outbreak period flow > the post-peak period flow > the outbreak period flow. Impacted by the lockdown measures, the population flow in various provinces plunged drastically and remained low until the post-peak period, at which time it gradually increased. (2) For the spatial pattern, the pattern of population flow is divided by the geographic demarcation line known as the Hu (Heihe-Tengchong) Line, with a high-density interconnected network in the southeast half and a low-density serial-connection network in the northwest half. During the outbreak period, Wuhan city appeared as a hollow region in the population flow network; during the post-peak period, the population flow increased gradually, but it was mainly focused on intra-provincial flow. (3) For the network characteristic changes, during the outbreak period, the gap in the network status between cities at different administrative levels narrowed significantly. Thus, the feasibility of Baidu migration data, comparison with non-epidemic periods, and optimal implications are discussed. This paper mainly described the difference and specific information under non-normal situation compared with existing results under a normal situation, and analyzed the impact mechanism, which can provide a reference for local governments to make policy recommendations for economic recovery in the future under the epidemic period.
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22
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Merrill RD, Bah Chabi AI, McIntyre E, Kouassi JV, Alleby MM, Codja C, Tante O, Primous Martial GT, Kone I, Ward S, Agbeko TT, Kakaı CG. An approach to integrate population mobility patterns and sociocultural factors in communicable disease preparedness and response. HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS 2021; 8:1-11. [PMID: 38617731 PMCID: PMC11010577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Complex human movement patterns driven by a range of economic, health, social, and environmental factors influence communicable disease spread. Further, cross-border movement impacts disparate public health systems of neighboring countries, making an effective response to disease importation or exportation more challenging. Despite the array of quantitative techniques and social science approaches available to analyze movement patterns, there continues to be a dearth of methods within the applied public health setting to gather and use information about community-level mobility dynamics. Population Connectivity Across Borders (PopCAB) is a rapidly-deployable toolkit to characterize multisectoral movement patterns through community engagement using focus group discussions or key informant interviews, each with participatory mapping, and apply the results to tailor preparedness and response strategies. The Togo and Benin Ministries of Health (MOH), in collaboration with the Abidjan Lagos Corridor Organization and the US Centers for Disease Control and Prevention, adapted and applied PopCAB to inform cross-border preparedness and response strategies for multinational Lassa fever outbreaks. Initially, the team implemented binational, national-level PopCAB activities in March 2017, highlighting details about a circular migration pathway across northern Togo, Benin, and Nigeria. After applying those results to respond to a cross-border Lassa fever outbreak in February 2018, the team designed an expanded PopCAB initiative in April 2018. In eight days, they trained 54 MOH staff who implemented 21 PopCAB focus group discussions in 14 cities with 224 community-level participants representing six stakeholder groups. Using the newly-identified 167 points of interest and 176 routes associated with a circular migration pathway across Togo, Benin, and Nigeria, the Togo and Benin MOH refined their cross-border information sharing and collaboration processes for Lassa fever and other communicable diseases, selected health facilities with increased community connectivity for enhanced training, and identified techniques to better integrate traditional healers in surveillance and community education strategies. They also integrated the final toolkit in national- and district-level public health preparedness plans. Integrating PopCAB in public health practice to better understand and accommodate population movement patterns can help countries mitigate the international spread of disease in support of improved global health security and International Health Regulations requirements.
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Affiliation(s)
| | | | - Elvira McIntyre
- Perspecta and US Centers for Disease Control and Prevention, Atlanta, USA
| | | | | | | | | | | | - Idriss Kone
- Abidjan Lagos Corridor Organization, Benin, Nigeria
| | - Sarah Ward
- US Centers for Disease Control and Prevention, Atlanta, USA
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23
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Internet of Things for Current COVID-19 and Future Pandemics: an Exploratory Study. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2020; 4:325-364. [PMID: 33204938 PMCID: PMC7659418 DOI: 10.1007/s41666-020-00080-6] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 10/01/2020] [Accepted: 10/07/2020] [Indexed: 01/10/2023]
Abstract
In recent years, the Internet of Things (IoT) has gained convincing research ground as a new research topic in a wide variety of academic and industrial disciplines, especially in healthcare. The IoT revolution is reshaping modern healthcare systems by incorporating technological, economic, and social prospects. It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily. The current global challenge of the pandemic caused by the novel severe respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. At the time this paper was written, the number of diagnosed COVID-19 cases around the world had reached more than 31 million. Since the pandemic started, there has been a rapid effort in different research communities to exploit a wide variety of technologies to combat this worldwide threat, and IoT technology is one of the pioneers in this area. In the context of COVID-19, IoT-enabled/linked devices/applications are utilized to lower the possible spread of COVID-19 to others by early diagnosis, monitoring patients, and practicing defined protocols after patient recovery. This paper surveys the role of IoT-based technologies in COVID-19 and reviews the state-of-the-art architectures, platforms, applications, and industrial IoT-based solutions combating COVID-19 in three main phases, including early diagnosis, quarantine time, and after recovery.
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24
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Nakashima A, Takeya M, Kuba K, Takano M, Nakashima N. Virus database annotations assist in tracing information on patients infected with emerging pathogens. INFORMATICS IN MEDICINE UNLOCKED 2020; 21:100442. [PMID: 33052312 PMCID: PMC7543791 DOI: 10.1016/j.imu.2020.100442] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/20/2020] [Accepted: 10/03/2020] [Indexed: 01/01/2023] Open
Abstract
The global pandemic of SARS-CoV-2 has disrupted human social activities. In restarting economic activities, successive outbreaks by new variants are concerning. Here, we evaluated the applicability of public database annotations to estimate the virulence, transmission trends and origins of emerging SARS-CoV-2 variants. Among the detectable multiple mutations, we retraced the mutation in the spike protein. With the aid of the protein database, structural modelling yielded a testable scientific hypothesis on viral entry to host cells. Simultaneously, annotations for locations and collection dates suggested that the variant virus emerged somewhere in the world in approximately February 2020, entered the USA and propagated nationwide with periodic sampling fluctuation likely due to an approximately 5-day incubation delay. Thus, public database annotations are useful for automated elucidation of the early spreading patterns in relation to human behaviours, which should provide objective reference for local governments for social decision making to contain emerging substrains. We propose that additional annotations for past paths and symptoms of the patients should further assist in characterizing the exact virulence and origins of emerging pathogens.
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Affiliation(s)
- Akiko Nakashima
- Department of Physiology, Kurume University School of Medicine, Asahi-machi 67, Kurume, Fukuoka, 830-0011, Japan
| | - Mitsue Takeya
- Department of Physiology, Kurume University School of Medicine, Asahi-machi 67, Kurume, Fukuoka, 830-0011, Japan
| | - Keiji Kuba
- Department of Biochemistry and Metabolic Science, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita, 010-8543, Japan
| | - Makoto Takano
- Department of Physiology, Kurume University School of Medicine, Asahi-machi 67, Kurume, Fukuoka, 830-0011, Japan
| | - Noriyuki Nakashima
- Department of Physiology, Kurume University School of Medicine, Asahi-machi 67, Kurume, Fukuoka, 830-0011, Japan
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25
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Carrasco-Escobar G, Fornace K, Wong D, Padilla-Huamantinco PG, Saldaña-Lopez JA, Castillo-Meza OE, Caballero-Andrade AE, Manrique E, Ruiz-Cabrejos J, Barboza JL, Rodriguez H, Henostroza G, Gamboa D, Castro MC, Vinetz JM, Llanos-Cuentas A. Open-Source 3D Printable GPS Tracker to Characterize the Role of Human Population Movement on Malaria Epidemiology in River Networks: A Proof-of-Concept Study in the Peruvian Amazon. Front Public Health 2020; 8:526468. [PMID: 33072692 PMCID: PMC7542225 DOI: 10.3389/fpubh.2020.526468] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 08/21/2020] [Indexed: 11/13/2022] Open
Abstract
Human movement affects malaria epidemiology at multiple geographical levels; however, few studies measure the role of human movement in the Amazon Region due to the challenging conditions and cost of movement tracking technologies. We developed an open-source low-cost 3D printable GPS-tracker and used this technology in a cohort study to characterize the role of human population movement in malaria epidemiology in a rural riverine village in the Peruvian Amazon. In this pilot study of 20 participants (mean age = 40 years old), 45,980 GPS coordinates were recorded over 1 month. Characteristic movement patterns were observed relative to the infection status and occupation of the participants. Applying two analytical animal movement ecology methods, utilization distributions (UDs) and integrated step selection functions (iSSF), we showed contrasting environmental selection and space use patterns according to infection status. These data suggested an important role of human movement in the epidemiology of malaria in the Peruvian Amazon due to high connectivity between villages of the same riverine network, suggesting limitations of current community-based control strategies. We additionally demonstrate the utility of this low-cost technology with movement ecology analysis to characterize human movement in resource-poor environments.
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Affiliation(s)
- Gabriel Carrasco-Escobar
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.,Division of Infectious Diseases, Department of Medicine, University of California, San Diego, La Jolla, CA, United States.,Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Kimberly Fornace
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Daniel Wong
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Pierre G Padilla-Huamantinco
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.,Departamento de Ingenieria, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jose A Saldaña-Lopez
- Departamento de Ingenieria, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Ober E Castillo-Meza
- Departamento de Ingenieria, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Armando E Caballero-Andrade
- Departamento de Ingenieria, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Edgar Manrique
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.,Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jorge Ruiz-Cabrejos
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.,Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jose Luis Barboza
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - German Henostroza
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Dionicia Gamboa
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru.,Departamento de Ciencias Celulares y Moleculares, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru.,Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Marcia C Castro
- Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Joseph M Vinetz
- Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru.,Section of Infectious Diseases, Yale School of Medicine, New Haven, CT, United States
| | - Alejandro Llanos-Cuentas
- Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru.,Facultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Peru
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26
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Ruktanonchai NW, Floyd JR, Lai S, Ruktanonchai CW, Sadilek A, Rente-Lourenco P, Ben X, Carioli A, Gwinn J, Steele JE, Prosper O, Schneider A, Oplinger A, Eastham P, Tatem AJ. Assessing the impact of coordinated COVID-19 exit strategies across Europe. Science 2020; 369:1465-1470. [PMID: 32680881 PMCID: PMC7402626 DOI: 10.1126/science.abc5096] [Citation(s) in RCA: 118] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 07/13/2020] [Indexed: 12/25/2022]
Abstract
As rates of new coronavirus disease 2019 (COVID-19) cases decline across Europe owing to nonpharmaceutical interventions such as social distancing policies and lockdown measures, countries require guidance on how to ease restrictions while minimizing the risk of resurgent outbreaks. We use mobility and case data to quantify how coordinated exit strategies could delay continental resurgence and limit community transmission of COVID-19. We find that a resurgent continental epidemic could occur as many as 5 weeks earlier when well-connected countries with stringent existing interventions end their interventions prematurely. Further, we find that appropriate coordination can greatly improve the likelihood of eliminating community transmission throughout Europe. In particular, synchronizing intermittent lockdowns across Europe means that half as many lockdown periods would be required to end continent-wide community transmission.
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Affiliation(s)
- N W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
- Population Health Sciences, Virginia Tech, Blacksburg, VA, USA
| | - J R Floyd
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
| | - S Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
| | - C W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | | | | | - X Ben
- Google, Mountain View, CA, USA
| | - A Carioli
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - J Gwinn
- College of Public Health, University of Kentucky, Lexington, KY, USA
| | - J E Steele
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - O Prosper
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| | | | | | | | - A J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
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27
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Uncovering temporal changes in Europe's population density patterns using a data fusion approach. Nat Commun 2020; 11:4631. [PMID: 32934205 PMCID: PMC7493994 DOI: 10.1038/s41467-020-18344-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 08/04/2020] [Indexed: 11/08/2022] Open
Abstract
The knowledge of the spatial and temporal distribution of human population is vital for the study of cities, disaster risk management or planning of infrastructure. However, information on the distribution of population is often based on place-of-residence statistics from official sources, thus ignoring the changing population densities resulting from human mobility. Existing assessments of spatio-temporal population are limited in their detail and geographical coverage, and the promising mobile-phone records are hindered by issues concerning availability and consistency. Here, we present a multi-layered dasymetric approach that combines official statistics with geospatial data from emerging sources to produce and validate a European Union-wide dataset of population grids taking into account intraday and monthly population variations at 1 km2 resolution. The results reproduce and systematically quantify known insights concerning the spatio-temporal population density structure of large European cities, whose daytime population we estimate to be, on average, 1.9 times higher than night time in city centers.
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28
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Bhadane C, Shah K. Context-aware next location prediction using data mining and metaheuristics. EVOLUTIONARY INTELLIGENCE 2020. [DOI: 10.1007/s12065-020-00469-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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29
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Kraemer MUG, Sadilek A, Zhang Q, Marchal NA, Tuli G, Cohn EL, Hswen Y, Perkins TA, Smith DL, Reiner RC, Brownstein JS. Mapping global variation in human mobility. Nat Hum Behav 2020; 4:800-810. [PMID: 32424257 DOI: 10.1038/s41562-020-0875-0] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Accepted: 03/30/2020] [Indexed: 01/11/2023]
Abstract
The geographic variation of human movement is largely unknown, mainly due to a lack of accurate and scalable data. Here we describe global human mobility patterns, aggregated from over 300 million smartphone users. The data cover nearly all countries and 65% of Earth's populated surface, including cross-border movements and international migration. This scale and coverage enable us to develop a globally comprehensive human movement typology. We quantify how human movement patterns vary across sociodemographic and environmental contexts and present international movement patterns across national borders. Fitting statistical models, we validate our data and find that human movement laws apply at 10 times shorter distances and movement declines 40% more rapidly in low-income settings. These results and data are made available to further understanding of the role of human movement in response to rapid demographic, economic and environmental changes.
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Affiliation(s)
- Moritz U G Kraemer
- Harvard Medical School, Harvard University, Boston, MA, USA.
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.
- Department of Zoology, University of Oxford, Oxford, UK.
| | | | - Qian Zhang
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Gaurav Tuli
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
| | - Emily L Cohn
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
| | - Yulin Hswen
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - T Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
- Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA.
| | - John S Brownstein
- Harvard Medical School, Harvard University, Boston, MA, USA.
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.
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30
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Agbehadji IE, Awuzie BO, Ngowi AB, Millham RC. Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E5330. [PMID: 32722154 PMCID: PMC7432484 DOI: 10.3390/ijerph17155330] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 06/24/2020] [Accepted: 06/29/2020] [Indexed: 12/23/2022]
Abstract
The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19's cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.
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Affiliation(s)
- Israel Edem Agbehadji
- Office of the Deputy Vice Chancellor: Research, Innovation and Engagement, Central University of Technology, Bloemfontein 9301, South Africa;
| | - Bankole Osita Awuzie
- Centre for Sustainable Smart Cities 4.0, Faculty of Engineering, Built Environment and Information Technology, Central University of Technology, Bloemfontein 9301, South Africa;
| | - Alfred Beati Ngowi
- Office of the Deputy Vice Chancellor: Research, Innovation and Engagement, Central University of Technology, Bloemfontein 9301, South Africa;
| | - Richard C. Millham
- ICT and Society Research Group, Department of Information Technology, Durban University of Technology, Durban 4001, South Africa;
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31
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Scott CEH, Holland G, Powell-Bowns MFR, Brennan CM, Gillespie M, Mackenzie SP, Clement ND, Amin AK, White TO, Duckworth AD. Population mobility and adult orthopaedic trauma services during the COVID-19 pandemic: fragility fracture provision remains a priority. Bone Jt Open 2020; 1:182-189. [PMID: 33225287 PMCID: PMC7677724 DOI: 10.1302/2633-1462.16.bjo-2020-0043.r1] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Aims This study aims to define the epidemiology of trauma presenting to a single centre providing all orthopaedic trauma care for a population of ∼ 900,000 over the first 40 days of the COVID-19 pandemic compared to that presenting over the same period one year earlier. The secondary aim was to compare this with population mobility data obtained from Google. Methods A cross-sectional study of consecutive adult (> 13 years) patients with musculoskeletal trauma referred as either in-patients or out-patients over a 40-day period beginning on 5 March 2020, the date of the first reported UK COVID-19 death, was performed. This time period encompassed social distancing measures. This group was compared to a group of patients referred over the same calendar period in 2019 and to publicly available mobility data from Google. Results Orthopaedic trauma referrals reduced by 42% (1,056 compared to 1,820) during the study period, and by 58% (405 compared to 967) following national lockdown. Outpatient referrals reduced by 44%, and inpatient referrals by 36%, and the number of surgeries performed by 36%. The regional incidence of traumatic injury fell from 5.07 (95% confidence interval (CI) 4.79 to 5.35) to 2.94 (95% CI 2.52 to 3.32) per 100,000 population per day. Significant reductions were seen in injuries related to sports and alcohol consumption. No admissions occurred relating to major trauma (Injury Severity Score > 16) or violence against the person. Changes in population mobility and trauma volume from baseline correlated significantly (Pearson's correlation 0.749, 95% CI 0.58 to 0.85, p < 0.001). However, admissions related to fragility fractures remained unchanged compared to the 2019 baseline. Conclusion The profound changes in social behaviour and mobility during the early stages of the COVID-19 pandemic have directly correlated with a significant decrease in orthopaedic trauma referrals, but fragility fractures remained unaffected and provision for these patients should be maintained.Cite this article: Bone Joint Open 2020;1-6:182-189.
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Affiliation(s)
- Chloe E H Scott
- Department of Orthopaedics, Royal Infirmary of Edinburgh, Edinburgh, UK.,Department of Orthopaedics, University of Edinburgh, Edinburgh, UK
| | - George Holland
- Department of Orthopaedics, Royal Infirmary of Edinburgh, Edinburgh, UK
| | | | - Caitlin M Brennan
- Department of Orthopaedics, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Matthew Gillespie
- Department of Orthopaedics, Royal Infirmary of Edinburgh, Edinburgh, UK
| | | | - Nick D Clement
- Department of Orthopaedics, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Anish K Amin
- Department of Orthopaedics, Royal Infirmary of Edinburgh, Edinburgh, UK.,Department of Orthopaedics, University of Edinburgh, Edinburgh, UK
| | - Tim O White
- Department of Orthopaedics, Royal Infirmary of Edinburgh, Edinburgh, UK.,Department of Orthopaedics, University of Edinburgh, Edinburgh, UK
| | - Andrew D Duckworth
- Department of Orthopaedics, Royal Infirmary of Edinburgh, Edinburgh, UK.,Department of Orthopaedics, University of Edinburgh, Edinburgh, UK
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32
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Floyd JR, Ogola J, Fèvre EM, Wardrop N, Tatem AJ, Ruktanonchai NW. Activity-specific mobility of adults in a rural region of western Kenya. PeerJ 2020; 8:e8798. [PMID: 32377444 PMCID: PMC7195828 DOI: 10.7717/peerj.8798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 02/25/2020] [Indexed: 11/25/2022] Open
Abstract
Improving rural household access to resources such as markets, schools and healthcare can help alleviate poverty in low-income settings. Current models of geographic accessibility to various resources rarely take individual variation into account due to a lack of appropriate data, yet understanding mobility at an individual level is key to knowing how people access their local resources. Our study used both an activity-specific survey and GPS trackers to evaluate how adults in a rural area of western Kenya accessed local resources. We calculated the travel time and time spent at six different types of resource and compared the GPS and survey data to see how well they matched. We found links between several demographic characteristics and the time spent at different resources, and that the GPS data reflected the survey data well for time spent at some types of resource, but poorly for others. We conclude that demography and activity are important drivers of mobility, and a better understanding of individual variation in mobility could be obtained through the use of GPS trackers on a wider scale.
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Affiliation(s)
- Jessica R Floyd
- WorldPop, Department of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - Joseph Ogola
- International Livestock Research Institute, Nairobi, Kenya
| | - Eric M Fèvre
- International Livestock Research Institute, Nairobi, Kenya.,Institute of Infection and Global Health, University of Liverpool, Liverpool, United Kingdom
| | - Nicola Wardrop
- Department for International Development, Glasgow, United Kingdom
| | - Andrew J Tatem
- WorldPop, Department of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - Nick W Ruktanonchai
- WorldPop, Department of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
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33
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Jim HSL, Hoogland AI, Brownstein NC, Barata A, Dicker AP, Knoop H, Gonzalez BD, Perkins R, Rollison D, Gilbert SM, Nanda R, Berglund A, Mitchell R, Johnstone PAS. Innovations in research and clinical care using patient-generated health data. CA Cancer J Clin 2020; 70:182-199. [PMID: 32311776 PMCID: PMC7488179 DOI: 10.3322/caac.21608] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 02/24/2020] [Accepted: 02/24/2020] [Indexed: 12/17/2022] Open
Abstract
Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.
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Affiliation(s)
- Heather S L Jim
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida
| | - Aasha I Hoogland
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida
| | - Naomi C Brownstein
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida
| | - Anna Barata
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Hans Knoop
- Department of Medical Psychology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Brian D Gonzalez
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida
| | - Randa Perkins
- Department of Clinical Informatics and Clinical Systems, Moffitt Cancer Center, Tampa, Florida
| | - Dana Rollison
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Scott M Gilbert
- Department of Genitourinary Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Ronica Nanda
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida
- BayCare Health Systems Inc, Morton Plant Hospital, Clearwater, Florida
| | - Anders Berglund
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida
| | - Ross Mitchell
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida
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34
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Yu X, Stuart AL, Liu Y, Ivey CE, Russell AG, Kan H, Henneman LRF, Sarnat SE, Hasan S, Sadmani A, Yang X, Yu H. On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2019; 252:924-930. [PMID: 31226517 DOI: 10.1016/j.envpol.2019.05.081] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 05/15/2019] [Accepted: 05/15/2019] [Indexed: 05/18/2023]
Abstract
Appropriately characterizing spatiotemporal individual mobility is important in many research areas, including epidemiological studies focusing on air pollution. However, in many retrospective air pollution health studies, exposure to air pollution is typically estimated at the subjects' residential addresses. Individual mobility is often neglected due to lack of data, and exposure misclassification errors are expected. In this study, we demonstrate the potential of using location history data collected from smartphones by the Google Maps application for characterizing historical individual mobility and exposure. Here, one subject carried a smartphone installed with Google Maps, and a reference GPS data logger which was configured to record location every 10 s, for a period of one week. The retrieved Google Maps Location History (GMLH) data were then compared with the GPS data to evaluate their effectiveness and accuracy of the GMLH data to capture individual mobility. We also conducted an online survey (n = 284) to assess the availability of GMLH data among smartphone users in the US. We found the GMLH data reasonably captured the spatial movement of the subject during the one-week time period at up to 200 m resolution. We were able to accurately estimate the time the subject spent in different microenvironments, as well as the time the subject spent driving during the week. The estimated time-weighted daily exposures to ambient particulate matter using GMLH and the GPS data logger were also similar (error less than 1.2%). Survey results showed that GMLH data may be available for 61% of the survey sample. Considering the popularity of smartphones and the Google Maps application, detailed historical location data are expected to be available for large portion of the population, and results from this study highlight the potential of these location history data to improve exposure estimation for retrospective epidemiological studies.
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Affiliation(s)
- Xiaonan Yu
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Amy L Stuart
- College of Public Health, University of South Florida, Tampa, FL, USA; Department of Civil & Environmental Engineering, University of South Florida, Tampa, FL, USA
| | - Yang Liu
- Department of Environmental Health, Emory University, Atlanta, GA, USA
| | - Cesunica E Ivey
- Department of Chemical and Environmental Engineering, University of California Riverside, Riverside, CA, USA
| | - Armistead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Haidong Kan
- School of Public Health, Fudan University, Shanghai, China
| | - Lucas R F Henneman
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | | | - Samiul Hasan
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Anwar Sadmani
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Xuchao Yang
- Institute of Island & Coastal Ecosystem, Zhejiang University, Hangzhou, Zhejiang, China
| | - Haofei Yu
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA.
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Lai S, Farnham A, Ruktanonchai NW, Tatem AJ. Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine. J Travel Med 2019; 26:taz019. [PMID: 30869148 PMCID: PMC6904325 DOI: 10.1093/jtm/taz019] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 03/08/2019] [Accepted: 03/08/2019] [Indexed: 11/15/2022]
Abstract
RATIONALE FOR REVIEW The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data. KEY FINDINGS Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel. CONCLUSIONS Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin-destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.
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Affiliation(s)
- Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- Flowminder Foundation, SE Stockholm, Sweden
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Dongan Road, Shanghai, China
| | - Andrea Farnham
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- Department of Public Health, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Nick W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- Flowminder Foundation, SE Stockholm, Sweden
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- Flowminder Foundation, SE Stockholm, Sweden
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Lai S, zu Erbach-Schoenberg E, Pezzulo C, Ruktanonchai NW, Sorichetta A, Steele J, Li T, Dooley CA, Tatem AJ. Exploring the use of mobile phone data for national migration statistics. PALGRAVE COMMUNICATIONS 2019; 5:34. [PMID: 31579302 PMCID: PMC6774788 DOI: 10.1057/s41599-019-0242-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Accepted: 03/01/2019] [Indexed: 05/22/2023]
Abstract
Statistics on internal migration are important for keeping estimates of subnational population numbers up-to-date as well as urban planning, infrastructure development and impact assessment, among other applications. However, migration flow statistics typically remain constrained by the logistics of infrequent censuses or surveys. The penetration rate of mobile phones is now high across the globe with rapid recent increases in ownership in low-income countries. Analysing the changing spatiotemporal distribution of mobile phone users through anonymized call detail records (CDRs) offers the possibility to measure migration at multiple temporal and spatial scales. Based on a dataset of 72 billion anonymized CDRs in Namibia from October 2010 to April 2014, we explore how internal migration estimates can be derived and modelled from CDRs at subnational and annual scales, and how precision and accuracy of these estimates compare to census-derived migration statistics. We also demonstrate the use of CDRs to assess how migration patterns change over time, with a finer temporal resolution compared to censuses. Moreover, we show how gravity-type spatial interaction models built using CDRs can accurately capture migration flows. Results highlight that estimates of migration flows made using mobile phone data is a promising avenue for complementing more traditional national migration statistics and obtaining more timely and local data.
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Affiliation(s)
- Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, 130 Dongan Road, Shanghai 200032, China
- Correspondence and requests for materials should be addressed to A.J.T () or S.L. ()
| | - Elisabeth zu Erbach-Schoenberg
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
| | - Carla Pezzulo
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Nick W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
| | - Alessandro Sorichetta
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
| | - Jessica Steele
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Tracey Li
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
| | - Claire A Dooley
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
- Correspondence and requests for materials should be addressed to A.J.T () or S.L. ()
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Carrasco-Escobar G, Castro MC, Barboza JL, Ruiz-Cabrejos J, Llanos-Cuentas A, Vinetz JM, Gamboa D. Use of open mobile mapping tool to assess human mobility traceability in rural offline populations with contrasting malaria dynamics. PeerJ 2019; 7:e6298. [PMID: 30697487 PMCID: PMC6346981 DOI: 10.7717/peerj.6298] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 12/18/2018] [Indexed: 11/20/2022] Open
Abstract
Infectious disease dynamics are affected by human mobility more powerfully than previously thought, and thus reliable traceability data are essential. In rural riverine settings, lack of infrastructure and dense tree coverage deter the implementation of cutting-edge technology to collect human mobility data. To overcome this challenge, this study proposed the use of a novel open mobile mapping tool, GeoODK. This study consists of a purposive sampling of 33 participants in six villages with contrasting patterns of malaria transmission that demonstrates a feasible approach to map human mobility. The self-reported traceability data allowed the construction of the first human mobility framework in rural riverine villages in the Peruvian Amazon. The mobility spectrum in these areas resulted in travel profiles ranging from 2 hours to 19 days; and distances between 10 to 167 km. Most Importantly, occupational-related mobility profiles with the highest displacements (in terms of time and distance) were observed in commercial, logging, and hunting activities. These data are consistent with malaria transmission studies in the area that show villages in watersheds with higher human movement are concurrently those with greater malaria risk. The approach we describe represents a potential tool to gather critical information that can facilitate malaria control activities.
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Affiliation(s)
- Gabriel Carrasco-Escobar
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru.,Division of Infectious Diseases, Department of Medicine, University of California, San Diego, La Jolla, CA, United States of America
| | - Marcia C Castro
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Jose Luis Barboza
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jorge Ruiz-Cabrejos
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Alejandro Llanos-Cuentas
- Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Joseph M Vinetz
- Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru.,Department of Infectious diseases, School of Medicine, Yale University, New Haven, CT, United States of America
| | - Dionicia Gamboa
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru.,Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru.,Departamento de Ciencias Celulares y Moleculares, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
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Fluctuations in anthropogenic nighttime lights from satellite imagery for five cities in Niger and Nigeria. Sci Data 2018; 5:180256. [PMID: 30422123 PMCID: PMC6233255 DOI: 10.1038/sdata.2018.256] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 09/27/2018] [Indexed: 11/23/2022] Open
Abstract
Dynamic measures of human populations are critical for global health management but are often overlooked, largely because they are difficult to quantify. Measuring human population dynamics can be prohibitively expensive in under-resourced communities. Satellite imagery can provide measurements of human populations, past and present, to complement public health analyses and interventions. We used anthropogenic illumination from publicly accessible, serial satellite nighttime images as a quantifiable proxy for seasonal population variation in five urban areas in Niger and Nigeria. We identified population fluxes as the mechanistic driver of regional seasonal measles outbreaks. Our data showed 1) urban illumination fluctuated seasonally, 2) corresponding population fluctuations were sufficient to drive seasonal measles outbreaks, and 3) overlooking these fluctuations during vaccination activities resulted in below-target coverage levels, incapable of halting transmission of the virus. We designed immunization solutions capable of achieving above-target coverage of both resident and mobile populations. Here, we provide detailed data on brightness from 2000–2005 for 5 cities in Niger and Nigeria and detailed methodology for application to other populations.
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Lai S, Johansson MA, Yin W, Wardrop NA, van Panhuis WG, Wesolowski A, Kraemer MUG, Bogoch II, Kain D, Findlater A, Choisy M, Huang Z, Mu D, Li Y, He Y, Chen Q, Yang J, Khan K, Tatem AJ, Yu H. Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005-2015. PLoS Negl Trop Dis 2018; 12:e0006743. [PMID: 30412575 PMCID: PMC6248995 DOI: 10.1371/journal.pntd.0006743] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 11/21/2018] [Accepted: 10/21/2018] [Indexed: 12/19/2022] Open
Abstract
Due to worldwide increased human mobility, air-transportation data and mathematical models have been widely used to measure risks of global dispersal of pathogens. However, the seasonal and interannual risks of pathogens importation and onward transmission from endemic countries have rarely been quantified and validated. We constructed a modelling framework, integrating air travel, epidemiological, demographical, entomological and meteorological data, to measure the seasonal probability of dengue introduction from endemic countries. This framework has been applied retrospectively to elucidate spatiotemporal patterns and increasing seasonal risk of dengue importation from South-East Asia into China via air travel in multiple populations, Chinese travelers and local residents, over a decade of 2005-15. We found that the volume of airline travelers from South-East Asia into China has quadrupled from 2005 to 2015 with Chinese travelers increased rapidly. Following the growth of air traffic, the probability of dengue importation from South-East Asia into China has increased dramatically from 2005 to 2015. This study also revealed seasonal asymmetries of transmission routes: Sri Lanka and Maldives have emerged as origins; neglected cities at central and coastal China have been increasingly vulnerable to dengue importation and onward transmission. Compared to the monthly occurrence of dengue reported in China, our model performed robustly for importation and onward transmission risk estimates. The approach and evidence could facilitate to understand and mitigate the changing seasonal threat of arbovirus from endemic regions.
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Affiliation(s)
- Shengjie Lai
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- WorldPop, Department of Geography and Environment, University of Southampton, Southampton, United Kingdom
- Division of Infectious Disease, Key Laboratory of Surveillance and Early–warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Changping District, Beijing, China
- Flowminder Foundation, Stockholm, Sweden
| | - Michael A. Johansson
- Dengue Branch, Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Wenwu Yin
- Division of Infectious Disease, Key Laboratory of Surveillance and Early–warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Changping District, Beijing, China
| | - Nicola A. Wardrop
- WorldPop, Department of Geography and Environment, University of Southampton, Southampton, United Kingdom
- Department for International Development, London, United Kingdom
| | - Willem G. van Panhuis
- Epidemiology and Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Moritz U. G. Kraemer
- Harvard Medical School, Harvard University, Boston, MA, United States of America
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA, United States of America
- Department of Zoology, University of Oxford, New Radcliffe House, Radcliffe Observatory Quarter, Oxford, United Kingdom
| | - Isaac I. Bogoch
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, ON, Canada
- Divisions of General Internal Medicine and Infectious Diseases, University Health Network, Toronto, ON, Canada
| | - Dylain Kain
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Aidan Findlater
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Marc Choisy
- MIVEGEC, IRD, CNRS, University of Montpellier, Montpellier, France
- Oxford University Clinical Research Unit, National Hospital for Tropical Diseases, Hanoi, Vietnam
| | - Zhuojie Huang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Di Mu
- Division of Infectious Disease, Key Laboratory of Surveillance and Early–warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Changping District, Beijing, China
| | - Yu Li
- Division of Infectious Disease, Key Laboratory of Surveillance and Early–warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Changping District, Beijing, China
| | - Yangni He
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Qiulan Chen
- Division of Infectious Disease, Key Laboratory of Surveillance and Early–warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Changping District, Beijing, China
| | - Juan Yang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Kamran Khan
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, Ontario, Canada
| | - Andrew J. Tatem
- WorldPop, Department of Geography and Environment, University of Southampton, Southampton, United Kingdom
- Flowminder Foundation, Stockholm, Sweden
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Division of Infectious Disease, Key Laboratory of Surveillance and Early–warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Changping District, Beijing, China
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