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Barreras F, Watts DJ. The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling. NATURE COMPUTATIONAL SCIENCE 2024; 4:398-411. [PMID: 38898315 DOI: 10.1038/s43588-024-00637-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 05/02/2024] [Indexed: 06/21/2024]
Abstract
Large-scale GPS location datasets hold immense potential for measuring human mobility and interpersonal contact, both of which are essential for data-driven epidemiology. However, despite their potential and widespread adoption during the COVID-19 pandemic, there are several challenges with these data that raise concerns regarding the validity and robustness of its applications. Here we outline two types of challenges-some related to accessing and processing these data, and some related to data quality-and propose several research directions to address them moving forward.
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Affiliation(s)
- Francisco Barreras
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Duncan J Watts
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
- Operations, Information and Decisions Department, Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
- Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA, USA.
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Nanyonjo G, Kwena Z, Nakamanya S, Okello E, Oketch B, Bahemuka UM, Ssetaala A, Okech B, Price MA, Kapiga S, Fast P, Bukusi E, Seeley J. Finding women in fishing communities around Lake Victoria: "Feasibility and acceptability of using phones and tracking devices". PLoS One 2024; 19:e0290634. [PMID: 38206982 PMCID: PMC10783786 DOI: 10.1371/journal.pone.0290634] [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: 02/18/2022] [Accepted: 08/01/2023] [Indexed: 01/13/2024] Open
Abstract
INTRODUCTION Women in fishing communities have both high HIV prevalence and incidence, hence they are a priority population for HIV prevention and treatment interventions. However, their mobility is likely to compromise the effectiveness of interventions. We assessed the acceptability, feasibility and of using phones and global positioning system (GPS) devices for tracking mobility, to inform future health research innovations. METHODS A mult-site formative qualitative study was conducted in six purposively selected Fishing Communities on the shores of Lake Victoria in Kenya, Tanzania, and Uganda. Participants were selected based on duration of stay in the community and frequency of movement. Sixty-four (64) women participated in the study (16 per fishing community). Twenty-four (24) participants were given a study phone; 24 were asked to use their own phones and 16 were provided with a portable GPS device to understand what is most preferred. Women were interviewed about their experiences and recommendations on carrying GPS devices or phones. Twenty four (24) Focus Group Discussions with 8-12 participants were conducted with community members to generate data on community perceptions regarding GPS devices and phones acceptability among women. Data were analyzed thematically and compared across sites/countries. RESULTS Women reported being willing to use tracking devices (both phones and GPS) because they are easy to carry. Their own phone was preferred compared to a study phone and GPS device because they were not required to carry an additional device, worry about losing it or be questioned about the extra device by their sexual partner. Women who carried GPS devices suggested more sensitization in communities to avoid domestic conflicts and public concern. Women suggested changing the GPS colour from white to a darker colour and, design to look like a commonly used object such as a telephone Subscriber Identity Module (SIM) card, a rosary/necklace or a ring for easy and safe storage. CONCLUSION Women in the study communities were willing to have their movements tracked, embraced the use of phones and GPS devices for mobility tracking. Devices need to be redesigned to be more discrete, but they could be valuable tools to understanding movement patterns and inform design of interventions for these mobile populations.
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Affiliation(s)
| | - Zachary Kwena
- Research Care and Training Program (RCTP), Kenya Medical Research Institute, Kisumu, Kenya
| | - Sarah Nakamanya
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine (MRC/UVRI & LSHTM) Uganda Research Unit, Entebbe, Uganda
| | - Elialilia Okello
- National Institute for Medical Research, Mwanza Intervention Trials Unit (MITU), Mwanza, Tanzania
| | - Bertha Oketch
- Research Care and Training Program (RCTP), Kenya Medical Research Institute, Kisumu, Kenya
| | - Ubaldo M. Bahemuka
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine (MRC/UVRI & LSHTM) Uganda Research Unit, Entebbe, Uganda
| | - Ali Ssetaala
- UVRI-IAVI HIV Vaccine Program Limited, Entebbe, Uganda
| | - Brenda Okech
- UVRI-IAVI HIV Vaccine Program Limited, Entebbe, Uganda
| | - Matt A. Price
- IAVI, New York, NY, United States of America
- Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, CA, United States of America
| | - Saidi Kapiga
- National Institute for Medical Research, Mwanza Intervention Trials Unit (MITU), Mwanza, Tanzania
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Pat Fast
- IAVI, New York, NY, United States of America
| | - Elizabeth Bukusi
- Research Care and Training Program (RCTP), Kenya Medical Research Institute, Kisumu, Kenya
| | - Janet Seeley
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine (MRC/UVRI & LSHTM) Uganda Research Unit, Entebbe, Uganda
- London School of Hygiene and Tropical Medicine, London, United Kingdom
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Ward T, Morris M, Gelman A, Carpenter B, Ferguson W, Overton C, Fyles M. Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England. PLoS Comput Biol 2023; 19:e1011580. [PMID: 37956206 PMCID: PMC10756685 DOI: 10.1371/journal.pcbi.1011580] [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: 02/02/2023] [Revised: 12/29/2023] [Accepted: 10/09/2023] [Indexed: 11/15/2023] Open
Abstract
In the early phases of growth, resurgent epidemic waves of SARS-CoV-2 incidence have been characterised by localised outbreaks. Therefore, understanding the geographic dispersion of emerging variants at the start of an outbreak is key for situational public health awareness. Using telecoms data, we derived mobility networks describing the movement patterns between local authorities in England, which we have used to inform the spatial structure of a Bayesian BYM2 model. Surge testing interventions can result in spatio-temporal sampling bias, and we account for this by extending the BYM2 model to include a random effect for each timepoint in a given area. Simulated-scenario modelling and real-world analyses of each variant that became dominant in England were conducted using our BYM2 model at local authority level in England. Simulated datasets were created using a stochastic metapopulation model, with the transmission rates between different areas parameterised using telecoms mobility data. Different scenarios were constructed to reproduce real-world spatial dispersion patterns that could prove challenging to inference, and we used these scenarios to understand the performance characteristics of the BYM2 model. The model performed better than unadjusted test positivity in all the simulation-scenarios, and in particular when sample sizes were small, or data was missing for geographical areas. Through the analyses of emerging variant transmission across England, we found a reduction in the early growth phase geographic clustering of later dominant variants as England became more interconnected from early 2022 and public health interventions were reduced. We have also shown the recent increased geographic spread and dominance of variants with similar mutations in the receptor binding domain, which may be indicative of convergent evolution of SARS-CoV-2 variants.
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Affiliation(s)
- Thomas Ward
- UK Health Security Agency, Infectious Disease Modelling Team, London, United Kingdom
| | - Mitzi Morris
- The University of Columbia, Institute for Social and Economic Research and Policy, New York, New York, United States of America
| | - Andrew Gelman
- The University of Columbia, Department of Statistics, New York, New York, United States of America
| | - Bob Carpenter
- The Flatiron Institute, Centre for Computational Mathematics, New York, New York, United Kingdom
| | - William Ferguson
- UK Health Security Agency, Infectious Disease Modelling Team, London, United Kingdom
| | - Christopher Overton
- UK Health Security Agency, Infectious Disease Modelling Team, London, United Kingdom
- The University of Liverpool, Department of Mathematics, Liverpool, United Kingdom
| | - Martyn Fyles
- UK Health Security Agency, Infectious Disease Modelling Team, London, United Kingdom
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Gibbs H, Musah A, Seidu O, Ampofo W, Asiedu-Bekoe F, Gray J, Adewole WA, Cheshire J, Marks M, Eggo RM. Call detail record aggregation methodology impacts infectious disease models informed by human mobility. PLoS Comput Biol 2023; 19:e1011368. [PMID: 37561812 PMCID: PMC10443843 DOI: 10.1371/journal.pcbi.1011368] [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: 01/25/2023] [Revised: 08/22/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023] Open
Abstract
This paper demonstrates how two different methods used to calculate population-level mobility from Call Detail Records (CDR) produce varying predictions of the spread of epidemics informed by these data. Our findings are based on one CDR dataset describing inter-district movement in Ghana in 2021, produced using two different aggregation methodologies. One methodology, "all pairs," is designed to retain long distance network connections while the other, "sequential" methodology is designed to accurately reflect the volume of travel between locations. We show how the choice of methodology feeds through models of human mobility to the predictions of a metapopulation SEIR model of disease transmission. We also show that this impact varies depending on the location of pathogen introduction and the transmissibility of infections. For central locations or highly transmissible diseases, we do not observe significant differences between aggregation methodologies on the predicted spread of disease. For less transmissible diseases or those introduced into remote locations, we find that the choice of aggregation methodology influences the speed of spatial spread as well as the size of the peak number of infections in individual districts. Our findings can help researchers and users of epidemiological models to understand how methodological choices at the level of model inputs may influence the results of models of infectious disease transmission, as well as the circumstances in which these choices do not alter model predictions.
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Affiliation(s)
- Hamish Gibbs
- Department of Geography, University College London, London, United Kingdom
| | - Anwar Musah
- Department of Geography, University College London, London, United Kingdom
| | | | - William Ampofo
- Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Accra, Ghana
| | | | | | | | - James Cheshire
- Department of Geography, University College London, London, United Kingdom
| | - Michael Marks
- Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Hospital for Tropical Diseases, University College London Hospital, London, United Kingdom
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Rosalind M. Eggo
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
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Wang M, Wang C, Peng X. Texting in a crisis-using SMS for information and emotional support during COVID-19: A mixed methods research study. FRONTIERS IN SOCIOLOGY 2022; 7:1053970. [PMID: 36530452 PMCID: PMC9748284 DOI: 10.3389/fsoc.2022.1053970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/07/2022] [Indexed: 06/17/2023]
Abstract
In the era of new media, short message service (SMS) is no longer seen as advantageous and it is no longer used very much by the Chinese public. However, as a traditional media, local governments managing public health crises used SMS as a way of meeting the public's need for emotional support during the COVID-19 pandemic. Our study examined 108 SMS texts pushed to phones in Chongqing between January and December 2020, and carried out in-depth interviews with ten interviewees. This mixed research method of descriptive and grounded theory analysis was designed to investigate how SMS was used to communicate prevention guidelines and give emotional support during COVID-19. The results show that Chongqing Municipal Health and Health Commission gained the public's attention with SMS messages consisting of neutral, objective advice, and guidance to reduce people's anxiety and panic. However, with the stabilization of COVID-19, SMS has once again been discarded by users, including the public health sector. The study found that the emotional support offered by SMS was limited to the elderly, a subset of the population considered to be weak users of the internet. SMS has been replaced by other technologies, but along with other media, such as official media and social media, it has shaped the media communication environment and served as an emotional support channel for the public. Undoubtedly,the use of SMS during COVID-19 presents a research opportunity for exploring its capacity for prevention, control and emotional support.
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Affiliation(s)
- Mengdi Wang
- School of Public Policy and Administration, Chongqing University, Chongqing, China
| | - Changzheng Wang
- School of Public Policy and Administration, Chongqing Technology and Business University, Chongqing, China
| | - Xiaobing Peng
- School of Public Policy and Administration, Chongqing University, Chongqing, China
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Obeidat Z, Obeidat MI. A typology of Jordanian consumers after Covid-19: The rational, the suspicious, and the cautious consumer. THE JOURNAL OF CONSUMER AFFAIRS 2022; 57:JOCA12493. [PMID: 36714886 PMCID: PMC9874892 DOI: 10.1111/joca.12493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 10/02/2022] [Accepted: 10/11/2022] [Indexed: 06/18/2023]
Abstract
This study presents a detailed typology of Jordanian consumers that identifies the effects of Covid-19 on their personal, social, and purchase and consumption patterns following the pandemic. Based on a qualitative approach using a sample of Jordanian consumers (N = 71), this study identifies three main types of consumers: the rational, suspicious, and cautious. All were distinguished by cognitive, emotional, and behavioral differences: the rational consumers viewed the pandemic as a natural occurrence, were willing to vaccinate, and took the opportunity to improve their lives and consumption behavior; the suspicious consumers viewed the pandemic as a man-made virus and refused to rationalize their behaviors and follow the social-distancing rules or vaccinate; and the cautious consumers were generally somewhere in between, and while they improved some aspects of their consumption, social, and personal lives, other aspects either remained the same or worsened. The findings have implications for managers and governmental bodies.
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Affiliation(s)
- Zaid Obeidat
- School of BusinessUniversity of JordanAmmanJordan
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7
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Wilber MQ, Yang A, Boughton R, Manlove KR, Miller RS, Pepin KM, Wittemyer G. A model for leveraging animal movement to understand spatio-temporal disease dynamics. Ecol Lett 2022; 25:1290-1304. [PMID: 35257466 DOI: 10.1111/ele.13986] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/27/2021] [Accepted: 02/04/2022] [Indexed: 12/19/2022]
Abstract
The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable model that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible model 'Movement-driven modelling of spatio-temporal infection risk' (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.
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Affiliation(s)
- Mark Q Wilber
- Forestry, Wildlife, and Fisheries, Institute of Agriculture, University of Tennessee, Knoxville, Tennessee, USA
| | - Anni Yang
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Fort Collins, Colorado, USA.,Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA.,Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, Oklahoma, USA
| | - Raoul Boughton
- Archbold Biological Station, Buck Island Ranch, Lake Placid, Florida, USA
| | - Kezia R Manlove
- Department of Wildland Resources and Ecology Center, Utah State University, Logan, Utah, USA
| | - Ryan S Miller
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Service, Center for Epidemiology and Animal Health, Fort Collins, Colorado, USA
| | - Kim M Pepin
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Fort Collins, Colorado, USA
| | - George Wittemyer
- Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
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8
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Yabe T, Tsubouchi K, Sekimoto Y, Ukkusuri SV. Early warning of COVID-19 hotspots using human mobility and web search query data. COMPUTERS, ENVIRONMENT AND URBAN SYSTEMS 2022; 92:101747. [PMID: 34931101 PMCID: PMC8673829 DOI: 10.1016/j.compenvurbsys.2021.101747] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 12/06/2021] [Accepted: 12/08/2021] [Indexed: 06/14/2023]
Abstract
COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts the locations of outbreaks is of crucial importance. Studies have shown the effectiveness of using large-scale mobility data to monitor the impacts of non-pharmaceutical interventions (e.g., lockdowns) through population density analysis. However, predicting the locations of potential outbreak occurrence is difficult using mobility data alone. Meanwhile, web search queries have been shown to be good predictors of the disease spread. In this study, we utilize a unique dataset of human mobility trajectories (GPS traces) and web search queries with common user identifiers (> 450 K users), to predict COVID-19 hotspot locations beforehand. More specifically, web search query analysis is conducted to identify users with high risk of COVID-19 contraction, and social contact analysis was further performed on the mobility patterns of these users to quantify the risk of an outbreak. Our approach is empirically tested using data collected from users in Tokyo, Japan. We show that by integrating COVID-19 related web search query analytics with social contact networks, we are able to predict COVID-19 hotspot locations 1-2 weeks beforehand, compared to just using social contact indexes or web search data analysis. This study proposes a novel method that can be used in early warning systems for disease outbreak hotspots, which can assist government agencies to prepare effective strategies to prevent further disease spread. Human mobility data and web search query data linked with common IDs are used to predict COVID-19 outbreaks. High risk social contact index captures both the contact density and COVID-19 contraction risks of individuals. Real world data was collected from 200 K individual users in Tokyo during the COVID-19 pandemic. Experiments showed that the index can be used for microscopic outbreak early warning.
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Affiliation(s)
- Takahiro Yabe
- Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Avenue, West Lafayette, IN 47907, USA
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, 50 Ames St, Cambridge, MA 02142, USA
| | - Kota Tsubouchi
- Yahoo Japan Corporation, Kioi Tower, Tokyo, Garden Terrace Kioicho, 1-3, Kioi-cho, Chiyoda-ku, Tokyo, Japan
| | - Yoshihide Sekimoto
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba Meguro-Ku, Tokyo 153-8505, Japan
| | - Satish V Ukkusuri
- Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Avenue, West Lafayette, IN 47907, USA
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Detecting People on the Street and the Streetscape Physical Environment from Baidu Street View Images and Their Effects on Community-Level Street Crime in a Chinese City. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11030151] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The occurrence of street crime is affected by socioeconomic and demographic characteristics and is also influenced by streetscape conditions. Understanding how the spatial distribution of street crime is associated with different streetscape features is significant for establishing crime prevention and city management strategies. Conventional data sources that quantify people on the street and streetscape characteristics, such as questionnaires, field surveys, or manual audits, are labor-intensive, time-consuming, and unable to cover a large area with a sufficient spatial resolution. Emerging cell phone and social media data have been used to measure ambient population, but they cannot distinguish between the street and indoor populations. This study addresses these limitations by combining Baidu Street View (BSV) images, deep learning algorithms, and spatial statistical regression models to examine the influences of people on the street and in the streetscape physical environment on street crime in a large Chinese city. First, we collected fine-grained street view images from the Baidu Map website. Then, we constructed a Faster R-CNN network to detect discrete elements with distinct outlines (such as persons) in each image. From this, we counted the number of people on the street in every BSV image and finally obtained the community-level total amounts. Additionally, the PSPNet network was developed for pixel-wise semantic segmentation to determine the proportions of other streetscape features such as buildings in each BSV image, based on which we obtained their community-level averages. The quantitative measurement of people on the street and a set of streetscape features that had potential influences on crime were finally derived by combining the outputs of two deep learning networks. To account for the spatial autocorrelation effect and distributional characteristics of crime data, we constructed a set of spatial lag negative binomial regression models to investigate how three types of street crime (i.e., total crime, property crime, and violent crime) were affected by the number of people on the street and the streetscape-built conditions. The models also controlled the effect of socioeconomic and demographic factors, land use features, the formal surveillance level, and transportation facilities. The models with people on the street and streetscape environment features had noticeable performance improvements, demonstrating the necessity for accounting for the effect of these factors when understanding street crime. Specifically, the number of people on the street had significantly positive impacts on the total street crime and street property crime. However, no statistically significant impact was found on street violent crime. The average proportions of the paths, buildings, and trees were associated with significantly lower street crime among physical streetscape features. Additionally, the statistical significances of most control variables conformed to previous research findings. This study is the first to combine Street View images and deep learning algorithms to retrieve the number of people on the street and the features of the visual streetscape environment to understand street crime.
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Yuan B, Lee H, Nishiura H. Analysis of international traveler mobility patterns in Tokyo to identify geographic foci of dengue fever risk. Theor Biol Med Model 2021; 18:17. [PMID: 34602095 PMCID: PMC8487561 DOI: 10.1186/s12976-021-00149-8] [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: 06/23/2021] [Accepted: 09/21/2021] [Indexed: 11/10/2022] Open
Abstract
Travelers play a role in triggering epidemics of imported dengue fever because they can carry the virus to other countries during the incubation period. If a traveler carrying dengue virus visits open green space and is bitten by mosquitoes, a local outbreak can ensue. In the present study, we aimed to understand the movement patterns of international travelers in Tokyo using mobile phone data, with the goal of identifying geographical foci of dengue transmission. We analyzed datasets based on mobile phone access to WiFi systems and measured the spatial distribution of international visitors in Tokyo on two specific dates (one weekday in July 2017 and another weekday in August 2017). Mobile phone users were classified by nationality into three groups according to risk of dengue transmission. Sixteen national parks were selected based on their involvement in a 2014 dengue outbreak and abundance of Aedes mosquitoes. We found that not all national parks were visited by international travelers and that visits to cemeteries were very infrequent. We also found that travelers from countries with high dengue prevalence were less likely to visit national parks compared with travelers from dengue-free countries. Travelers from countries with sporadic dengue cases and countries with regional transmission tended to visit common destinations. By contrast, the travel footprints of visitors from countries with continuous dengue transmission were focused on non-green spaces. Entomological surveillance in Tokyo has been restricted to national parks since the 2014 dengue outbreak. However, our results indicate that areas subject to surveillance should include both public and private green spaces near tourist sites.
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Affiliation(s)
- Baoyin Yuan
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido, 060-8638, Japan.,CREST, Japan Science and Technology Agency, Honcho 4-1-8, Kawaguchi, Saitama, 332-0012, Japan.,School of Mathematics, South China University of Technology, 381 Wushan Rd, Tianhe District, Guangzhou, China
| | - Hyojung Lee
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido, 060-8638, Japan.,CREST, Japan Science and Technology Agency, Honcho 4-1-8, Kawaguchi, Saitama, 332-0012, Japan.,Department of Statistics, Kyungpook National University, Daegu, 41566, South Korea
| | - Hiroshi Nishiura
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido, 060-8638, Japan. .,CREST, Japan Science and Technology Agency, Honcho 4-1-8, Kawaguchi, Saitama, 332-0012, Japan. .,Kyoto University School of Public Health, Yoshidakonoecho, Sakyoku, Kyoto, 6068501, Japan.
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11
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Meredith HR, Giles JR, Perez-Saez J, Mande T, Rinaldo A, Mutembo S, Kabalo EN, Makungo K, Buckee CO, Tatem AJ, Metcalf CJE, Wesolowski A. Characterizing human mobility patterns in rural settings of sub-Saharan Africa. eLife 2021; 10:e68441. [PMID: 34533456 PMCID: PMC8448534 DOI: 10.7554/elife.68441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 08/21/2021] [Indexed: 11/27/2022] Open
Abstract
Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.
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Affiliation(s)
- Hannah R Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - John R Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Théophile Mande
- Bureau d'Etudes Scientifiques et Techniques - Eau, Energie, Environnement (BEST-3E), Ouagadougou, Burkina Faso
| | - Andrea Rinaldo
- Dipartimento di Ingegneria Civile Edile ed Ambientale, Università di Padova, Padova, Italy
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Simon Mutembo
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
- Macha Research Trust, Choma, Zambia
| | - Elliot N Kabalo
- Zambia Information and Communications Technology Authority, Lusaka, Zambia
| | | | - Caroline O Buckee
- Department of Epidemiology and the Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, United States
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology and the Princeton School of Public and International Affairs, Princeton University, Princeton, United States
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
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12
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Yin L, Zhang H, Li Y, Liu K, Chen T, Luo W, Lai S, Li Y, Tang X, Ning L, Feng S, Wei Y, Zhao Z, Wen Y, Mao L, Mei S. A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities. J R Soc Interface 2021; 18:20210112. [PMID: 34428950 PMCID: PMC8385367 DOI: 10.1098/rsif.2021.0112] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 08/02/2021] [Indexed: 12/12/2022] Open
Abstract
Before herd immunity against Coronavirus disease 2019 (COVID-19) is achieved by mass vaccination, science-based guidelines for non-pharmaceutical interventions are urgently needed to reopen megacities. This study integrated massive mobile phone tracking records, census data and building characteristics into a spatially explicit agent-based model to simulate COVID-19 spread among 11.2 million individuals living in Shenzhen City, China. After validation by local epidemiological observations, the model was used to assess the probability of COVID-19 resurgence if sporadic cases occurred in a fully reopened city. Combined scenarios of three critical non-pharmaceutical interventions (contact tracing, mask wearing and prompt testing) were assessed at various levels of public compliance. Our results show a greater than 50% chance of disease resurgence if the city reopened without contact tracing. However, tracing household contacts, in combination with mandatory mask use and prompt testing, could suppress the probability of resurgence under 5% within four weeks. If household contact tracing could be expanded to work/class group members, the COVID resurgence could be avoided if 80% of the population wear facemasks and 40% comply with prompt testing. Our assessment, including modelling for different scenarios, helps public health practitioners tailor interventions within Shenzhen City and other world megacities under a variety of suppression timelines, risk tolerance, healthcare capacity and public compliance.
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Affiliation(s)
- Ling Yin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, People's Republic of China
| | - Hao Zhang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Yuan Li
- Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, Guangdong, People's Republic of China
| | - Kang Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, Fujian, People's Republic of China
| | - Wei Luo
- Geography Department, National University of Singapore, AS2-03-01, 1 Arts Link, Singapore 117570, Republic of Singapore
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Ye Li
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, People's Republic of China
| | - Xiujuan Tang
- Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, Guangdong, People's Republic of China
| | - Li Ning
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, People's Republic of China
| | - Shengzhong Feng
- National Supercomputing Center in Shenzhen, Shenzhen 518055, Guangdong, People's Republic of China
| | - Yanjie Wei
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, People's Republic of China
| | - Zhiyuan Zhao
- The Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, Fujian, People's Republic of China
| | - Ying Wen
- Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, Guangdong, People's Republic of China
| | - Liang Mao
- Department of Geography, University of Florida, Gainesville, FL 32611, USA
| | - Shujiang Mei
- Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, Guangdong, People's Republic of China
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13
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The contribution of telco data to fight the COVID-19 pandemic: Experience of Telefonica throughout its footprint. DATA & POLICY 2021. [DOI: 10.1017/dap.2021.6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Abstract
The COVID-19 pandemic is a global challenge for humanity, in which a large number of resources are invested to develop effective vaccines and treatments. At the same time, governments try to manage the spread of the disease while alleviating the strong impact derived from the slowdown in economic activity. Governments were forced to impose strict lockdown measures to tackle the pandemic. This significantly changed people’s mobility and habits, subsequently impacting the economy. In this context, the availability of tools to effectively monitor and quantify mobility was key for public institutions to decide which policies to implement and for how long. Telefonica has promoted different initiatives to offer governments mobility insights throughout many of the countries where it operates in Europe and Latin America. Mobility indicators with high spatial granularity and frequency of updates were successfully deployed in different formats. However, Telefonica faced many challenges (not only technical) to put these tools into service in a short timing: from reducing latency in insights to ensuring the security and privacy of information. In this article, we provide details on how Telefonica engaged with governments and other stakeholders in different countries as a response to the pandemic. We also cover the challenges faced and the shared learnings from Telefonica’s experience in those countries.
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14
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Improving influenza surveillance based on multi-granularity deep spatiotemporal neural network. Comput Biol Med 2021; 134:104482. [PMID: 34051452 DOI: 10.1016/j.compbiomed.2021.104482] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/16/2021] [Accepted: 05/06/2021] [Indexed: 11/23/2022]
Abstract
Influenza is a common respiratory disease that can cause human illness and death. Timely and accurate prediction of disease risk is of great importance for public health management and prevention. The influenza data belong to typical spatiotemporal data in that influenza transmission is influenced by regional and temporal interactions. Many existing methods only use the historical time series information for prediction, which ignores the effect of spatial correlations of neighboring regions and temporal correlations of different time periods. Mining spatiotemporal information for risk prediction is a significant and challenging issue. In this paper, we propose a new end-to-end spatiotemporal deep neural network structure for influenza risk prediction. The proposed model mainly consists of two parts. The first stage is the spatiotemporal feature extraction stage where two-stream convolutional and recurrent neural networks are constructed to extract the different regions and time granularity information. Then, a dynamically parametric-based fusion method is adopted to integrate the two-stream features and making predictions. In our work, we demonstrate that our method, tested on two influenza-like illness (ILI) datasets (US-HHS and SZ-HIC), achieved the best performance across all evaluation metrics. The results imply that our method has outstanding performance for spatiotemporal feature extraction and enables accurate predictions compared to other well-known influenza forecasting models.
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15
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Lieberthal B, Gardner AM. Connectivity, reproduction number, and mobility interact to determine communities' epidemiological superspreader potential in a metapopulation network. PLoS Comput Biol 2021; 17:e1008674. [PMID: 33735223 PMCID: PMC7971523 DOI: 10.1371/journal.pcbi.1008674] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 01/05/2021] [Indexed: 12/23/2022] Open
Abstract
Disease epidemic outbreaks on human metapopulation networks are often driven by a small number of superspreader nodes, which are primarily responsible for spreading the disease throughout the network. Superspreader nodes typically are characterized either by their locations within the network, by their degree of connectivity and centrality, or by their habitat suitability for the disease, described by their reproduction number (R). Here we introduce a model that considers simultaneously the effects of network properties and R on superspreaders, as opposed to previous research which considered each factor separately. This type of model is applicable to diseases for which habitat suitability varies by climate or land cover, and for direct transmitted diseases for which population density and mitigation practices influences R. We present analytical models that quantify the superspreader capacity of a population node by two measures: probability-dependent superspreader capacity, the expected number of neighboring nodes to which the node in consideration will randomly spread the disease per epidemic generation, and time-dependent superspreader capacity, the rate at which the node spreads the disease to each of its neighbors. We validate our analytical models with a Monte Carlo analysis of repeated stochastic Susceptible-Infected-Recovered (SIR) simulations on randomly generated human population networks, and we use a random forest statistical model to relate superspreader risk to connectivity, R, centrality, clustering, and diffusion. We demonstrate that either degree of connectivity or R above a certain threshold are sufficient conditions for a node to have a moderate superspreader risk factor, but both are necessary for a node to have a high-risk factor. The statistical model presented in this article can be used to predict the location of superspreader events in future epidemics, and to predict the effectiveness of mitigation strategies that seek to reduce the value of R, alter host movements, or both.
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16
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Brown TS, Engø-Monsen K, Kiang MV, Mahmud AS, Maude RJ, Buckee CO. The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok. Epidemics 2021; 35:100441. [PMID: 33667878 PMCID: PMC7899011 DOI: 10.1016/j.epidem.2021.100441] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 01/20/2021] [Accepted: 02/01/2021] [Indexed: 11/24/2022] Open
Abstract
Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics.
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Affiliation(s)
- Tyler S Brown
- Massachusetts General Hospital, Infectious Diseases Division, United States of America; Harvard T.H. Chan School of Public Health, United States of America.
| | | | - Mathew V Kiang
- Stanford University School of Medicine, Department of Epidemiology and Population Health, United States of America
| | - Ayesha S Mahmud
- University of California, Berkeley, Demography Department, United States of America
| | - Richard J Maude
- Harvard T.H. Chan School of Public Health, United States of America; Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Thailand; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, United Kingdom
| | - Caroline O Buckee
- Harvard T.H. Chan School of Public Health, United States of America.
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17
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Engebretsen S, Engø-Monsen K, Aleem MA, Gurley ES, Frigessi A, de Blasio BF. Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh. J R Soc Interface 2020; 17:20190809. [PMID: 32546112 PMCID: PMC7328378 DOI: 10.1098/rsif.2019.0809] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Human mobility plays a major role in the spatial dissemination of infectious diseases. We develop a spatio-temporal stochastic model for influenza-like disease spread based on estimates of human mobility. The model is informed by mobile phone mobility data collected in Bangladesh. We compare predictions of models informed by daily mobility data (reference) with that of models informed by time-averaged mobility data, and mobility model approximations. We find that the gravity model overestimates the spatial synchrony, while the radiation model underestimates the spatial synchrony. Using time-averaged mobility resulted in spatial spreading patterns comparable to the daily mobility model. We fit the model to 2014–2017 influenza data from sentinel hospitals in Bangladesh, using a sequential version of approximate Bayesian computation. We find a good agreement between our estimated model and the case data. We estimate transmissibility and regional spread of influenza in Bangladesh, which are useful for policy planning. Time-averaged mobility appears to be a good proxy for human mobility when modelling infectious diseases. This motivates a more general use of the time-averaged mobility, with important implications for future studies and outbreak control. Moreover, time-averaged mobility is subject to less privacy concerns than daily mobility, containing less temporal information on individual movements.
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Affiliation(s)
- Solveig Engebretsen
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway.,Norwegian Computing Center, Oslo, Norway
| | | | - Mohammad Abdul Aleem
- International Centre for Diarrhoeal Disease Research, Bangladesh, ICDDR,B, Dhaka, Bangladesh
| | - Emily Suzanne Gurley
- International Centre for Diarrhoeal Disease Research, Bangladesh, ICDDR,B, Dhaka, Bangladesh.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
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18
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Kwena Z, Nakamanya S, Nanyonjo G, Okello E, Fast P, Ssetaala A, Oketch B, Price M, Kapiga S, Bukusi E, Seeley J. Understanding mobility and sexual risk behaviour among women in fishing communities of Lake Victoria in East Africa: a qualitative study. BMC Public Health 2020; 20:944. [PMID: 32539818 PMCID: PMC7296721 DOI: 10.1186/s12889-020-09085-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 06/10/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND HIV-prevalence and incidence is high in many fishing communities around Lake Victoria in East Africa. In these settings, mobility among women is high and may contribute to increased risk of HIV infection and poor access to effective prevention and treatment services. Understanding the nature and patterns of this mobility is important for the design of interventions. We conducted an exploratory study to understand the nature and patterns of women's mobility to inform the design of HIV intervention trials in fishing communities of Lake Victoria. METHODS This was a cross-sectional formative qualitative study conducted in six purposively selected fishing communities in Kenya, Tanzania and Uganda. Potential participants were screened for eligibility on age (18+ years) and having stayed in the fishing community for more than 6 months. We collected data using introductory and focus group discussions, and in-depth interviews with key informants. Data focused on: history and patterns of mobility, migration in and out of fishing communities and the relationship between mobility and HIV infection. Since the interviews and discussions were not audio-recorded, detailed notes were taken and written up into full scripts for analysis. We conducted a thematic analysis using constant comparison analysis. RESULTS Participants reported that women in fishing communities were highly mobile for work-related activities. Overall, we categorized mobility as travels over long and short distances or periods depending on the kind of livelihood activity women were involved in. Participants reported that women often travelled to new places, away from familiar contacts and far from healthcare access. Some women were reported to engage in high risk sexual behaviour and disengaging from HIV care. However, participants reported that women often returned to the fishing communities they considered home, or followed a seasonal pattern of work, which would facilitate contact with service providers. CONCLUSION Women exhibited circular and seasonal mobility patterns over varying distances and duration away from their home communities. These mobility patterns may limit women's access to trial/health services and put them at risk of HIV-infection. Interventions should be tailored to take into account mobility patterns of seasonal work observed in this study.
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Affiliation(s)
- Zachary Kwena
- Research Care and Training Program (RCTP), Kenya Medical Research Institute, Kisumu, Kenya.
| | - Sarah Nakamanya
- Medical Research Council/Uganda Virus Research Institute MRC/UVRI and London School of Hygiene and Tropical Medicine (LSHTM) Uganda Research Unit, Entebbe, Uganda
| | - Gertrude Nanyonjo
- Uganda Virus Research Institute-International AIDS Vaccine Initiative (UVRI-IAVI) Project, Uganda Virus Research Institute, Entebbe, Uganda
| | - Elialilia Okello
- Mwanza Intervention Trials Unit (MITU), National Institute for Medical Research, Mwanza, Tanzania
| | - Pat Fast
- International AIDS Vaccine Initiative (IAVI), New York, USA
| | - Ali Ssetaala
- Uganda Virus Research Institute-International AIDS Vaccine Initiative (UVRI-IAVI) Project, Uganda Virus Research Institute, Entebbe, Uganda
| | - Bertha Oketch
- Research Care and Training Program (RCTP), Kenya Medical Research Institute, Kisumu, Kenya
| | - Matt Price
- International AIDS Vaccine Initiative (IAVI), New York, USA
| | - Saidi Kapiga
- Mwanza Intervention Trials Unit (MITU), National Institute for Medical Research, Mwanza, Tanzania
- London School of Hygiene and Tropical Medicine, London, UK
| | - Elizabeth Bukusi
- Research Care and Training Program (RCTP), Kenya Medical Research Institute, Kisumu, Kenya
| | - Janet Seeley
- Medical Research Council/Uganda Virus Research Institute MRC/UVRI and London School of Hygiene and Tropical Medicine (LSHTM) Uganda Research Unit, Entebbe, Uganda
- London School of Hygiene and Tropical Medicine, London, UK
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19
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Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2020. [DOI: 10.3390/ijgi9060342] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In the spatial analysis of crime, the residential population has been a conventional measure of the population at risk. Recent studies suggest that the ambient population is a useful alternative measure of the population at risk that can better capture the activity patterns of a population. However, current studies are limited by the availability of high precision demographic characteristics, such as social activities and the origins of residents. In this research, we use spatially referenced mobile phone data to measure the size and activity patterns of various types of ambient population, and further investigate the link between urban larceny-theft and population with multiple demographic and activity characteristics. A series of crime attractors, generators, and detractors are also considered in the analysis to account for the spatial variation of crime opportunities. The major findings based on a negative binomial model are three-fold. (1) The size of the non-local population and people’s social regularity calculated from mobile phone big data significantly correlate with the spatial variation of larceny-theft. (2) Crime attractors, generators, and detractors, measured by five types of Points of Interest (POIs), significantly depict the criminality of places and impact opportunities for crime. (3) Higher levels of nighttime light are associated with increased levels of larceny-theft. The results have practical implications for linking the ambient population to crime, and the insights are informative for several theories of crime and crime prevention efforts.
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20
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Ekong I, Chukwu E, Chukwu M. COVID-19 Mobile Positioning Data Contact Tracing and Patient Privacy Regulations: Exploratory Search of Global Response Strategies and the Use of Digital Tools in Nigeria. JMIR Mhealth Uhealth 2020; 8:e19139. [PMID: 32310817 PMCID: PMC7187764 DOI: 10.2196/19139] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 04/17/2020] [Accepted: 04/19/2020] [Indexed: 01/21/2023] Open
Abstract
Background The coronavirus disease (COVID-19) pandemic is the biggest global economic and health challenge of the century. Its effect and impact are still evolving, with deaths estimated to reach 40 million if unchecked. One effective and complementary strategy to slow the spread and reduce the impact is to trace the primary and secondary contacts of confirmed COVID-19 cases using contact tracing technology. Objective The objective of this paper is to survey strategies for digital contact tracing for the COVID-19 pandemic and to present how using mobile positioning data conforms with Nigeria’s data privacy regulations. Methods We conducted an exploratory review of current measures for COVID-19 contact tracing implemented around the world. We then analyzed how countries are using mobile positioning data technology to reduce the spread of COVID-19. We made recommendations on how Nigeria can adopt this approach while adhering to the guidelines provided by the National Data Protection Regulation (NDPR). Results Despite the potential of digital contact tracing, it always conflicts with patient data privacy regulations. We found that Nigeria’s response complies with the NDPR, and that it is possible to leverage call detail records to complement current strategies within the NDPR. Conclusions Our study shows that mobile position data contact tracing is important for epidemic control as long as it conforms to relevant data privacy regulations. Implementation guidelines will limit data misuse.
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Affiliation(s)
- Iniobong Ekong
- Department of Health Planning, Research and Statistics, FCT Health and Human Services Secretariat, Abuja, Nigeria
| | - Emeka Chukwu
- Department of Computer Information System, Faculty of Information & Communication Technology, University of Malta, Msida, Malta
| | - Martha Chukwu
- Ragnar Nurkse Department of Innovation and Governance, School of Business and Governance, Tallinn University of Technology, Tallinn, Estonia
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21
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Lenormand M, Samaniego H, Chaves JC, da Fonseca Vieira V, da Silva MAHB, Evsukoff AG. Entropy as a Measure of Attractiveness and Socioeconomic Complexity in Rio de Janeiro Metropolitan Area. ENTROPY 2020; 22:e22030368. [PMID: 33286142 PMCID: PMC7516840 DOI: 10.3390/e22030368] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 03/06/2020] [Accepted: 03/13/2020] [Indexed: 02/02/2023]
Abstract
Defining and measuring spatial inequalities across the urban environment remains a complex and elusive task which has been facilitated by the increasing availability of large geolocated databases. In this study, we rely on a mobile phone dataset and an entropy-based metric to measure the attractiveness of a location in the Rio de Janeiro Metropolitan Area (Brazil) as the diversity of visitors’ location of residence. The results show that the attractiveness of a given location measured by entropy is an important descriptor of the socioeconomic status of the location, and can thus be used as a proxy for complex socioeconomic indicators.
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Affiliation(s)
- Maxime Lenormand
- TETIS, Univ Montpellier, AgroParisTech, Cirad, CNRS, INRAE, 34000 Montpellier, France
| | - Horacio Samaniego
- Laboratorio de Ecoinformática, Instituto de Conservación Biodiersidad y Territorio, Campus Isla Teja s/n, Valdivia 5110290, Chile;
- Instituto de Ecología y Biodiversidad, Facultad de Ciencias, Universidad de Chile, Las Palmeras, Ñuñoa, Santiago 7800003, Chile
- Instituto de Sistemas Complejos de Valparaíso, Subida Artillería 470, Valparaíso 2360448, Chile
| | - Júlio César Chaves
- Getulio Vargas Foundation, Praia de Botafogo 190, Rio de Janeiro, RJ, 22250-900, Brazil; (J.C.C.); (M.A.H.B.d.S.)
| | - Vinícius da Fonseca Vieira
- Department of Computer Science, Federal University of São João Del Rey, Sao João Del Rey, MG, 36301-360, Brazil;
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22
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Matsuki A, Tanaka G. Intervention threshold for epidemic control in susceptible-infected-recovered metapopulation models. Phys Rev E 2020; 100:022302. [PMID: 31574659 PMCID: PMC7217496 DOI: 10.1103/physreve.100.022302] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Indexed: 12/26/2022]
Abstract
Metapopulation epidemic models describe epidemic dynamics in networks of spatially distant patches connected via pathways for migration of individuals. In the present study, we deal with a susceptible-infected-recovered (SIR) metapopulation model where the epidemic process in each patch is represented by an SIR model and the mobility of individuals is assumed to be a homogeneous diffusion. We consider two types of patches including high-risk and low-risk ones under the assumption that a local patch is changed from a high-risk one to a low-risk one by an intervention. We theoretically analyze the intervention threshold which indicates the critical fraction of low-risk patches for preventing a global epidemic outbreak. We show that an intervention targeted to high-degree patches is more effective for epidemic control than a random intervention. The theoretical results are validated by Monte Carlo simulations for synthetic and realistic scale-free patch networks. The theoretical results also reveal that the intervention threshold depends on the human mobility network and the mobility rate. Our approach is useful for exploring better local interventions aimed at containment of epidemics.
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Affiliation(s)
- Akari Matsuki
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan
| | - Gouhei Tanaka
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan.,Institute for Innovation in International Engineering Education, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
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23
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Wang Y, Li J, Zhao X, Feng G, Luo X(R. Using Mobile Phone Data for Emergency Management: a Systematic Literature Review. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2020; 22:1539-1559. [PMID: 32952439 PMCID: PMC7493063 DOI: 10.1007/s10796-020-10057-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Emergency management (EM) has always been a concern of people from all walks of life due to the devastating impacts emergencies can have. The global outbreak of COVID-19 in 2020 has pushed EM to the top topic. As mobile phones have become ubiquitous, many scholars have shown interest in using mobile phone data for EM. This paper presents a systematic literature review about the use of mobile phone data for EM that includes 65 related articles written between 2014 and 2019 from six electronic databases. Five themes in using mobile phone data for EM emerged from the reviewed articles, and a systematic framework is proposed to illustrate the current state of the research. This paper also discusses EM under COVID-19 pandemic and five future implications of the proposed framework to guide future work.
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Affiliation(s)
- Yanxin Wang
- School of Management, Xi’an Jiaotong University, Xi’an, 710049 China
| | - Jian Li
- School of Management, Xi’an Jiaotong University, Xi’an, 710049 China
| | - Xi Zhao
- School of Management, Xi’an Jiaotong University, Xi’an, 710049 China
- The Key Lab of the Ministry of Education for process control & Efficiency Engineering, Xi’an, 710049 China
| | - Gengzhong Feng
- School of Management, Xi’an Jiaotong University, Xi’an, 710049 China
| | - Xin (Robert) Luo
- Anderson School of Management, University of New Mexico, Albuquerque, NM USA
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24
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Feng S, Jin Z. Infectious Diseases Spreading on an Adaptive Metapopulation Network. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:153425-153435. [PMID: 34812348 PMCID: PMC8545316 DOI: 10.1109/access.2020.3016016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 08/03/2020] [Indexed: 05/15/2023]
Abstract
When an emerging acute infectious disease occurs, travel restrictions, one-way or two-way, are often taken to prevent its global spread. In order to investigate the impact of two-way travel restrictions in the global spread of infectious diseases, this paper defines a risk indicator according to the relative infection density. Based on this risk indicator and an intervention time on two-way travel restrictions, we define an adaptive metapopulation network. Then a susceptible-infectious-removed (SIR) metapopulation model on this network is proposed. The mathematical analysis shows that the basic reproduction number is independent of human mobility. Furthermore, this essay compares the effects of one-way travel restrictions and two-way travel restrictions on the global spread of infectious diseases. It is shown that the adaptive metapopulation network under two-way travel restrictions can effectively suppress the global spread of infectious diseases. We also obtain a threshold of risk indicator to prevent the global spread of infectious diseases by simulations. The earlier the intervention time on two-way travel restriction is, the better to curb the global spread of the disease. Even if two-way travel restrictions are not implemented, controlling the mobility of infectious persons would help prevent the global spread of the disease. This work will throw lights on the prevention and control of the globally spreading of an emerging infectious disease.
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Affiliation(s)
- Shanshan Feng
- School of Data Science and TechnologyNorth University of China Taiyuan 030051 China
- Complex Systems Research CenterShanxi University Taiyuan 030006 China
| | - Zhen Jin
- Complex Systems Research CenterShanxi University Taiyuan 030006 China
- Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and PreventionShanxi University Taiyuan 030006 China
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25
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Hamedmoghadam H, Ramezani M, Saberi M. Revealing latent characteristics of mobility networks with coarse-graining. Sci Rep 2019; 9:7545. [PMID: 31101843 PMCID: PMC6525175 DOI: 10.1038/s41598-019-44005-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 05/07/2019] [Indexed: 11/09/2022] Open
Abstract
Previous theoretical and data-driven studies on urban mobility uncovered the repeating patterns in individual and collective human behavior. This paper analyzes the travel demand characteristics of mobility networks through studying a coarse-grained representation of individual trips. Building on the idea of reducing the complexity of the mobility network, we investigate the preserved spatial and temporal information in a simplified representations of large-scale origin-destination matrices derived from more than 16 million taxi trip records from New York and Chicago. We reduce the numerous individual flows on the network into four major groups, to uncover latent collective mobility patterns in those cities. The new simplified representation of the origin-destination matrices leads to categorization of trips into distinctive flow types with specific temporal and spatial properties in each city under study. Collocation of the descriptive statistics of flow types within the two cities suggests the generalizability of the proposed approach. We extract an overall displacement metric from each of the major flows to analyze the evolution of their temporal attributes. The new representation of the demand network reveals insightful properties of the mobility system which could not have been identified from the original disaggregated representation.
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Affiliation(s)
| | - Mohsen Ramezani
- The University of Sydney, School of Civil Engineering, Sydney, NSW, 2006, Australia
| | - Meead Saberi
- University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, 2052, Australia.
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26
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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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27
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Dannemann T, Sotomayor-Gómez B, Samaniego H. The time geography of segregation during working hours. ROYAL SOCIETY OPEN SCIENCE 2018; 5:180749. [PMID: 30473825 PMCID: PMC6227938 DOI: 10.1098/rsos.180749] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 09/06/2018] [Indexed: 06/01/2023]
Abstract
While segregation is usually evaluated at the residential level, the recent influx of large streams of data describing urbanites' movement across the city allows to generate detailed descriptions of spatio-temporal segregation patterns across the activity space of individuals. For instance, segregation across the activity space is usually thought to be lower compared with residential segregation given the importance of social complementarity, among other factors, shaping the economies of cities. However, these new dynamic approaches to segregation convey important methodological challenges. This paper proposes a methodological framework to investigate segregation during working hours. Our approach combines three well-known mathematical tools: community detection algorithms, segregation metrics and random walk analysis. Using Santiago (Chile) as our model system, we build a detailed home-work commuting network from a large dataset of mobile phone pings and spatially partition the city into several communities. We then evaluate the probability that two persons at their work location will come from the same community. Finally, a randomization analysis of commuting distances and angles corroborates the strong segregation description for Santiago provided by the sociological literature. While our findings highlights the benefit of developing new approaches to understand dynamic processes in the urban environment, unveiling counterintuitive patterns such as segregation at our workplace also shows a specific example in which the exposure dimension of segregation is successfully studied using the growingly available streams of highly detailed anonymized mobile phone registries.
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Affiliation(s)
| | | | - Horacio Samaniego
- Laboratorio de Ecoinformática, Universidad Austral de Chile, Campus Isla Teja, Valdivia, Chile
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