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Liu C, Holme P, Lehmann S, Yang W, Lu X. Nonrepresentativeness of Human Mobility Data and its Impact on Modeling Dynamics of the COVID-19 Pandemic: Systematic Evaluation. JMIR Form Res 2024; 8:e55013. [PMID: 38941609 DOI: 10.2196/55013] [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: 12/05/2023] [Revised: 03/31/2024] [Accepted: 04/19/2024] [Indexed: 06/30/2024] Open
Abstract
BACKGROUND In recent years, a range of novel smartphone-derived data streams about human mobility have become available on a near-real-time basis. These data have been used, for example, to perform traffic forecasting and epidemic modeling. During the COVID-19 pandemic in particular, human travel behavior has been considered a key component of epidemiological modeling to provide more reliable estimates about the volumes of the pandemic's importation and transmission routes, or to identify hot spots. However, nearly universally in the literature, the representativeness of these data, how they relate to the underlying real-world human mobility, has been overlooked. This disconnect between data and reality is especially relevant in the case of socially disadvantaged minorities. OBJECTIVE The objective of this study is to illustrate the nonrepresentativeness of data on human mobility and the impact of this nonrepresentativeness on modeling dynamics of the epidemic. This study systematically evaluates how real-world travel flows differ from census-based estimations, especially in the case of socially disadvantaged minorities, such as older adults and women, and further measures biases introduced by this difference in epidemiological studies. METHODS To understand the demographic composition of population movements, a nationwide mobility data set from 318 million mobile phone users in China from January 1 to February 29, 2020, was curated. Specifically, we quantified the disparity in the population composition between actual migrations and resident composition according to census data, and shows how this nonrepresentativeness impacts epidemiological modeling by constructing an age-structured SEIR (Susceptible-Exposed-Infected- Recovered) model of COVID-19 transmission. RESULTS We found a significant difference in the demographic composition between those who travel and the overall population. In the population flows, 59% (n=20,067,526) of travelers are young and 36% (n=12,210,565) of them are middle-aged (P<.001), which is completely different from the overall adult population composition of China (where 36% of individuals are young and 40% of them are middle-aged). This difference would introduce a striking bias in epidemiological studies: the estimation of maximum daily infections differs nearly 3 times, and the peak time has a large gap of 46 days. CONCLUSIONS The difference between actual migrations and resident composition strongly impacts outcomes of epidemiological forecasts, which typically assume that flows represent underlying demographics. Our findings imply that it is necessary to measure and quantify the inherent biases related to nonrepresentativeness for accurate epidemiological surveillance and forecasting.
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Affiliation(s)
- Chuchu Liu
- School of Economics and Management, Changsha University of Science and Technology, Changsha, China
- College of Systems Engineering, National University of Defense Technology, Changsha, China
| | - Petter Holme
- Department of Computer Science, Aalto University, Espoo, Finland
- Center for Computational Social Science, Kobe University, Kobe, Japan
| | - Sune Lehmann
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Copenhagen, Denmark
| | - Wenchuan Yang
- College of Systems Engineering, National University of Defense Technology, Changsha, China
| | - Xin Lu
- College of Systems Engineering, National University of Defense Technology, Changsha, China
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2
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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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3
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Omori R, Ito K, Kanemitsu S, Kimura R, Iwasa Y. Human movement avoidance decisions during Coronavirus disease 2019 in Japan. J Theor Biol 2024; 585:111795. [PMID: 38493888 DOI: 10.1016/j.jtbi.2024.111795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 03/12/2024] [Accepted: 03/14/2024] [Indexed: 03/19/2024]
Abstract
Understanding host behavioral change in response to epidemics is important to forecast the disease dynamics. To predict the behavioral change relevant to the epidemic situation (e.g., the number of reported cases), we need to know the epidemic situation at the moment of decision, which is difficult to identify from the records of actually performed human mobility. In this study, the largest travel accommodation reservation data covering half of the existed accommodations in Japan was analyzed to observe decision-making timings and how it responded to the changing epidemic situation during Japan's Coronavirus Disease 2019 until February 2023. To this end, we measured mobility avoidance index proposed in Ito et al., 2022 to indicate people's decision of mobility avoidance and quantified it using the time-series of the accommodation booking/cancellation data. We observed matches of the peak dates of the mobility avoidance and the number of reported cases, and mobility avoidance changed proportional to the logarithmic number of reported cases. We also found that the slope of mobility avoidance against the change of the logarithmic number of reported cases were similar among the epidemic waves, while the intercept of that was much reduced as the first epidemic wave passed by. People measure the intensity of epidemic by logarithm of the number of reported cases. The sensitivity of their response is established during the first wave and the people's response became weakened after the first experience, as if the number of reported cases were multiplied by a constant small factor.
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Affiliation(s)
- Ryosuke Omori
- Division of Bioinformatics, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Hokkaido 001-0020, Japan.
| | - Koichi Ito
- Division of Bioinformatics, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Hokkaido 001-0020, Japan; Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
| | - Shunsuke Kanemitsu
- Data Solution Unit 2(Marriage & Family/Automobile Business/Travel), Data Management & Planning Office, Product Development Management Office, Recruit Co., Ltd, Chiyoda-ku, Tokyo 100-6640, Japan
| | - Ryusuke Kimura
- SaaS Data Solution Unit, Data Management & Planning Office, Product Development Management Office, Recruit Co., Ltd, Chiyoda-ku, Tokyo 100-6640, Japan
| | - Yoh Iwasa
- Department of Biology, Faculty of Science, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
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Perofsky AC, Hansen CL, Burstein R, Boyle S, Prentice R, Marshall C, Reinhart D, Capodanno B, Truong M, Schwabe-Fry K, Kuchta K, Pfau B, Acker Z, Lee J, Sibley TR, McDermot E, Rodriguez-Salas L, Stone J, Gamboa L, Han PD, Adler A, Waghmare A, Jackson ML, Famulare M, Shendure J, Bedford T, Chu HY, Englund JA, Starita LM, Viboud C. Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years. Nat Commun 2024; 15:4164. [PMID: 38755171 PMCID: PMC11098821 DOI: 10.1038/s41467-024-48528-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 05/02/2024] [Indexed: 05/18/2024] Open
Abstract
Many studies have used mobile device location data to model SARS-CoV-2 dynamics, yet relationships between mobility behavior and endemic respiratory pathogens are less understood. We studied the effects of population mobility on the transmission of 17 endemic viruses and SARS-CoV-2 in Seattle over a 4-year period, 2018-2022. Before 2020, visits to schools and daycares, within-city mixing, and visitor inflow preceded or coincided with seasonal outbreaks of endemic viruses. Pathogen circulation dropped substantially after the initiation of COVID-19 stay-at-home orders in March 2020. During this period, mobility was a positive, leading indicator of transmission of all endemic viruses and lagging and negatively correlated with SARS-CoV-2 activity. Mobility was briefly predictive of SARS-CoV-2 transmission when restrictions relaxed but associations weakened in subsequent waves. The rebound of endemic viruses was heterogeneously timed but exhibited stronger, longer-lasting relationships with mobility than SARS-CoV-2. Overall, mobility is most predictive of respiratory virus transmission during periods of dramatic behavioral change and at the beginning of epidemic waves.
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Affiliation(s)
- Amanda C Perofsky
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA.
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
| | - Chelsea L Hansen
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
- PandemiX Center, Department of Science & Environment, Roskilde University, Roskilde, Denmark
| | - Roy Burstein
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Shanda Boyle
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Robin Prentice
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Cooper Marshall
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - David Reinhart
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Ben Capodanno
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Melissa Truong
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Kristen Schwabe-Fry
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Kayla Kuchta
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Brian Pfau
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Zack Acker
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Jover Lee
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Thomas R Sibley
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Evan McDermot
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Leslie Rodriguez-Salas
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Jeremy Stone
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Luis Gamboa
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Peter D Han
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Amanda Adler
- Seattle Children's Research Institute, Seattle, WA, USA
| | - Alpana Waghmare
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Seattle Children's Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington, Seattle, WA, USA
| | | | - Michael Famulare
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Jay Shendure
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | - Trevor Bedford
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | - Helen Y Chu
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Janet A Englund
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
- Seattle Children's Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Lea M Starita
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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Kiwanuka N, Zalwango S, Kakaire R, Castellanos ME, Quach THT, Whalen CC. M. tuberculosis Infection Attributable to Exposure in Social Networks of Tuberculosis Cases in an Urban African Community. Open Forum Infect Dis 2024; 11:ofae200. [PMID: 38737427 PMCID: PMC11083641 DOI: 10.1093/ofid/ofae200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 04/11/2024] [Indexed: 05/14/2024] Open
Abstract
Background The persistence of tuberculosis today and its global disparity send a powerful message that effective tuberculosis control must respond to its regional epidemiology. Active case finding through contact investigation is a standard protocol used for tuberculosis control, but its effectiveness has not been established, especially in endemic areas. Methods To quantify the potential effectiveness of contact investigation in Kampala, Uganda, we used a cross-sectional design to evaluate the social networks of 123 tuberculosis index cases and 124 controls without tuberculosis. Results Tuberculous infection was present in 515 of 989 tuberculosis case contacts (52.1%) and 396 of 1026 control contacts (38.6%; adjusted prevalence ratio, 1.4; 95% CI, 1.3-1.6). The proportion of infected participants with known exposure within the social network of the tuberculosis case was 35%. The population-attributable fraction was 11.1% for any known exposure, with 7.3% attributable to household exposure and 3.4% attributable to extrahousehold exposure. Conclusions This low population-attributable fraction indicates that contact tracing in the social networks of index cases will have only a modest effect in reducing tuberculous infection in a community. New approaches to community-level active case finding are needed.
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Affiliation(s)
- Noah Kiwanuka
- Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Sarah Zalwango
- Department of Public Health and Environment, Kampala Capital City Authority, Kampala, Uganda
| | - Robert Kakaire
- Global Health Institute, College of Public Health, University of Georgia, Athens, Georgia, USA
| | - Maria Eugenia Castellanos
- Public Health and Tropical Medicine, College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Australia
| | - Trang Ho Thu Quach
- Global Health Institute, College of Public Health, University of Georgia, Athens, Georgia, USA
| | - Christopher C Whalen
- Global Health Institute, College of Public Health, University of Georgia, Athens, Georgia, USA
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Swanson T, Guikema S. Using mobile phone data to evaluate access to essential services following natural hazards. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2024; 44:883-906. [PMID: 37515569 DOI: 10.1111/risa.14201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 04/11/2023] [Accepted: 07/14/2023] [Indexed: 07/31/2023]
Abstract
Natural hazards bring about changes in the access to essential services such as grocery stores, healthcare, schools, and day care because of facility closures, transportation system disruption, evacuation orders, power outages, and other barriers to access. Understanding changes in access to essential services following a disruption is critical to ensure equitable recovery and more resilient communities. However, past approaches to understanding facility closures and inaccessibility such as surveys and interviews are labor-intensive and of limited geographic scope. In this article, we develop an approach to understanding facility-level inaccessibility across a broad geographic area based on location-based services data collected from cell phones. This approach supplements current approaches and helps both researchers and emergency response planners better understand which communities lose access to essential services and for how long. We demonstrate our approach by analyzing loss of access to supermarkets, schools, healthcare facilities, and home improvement stores in Southwest Florida leading up to and following the landfall of Hurricane Irma in 2017.
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Affiliation(s)
- Tessa Swanson
- Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Seth Guikema
- Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan, USA
- Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA
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7
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Hassler J, Andersson Granberg T, Steins K, Ceccato V. Towards more realistic measures of accessibility to emergency departments in Sweden. Int J Health Geogr 2024; 23:6. [PMID: 38431597 PMCID: PMC10909287 DOI: 10.1186/s12942-024-00364-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/19/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Assuring that emergency health care (EHC) is accessible is a key objective for health care planners. Conventional accessibility analysis commonly relies on resident population data. However, the allocation of resources based on stationary population data may lead to erroneous assumptions of population accessibility to EHC. METHOD Therefore, in this paper, we calculate population accessibility to emergency departments in Sweden with a geographical information system based network analysis. Utilizing static population data and dynamic population data, we investigate spatiotemporal patterns of how static population data over- or underestimates population sizes derived from temporally dynamic population data. RESULTS Our findings show that conventional measures of population accessibility tend to underestimate population sizes particularly in rural areas and in smaller ED's catchment areas compared to urban, larger ED's-especially during vacation time in the summer. CONCLUSIONS Planning based on static population data may thus lead to inequitable distributions of resources. This study is motivated in light of the ongoing centralization of ED's in Sweden, which largely depends on population sizes in ED's catchment areas.
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Affiliation(s)
- Jacob Hassler
- Department of Urban Planning and Environment, KTH Royal Institute of Technology, Teknikringen 10 A, 10044, Stockholm, Sweden.
| | | | - Krisjanis Steins
- Department of Science and Technology, Linköping University/ITN, 60174, Norrköping, Sweden
| | - Vania Ceccato
- Department of Urban Planning and Environment, KTH Royal Institute of Technology, Teknikringen 10 A, 10044, Stockholm, Sweden
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Tirabassi JN, Wang J, Zhenqi Zhou R, Hu Y. Human mobility data demonstrates increase in park visitation since start of COVID-19 pandemic in Buffalo, New York. Prev Med Rep 2024; 39:102650. [PMID: 38380386 PMCID: PMC10876662 DOI: 10.1016/j.pmedr.2024.102650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 02/22/2024] Open
Abstract
Background The COVID-19 pandemic highlighted the importance of urban parks to provide safe places to visit and recreate. Recent research has suggested that park visitation over this time may not have occurred equitably, which may exacerbate existing health disparities. However, usual methods of estimating park visitation are labor intensive, requiring better solutions.. The objective of this study was to assess how park visitation changed in Buffalo, New York after the start of the COVID-19 pandemic utilizing human mobility data. Methods Monthly mobile phone location data from January 2018 through October of 2021 from residents of Buffalo were analyzed to estimate total park visits for each census tract. A generalized linear mixed effect model was utilized to examine if selected factors affected park visitation. Factors examined included demographic, health, park, and crime data at the census tract level. Results Across 587,487 park visits that were captured in the 79 census tracts, park visitation increased by 25% since March 2020. In our regression model, having cancer and currently smoking had negative effects on park visitation. The start of the COVID-19 pandemic positively affected park visitation. Season (of the year), was the other statistically significant variable that affected park visitation. Conclusions Anonymous mobile phone data demonstrated that park visitation has increased by 25% since the beginning of the pandemic when we looked at census tract level data. While some data limitations must be addressed, mobile phone data is a novel method that can be used to understand behavioral and public health trends.
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Affiliation(s)
- Jill N. Tirabassi
- Department of Family Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Jia Wang
- Department of Biostatistics, School of Public Health and Health Professions, University at Buffalo, Buffalo NY, USA
| | - Ryan Zhenqi Zhou
- Department of Geography, College of Arts and Sciences, University at Buffalo, Buffalo, NY, USA
| | - Yingjie Hu
- Department of Geography, College of Arts and Sciences, University at Buffalo, Buffalo, NY, USA
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Osi A, Ghaffarzadegan N. Parameter estimation in behavioral epidemic models with endogenous societal risk-response. PLoS Comput Biol 2024; 20:e1011992. [PMID: 38551972 PMCID: PMC11006122 DOI: 10.1371/journal.pcbi.1011992] [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: 07/13/2023] [Revised: 04/10/2024] [Accepted: 03/11/2024] [Indexed: 04/11/2024] Open
Abstract
Behavioral epidemic models incorporating endogenous societal risk-response, where changes in risk perceptions prompt adjustments in contact rates, are crucial for predicting pandemic trajectories. Accurate parameter estimation in these models is vital for validation and precise projections. However, few studies have examined the problem of identifiability in models where disease and behavior parameters must be jointly estimated. To address this gap, we conduct simulation experiments to assess the effect on parameter estimation accuracy of a) delayed risk response, b) neglecting behavioral response in model structure, and c) integrating disease and public behavior data. Our findings reveal systematic biases in estimating behavior parameters even with comprehensive and accurate disease data and a well-structured simulation model when data are limited to the first wave. This is due to the significant delay between evolving risks and societal reactions, corresponding to the duration of a pandemic wave. Moreover, we demonstrate that conventional SEIR models, which disregard behavioral changes, may fit well in the early stages of a pandemic but exhibit significant errors after the initial peak. Furthermore, early on, relatively small data samples of public behavior, such as mobility, can significantly improve estimation accuracy. However, the marginal benefits decline as the pandemic progresses. These results highlight the challenges associated with the joint estimation of disease and behavior parameters in a behavioral epidemic model.
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Affiliation(s)
- Ann Osi
- Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Navid Ghaffarzadegan
- Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States of America
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Kufoof L, Hajjeh R, Al Nsour M, Saad R, Bélorgeot V, Abubakar A, Khader Y, Rawaf S. Learning From COVID-19: What Would It Take to Be Better Prepared in the Eastern Mediterranean Region? JMIR Public Health Surveill 2024; 10:e40491. [PMID: 38359418 PMCID: PMC10871069 DOI: 10.2196/40491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/24/2023] [Accepted: 11/21/2023] [Indexed: 02/17/2024] Open
Abstract
The COVID-19 transmission in the Eastern Mediterranean Region (EMR) was influenced by various factors such as conflict, demographics, travel and social restrictions, migrant workers, weak health systems, and mass gatherings. The countries that responded well to COVID-19 had high-level political commitment, multisectoral coordination, and existing infrastructures that could quickly mobilize. However, some EMR countries faced challenges due to political instability and fragile health systems, which hindered their response strategies. The pandemic highlighted the region's weak health systems and preparedness, fragmented surveillance systems, and lack of trust in information sharing. COVID-19 exposed the disruption of access and delivery of essential health services as a major health system fragility. In 2020, the World Health Organization (WHO) conducted a global pulse survey, which demonstrated that the EMR experienced the highest disruption in health services compared to other WHO regions. However, thanks to prioritization by the WHO and its member states, significant improvement was observed in 2021 during the second round of the WHO's National Pulse Survey. The pandemic underscored the importance of political leadership, community engagement, and trust and emphasized that investing in health security benefits everyone. Increasing vaccine coverage, building regional capacities, strengthening health systems, and working toward universal health coverage and health security are all priorities in the EMR. Emergency public health plays a key role in preparing for and responding to pandemics and biological threats. Integrating public health into primary care and investing in public health workforce capacity building is essential to reshaping public health and health emergency preparedness.
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Affiliation(s)
- Lara Kufoof
- Project Management Office, Global Health Development, Eastern Mediterranean Public Health Network, Amman, Jordan
| | - Rana Hajjeh
- Department of Program Management, Regional Office for the Eastern Mediterranean, World Health Organization, Cairo, Egypt
| | - Mohannad Al Nsour
- Global Health Development, Eastern Mediterranean Public Health Network, Amman, Jordan
| | - Randa Saad
- Department of Research and Policy, Global Health Development, Eastern Mediterranean Public Health Network, Amman, Jordan
| | - Victoria Bélorgeot
- Regional Office for the Eastern Mediterranean, World Health Organization, Cairo, Egypt
| | - Abdinasir Abubakar
- Regional Office for the Eastern Mediterranean, World Health Organization, Cairo, Egypt
| | - Yousef Khader
- Department of Public Health, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Salman Rawaf
- Department of Primary Care and Public Health, School of Public Health at Imperial College London, London, United Kingdom
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11
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Li Z, Ning H, Jing F, Lessani MN. Understanding the bias of mobile location data across spatial scales and over time: A comprehensive analysis of SafeGraph data in the United States. PLoS One 2024; 19:e0294430. [PMID: 38241418 PMCID: PMC10798630 DOI: 10.1371/journal.pone.0294430] [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: 05/29/2023] [Accepted: 11/01/2023] [Indexed: 01/21/2024] Open
Abstract
Mobile location data has emerged as a valuable data source for studying human mobility patterns in various contexts, including virus spreading, urban planning, and hazard evacuation. However, these data are often anonymized overviews derived from a panel of traced mobile devices, and the representativeness of these panels is not well documented. Without a clear understanding of the data representativeness, the interpretations of research based on mobile location data may be questionable. This article presents a comprehensive examination of the potential biases associated with mobile location data using SafeGraph Patterns data in the United States as a case study. The research rigorously scrutinizes and documents the bias from multiple dimensions, including spatial, temporal, urbanization, demographic, and socioeconomic, over a five-year period from 2018 to 2022 across diverse geographic levels, including state, county, census tract, and census block group. Our analysis of the SafeGraph Patterns dataset revealed an average sampling rate of 7.5% with notable temporal dynamics, geographic disparities, and urban-rural differences. The number of sampled devices was strongly correlated with the census population at the county level over the five years for both urban (r > 0.97) and rural counties (r > 0.91), but less so at the census tract and block group levels. We observed minor sampling biases among groups such as gender, age, and moderate-income, with biases typically ranging from -0.05 to +0.05. However, minority groups such as Hispanic populations, low-income households, and individuals with low levels of education generally exhibited higher levels of underrepresentation bias that varied over space, time, urbanization, and across geographic levels. These findings provide important insights for future studies that utilize SafeGraph data or other mobile location datasets, highlighting the need to thoroughly evaluate the spatiotemporal dynamics of the bias across spatial scales when employing such data sources.
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Affiliation(s)
- Zhenlong Li
- Geoinformation and Big Data Research Laboratory, Department of Geography, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Huan Ning
- Geoinformation and Big Data Research Laboratory, Department of Geography, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Fengrui Jing
- Geoinformation and Big Data Research Laboratory, Department of Geography, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - M. Naser Lessani
- Geoinformation and Big Data Research Laboratory, Department of Geography, The Pennsylvania State University, University Park, Pennsylvania, United States of America
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Gholipour S, Shamsizadeh Z, Halabowski D, Gwenzi W, Nikaeen M. Combating antibiotic resistance using wastewater surveillance: Significance, applications, challenges, and future directions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168056. [PMID: 37914125 DOI: 10.1016/j.scitotenv.2023.168056] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/19/2023] [Accepted: 10/21/2023] [Indexed: 11/03/2023]
Abstract
The global increase of antibiotic resistance (AR) and resistant infections call for effective surveillance methods for understanding and mitigating (re)-emerging public health risks. Wastewater surveillance (WS) of antibiotic resistance is an emerging, but currently under-utilized decision-support tool in public health systems. Recent years have witnessed an increase in evidence linking antibiotic resistance in wastewaters to that of the community. To date, very few comprehensive reviews exist on the application of WS to understand AR and resistant infections in population. Current and emerging AR detection methods, and their merits and limitations are discussed. Wastewater surveillance has several merits relative to individual testing, including; (1) low per capita testing cost, (2) high spatial coverage, (3) low requirement for diagnostic equipment, and (4) detection of health threats ahead of real outbreaks. The applications of WS as an early warning system and decision support tool to understand and mitigate AR are discussed. Wastewater surveillance could be a tool of choice in low-income settings lacking resources and diagnostic facilities for individual testing. To demonstrate the utility of WS, empirical evidence from field case studies is presented. However, constraints still exist, including; (1) lack of standardized protocols, (2) the clinical utility and sensitivity of WS-based data, (3) uncertainties in relating WS data to pathogenic and virulent bacteria, and (4) whether or not AR in stools and ultimately wastewater represent the complete human resistome. Finally, further prospects are presented, include knowledge gaps on; (1) development of low-cost biosensors for AR, (2) development of WS protocols (sampling, processing, interpretation), (3) further pilot scale studies to understand the opportunities and limits of WS, and (4) development of computer-based analytical tools to facilitate rapid data collection, visualization and interpretation. Therefore, the present paper discusses the principles, opportunities, and constraints of wastewater surveillance applications to understand AR and safeguard public health.
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Affiliation(s)
- Sahar Gholipour
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Zahra Shamsizadeh
- Department of Environmental Health Engineering, School of Health, Larestan University of Medical Sciences, Larestan, Iran
| | - Dariusz Halabowski
- University of Lodz, Faculty of Biology and Environmental Protection, Department of Ecology and Vertebrate Zoology, Lodz, Poland
| | - Willis Gwenzi
- Universität Kassel, Fachbereich Ökologische Agrarwissenschaften Fachgebiet Grünlandwissenschaft und Nachwachsende Rohstoffe, Steinstr. 19, 37249 Witzenhausen, Germany; Leibniz-Institut für Agrartechnik und Bioökonomie e.V. Max-Eyth-Allee 100, D-14469 Potsdam, Germany.
| | - Mahnaz Nikaeen
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran; Environment Research Center, Research Institute for Primordial Prevention of Non-Communicable Diseases, Isfahan University of Medical Sciences, Isfahan, Iran.
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13
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Liao J, Liu XF, Xu XK, Zhou T. COVID-19 spreading patterns in family clusters reveal gender roles in China. J R Soc Interface 2023; 20:20230336. [PMID: 38086400 PMCID: PMC10715915 DOI: 10.1098/rsif.2023.0336] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023] Open
Abstract
Understanding different gender roles forms part of the efforts to reduce gender inequality. This paper analyses COVID-19 family clusters outside Hubei Province in mainland China during the 2020 outbreak, revealing significant differences in spreading patterns across gender and family roles. Results show that men are more likely to be the imported cases of a family cluster, and women are more likely to be infected within the family. This finding provides new supportive evidence of the 'men as breadwinner and women as homemaker' (MBWH) gender roles in China. Further analyses reveal that the MBWH pattern is stronger in eastern than in western China, stronger for younger than for elder people. This paper offers not only valuable references for formulating gender-differentiated epidemic prevention policies but also an exemplification for studying group differences in similar scenarios.
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Affiliation(s)
- Jingyi Liao
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China
| | - Xiao Fan Liu
- Web Mining Laboratory, Department of Media and Communication, City University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Xiao-Ke Xu
- Computational Communication Research Center, Beijing Normal University, Zhuhai 519087, People's Republic of China
- School of Journalism and Communication, Beijing Normal University, Beijing 100875, People's Republic of China
- College of Information and Communication Engineering, Dalian Minzu University, Dalian, People's Republic of China
| | - Tao Zhou
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
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14
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Zhang D, Ge Y, Wang J, Liu H, Zhang WB, Wu X, Heuvelink G, Wu C, Yang J, Ruktanonchai N, Qader S, Ruktanonchai C, Cleary E, Yao Y, Liu J, Nnanatu C, Wesolowski A, Cummings D, Tatem A, Lai S. Optimizing the detection of emerging infections using mobility-based spatial sampling. RESEARCH SQUARE 2023:rs.3.rs-3597070. [PMID: 38014322 PMCID: PMC10680910 DOI: 10.21203/rs.3.rs-3597070/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Background Timely and precise detection of emerging infections is crucial for effective outbreak management and disease control. Human mobility significantly influences infection risks and transmission dynamics, and spatial sampling is a valuable tool for pinpointing potential infections in specific areas. This study explored spatial sampling methods, informed by various mobility patterns, to optimize the allocation of testing resources for detecting emerging infections. Methods Mobility patterns, derived from clustering point-of-interest data and travel data, were integrated into four spatial sampling approaches to detect emerging infections at the community level. To evaluate the effectiveness of the proposed mobility-based spatial sampling, we conducted analyses using actual and simulated outbreaks under different scenarios of transmissibility, intervention timing, and population density in cities. Results By leveraging inter-community movement data and initial case locations, the proposed case flow intensity (CFI) and case transmission intensity (CTI)-informed sampling approaches could considerably reduce the number of tests required for both actual and simulated outbreaks. Nonetheless, the prompt use of CFI and CTI within communities is imperative for effective detection, particularly for highly contagious infections in densely populated areas. Conclusions The mobility-based spatial sampling approach can substantially improve the efficiency of community-level testing for detecting emerging infections. It achieves this by reducing the number of individuals screened while maintaining a high accuracy rate of infection identification. It represents a cost-effective solution to optimize the deployment of testing resources, when necessary, to contain emerging infectious diseases in diverse settings.
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15
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Erfani A, Frias-Martinez V. A fairness assessment of mobility-based COVID-19 case prediction models. PLoS One 2023; 18:e0292090. [PMID: 37851681 PMCID: PMC10584164 DOI: 10.1371/journal.pone.0292090] [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: 02/16/2023] [Accepted: 09/12/2023] [Indexed: 10/20/2023] Open
Abstract
In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models' performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups.
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Affiliation(s)
- Abdolmajid Erfani
- Department of Civil, Environmental, and Geospatial Engineering, Michigan Technological University, Houghton, MI, United States of America
| | - Vanessa Frias-Martinez
- College of Information Studies, University of Maryland, College Park, MD, United States of America
- University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, MD, United States of America
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16
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Jiang WM, Wen TH, Huang YC, Chiou HY, Chen WJ, Hsiung CA, Sytwu HK, Tsou HH. Interregional mobility in different age groups is associated with COVID-19 transmission in the Taipei metropolitan area, Taiwan. Sci Rep 2023; 13:17285. [PMID: 37828352 PMCID: PMC10570333 DOI: 10.1038/s41598-023-44474-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 10/09/2023] [Indexed: 10/14/2023] Open
Abstract
Before vaccines were introduced, mobility restriction was one of the primary control measures in the early stage of the coronavirus disease 2019 (COVID-19) pandemic. Because different age groups face disproportionate health risks, differences in their mobility changes affect the effectiveness of pandemic control measures. This study aimed to investigate the relationship between multiscale mobility patterns in different age groups and COVID-19 transmission before and after control measures implementation. Data on daily confirmed case numbers, anonymized mobile phone data, and 38 socioeconomic factors were used to construct negative binomial regression models of these relationships in the Taipei metropolitan area in May 2021. To avoid overfitting, the socioeconomic factor dimensions were reduced by principal component analysis. The results showed that inter-district mobility was a greater promoter of COVID-19 transmission than was intra-district mobility (coefficients: pre-alert, 0.52 and 0.43; post-alert, 0.41 and 0.36, respectively). Moreover, both the inter-district mobility of people aged 15-59 and ≥ 60 years were significantly related to the number of confirmed cases (coefficients: pre-alert, 0.82 and 1.05; post-alert, 0.48 and 0.66, respectively). The results can help agencies worldwide formulate public health responses to emerging infectious diseases.
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Affiliation(s)
- Wei-Ming Jiang
- Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, 350, Miaoli County, Taiwan
| | - Tzai-Hung Wen
- Department of Geography, National Taiwan University, Taipei, Taiwan
| | - Ying-Chi Huang
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
| | - Hung-Yi Chiou
- Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, 350, Miaoli County, Taiwan
- School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan
- Master's Program in Applied Epidemiology, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Wei J Chen
- Center for Neuropsychiatric Research, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Chao A Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, 350, Miaoli County, Taiwan
| | - Huey-Kang Sytwu
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
| | - Hsiao-Hui Tsou
- Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, 350, Miaoli County, Taiwan.
- Graduate Institute of Biostatistics, College of Public Health, China Medical University, Taichung, Taiwan.
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17
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Delussu F, Tizzoni M, Gauvin L. The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach. PNAS NEXUS 2023; 2:pgad302. [PMID: 37811338 PMCID: PMC10558401 DOI: 10.1093/pnasnexus/pgad302] [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: 02/17/2023] [Accepted: 08/17/2023] [Indexed: 10/10/2023]
Abstract
Mobile phone data have been widely used to model the spread of COVID-19; however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. Using multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on COVID-19 spread. Our approach allows us to determine the best metric for predicting disease incidence in a particular location, at different spatial scales. Additionally, we identify geographic and demographic factors, such as users' coverage and commuting patterns, that explain the (non)observed relationship between mobility and epidemic patterns. Our work provides epidemiologists and public health officials with a general-not limited to COVID-19-framework to evaluate the usefulness of human mobility data in responding to epidemics.
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Affiliation(s)
- Federico Delussu
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- Department of Applied Mathematics and Computer Science, DTU, Richard Petersens Plads, DK-2800 Copenhagen, Denmark
| | - Michele Tizzoni
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- Department of Sociology and Social Research, University of Trento, via Verdi 26, I-38122 Trento, Italy
| | - Laetitia Gauvin
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- UMR 215 PRODIG, Institute for Research on Sustainable Development - IRD, 5 cours des Humanités, F-93 322 Aubervilliers Cedex, France
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18
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Savi MK, Yadav A, Zhang W, Vembar N, Schroeder A, Balsari S, Buckee CO, Vadhan S, Kishore N. A standardised differential privacy framework for epidemiological modeling with mobile phone data. PLOS DIGITAL HEALTH 2023; 2:e0000233. [PMID: 37889905 PMCID: PMC10610440 DOI: 10.1371/journal.pdig.0000233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/03/2023] [Indexed: 10/29/2023]
Abstract
During the COVID-19 pandemic, the use of mobile phone data for monitoring human mobility patterns has become increasingly common, both to study the impact of travel restrictions on population movement and epidemiological modeling. Despite the importance of these data, the use of location information to guide public policy can raise issues of privacy and ethical use. Studies have shown that simple aggregation does not protect the privacy of an individual, and there are no universal standards for aggregation that guarantee anonymity. Newer methods, such as differential privacy, can provide statistically verifiable protection against identifiability but have been largely untested as inputs for compartment models used in infectious disease epidemiology. Our study examines the application of differential privacy as an anonymisation tool in epidemiological models, studying the impact of adding quantifiable statistical noise to mobile phone-based location data on the bias of ten common epidemiological metrics. We find that many epidemiological metrics are preserved and remain close to their non-private values when the true noise state is less than 20, in a count transition matrix, which corresponds to a privacy-less parameter ϵ = 0.05 per release. We show that differential privacy offers a robust approach to preserving individual privacy in mobility data while providing useful population-level insights for public health. Importantly, we have built a modular software pipeline to facilitate the replication and expansion of our framework.
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Affiliation(s)
- Merveille Koissi Savi
- Department of Medical Oncology, Dana Farber Cancer Institute, Harvard School of Medicine, Boston, Massachusetts, United States of America
| | - Akash Yadav
- Direct Relief, Santa Barbara, California, United States of America
| | - Wanrong Zhang
- Department of Computer Sciences, Harvard John A. Paulson School of Engineering & Applied Sciences, Boston, Massachusetts, United States of America
| | - Navin Vembar
- Camber Systems, Washington, District of Columbia, United States of America
| | - Andrew Schroeder
- Direct Relief, Santa Barbara, California, United States of America
| | - Satchit Balsari
- Department of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Caroline O. Buckee
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Salil Vadhan
- Department of Computer Sciences, Harvard John A. Paulson School of Engineering & Applied Sciences, Boston, Massachusetts, United States of America
| | - Nishant Kishore
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
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19
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Arambepola R, Schaber KL, Schluth C, Huang AT, Labrique AB, Mehta SH, Solomon SS, Cummings DAT, Wesolowski A. Fine scale human mobility changes within 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0002151. [PMID: 37478056 PMCID: PMC10361529 DOI: 10.1371/journal.pgph.0002151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 06/18/2023] [Indexed: 07/23/2023]
Abstract
Human mobility patterns changed greatly due to the COVID-19 pandemic. Despite many analyses investigating general mobility trends, there has been less work characterising changes in mobility on a fine spatial scale and developing frameworks to model these changes. We analyse zip code-level within-city mobility data from 26 US cities between February 2 -August 31, 2020. We use Bayesian models to characterise the initial decrease in mobility and mobility patterns between June-August at this fine spatial scale. There were similar temporal trends across cities but large variations in the magnitude of mobility reductions. Long-distance routes and higher-income subscribers, but not age, were associated with greater mobility reductions. At the city level, mobility rates around early April, when mobility was lowest, and over summer showed little association with non-pharmaceutical interventions or case rates. Changes in mobility patterns lasted until the end of the study period, despite overall numbers of trips recovering to near baseline levels in many cities.
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Affiliation(s)
- Rohan Arambepola
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Kathryn L. Schaber
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Catherine Schluth
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Angkana T. Huang
- Department of Genetics, Cambridge University, Cambridge, United Kingdom
| | - Alain B. Labrique
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Shruti H. Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Sunil S. Solomon
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Department of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD, United States of America
| | - Derek A. T. Cummings
- Department of Biology and the Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States of America
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
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20
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Wang J, Huang Y, Dong Y, Wu B. Assessment of the impact of reopening strategies on the spatial transmission risk of COVID-19 based on a data-driven transmission model. Sci Rep 2023; 13:11146. [PMID: 37429885 DOI: 10.1038/s41598-023-37297-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 06/19/2023] [Indexed: 07/12/2023] Open
Abstract
COVID-19 has dramatically changed people's mobility geste patterns and affected the operations of different functional spots. In the environment of the successful reopening of countries around the world since 2022, it's pivotal to understand whether the reopening of different types of locales poses a threat of wide epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the data handed by the Safegraph website, and taking into account the crowd inflow characteristics and the changes of susceptible and latent populations, the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the perpetration of continuing strategies were simulated. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the perpetration of the continuing strategy, especially the general dine-in restaurants were at higher risk levels. Religious exertion centers were the points of interest with the loftiest average infection rates after the perpetration of the continuing strategy. Points of interest such as convenience stores, large shopping malls, and pharmacies were at a lower risk for outbreak impact after the continuing strategy was enforced. Based on this, continuing forestallment and control strategies for different functional points of interest are proposed to provide decision support for the development of precise forestallment and control measures for different spots.
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Affiliation(s)
- Jing Wang
- School of Economics and Management, Fuzhou University, Fuzhou, 350116, China.
- Emergency Management Research Center, Fuzhou University, Fuzhou, 350116, China.
| | - YuHui Huang
- School of Economics and Management, Fuzhou University, Fuzhou, 350116, China
| | - Ying Dong
- School of Economics and Management, Fuzhou University, Fuzhou, 350116, China
| | - BingYing Wu
- School of Economics and Management, Fuzhou University, Fuzhou, 350116, China
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21
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Choi S, Kim C, Park KH, Kim JH. Direct indicators of social distancing effectiveness in COVID-19 outbreak stages: a correlational analysis of case contacts and population mobility in Korea. Epidemiol Health 2023; 45:e2023065. [PMID: 37448123 PMCID: PMC10876423 DOI: 10.4178/epih.e2023065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/25/2023] [Indexed: 07/15/2023] Open
Abstract
OBJECTIVES The effectiveness of social distancing during the coronavirus disease 2019 (COVID-19) pandemic has been evaluated using the magnitude of changes in population mobility. This study aimed to investigate a direct indicator-namely, the number of close contacts per patient with confirmed COVID-19. METHODS From week 7, 2020 to week 43, 2021, population movement changes were calculated from the data of two Korean telecommunication companies and Google in accordance with social distancing stringency levels. Data on confirmed cases and their close contacts among residents of Gyeonggi Province, Korea were combined at each stage. Pearson correlation analysis was conducted to compare the movement data with the change in the number of contacts for each confirmed case calculated by stratification according to age group. The reference value of the population movement data was set using the value before mid-February 2020, considering each data's characteristics. RESULTS In the age group of 18 or younger, the number of close contacts per confirmed case decreased or increased when the stringency level was strengthened or relaxed, respectively. In adults, the correlation was relatively low, with no correlation between the change in the number of close contacts per confirmed case and the change in population movement after the commencement of vaccination for adults. CONCLUSIONS The effectiveness of governmental social distancing policies against COVID-19 can be evaluated using the number of close contacts per confirmed case as a direct indicator, especially for each age group. Such an analysis can facilitate policy changes for specific groups.
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Affiliation(s)
- Sojin Choi
- Gyeonggi Infectious Disease Control Center, Health Bureau, Gyeonggi Provincial Government, Suwon, Korea
| | - Chanhee Kim
- Gyeonggi Infectious Disease Control Center, Health Bureau, Gyeonggi Provincial Government, Suwon, Korea
| | - Kun-Hee Park
- Gyeonggi Infectious Disease Control Center, Health Bureau, Gyeonggi Provincial Government, Suwon, Korea
| | - Jong-Hun Kim
- Department of Social and Preventive Medicine, Sungkyunkwan University School of Medicine, Suwon, Korea
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22
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Loedy N, Coletti P, Wambua J, Hermans L, Willem L, Jarvis CI, Wong KLM, Edmunds W, Robert A, Leclerc QJ, Gimma A, Molenberghs G, Beutels P, Faes C, Hens N. Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic. BMC Public Health 2023; 23:1298. [PMID: 37415096 PMCID: PMC10326964 DOI: 10.1186/s12889-023-16193-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 06/26/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND During the COVID-19 pandemic, the CoMix study, a longitudinal behavioral survey, was designed to monitor social contacts and public awareness in multiple countries, including Belgium. As a longitudinal survey, it is vulnerable to participants' "survey fatigue", which may impact inferences. METHODS A negative binomial generalized additive model for location, scale, and shape (NBI GAMLSS) was adopted to estimate the number of contacts reported between age groups and to deal with under-reporting due to fatigue within the study. The dropout process was analyzed with first-order auto-regressive logistic regression to identify factors that influence dropout. Using the so-called next generation principle, we calculated the effect of under-reporting due to fatigue on estimating the reproduction number. RESULTS Fewer contacts were reported as people participated longer in the survey, which suggests under-reporting due to survey fatigue. Participant dropout is significantly affected by household size and age categories, but not significantly affected by the number of contacts reported in any of the two latest waves. This indicates covariate-dependent missing completely at random (MCAR) in the dropout pattern, when missing at random (MAR) is the alternative. However, we cannot rule out more complex mechanisms such as missing not at random (MNAR). Moreover, under-reporting due to fatigue is found to be consistent over time and implies a 15-30% reduction in both the number of contacts and the reproduction number ([Formula: see text]) ratio between correcting and not correcting for under-reporting. Lastly, we found that correcting for fatigue did not change the pattern of relative incidence between age groups also when considering age-specific heterogeneity in susceptibility and infectivity. CONCLUSIONS CoMix data highlights the variability of contact patterns across age groups and time, revealing the mechanisms governing the spread/transmission of COVID-19/airborne diseases in the population. Although such longitudinal contact surveys are prone to the under-reporting due to participant fatigue and drop-out, we showed that these factors can be identified and corrected using NBI GAMLSS. This information can be used to improve the design of similar, future surveys.
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Affiliation(s)
- Neilshan Loedy
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Pietro Coletti
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - James Wambua
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Lisa Hermans
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Lander Willem
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Christopher I. Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Kerry L. M. Wong
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - W. John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Alexis Robert
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Quentin J. Leclerc
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Public Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Epidemiology and Modelling of Bacterial Escape to Antimicrobials, Institut Pasteur, Paris, France
| | - Amy Gimma
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Geert Molenberghs
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- L-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Niel Hens
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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23
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Xu Y, Olmos LE, Mateo D, Hernando A, Yang X, González MC. Urban dynamics through the lens of human mobility. NATURE COMPUTATIONAL SCIENCE 2023; 3:611-620. [PMID: 38177741 DOI: 10.1038/s43588-023-00484-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 06/06/2023] [Indexed: 01/06/2024]
Abstract
The urban spatial structure represents the distribution of public and private spaces in cities and how people move within them. Although it usually evolves slowly, it can change quickly during large-scale emergency events, as well as due to urban renewal in rapidly developing countries. Here we present an approach to delineate such urban dynamics in quasi-real time through a human mobility metric, the mobility centrality index ΔKS. As a case study, we tracked the urban dynamics of eleven Spanish cities during the COVID-19 pandemic. The results revealed that their structures became more monocentric during the lockdown in the first wave, but kept their regular spatial structures during the second wave. To provide a more comprehensive understanding of mobility from home, we also introduce a dimensionless metric, KSHBT, which measures the extent of home-based travel and provides statistical insights into the transmission of COVID-19. By utilizing individual mobility data, our metrics enable the detection of changes in the urban spatial structure.
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Affiliation(s)
- Yanyan Xu
- MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
- Department of City and Regional Planning, University of California, Berkeley, CA, USA
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Luis E Olmos
- Department of City and Regional Planning, University of California, Berkeley, CA, USA
- Facultad de Ciencias Básicas, Universidad de Medellín, Medellín, Colombia
| | | | | | - Xiaokang Yang
- MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Marta C González
- Department of City and Regional Planning, University of California, Berkeley, CA, USA.
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA.
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24
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Li Y, Ran Z, Tsai L, Williams S. Using call detail records to determine mobility patterns of different socio-demographic groups in the western area of Sierra Leone during early COVID-19 crisis. ENVIRONMENT AND PLANNING. B, URBAN ANALYTICS AND CITY SCIENCE 2023; 50:1298-1312. [PMID: 38603005 PMCID: PMC10247678 DOI: 10.1177/23998083231158377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Human mobility patterns created from mobile phone call detail records (CDRs) can provide an essential resource in data-poor environments to monitor the effects of health outbreaks. Analysis of this data can be instrumental for understanding the movement pattern of populations allowing governments to set and refine policies to respond to community health risks. Building on CDR mobility analysis techniques, this research set out to test whether combining CDR mobility indicators with socio-economic information can illustrate differences between different socio-economic groups' exposure risks to COVID-19. The work focuses on the Western Area of Sierra Leone which houses the capital Freetown because it lacks existing mobility data and therefore can be a great example of how CDR can be transformed for this use. To determine mobility patterns, we applied the radius of gyration, regularity of movement, and motif types analytics commonly used in CDR research. We then applied a clustering algorithm to these results to understand user trends. Then we compared the results of the three methods with socio-economic status determined from census data in the same geography. The results show the daily movement of cell phone users of lower socio-economic status covered greater distances in the Western Area before and after lockdown, thereby showing a greater risk to COVID-19. The research also shows that groups of higher social status decreased mobility significantly after lockdown and did not return to pre-COVID-19 levels, unlike lower-social status groups.
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Affiliation(s)
- Yanchao Li
- Department of Urban Studies and Planning, Massachusetts Institute of Technology, USA
| | - Ziyu Ran
- Department of Urban Studies and Planning, Massachusetts Institute of Technology, USA
| | - Lily Tsai
- Department of Political Science, Massachusetts Institute of Technology, USA
| | - Sarah Williams
- Department of Urban Studies and Planning, Massachusetts Institute of Technology, USA
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25
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Saw VL, Vismara L, Yang B, Johansson M, Chew LY. Inferring origin-destination distribution of agent transfer in a complex network using deep gated recurrent units. Sci Rep 2023; 13:8287. [PMID: 37217647 DOI: 10.1038/s41598-023-35417-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 05/17/2023] [Indexed: 05/24/2023] Open
Abstract
Predicting the origin-destination (OD) probability distribution of agent transfer is an important problem for managing complex systems. However, prediction accuracy of associated statistical estimators suffer from underdetermination. While specific techniques have been proposed to overcome this deficiency, there still lacks a general approach. Here, we propose a deep neural network framework with gated recurrent units (DNNGRU) to address this gap. Our DNNGRU is network-free, as it is trained by supervised learning with time-series data on the volume of agents passing through edges. We use it to investigate how network topologies affect OD prediction accuracy, where performance enhancement is observed to depend on the degree of overlap between paths taken by different ODs. By comparing against methods that give exact results, we demonstrate the near-optimal performance of our DNNGRU, which we found to consistently outperform existing methods and alternative neural network architectures, under diverse data generation scenarios.
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Affiliation(s)
- Vee-Liem Saw
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore
| | - Luca Vismara
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore
| | - Bo Yang
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore
| | - Mikael Johansson
- School of Electrical Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Lock Yue Chew
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore.
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26
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An R, Tong Z, Liu X, Tan B, Xiong Q, Pang H, Liu Y, Xu G. Post COVID-19 pandemic recovery of intracity human mobility in Wuhan: Spatiotemporal characteristic and driving mechanism. TRAVEL BEHAVIOUR & SOCIETY 2023; 31:37-48. [PMID: 36405767 PMCID: PMC9650583 DOI: 10.1016/j.tbs.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 09/27/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
After successfully inhibiting the first wave of COVID-19 transmission through a city lockdown, Wuhan implemented a series of policies to gradually lift restrictions and restore daily activities. Existing studies mainly focus on the intercity recovery under a macroscopic view. How does the intracity mobility return to normal? Is the recovery process consistent among different subareas, and what factor affects the post-pandemic recovery? To answer these questions, we sorted out policies adopted during the Wuhan resumption, and collected the long-time mobility big data in 1105 traffic analysis zones (TAZs) to construct an observation matrix (A). We then used the nonnegative matrix factorization (NMF) method to approximate A as the product of two condensed matrices (WH). The column vectors of W matrix were visualized as five typical recovery curves to reveal the temporal change. The row vectors of H matrix were visualized to identify the spatial distribution of each recovery type, and were analyzed with variables of population, GDP, land use, and key facility to explain the recovery driving mechanisms. We found that the "staggered time" policies implemented in Wuhan effectively staggered the peak mobility of several recovery types ("staggered peak"). Besides, different TAZs had heterogeneous response intensities to these policies ("staggered area") which were closely related to land uses and key facilities. The creative policies taken by Wuhan highlight the wisdom of public health crisis management, and could provide an empirical reference for the adjustment of post-pandemic intervention measures in other cities.
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Affiliation(s)
- Rui An
- School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, PR China
| | - Zhaomin Tong
- School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, PR China
| | - Xiaoyan Liu
- Institute of Disaster Risk Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, PR China
| | - Bo Tan
- Wuhan Geomatics Institute, 209 Wansongyuan Road, Wuhan 430022, PR China
| | - Qiangqiang Xiong
- School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, PR China
| | - Huixin Pang
- School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, PR China
| | - Yaolin Liu
- School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, PR China
| | - Gang Xu
- School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, PR China
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27
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Panik RT, Watkins K, Ederer D. Metrics of Mobility: Assessing the Impact of COVID-19 on Travel Behavior. TRANSPORTATION RESEARCH RECORD 2023; 2677:583-596. [PMID: 38603318 PMCID: PMC9666410 DOI: 10.1177/03611981221131812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
The COVID-19 pandemic disrupted typical travel behavior worldwide. In the United States (U.S.), government entities took action to limit its spread through public health messaging to encourage reduced mobility and thus reduce the spread of the virus. Within statewide responses to COVID-19, however, there were different responses locally. Likely some of these variations were a result of individual attitudes toward the government and health messaging, but there is also likely a portion of the effects that were because of the character of the communities. In this research, we summarize county-level characteristics that are known to affect travel behavior for 404 counties in the U.S., and we investigate correlates of mobility between April and September (2020). We do this through application of three metrics that are derived via changepoint analysis-initial post-disruption mobility index, changepoint on restoration of a "new normal," and recovered mobility index. We find that variables for employment sectors are significantly correlated and had large effects on mobility during the pandemic. The state dummy variables are significant, suggesting that counties within the same state behaved more similarly to one another than to counties in different states. Our findings indicate that few travel characteristics that typically correlate with travel behavior are related to pandemic mobility, and that the number of COVID-19 cases may not be correlated with mobility outcomes.
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Affiliation(s)
- Rachael Thompson Panik
- School of Civil and Environmental
Engineering, Georgia Institute of Technology, Atlanta, GA
| | - Kari Watkins
- School of Civil and Environmental
Engineering, Georgia Institute of Technology, Atlanta, GA
| | - David Ederer
- School of Civil and Environmental
Engineering, Georgia Institute of Technology, Atlanta, GA
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28
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Ali Y, Khan HU. A Survey on harnessing the Applications of Mobile Computing in Healthcare during the COVID-19 Pandemic: Challenges and Solutions. COMPUTER NETWORKS 2023; 224:109605. [PMID: 36776582 PMCID: PMC9894776 DOI: 10.1016/j.comnet.2023.109605] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 11/17/2022] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
The COVID-19 pandemic ravaged almost every walk of life but it triggered many challenges for the healthcare system, globally. Different cutting-edge technologies such as Internet of things (IoT), machine learning, Virtual Reality (VR), Big data, Blockchain etc. have been adopted to cope with this menace. In this regard, various surveys have been conducted to highlight the importance of these technologies. However, among these technologies, the role of mobile computing is of paramount importance which is not found in the existing literature. Hence, this survey in mainly targeted to highlight the significant role of mobile computing in alleviating the impacts of COVID-19 in healthcare sector. The major applications of mobile computing such as software-based solutions, hardware-based solutions and wireless communication-based support for diagnosis, prevention, self-symptom reporting, contact tracing, social distancing, telemedicine and treatment related to coronavirus are discussed in detailed and comprehensive fashion. A state-of-the-art work is presented to identify the challenges along with possible solutions in adoption of mobile computing with respect to COVID-19 pandemic. Hopefully, this research will help the researchers, policymakers and healthcare professionals to understand the current research gaps and future research directions in this domain. To the best level of our knowledge, this is the first survey of its type to address the COVID-19 pandemic by exploring the holistic contribution of mobile computing technologies in healthcare area.
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Affiliation(s)
- Yasir Ali
- Higher Education Department, Khyber Pakhtunkhwa, Government Degree College Kotha Swabi, KP, Pakistan
- Higher Education Department, Shahzeb Shaheed Government Degree College Razzar, Swabi, KP, Pakistan
| | - Habib Ullah Khan
- Accounting and Information, College of Business and Economics, Qatar University, Doha Qatar
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29
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Lind A, Wu S, Hadachi A. Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:3603. [PMID: 37050661 PMCID: PMC10098955 DOI: 10.3390/s23073603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/17/2023] [Accepted: 03/27/2023] [Indexed: 06/19/2023]
Abstract
Kalman filter is a well-established accuracy correction method in control, guidance, and navigation. With the popularity of mobile communication and ICT, Kalman Filter has been used in many new applications related to positioning based on spatiotemporal data from the cellular network. Despite the low accuracy compared to Global Positioning System, the method is an excellent supplement to other positioning technologies. It is often used in sensor fusion setups as a complementary source. One of the reasons for the Kalman Filter's inaccuracy lies in naive radio coverage approximation techniques based on multivariate normal distributions assumed by previous studies. Therefore, in this paper, we evaluated those disadvantages and proposed a Gaussian mixtures model to address the non-arbitrary shape of the radio cells' coverage area. Having incorporated the Gaussian mixtures model into Switching Kalman Filter, we achieved better accuracy in positioning within the cellular network.
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30
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Ren J, Liu M, Liu Y, Liu J. TransCode: Uncovering COVID-19 transmission patterns via deep learning. Infect Dis Poverty 2023; 12:14. [PMID: 36855184 PMCID: PMC9971690 DOI: 10.1186/s40249-023-01052-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 01/03/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns via deep learning. METHODS We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors. First, in Hong Kong, China, we construct the mobility trajectories of confirmed cases using their visiting records. Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution. Integrating the spatial and temporal information, we represent the TransCode via spatiotemporal transmission networks. Further, we propose a deep transfer learning model to adapt the TransCode of Hong Kong, China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises: New York City, San Francisco, Toronto, London, Berlin, and Tokyo, where fine-scale data are limited. All the data used in this study are publicly available. RESULTS The TransCode of Hong Kong, China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns (e.g., the imported and exported transmission intensities) at the district and constituency levels during different COVID-19 outbreaks waves. By adapting the TransCode of Hong Kong, China to other data-limited densely populated metropolises, the proposed method outperforms other representative methods by more than 10% in terms of the prediction accuracy of the disease dynamics (i.e., the trend of case numbers), and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level. CONCLUSIONS The fine-scale transmission patterns due to the metapopulation level mobility (e.g., travel across different districts) and contact behaviors (e.g., gathering in social-economic centers) are one of the main contributors to the rapid spread of the virus. Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.
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Affiliation(s)
- Jinfu Ren
- grid.221309.b0000 0004 1764 5980Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
| | - Mutong Liu
- grid.221309.b0000 0004 1764 5980Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
| | - Yang Liu
- grid.221309.b0000 0004 1764 5980Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
| | - Jiming Liu
- Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China.
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31
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Del-Águila-Mejía J, García-García D, Rojas-Benedicto A, Rosillo N, Guerrero-Vadillo M, Peñuelas M, Ramis R, Gómez-Barroso D, Donado-Campos JDM. Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4356. [PMID: 36901366 PMCID: PMC10001675 DOI: 10.3390/ijerph20054356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
Human mobility drives the geographical diffusion of infectious diseases at different scales, but few studies focus on mobility itself. Using publicly available data from Spain, we define a Mobility Matrix that captures constant flows between provinces by using a distance-like measure of effective distance to build a network model with the 52 provinces and 135 relevant edges. Madrid, Valladolid and Araba/Álaba are the most relevant nodes in terms of degree and strength. The shortest routes (most likely path between two points) between all provinces are calculated. A total of 7 mobility communities were found with a modularity of 63%, and a relationship was established with a cumulative incidence of COVID-19 in 14 days (CI14) during the study period. In conclusion, mobility patterns in Spain are governed by a small number of high-flow connections that remain constant in time and seem unaffected by seasonality or restrictions. Most of the travels happen within communities that do not completely represent political borders, and a wave-like spreading pattern with occasional long-distance jumps (small-world properties) can be identified. This information can be incorporated into preparedness and response plans targeting locations that are at risk of contagion preventively, underscoring the importance of coordination between administrations when addressing health emergencies.
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Affiliation(s)
- Javier Del-Águila-Mejía
- Departamento de Medicina Preventiva y Salud Pública y Microbiología, Facultad de Medicina, Universidad Autónoma de Madrid. C. Arzobispo Morcillo 4, 28029 Madrid, Spain
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Servicio de Medicina Preventiva, Hospital Universitario de Móstoles, Calle Río Júcar s/n, 28935 Móstoles, Spain
| | - David García-García
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Ayelén Rojas-Benedicto
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
- Universidad Nacional de Educación a Distancia (UNED), Calle de Bravo Murillo 38, 28015 Madrid, Spain
| | - Nicolás Rosillo
- Servicio de Medicina Preventiva, Hospital Universitario 12 de Octubre, Avenida de Córdoba s/n, 28041 Madrid, Spain
| | - María Guerrero-Vadillo
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Marina Peñuelas
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Rebeca Ramis
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Diana Gómez-Barroso
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Juan de Mata Donado-Campos
- Departamento de Medicina Preventiva y Salud Pública y Microbiología, Facultad de Medicina, Universidad Autónoma de Madrid. C. Arzobispo Morcillo 4, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
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32
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Yücel SG, Pereira RHM, Peixoto PS, Camargo CQ. Impact of network centrality and income on slowing infection spread after outbreaks. APPLIED NETWORK SCIENCE 2023; 8:16. [PMID: 36855413 PMCID: PMC9951146 DOI: 10.1007/s41109-023-00540-z] [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: 08/09/2022] [Accepted: 02/08/2023] [Indexed: 06/18/2023]
Abstract
The COVID-19 pandemic has shed light on how the spread of infectious diseases worldwide are importantly shaped by both human mobility networks and socio-economic factors. However, few studies look at how both socio-economic conditions and the complex network properties of human mobility patterns interact, and how they influence outbreaks together. We introduce a novel methodology, called the Infection Delay Model, to calculate how the arrival time of an infection varies geographically, considering both effective distance-based metrics and differences in regions' capacity to isolate-a feature associated with socio-economic inequalities. To illustrate an application of the Infection Delay Model, this paper integrates household travel survey data with cell phone mobility data from the São Paulo metropolitan region to assess the effectiveness of lockdowns to slow the spread of COVID-19. Rather than operating under the assumption that the next pandemic will begin in the same region as the last, the model estimates infection delays under every possible outbreak scenario, allowing for generalizable insights into the effectiveness of interventions to delay a region's first case. The model sheds light on how the effectiveness of lockdowns to slow the spread of disease is influenced by the interaction of mobility networks and socio-economic levels. We find that a negative relationship emerges between network centrality and the infection delay after a lockdown, irrespective of income. Furthermore, for regions across all income and centrality levels, outbreaks starting in less central locations were more effectively slowed by a lockdown. Using the Infection Delay Model, this paper identifies and quantifies a new dimension of disease risk faced by those most central in a mobility network.
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Affiliation(s)
- Shiv G. Yücel
- School of Geography and the Environment, University of Oxford, Oxford, UK
| | | | - Pedro S. Peixoto
- Applied Mathematics Department, University of São Paulo, São Paulo, Brazil
| | - Chico Q. Camargo
- Department of Computer Science, University of Exeter, Exeter, UK
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33
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An Optimization Method Combining RSSI and PDR Data to Estimate Distance between Smart Devices for COVID-19 Contact Tracing. JOURNAL OF HEALTHCARE ENGINEERING 2023. [DOI: 10.1155/2023/7711901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Distance estimation methods arise in many applications, such as indoor positioning and COVID-19 contact tracing. The received signal strength indicator (RSSI) is favored in distance estimation. However, the accuracy is not satisfactory due to the signal fluctuation. Besides, the RSSI-only method has a large-ranging error because it uses fixed parameters of the path loss model. Here, we propose an optimization method combining RSSI and pedestrian dead reckoning (PDR) data to estimate the distance between smart devices. The PDR may provide high accuracy of walking distance and direction. Moreover, the parameters of the path loss model are optimized to dynamically fit the complex electromagnetic environment. The proposed method is evaluated in outdoor and indoor environments and compared with the RSSI-only method. The results show that the mean absolute error is reduced up to 0.51 m and 1.02 m, with an improvement of 10.60% and 64.55% for outdoor and indoor environments, respectively, compared with the RSSI-only method. Consequently, the proposed optimization method has better accuracy of distance estimation than the RSSI-only method, and its feasibility is demonstrated through real-world evaluations.
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34
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Kim H, Shoji Y, Mameno K, Kubo T, Aikoh T. Changes in visits to green spaces due to the COVID-19 pandemic: Focusing on the proportion of repeat visitors and the distances between green spaces and visitors' places of residences. URBAN FORESTRY & URBAN GREENING 2023; 80:127828. [PMID: 36619347 PMCID: PMC9806923 DOI: 10.1016/j.ufug.2022.127828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 12/21/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
Through a quantitative approach, this study aimed to clarify the changes in the number of visitors and visits to green spaces according to green space type before and after the COVID-19 pandemic. We explored the changes in the proportion of repeat visitors and the distance between green spaces and visitors' places of residence. We used KDDI Location Analyzer, which performs novel analysis using mobile phone GPS and census data. The study area included 10 target sites (urban parks and nature trails in the backcountry) located in the Sapporo metropolitan area in Japan. The survey period included snow-free seasons from 2019 to 2021, and 2019 was considered the period "before the pandemic." The results revealed that the number of visits during the pandemic increased compared with those before the pandemic, except for those of urban parks near the city center. In 2020, the proportion of repeat visitors increased for all 10 target sites. In addition, since the outbreak of the pandemic, distances between all urban parks and visitors' residences decreased. The same trend was observed for nature trails in the backcountry close to the city center. These findings indicate a generally decreasing trend in the number of visits to green spaces as many people have been refraining from visiting the site since the outbreak of the pandemic. Contrastingly, the number of visits by repeat visitors who reside close to the target sites has increased in some cases, which compensated for the general decreases.
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Affiliation(s)
- Hyerin Kim
- Research Faculty of Agriculture, Hokkaido University, Kita 9 Nishi 9, Kita-ku, Sapporo 060-8589, Japan
| | - Yasushi Shoji
- Research Faculty of Agriculture, Hokkaido University, Kita 9 Nishi 9, Kita-ku, Sapporo 060-8589, Japan
| | - Kota Mameno
- Graduate School of Agricultural Science, Tohoku University, 468-1 Aramaki Azaaoba, Aobaku, Sendai 980-8572, Japan
| | - Takahiro Kubo
- Biodiversity Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba 305-8506, Japan
- Durrell Institute of Conservation and Ecology (DICE), School of Anthropology and Conservation, University of Kent, Canterbury CT2 7NZ, UK
- Department of Biology, University of Oxford, 11a Mansfield Road, Oxford OX1 3SZ, UK
| | - Tetsuya Aikoh
- Research Faculty of Agriculture, Hokkaido University, Kita 9 Nishi 9, Kita-ku, Sapporo 060-8589, Japan
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35
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Yang L, Iwami M, Chen Y, Wu M, van Dam KH. Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review. PROGRESS IN PLANNING 2023; 168:100657. [PMID: 35280114 PMCID: PMC8904142 DOI: 10.1016/j.progress.2022.100657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, impelling behaviour change and facilitating the construction of lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, have been used to support responses to the current pandemic as well as to the spread of previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. We selected 109 out of 8,737 studies retrieved from databases and analysed them based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design, as well as computational modelling support, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches for evaluating design decisions depending on the target disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for fighting against COVID-19, or be incorporated into broader frameworks to help cities become more resilient to future disasters.
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Affiliation(s)
- Liu Yang
- School of Architecture, Southeast University, Nanjing, China
- Research Center of Urban Design, Southeast University, Nanjing, China
| | - Michiyo Iwami
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, UK
| | - Yishan Chen
- Architecture and Urban Design Research Center, China IPPR International Engineering CO., LTD, Beijing, China
| | - Mingbo Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Koen H van Dam
- Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, UK
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Chen T, Zhu D, Cheng T, Gao X, Chen H. Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic. PLoS One 2023; 18:e0277913. [PMID: 36662785 PMCID: PMC9858062 DOI: 10.1371/journal.pone.0277913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 11/05/2022] [Indexed: 01/21/2023] Open
Abstract
Exploration of dynamic human activity gives significant insights into understanding the urban environment and can help to reinforce scientific urban management strategies. Lots of studies are arising regarding the significant human activity changes in global metropolises and regions affected by COVID-19 containment policies. However, the variations of human activity dynamics amid different phases divided by the non-pharmaceutical intervention policies (e.g., stay-at-home, lockdown) have not been investigated across urban areas in space and time and discussed with the urban characteristic determinants. In this study, we aim to explore the influence of different restriction phases on dynamic human activity through sensing human activity zones (HAZs) and their dominated urban characteristics. Herein, we proposed an explainable analysis framework to explore the HAZ variations consisting of three parts, i.e., footfall detection, HAZs delineation and the identification of relationships between urban characteristics and HAZs. In our study area of Greater London, United Kingdom, we first utilised the footfall detection method to extract human activity metrics (footfalls) counted by visits/stays at space and time from the anonymous mobile phone GPS trajectories. Then, we characterised HAZs based on the homogeneity of daily human footfalls at census output areas (OAs) during the predefined restriction phases in the UK. Lastly, we examined the feature importance of explanatory variables as the metric of the relationship between human activity and urban characteristics using machine learning classifiers. The results show that dynamic human activity exhibits statistically significant differences in terms of the HAZ distributions across restriction phases and is strongly associated with urban characteristics (e.g., specific land use types) during the COVID-19 pandemic. These findings can improve the understanding of the variation of human activity patterns during the pandemic and offer insights into city management resource allocation in urban areas concerning dynamic human activity.
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Affiliation(s)
- Tongxin Chen
- SpaceTimeLab for Big Data Analytics, Department of Civil, Environmental and Geomatic Engineering, University College London, London, United Kingdom
| | - Di Zhu
- Department of Geography, Environment and Society, University of Minnesota, Twin Cities, MN, United States of America
| | - Tao Cheng
- SpaceTimeLab for Big Data Analytics, Department of Civil, Environmental and Geomatic Engineering, University College London, London, United Kingdom
| | - Xiaowei Gao
- SpaceTimeLab for Big Data Analytics, Department of Civil, Environmental and Geomatic Engineering, University College London, London, United Kingdom
| | - Huanfa Chen
- Centre for Advanced Spatial Analysis, Bartlett School of Architecture, University College London, London, United Kingdom
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Brooks-Pollock E, Northstone K, Pellis L, Scarabel F, Thomas A, Nixon E, Matthews DA, Bowyer V, Garcia MP, Steves CJ, Timpson NJ, Danon L. Voluntary risk mitigation behaviour can reduce impact of SARS-CoV-2: a real-time modelling study of the January 2022 Omicron wave in England. BMC Med 2023; 21:25. [PMID: 36658548 PMCID: PMC9851586 DOI: 10.1186/s12916-022-02714-5] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 12/15/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Predicting the likely size of future SARS-CoV-2 waves is necessary for public health planning. In England, voluntary "plan B" mitigation measures were introduced in December 2021 including increased home working and face coverings in shops but stopped short of restrictions on social contacts. The impact of voluntary risk mitigation behaviours on future SARS-CoV-2 burden is unknown. METHODS We developed a rapid online survey of risk mitigation behaviours ahead of the winter 2021 festive period and deployed in two longitudinal cohort studies in the UK (Avon Longitudinal Study of Parents and Children (ALSPAC) and TwinsUK/COVID Symptom Study (CSS) Biobank) in December 2021. Using an individual-based, probabilistic model of COVID-19 transmission between social contacts with SARS-CoV-2 Omicron variant parameters and realistic vaccine coverage in England, we predicted the potential impact of the SARS-CoV-2 Omicron wave in England in terms of the effective reproduction number and cumulative infections, hospital admissions and deaths. Using survey results, we estimated in real-time the impact of voluntary risk mitigation behaviours on the Omicron wave in England, if implemented for the entire epidemic wave. RESULTS Over 95% of survey respondents (NALSPAC = 2686 and NTwins = 6155) reported some risk mitigation behaviours, with vaccination and using home testing kits reported most frequently. Less than half of those respondents reported that their behaviour was due to "plan B". We estimate that without risk mitigation behaviours, the Omicron variant is consistent with an effective reproduction number between 2.5 and 3.5. Due to the reduced vaccine effectiveness against infection with the Omicron variant, our modelled estimates suggest that between 55% and 60% of the English population could be infected during the current wave, translating into between 12,000 and 46,000 cumulative deaths, depending on assumptions about severity and vaccine effectiveness. The actual number of deaths was 15,208 (26 November 2021-1 March 2022). We estimate that voluntary risk reduction measures could reduce the effective reproduction number to between 1.8 and 2.2 and reduce the cumulative number of deaths by up to 24%. CONCLUSIONS Predicting future infection burden is affected by uncertainty in disease severity and vaccine effectiveness estimates. In addition to biological uncertainty, we show that voluntary measures substantially reduce the projected impact of the SARS-CoV-2 Omicron variant but that voluntary measures alone would be unlikely to completely control transmission.
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Affiliation(s)
- Ellen Brooks-Pollock
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Kate Northstone
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, UK
- School of Biological Sciences, University of Bristol, Bristol, UK
| | | | - Amy Thomas
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Emily Nixon
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- The Alan Turing Institute, London, UK
| | - David A Matthews
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Vicky Bowyer
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Maria Paz Garcia
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Claire J Steves
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Nicholas J Timpson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, UK
| | - Leon Danon
- Department of Engineering Mathematics, University of Bristol, Bristol, UK
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Ribeiro M, Teixeira D, Barbosa P, Nunes NJ. Using passive Wi-Fi for community crowd sensing during the COVID-19 pandemic. JOURNAL OF BIG DATA 2023; 10:7. [PMID: 36686620 PMCID: PMC9844951 DOI: 10.1186/s40537-022-00675-3] [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: 02/18/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Sensing passersby and detecting crowded locations is a growing area of research and development in the last decades. The COVID-19 pandemic compelled authorities and public and private institutions to monitor access and occupancy of crowded spaces. This work addresses the detection of crowds in points of interest (POI) by using a territory grid analysis categorizing POIs by the services available in each location and comparing data gathered from a community passive Wi-Fi infrastructure against mobile cellular tower association data from telecom companies. In Madeira islands (Portugal), we used data from the telecom provider NOS for the timespan of 4 months as ground truth and found a strong correlation with sparse passive Wi-Fi. An official regional mobile application shows the occupancy data to end-users based on the territory categorization and the passive Wi-Fi infrastructure in POIs. Occupancy data shows historical hourly trends of each location, and the real-time occupation, helping visitors and locals plan their commutes better to avoid crowded spaces.
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Affiliation(s)
- Miguel Ribeiro
- ITI/LARSyS, Instituto Superior Técnico, Lisbon, Portugal
| | - Diogo Teixeira
- Mathematics, Instituto Superior Técnico, Lisbon, Portugal
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Finazzi F. Replacing discontinued Big Tech mobility reports: a penetration-based analysis. Sci Rep 2023; 13:935. [PMID: 36650298 PMCID: PMC9844950 DOI: 10.1038/s41598-023-28137-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
People mobility data sets played a role during the COVID-19 pandemic in assessing the impact of lockdown measures and correlating mobility with pandemic trends. Two global data sets were Apple's Mobility Trends Reports and Google's Community Mobility Reports. The former is no longer available online, while the latter is no longer updated since October 2022. Thus, new products are required. To establish a lower bound on data set penetration guaranteeing high adherence between new products and the Big Tech products, an independent mobility data set based on 3.8 million smartphone trajectories is analysed to compare its information content with that of the Google data set. This lower bound is determined to be around 10-4 (1 trajectory every 10,000 people) suggesting that relatively small data sets are suitable for replacing Big Tech reports.
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40
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Okmi M, Por LY, Ang TF, Ku CS. Mobile Phone Data: A Survey of Techniques, Features, and Applications. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020908. [PMID: 36679703 PMCID: PMC9865984 DOI: 10.3390/s23020908] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/22/2022] [Accepted: 12/29/2022] [Indexed: 05/27/2023]
Abstract
Due to the rapid growth in the use of smartphones, the digital traces (e.g., mobile phone data, call detail records) left by the use of these devices have been widely employed to assess and predict human communication behaviors and mobility patterns in various disciplines and domains, such as urban sensing, epidemiology, public transportation, data protection, and criminology. These digital traces provide significant spatiotemporal (geospatial and time-related) data, revealing people's mobility patterns as well as communication (incoming and outgoing calls) data, revealing people's social networks and interactions. Thus, service providers collect smartphone data by recording the details of every user activity or interaction (e.g., making a phone call, sending a text message, or accessing the internet) done using a smartphone and storing these details on their databases. This paper surveys different methods and approaches for assessing and predicting human communication behaviors and mobility patterns from mobile phone data and differentiates them in terms of their strengths and weaknesses. It also gives information about spatial, temporal, and call characteristics that have been extracted from mobile phone data and used to model how people communicate and move. We survey mobile phone data research published between 2013 and 2021 from eight main databases, namely, the ACM Digital Library, IEEE Xplore, MDPI, SAGE, Science Direct, Scopus, SpringerLink, and Web of Science. Based on our inclusion and exclusion criteria, 148 studies were selected.
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Affiliation(s)
- Mohammed Okmi
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
- Department of Information Technology and Security, Jazan University, Jazan 45142, Saudi Arabia
| | - Lip Yee Por
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Tan Fong Ang
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Chin Soon Ku
- Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
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41
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Sovereignty in the Digital and Contact Tracing Apps. DIGITAL SOCIETY : ETHICS, SOCIO-LEGAL AND GOVERNANCE OF DIGITAL TECHNOLOGY 2023; 2:2. [PMID: 36590274 PMCID: PMC9791621 DOI: 10.1007/s44206-022-00030-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 11/15/2022] [Indexed: 12/28/2022]
Abstract
Recently, the concept of sovereignty in the digital has attracted much attention. Several publications dealing with this concept assume that it can best be described as a network of different, overlapping exercises of power. Nevertheless, there is a need for further research on how exactly sovereignty in the digital can be understood. In order to contribute to a better understanding of this concept, I illustrate its complex structure using contact tracing apps as a paradigmatic example. I conduct a narrative review to show what sovereignty looks like in the context of these apps. In the context of digital contact tracing apps, sovereignty is best understood as a complex network of three actors-nations, (big tech) companies, and individuals-that exercise various forms of power against or on behalf of each other to claim sovereignty for themselves and to either weaken or strengthen the sovereignty claims of other actors. Since large parts of the results can be generalized from the particular context of contact tracing apps, they contribute to a better overall understanding of the concept of sovereignty in digital. This might, in turn, be helpful for discussions about this technology as well as about the regulation and governance of the digital in general.
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Liu X, Yang S, Huang X, An R, Xiong Q, Ye T. Quantifying COVID-19 recovery process from a human mobility perspective: An intra-city study in Wuhan. CITIES (LONDON, ENGLAND) 2023; 132:104104. [PMID: 36407935 PMCID: PMC9659556 DOI: 10.1016/j.cities.2022.104104] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 11/05/2022] [Accepted: 11/07/2022] [Indexed: 05/20/2023]
Abstract
The COVID-19 pandemic has brought huge challenges to sustainable urban and community development. Although some recovery signals and patterns have been uncovered, the intra-city recovery process remains underexploited. This study proposes a comprehensive approach to quantify COVID-19 recovery leveraging fine-grained human mobility records. Taking Wuhan, a typical COVID-19 affected megacity in China, as the study area, we identify accurate recovery phases and select appropriate recovery functions in a data-driven manner. We observe that recovery characteristics regarding duration, amplitude, and velocity exhibit notable differences among urban blocks. We also notice that the recovery process under a one-wave outbreak lasts at least 84 days and has an S-shaped form best fitted with four-parameter Logistic functions. More than half of the recovery variance can be well explained and estimated by common variables from auxiliary data, including population, economic level, and built environments. Our study serves as a valuable reference that supports data-driven recovery quantification for COVID-19 and other crises.
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Affiliation(s)
- Xiaoyan Liu
- State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China
- Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, China
- Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing 100875, China
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Saini Yang
- School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China
| | - Xiao Huang
- Department of Geosciences, University of Arkansas, Fayetteville 72762, USA
| | - Rui An
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
| | - Qiangqiang Xiong
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
| | - Tao Ye
- State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China
- Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, China
- Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing 100875, China
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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43
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Showalter E, Vigil-Hayes M, Zegura E, Sutton R, Belding E. Pandemic-influenced human mobility on tribal lands in California: Data sparsity and analytical precision. PLoS One 2022; 17:e0276644. [PMID: 36516118 PMCID: PMC9750007 DOI: 10.1371/journal.pone.0276644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 10/11/2022] [Indexed: 12/15/2022] Open
Abstract
Human mobility datasets collected from personal mobile device locations are integral to understanding how states, counties, and cities have collectively adapted to pervasive social disruption stemming from the COVID-19 pandemic. However, while indigenous tribal communities in the United States have been disproportionately devastated by the pandemic, the relatively sparse populations and data available in these hard-hit tribal areas often exclude them from mobility studies. We explore the effects of sparse mobility data in untangling the often inter-correlated relationship between human mobility, distancing orders, and case growth throughout 2020 in tribal and rural areas of California. Our findings account for data sparsity imprecision to show: 1) Mobility through legal tribal boundaries was unusually low but still correlated highly with case growth; 2) Case growth correlated less strongly with mobility later in the the year in all areas; and 3) State-mandated distancing orders later in the year did not necessarily precede lower mobility medians, especially in tribal areas. It is our hope that with more timely feedback offered by mobile device datasets even in sparse areas, health policy makers can better plan health emergency responses that still keep the economy vibrant across all sectors.
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Affiliation(s)
- Esther Showalter
- Computer Science Dept, University of California, Santa Barbara, California, United States of America
- * E-mail:
| | - Morgan Vigil-Hayes
- School of Informatics, Computing, and Cybersystems, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Ellen Zegura
- College of Computing, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Richard Sutton
- Skyhook, Boston, Massachusetts, United States of America
| | - Elizabeth Belding
- Computer Science Dept, University of California, Santa Barbara, California, United States of America
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44
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Kalleitner F, Schiestl DW, Heiler G. Varieties of Mobility Measures: Comparing Survey and Mobile Phone Data during the COVID-19 Pandemic. PUBLIC OPINION QUARTERLY 2022; 86:913-931. [PMID: 36814551 PMCID: PMC9940778 DOI: 10.1093/poq/nfac042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Human mobility has become a major variable of interest during the COVID-19 pandemic and central to policy decisions all around the world. To measure individual mobility, research relies on a variety of indicators that commonly stem from two main data sources: survey self-reports and behavioral mobility data from mobile phones. However, little is known about how mobility from survey self-reports relates to popular mobility estimates using data from the Global System for Mobile Communications (GSM) and the Global Positioning System (GPS). Spanning March 2020 until April 2021, this study compares self-reported mobility from a panel survey in Austria to aggregated mobility estimates utilizing (1) GSM data and (2) Google's GPS-based Community Mobility Reports. Our analyses show that correlations in mobility changes over time are high, both in general and when comparing subgroups by age, gender, and mobility category. However, while these trends are similar, the size of relative mobility changes over time differs substantially between different mobility estimates. Overall, while our findings suggest that these mobility estimates manage to capture similar latent variables, especially when focusing on changes in mobility over time, researchers should be aware of the specific form of mobility different data sources capture.
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Affiliation(s)
- Fabian Kalleitner
- Corresponding author: Fabian Kalleitner, Department of Economic Sociology, University of Vienna, Kolingasse 14-16, Vienna 1090, Austria.
| | - David W Schiestl
- PhD Candidate, Department of Economic Sociology, University of Vienna, Vienna, Austria
| | - Georg Heiler
- PhD Candidate, Complexity Science Hub Vienna, Vienna, Austria, and Institute of Information Systems Engineering, Technical University Wien, Vienna, Austria
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Luisa Vissat L, Horvitz N, Phillips RV, Miao Z, Mgbara W, You Y, Salter R, Hubbard AE, Getz WM. A comparison of COVID-19 outbreaks across US Combined Statistical Areas using new methods for estimating R 0 and social distancing behaviour. Epidemics 2022; 41:100640. [PMID: 36274569 PMCID: PMC9550289 DOI: 10.1016/j.epidem.2022.100640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 10/03/2022] [Accepted: 10/03/2022] [Indexed: 02/05/2023] Open
Abstract
We investigated the initial outbreak rates and subsequent social distancing behaviour over the initial phase of the COVID-19 pandemic across 29 Combined Statistical Areas (CSAs) of the United States. We used the Numerus Model Builder Data and Simulation Analysis (NMB-DASA) web application to fit the exponential phase of a SCLAIV+D (Susceptible, Contact, Latent, Asymptomatic infectious, symptomatic Infectious, Vaccinated, Dead) disease classes model to outbreaks, thereby allowing us to obtain an estimate of the basic reproductive number R0 for each CSA. Values of R0 ranged from 1.9 to 9.4, with a mean and standard deviation of 4.5±1.8. Fixing the parameters from the exponential fit, we again used NMB-DASA to estimate a set of social distancing behaviour parameters to compute an epidemic flattening index cflatten. Finally, we applied hierarchical clustering methods using this index to divide CSA outbreaks into two clusters: those presenting a social distancing response that was either weaker or stronger. We found cflatten to be more influential in the clustering process than R0. Thus, our results suggest that the behavioural response after a short initial exponential growth phase is likely to be more determinative of the rise of an epidemic than R0 itself.
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Affiliation(s)
- Ludovica Luisa Vissat
- Department of Environmental Science, Policy, and Management, UC Berkeley, CA 94720, USA
| | - Nir Horvitz
- Department of Environmental Science, Policy, and Management, UC Berkeley, CA 94720, USA
| | | | - Zhongqi Miao
- Department of Environmental Science, Policy, and Management, UC Berkeley, CA 94720, USA
| | - Whitney Mgbara
- Department of Environmental Science, Policy, and Management, UC Berkeley, CA 94720, USA
| | - Yue You
- Division Environmental Health Sciences, UC Berkeley, CA 94720, USA
| | - Richard Salter
- Computer Science Department, Oberlin College, Oberlin, Ohio, OH 44074, USA
| | - Alan E Hubbard
- Division Environmental Health Sciences, UC Berkeley, CA 94720, USA
| | - Wayne M Getz
- Department of Environmental Science, Policy, and Management, UC Berkeley, CA 94720, USA; Division Environmental Health Sciences, UC Berkeley, CA 94720, USA; School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa.
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46
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Safranek CW, Scheinker D. A computer modeling method to analyze rideshare data for the surveillance of novel strains of SARS-CoV-2. Ann Epidemiol 2022; 76:136-142. [PMID: 36087658 PMCID: PMC9452418 DOI: 10.1016/j.annepidem.2022.08.051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 07/15/2022] [Accepted: 08/29/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE No method is available to systematically study SARS-CoV-2 transmission dynamics using the data that rideshare companies share with government agencies. We developed a proof-of-concept method for the analysis of SARS-CoV-2 transmissions between rideshare passengers and drivers. METHOD To assess whether this method could enable hypothesis testing about SARS-CoV-2, we repeated ten 200-day agent-based simulations of SARS-CoV-2 propagation within the Los Angeles County rideshare network. Assuming data access for 25% of infections, we estimated an epidemiologist's ability to analyze the observable infection patterns to correctly identify a baseline viral variant A, as opposed to viral variant A with mask use (50% reduction in viral particle exchange), or a more infectious viral variant B (300% higher cumulative viral load). RESULTS Simulations had an average of 190,387 potentially infectious rideshare interactions, resulting in 409 average diagnosed infections. Comparison of the number of observed and expected passenger-to-driver infections under each hypothesis demonstrated our method's ability to consistently discern large infectivity differences (viral variant A vs. viral variant B) given partial data from one large city, and to discern smaller infectivity differences (viral variant A vs. viral variant A with masks) given partial data aggregated across multiple cities. CONCLUSIONS This novel statistical method suggests that, for the present and subsequent pandemics, government-facilitated analysis of rideshare data combined with diagnosis records may augment efforts to better understand viral transmission dynamics and to measure changes in infectivity associated with nonpharmaceutical interventions and emergent viral strains.
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Affiliation(s)
- Conrad W. Safranek
- Department of Biology, Computational Biology, Stanford University, CA,Department of Management Science and Engineering, Stanford University School of Engineering, CA
| | - David Scheinker
- Department of Management Science and Engineering, Stanford University School of Engineering, CA; Department of Pediatrics, Stanford University School of Medicine, CA; Clinical Excellence Research Center, Stanford University School of Medicine, CA.
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47
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Haw DJ, Morgenstern C, Forchini G, Johnson R, Doohan P, Smith PC, Hauck KD. Data needs for integrated economic-epidemiological models of pandemic mitigation policies. Epidemics 2022; 41:100644. [PMID: 36375311 PMCID: PMC9624062 DOI: 10.1016/j.epidem.2022.100644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 10/11/2022] [Accepted: 10/14/2022] [Indexed: 11/13/2022] Open
Abstract
The COVID-19 pandemic and the mitigation policies implemented in response to it have resulted in economic losses worldwide. Attempts to understand the relationship between economics and epidemiology has led to a new generation of integrated mathematical models. The data needs for these models transcend those of the individual fields, especially where human interaction patterns are closely linked with economic activity. In this article, we reflect upon modelling efforts to date, discussing the data needs that they have identified, both for understanding the consequences of the pandemic and policy responses to it through analysis of historic data and for the further development of this new and exciting interdisciplinary field.
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Affiliation(s)
- David J. Haw
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, United Kingdom,Corresponding author
| | - Christian Morgenstern
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, United Kingdom
| | - Giovanni Forchini
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, United Kingdom,USBE, Umeå Universitet, SE-901 87 Umeå, Sweden
| | - Rob Johnson
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, United Kingdom
| | - Patrick Doohan
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, United Kingdom
| | - Peter C. Smith
- Department of Economics and Public Policy, Imperial College Business School, United Kingdom,Centre for Health Economics, University of York, United Kingdom
| | - Katharina D. Hauck
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, United Kingdom
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48
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Future behaviours decision-making regarding travel avoidance during COVID-19 outbreaks. Sci Rep 2022; 12:19780. [PMID: 36396687 PMCID: PMC9671889 DOI: 10.1038/s41598-022-24323-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
Abstract
Human behavioural changes are poorly understood, and this limitation has been a serious obstacle to epidemic forecasting. It is generally understood that people change their respective behaviours to reduce the risk of infection in response to the status of an epidemic or government interventions. We must first identify the factors that lead to such decision-making to predict these changes. However, due to an absence of a method to observe decision-making for future behaviour, understanding the behavioural responses to disease is limited. Here, we show that accommodation reservation data could reveal the decision-making process that underpins behavioural changes, travel avoidance, for reducing the risk of COVID-19 infections. We found that the motivation to avoid travel with respect to only short-term future behaviours dynamically varied and was associated with the outbreak status and/or the interventions of the government. Our developed method can quantitatively measure and predict a large-scale population's behaviour to determine the future risk of COVID-19 infections. These findings enable us to better understand behavioural changes in response to disease spread, and thus, contribute to the development of reliable long-term forecasting of disease spread.
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Schaber KL, Kobayashi T, Hast M, Searle KM, Shields TM, Hamapumbu H, Lubinda J, Thuma PE, Lupiya J, Chaponda M, Munyati S, Gwanzura L, Mharakurwa S, Moss WJ, Wesolowski A. What Heterogeneities in Individual-level Mobility Are Lost During Aggregation? Leveraging GPS Logger Data to Understand Fine-scale and Aggregated Patterns of Mobility. Am J Trop Med Hyg 2022; 107:1145-1153. [PMID: 36252797 PMCID: PMC9709031 DOI: 10.4269/ajtmh.22-0202] [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: 03/14/2022] [Accepted: 08/18/2022] [Indexed: 01/25/2023] Open
Abstract
Human movement drives spatial transmission patterns of infectious diseases. Population-level mobility patterns are often quantified using aggregated data sets, such as census migration surveys or mobile phone data. These data are often unable to quantify individual-level travel patterns and lack the information needed to discern how mobility varies by demographic groups. Individual-level datasets can capture additional, more precise, aspects of mobility that may impact disease risk or transmission patterns and determine how mobility differs across cohorts; however, these data are rare, particularly in locations such as sub-Saharan Africa. Using detailed GPS logger data collected from three sites in southern Africa, we explore metrics of mobility such as percent time spent outside home, number of locations visited, distance of locations, and time spent at locations to determine whether they vary by demographic, geographic, or temporal factors. We further create a composite mobility score to identify how well aggregated summary measures would capture the full extent of mobility patterns. Although sites had significant differences in all mobility metrics, no site had the highest mobility for every metric, a distinction that was not captured by the composite mobility score. Further, the effects of sex, age, and season on mobility were all dependent on site. No factor significantly influenced the number of trips to locations, a common way to aggregate datasets. When collecting and analyzing human mobility data, it is difficult to account for all the nuances; however, these analyses can help determine which metrics are most helpful and what underlying differences may be present.
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Affiliation(s)
- Kathryn L. Schaber
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Tamaki Kobayashi
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Marisa Hast
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Kelly M. Searle
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Timothy M. Shields
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Jailos Lubinda
- Telethon Kids Institute, Malaria Atlas Project, Nedlands, Australia
| | - Philip E. Thuma
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - James Lupiya
- The Tropical Diseases Research Centre, Ndola, Zambia
| | - Mike Chaponda
- The Tropical Diseases Research Centre, Ndola, Zambia
| | - Shungu Munyati
- Biomedical Research and Training Institute, Harare, Zimbabwe
| | - Lovemore Gwanzura
- Biomedical Research and Training Institute, Harare, Zimbabwe
- College of Health Sciences, University of Zimbabwe, Harare, Zimbabwe
| | - Sungano Mharakurwa
- Biomedical Research and Training Institute, Harare, Zimbabwe
- College of Health, Agriculture and Natural Sciences, Africa University, Mutare, Zimbabwe
| | - William J. Moss
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Strzelecki A, Azevedo A, Rizun M, Rutecka P, Zagała K, Cicha K, Albuquerque A. Human Mobility Restrictions and COVID-19 Infection Rates: Analysis of Mobility Data and Coronavirus Spread in Poland and Portugal. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14455. [PMID: 36361333 PMCID: PMC9655221 DOI: 10.3390/ijerph192114455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/22/2022] [Accepted: 10/31/2022] [Indexed: 06/16/2023]
Abstract
This study examines the possibility of correlation between the data on human mobility restrictions and the COVID-19 infection rates in two European countries: Poland and Portugal. The aim of this study is to verify the correlation and causation between mobility changes and the infection spread as well as to investigate the impact of the introduced restrictions on changes in human mobility. The data were obtained from Google Community Mobility Reports, Apple Mobility Trends Reports, and The Humanitarian Data Exchange along with other reports published online. All the data were organized in one dataset, and three groups of variables were distinguished: restrictions, mobility, and intensity of the disease. The causal-comparative research design method is used for this study. The results show that in both countries the state restrictions reduced human mobility, with the strongest impact in places related to retail and recreation, grocery, pharmacy, and transit stations. At the same time, the data show that the increase in restrictions had strong positive correlation with stays in residential places both in Poland and Portugal.
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Affiliation(s)
- Artur Strzelecki
- Department of Informatics, University of Economics in Katowice, 40-287 Katowice, Poland
| | - Ana Azevedo
- CEOS.PP, Porto Accounting and Business School, Polytechnic Institute of Porto, 4200-465 Porto, Portugal
| | - Mariia Rizun
- Department of Informatics, University of Economics in Katowice, 40-287 Katowice, Poland
| | - Paulina Rutecka
- Department of Informatics, University of Economics in Katowice, 40-287 Katowice, Poland
| | - Kacper Zagała
- Department of Informatics, University of Economics in Katowice, 40-287 Katowice, Poland
| | - Karina Cicha
- Department of Communication Design and Analysis, University of Economics in Katowice, 40-287 Katowice, Poland
| | - Alexandra Albuquerque
- CEOS.PP, Porto Accounting and Business School, Polytechnic Institute of Porto, 4200-465 Porto, Portugal
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