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Berke A, Calacci D, Mahari R, Yabe T, Larson K, Pentland S. Open e-commerce 1.0, five years of crowdsourced U.S. Amazon purchase histories with user demographics. Sci Data 2024; 11:491. [PMID: 38740768 DOI: 10.1038/s41597-024-03329-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/29/2024] [Indexed: 05/16/2024] Open
Abstract
This is a first-of-its-kind dataset containing detailed purchase histories from 5027 U.S. Amazon.com consumers, spanning 2018 through 2022, with more than 1.8 million purchases. Consumer spending data are customarily collected through government surveys to produce public datasets and statistics, which serve public agencies and researchers. Companies now collect similar data through consumers' use of digital platforms at rates superseding data collection by public agencies. We published this dataset in an effort towards democratizing access to rich data sources routinely used by companies. The data were crowdsourced through an online survey and shared with participants' informed consent. Data columns include order date, product code, title, price, quantity, and shipping address state. Each purchase history is linked to survey data with information about participants' demographics, lifestyle, and health. We validate the dataset by showing expenditure correlates with public Amazon sales data (Pearson r = 0.978, p < 0.001) and conduct analyses of specific product categories, demonstrating expected seasonal trends and strong relationships to other public datasets.
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Affiliation(s)
- Alex Berke
- MIT Media Lab, Cambridge, MA, 02139, USA.
| | - Dan Calacci
- MIT Media Lab, Cambridge, MA, 02139, USA
- Princeton University, Princeton, NJ, 08544, USA
| | - Robert Mahari
- MIT Media Lab, Cambridge, MA, 02139, USA
- Harvard Law School, Cambridge, MA, 02138, USA
| | - Takahiro Yabe
- MIT Institute of Data, Systems, and Society (IDSS), Cambridge, MA, 02139, USA
- New York University Center for Urban Science and Progress, Brooklyn, NY, 11201, USA
| | | | - Sandy Pentland
- MIT Media Lab, Cambridge, MA, 02139, USA
- MIT Connection Science, Cambridge, MA, 02139, USA
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WANG J, HU L, ZHANG T, LIU J, YU C, ZHAO N, QI J, LIU L. Prevalence and predictors of prenatal depression during the COVID-19 pandemic: A multistage observational study in Beijing, China. PLoS One 2024; 19:e0298314. [PMID: 38662750 PMCID: PMC11045078 DOI: 10.1371/journal.pone.0298314] [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: 07/28/2022] [Accepted: 01/19/2024] [Indexed: 04/28/2024] Open
Abstract
OBJECTIVE While growing psychological health issues among pregnant women during the COVID-19 pandemic have been clearly validated, most research was conducted in countries with relatively lax quarantine measures. This study aimed to compare the prevalence of prenatal depression among pre-, peak-, and post-COVID-19 in Beijing, the region with a stringent response policy in China. We also explore predictors of prenatal depression throughout the outbreak. METHODS We investigated prenatal depression among 742 pregnant women who received antenatal checkups in Beijing from March 28, 2019 to May 07, 2021 using the Edinburgh Postnatal Depression Scale and associative demographic, pregnancy-related, and psychosocial characteristics were measured. The phase was divided into pre-, peak-, and post-COVID-19 in light of the trajectory of COVID-19. Pearson's Chi-square test was used after the examination of confounders homogeneity. The bivariable and multivariable logistic regression was conducted to explore predictors. RESULTS The pooled prevalence of prenatal depression was 11.9% throughout the COVID-19 pandemic. Rates at different phases were 10.6%, 15.2%, and 11.1% respectively and no significant difference was observed. Multivariable logistic regression revealed that history of mental illness, number of boy-preference from both pregnant women and husband's family, social support, occupation, and living space were independent predictors of prenatal depression in Beijing. CONCLUSION Our data suggested that the impact of this pandemic on prenatal depression in Beijing appears to be not significant, which will strengthen confidence in adhering to current policy for decision-makers and provide important guidance for the development of major outbreak control and management policies in the future. Our findings may also provide a more efficient measure to identify high-risk pregnant women for professionals and help raise gender equity awareness of pregnant women and their husbands' families. Future studies should focus on the value of targeted care and family relations on the mental health of pregnant women.
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Affiliation(s)
- Jin WANG
- Institution of Hospital Management, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, China
- Department of Aviation Psychology, Air Force Medical Center, Air Force Medical University, Beijing, China
| | - Libin HU
- Institution of Hospital Management, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, China
| | - Tianyi ZHANG
- Institution of Hospital Management, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, China
| | - Jiajia LIU
- Department of Aviation Psychology, Air Force Medical Center, Air Force Medical University, Beijing, China
| | - Chuan YU
- Department of Aviation Psychology, Air Force Medical Center, Air Force Medical University, Beijing, China
| | - Ningxin ZHAO
- Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Jianlin QI
- Department of Aviation Psychology, Air Force Medical Center, Air Force Medical University, Beijing, China
| | - Lihua LIU
- Institution of Hospital Management, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, China
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3
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Sahu KS, Dubin JA, Majowicz SE, Liu S, Morita PP. Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights. JMIR Public Health Surveill 2024; 10:e46903. [PMID: 38506901 PMCID: PMC10993118 DOI: 10.2196/46903] [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: 03/02/2023] [Revised: 09/27/2023] [Accepted: 01/03/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. OBJECTIVE This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. METHODS Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. RESULTS The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. CONCLUSIONS This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.
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Affiliation(s)
- Kirti Sundar Sahu
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Joel A Dubin
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada
| | - Shannon E Majowicz
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Sam Liu
- School of Exercise Science, Physical and Health Education, University of Victoria, Victoria, BC, Canada
| | - Plinio P Morita
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Research Institute of Aging, University of Waterloo, Waterloo, ON, Canada
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
- eHealth Innovation, University Health Network, Toronto, ON, Canada
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4
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Anupong S, Chadsuthi S, Hongsing P, Hurst C, Phattharapornjaroen P, Rad S.M. AH, Fernandez S, Huang AT, Vatanaprasan P, Saethang T, Luk-in S, Storer RJ, Ounjai P, Devanga Ragupathi NK, Kanthawee P, Ngamwongsatit N, Badavath VN, Thuptimdang W, Leelahavanichkul A, Kanjanabuch T, Miyanaga K, Cui L, Nanbo A, Shibuya K, Kupwiwat R, Sano D, Furukawa T, Sei K, Higgins PG, Kicic A, Singer AC, Chatsuwan T, Trowsdale S, Abe S, Ishikawa H, Amarasiri M, Modchang C, Wannigama DL. Exploring indoor and outdoor dust as a potential tool for detection and monitoring of COVID-19 transmission. iScience 2024; 27:109043. [PMID: 38375225 PMCID: PMC10875567 DOI: 10.1016/j.isci.2024.109043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/09/2023] [Accepted: 01/23/2024] [Indexed: 02/21/2024] Open
Abstract
This study investigated the potential of using SARS-CoV-2 viral concentrations in dust as an additional surveillance tool for early detection and monitoring of COVID-19 transmission. Dust samples were collected from 8 public locations in 16 districts of Bangkok, Thailand, from June to August 2021. SARS-CoV-2 RNA concentrations in dust were quantified, and their correlation with community case incidence was assessed. Our findings revealed a positive correlation between viral concentrations detected in dust and the relative risk of COVID-19. The highest risk was observed with no delay (0-day lag), and this risk gradually decreased as the lag time increased. We observed an overall decline in viral concentrations in public places during lockdown, closely associated with reduced human mobility. The effective reproduction number for COVID-19 transmission remained above one throughout the study period, suggesting that transmission may persist in locations beyond public areas even after the lockdown measures were in place.
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Affiliation(s)
- Suparinthon Anupong
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - Sudarat Chadsuthi
- Department of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand
| | - Parichart Hongsing
- Mae Fah Luang University Hospital, Chiang Rai, Thailand
- School of Integrative Medicine, Mae Fah Luang University, Chiang Rai, Thailand
| | - Cameron Hurst
- Molly Wardaguga Research Centre, Charles Darwin University, Brisbane, QLD, Australia
- Statistics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Phatthranit Phattharapornjaroen
- Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
- Institute of Clinical Sciences, Department of Surgery, Sahlgrenska Academy, Gothenburg University, 40530 Gothenburg, Sweden
| | - Ali Hosseini Rad S.M.
- Department of Microbiology and Immunology, University of Otago, Dunedin, Otago 9010, New Zealand
- Center of Excellence in Immunology and Immune-Mediated Diseases, Chulalongkorn University, Bangkok 10330, Thailand
| | - Stefan Fernandez
- Department of Virology, U.S. Army Medical Directorate, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Angkana T. Huang
- Department of Virology, U.S. Army Medical Directorate, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
- Department of Genetics, University of Cambridge, Cambridge, UK
| | | | - Thammakorn Saethang
- Department of Computer Science, Faculty of Science, Kasetsart University, Bangkok, Thailand
| | - Sirirat Luk-in
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Robin James Storer
- Office of Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Puey Ounjai
- Department of Biology, Faculty of Science, Mahidol University, Bangkok, Thailand
| | - Naveen Kumar Devanga Ragupathi
- Department of Chemical and Biological Engineering, The University of Sheffield, Sheffield, UK
- Biofilms and Antimicrobial Resistance Consortium of ODA Receiving Countries, The University of Sheffield, Sheffield, UK
- Division of Microbial Interactions, Department of Research and Development, Bioberrys Healthcare and Research Centre, Vellore 632009, India
| | - Phitsanuruk Kanthawee
- Public Health Major, School of Health Science, Mae Fah Luang University, Chiang Rai 57100, Thailand
| | - Natharin Ngamwongsatit
- Department of Clinical Sciences and Public Health, Faculty of Veterinary Science, Mahidol University, Nakhon Pathom, Thailand
| | - Vishnu Nayak Badavath
- School of Pharmacy & Technology Management, SVKM’s Narsee Monjee Institute of Management Studies (NMIMS), Hyderabad 509301, India
| | - Wanwara Thuptimdang
- Institute of Biomedical Engineering, Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand
| | - Asada Leelahavanichkul
- Department of Microbiology, Faculty of Medicine, Chulalongkorn University, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- Translational Research in Inflammation and Immunology Research Unit (TRIRU), Department of Microbiology, Chulalongkorn University, Bangkok, Thailand
| | - Talerngsak Kanjanabuch
- Division of Nephrology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Kidney Metabolic Disorders, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Dialysis Policy and Practice Program (DiP3), School of Global Health, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Peritoneal Dialysis Excellence Center, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Kazuhiko Miyanaga
- Division of Bacteriology, School of Medicine, Jichi Medical University, Tochigi, Japan
| | - Longzhu Cui
- Division of Bacteriology, School of Medicine, Jichi Medical University, Tochigi, Japan
| | - Asuka Nanbo
- The National Research Center for the Control and Prevention of Infectious Diseases, Nagasaki University, Nagasaki, Japan
| | - Kenji Shibuya
- Tokyo Foundation for Policy Research, Minato-ku, Tokyo, Japan
| | - Rosalyn Kupwiwat
- Department of Dermatology. Faculty of Medicine Siriraj Hospital. Mahidol University, Bangkok, Thailand
| | - Daisuke Sano
- Department of Frontier Sciences for Advanced Environment, Graduate School of Environmental Studies, Tohoku University, Sendai, Miyagi, Japan
- Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, Sendai, Miyagi, Japan
| | - Takashi Furukawa
- Laboratory of Environmental Hygiene, Department of Health Science, School of Allied Health Sciences, Graduate School of Medical Sciences, Kitasato University, Minato City, Tokyo 108-8641, Japan
| | - Kazunari Sei
- Laboratory of Environmental Hygiene, Department of Health Science, School of Allied Health Sciences, Graduate School of Medical Sciences, Kitasato University, Minato City, Tokyo 108-8641, Japan
| | - Paul G. Higgins
- Institute for Medical Microbiology, Immunology and Hygiene, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Centre for Infection Research, Partner Site Bonn-Cologne, Cologne, Germany
| | - Anthony Kicic
- Wal-Yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Nedlands WA 6009, Australia
- Centre for Cell Therapy and Regenerative Medicine, Medical School, The University of Western Australia, Nedlands, WA 6009, Australia
- Department of Respiratory and Sleep Medicine, Perth Children’s Hospital, Nedlands WA 6009, Australia
- School of Population Health, Curtin University, Bentley WA 6102, Australia
| | | | - Tanittha Chatsuwan
- Department of Microbiology, Faculty of Medicine, Chulalongkorn University, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- Center of Excellence in Antimicrobial Resistance and Stewardship, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sam Trowsdale
- Department of Environmental Science, University of Auckland, Auckland 1010, New Zealand
| | - Shuichi Abe
- Department of Infectious Diseases and Infection Control, Yamagata Prefectural Central Hospital, Yamagata, Japan
| | - Hitoshi Ishikawa
- Yamagata Prefectural University of Health Sciences, Kamiyanagi, Yamagata 990-2212, Japan
| | - Mohan Amarasiri
- Laboratory of Environmental Hygiene, Department of Health Science, School of Allied Health Sciences, Graduate School of Medical Sciences, Kitasato University, Minato City, Tokyo 108-8641, Japan
| | - Charin Modchang
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
- Centre of Excellence in Mathematics, MHESI, Bangkok 10400, Thailand
- Thailand Center of Excellence in Physics, Ministry of Higher Education, Science, Research and Innovation, 328 Si Ayutthaya Road, Bangkok 10400, Thailand
| | - Dhammika Leshan Wannigama
- Biofilms and Antimicrobial Resistance Consortium of ODA Receiving Countries, The University of Sheffield, Sheffield, UK
- Department of Microbiology, Faculty of Medicine, Chulalongkorn University, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- Center of Excellence in Antimicrobial Resistance and Stewardship, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Department of Infectious Diseases and Infection Control, Yamagata Prefectural Central Hospital, Yamagata, Japan
- Yamagata Prefectural University of Health Sciences, Kamiyanagi, Yamagata 990-2212, Japan
- School of Medicine, Faculty of Health and Medical Sciences, The University of Western Australia, Nedlands, WA, Australia
- Pathogen Hunter’s Research Collaborative Team, Department of Infectious Diseases and Infection Control, Yamagata Prefectural Central Hospital, Yamagata, Japan
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Kuwahara B, Bauch CT. Predicting Covid-19 pandemic waves with biologically and behaviorally informed universal differential equations. Heliyon 2024; 10:e25363. [PMID: 38370214 PMCID: PMC10869765 DOI: 10.1016/j.heliyon.2024.e25363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 12/29/2023] [Accepted: 01/25/2024] [Indexed: 02/20/2024] Open
Abstract
During the COVID-19 pandemic, it became clear that pandemic waves and population responses were locked in a mutual feedback loop in a classic example of a coupled behavior-disease system. We demonstrate for the first time that universal differential equation (UDE) models are able to extract this interplay from data. We develop a UDE model for COVID-19 and test its ability to make predictions of second pandemic waves. We find that UDEs are capable of learning coupled behavior-disease dynamics and predicting second waves in a variety of populations, provided they are supplied with learning biases describing simple assumptions about disease transmission and population response. Though not yet suitable for deployment as a policy-guiding tool, our results demonstrate potential benefits, drawbacks, and useful techniques when applying universal differential equations to coupled systems.
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Affiliation(s)
- Bruce Kuwahara
- Department of Applied Mathematics, University of Waterloo, 200 University Ave West, Waterloo, Ontario, Canada
| | - Chris T. Bauch
- Department of Applied Mathematics, University of Waterloo, 200 University Ave West, Waterloo, Ontario, Canada
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6
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Umutoni B, Tumushime JC, Hewins B, Udahemuka JC, Ndishimye P, Kelvin DJ, Sganzerla Martinez G. The impact of public transportation on the transmission of COVID-19 in Rwanda. Front Public Health 2024; 12:1345433. [PMID: 38476489 PMCID: PMC10927834 DOI: 10.3389/fpubh.2024.1345433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 02/06/2024] [Indexed: 03/14/2024] Open
Abstract
Introduction The onset of the COVID-19 pandemic has placed a significant burden on healthcare systems worldwide, particularly in sub-Saharan regions where healthcare resources are limited. The transmission of SARS-CoV-2 is facilitated by the movement of people from place to place. Therefore, implementing measures that restrict movement of people and contacts is crucial in controlling the spread of the disease. Following the identification of the first COVID-19 case in Rwanda, the government implemented stringent measures, including a complete nationwide lockdown, border closures, curfews, reduced capacity in public transportation and businesses, and mandatory testing. This study aims to assess epidemiological trends in COVID-19 cases in relation to changes in population mobility within the public transportation system. Methods A descriptive analysis using publicly available data on COVID-19 epidemiological indicators (cases, deaths, vaccinations, and stringency index) and mobility data was conducted. Results The results reveal a strong correlation between mobility in public transportation and other activities, underscoring Rwanda's reliance on its public transportation system. The study also identifies a pattern where increases in transit station mobility preceded spikes in COVID-19 cases, suggesting that the subsequent rise in public transportation usage may contribute to higher infection rates. Discussion Therefore, this study emphasizes the importance of ongoing vigilance and regulatory measures regarding public transportation during infectious disease outbreaks.
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Affiliation(s)
- Brigitte Umutoni
- College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda
- Center for Research and Innovation, African Institute for Mathematical Sciences (AIMS), Kigali, Rwanda
| | - Jean Claude Tumushime
- College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda
- Center for Research and Innovation, African Institute for Mathematical Sciences (AIMS), Kigali, Rwanda
| | - Benjamin Hewins
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada
- Department of Pediatrics, Izaak Walton Killam (IWK) Health Center, Canadian Center for Vaccinology (CCfV), Halifax, NS, Canada
| | | | - Pacifique Ndishimye
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada
- Department of Pediatrics, Izaak Walton Killam (IWK) Health Center, Canadian Center for Vaccinology (CCfV), Halifax, NS, Canada
| | - David J. Kelvin
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada
- Department of Pediatrics, Izaak Walton Killam (IWK) Health Center, Canadian Center for Vaccinology (CCfV), Halifax, NS, Canada
| | - Gustavo Sganzerla Martinez
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada
- Department of Pediatrics, Izaak Walton Killam (IWK) Health Center, Canadian Center for Vaccinology (CCfV), Halifax, NS, Canada
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7
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Aguilar-Ruiz JS, Ruiz R, Giráldez R. Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure. Healthcare (Basel) 2024; 12:517. [PMID: 38470628 PMCID: PMC10930786 DOI: 10.3390/healthcare12050517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/27/2024] [Accepted: 02/13/2024] [Indexed: 03/14/2024] Open
Abstract
The COVID-19 pandemic has had a profound impact on various aspects of our lives, affecting personal, occupational, economic, and social spheres. Much has been learned since the early 2020s, which will be very useful when the next pandemic emerges. In general, mobility and virus spread are strongly related. However, most studies analyze the impact of COVID-19 on mobility, but not much research has focused on analyzing the impact of mobility on virus transmission, especially from the point of view of monitoring virus incidence, which is extremely important for making sound decisions to control any epidemiological threat to public health. As a result of a thorough analysis of COVID-19 and mobility data, this work introduces a novel measure, the Infection Ratio (IR), which is not sensitive to underestimation of positive cases and is very effective in monitoring the pandemic's upward or downward evolution when it appears to be more stable, thus anticipating possible risk situations. For a bounded spatial context, we can infer that there is a significant threshold in the restriction of mobility that determines a change of trend in the number of infections that, if maintained for a minimum period, would notably increase the chances of keeping the spread of disease under control. Results show that IR is a reliable indicator of the intensity of infection, and an effective measure for early monitoring and decision making in smart cities.
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Affiliation(s)
- Jesus S. Aguilar-Ruiz
- School of Engineering, Pablo de Olavide University, 41013 Seville, Spain; (R.R.); (R.G.)
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8
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Klein B, LaRock T, McCabe S, Torres L, Friedland L, Kos M, Privitera F, Lake B, Kraemer MUG, Brownstein JS, Gonzalez R, Lazer D, Eliassi-Rad T, Scarpino SV, Vespignani A, Chinazzi M. Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic. PLOS DIGITAL HEALTH 2024; 3:e0000430. [PMID: 38319890 PMCID: PMC10846712 DOI: 10.1371/journal.pdig.0000430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 12/11/2023] [Indexed: 02/08/2024]
Abstract
The COVID-19 pandemic offers an unprecedented natural experiment providing insights into the emergence of collective behavioral changes of both exogenous (government mandated) and endogenous (spontaneous reaction to infection risks) origin. Here, we characterize collective physical distancing-mobility reductions, minimization of contacts, shortening of contact duration-in response to the COVID-19 pandemic in the pre-vaccine era by analyzing de-identified, privacy-preserving location data for a panel of over 5.5 million anonymized, opted-in U.S. devices. We define five indicators of users' mobility and proximity to investigate how the emerging collective behavior deviates from typical pre-pandemic patterns during the first nine months of the COVID-19 pandemic. We analyze both the dramatic changes due to the government mandated mitigation policies and the more spontaneous societal adaptation into a new (physically distanced) normal in the fall 2020. Using the indicators here defined we show that: a) during the COVID-19 pandemic, collective physical distancing displayed different phases and was heterogeneous across geographies, b) metropolitan areas displayed stronger reductions in mobility and contacts than rural areas; c) stronger reductions in commuting patterns are observed in geographical areas with a higher share of teleworkable jobs; d) commuting volumes during and after the lockdown period negatively correlate with unemployment rates; and e) increases in contact indicators correlate with future values of new deaths at a lag consistent with epidemiological parameters and surveillance reporting delays. In conclusion, this study demonstrates that the framework and indicators here presented can be used to analyze large-scale social distancing phenomena, paving the way for their use in future pandemics to analyze and monitor the effects of pandemic mitigation plans at the national and international levels.
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Affiliation(s)
- Brennan Klein
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Timothy LaRock
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Stefan McCabe
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Leo Torres
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Lisa Friedland
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Maciej Kos
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | | | - Brennan Lake
- Cuebiq Inc., New York, New York, United States of America
| | | | - John S. Brownstein
- Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Richard Gonzalez
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Tina Eliassi-Rad
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Samuel V. Scarpino
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - Alessandro Vespignani
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
- ISI Foundation, Turin, Italy
| | - Matteo Chinazzi
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
- The Roux Institute, Northeastern University, Portland, Maine, United States of America
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9
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Du H, Saiyed S, Gardner LM. Association between vaccination rates and COVID-19 health outcomes in the United States: a population-level statistical analysis. BMC Public Health 2024; 24:220. [PMID: 38238709 PMCID: PMC10797940 DOI: 10.1186/s12889-024-17790-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/16/2024] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Population-level vaccine efficacy is a critical component of understanding COVID-19 risk, informing public health policy, and mitigating disease impacts. Unlike individual-level clinical trials, population-level analysis characterizes how well vaccines worked in the face of real-world challenges like emerging variants, differing mobility patterns, and policy changes. METHODS In this study, we analyze the association between time-dependent vaccination rates and COVID-19 health outcomes for 48 U.S. states. We primarily focus on case-hospitalization risk (CHR) as the outcome of interest, using it as a population-level proxy for disease burden on healthcare systems. Performing the analysis using Generalized Additive Models (GAMs) allowed us to incorporate real-world nonlinearities and control for critical dynamic (time-changing) and static (temporally constant) factors. Dynamic factors include testing rates, activity-related engagement levels in the population, underlying population immunity, and policy. Static factors incorporate comorbidities, social vulnerability, race, and state healthcare expenditures. We used SARS-CoV-2 genomic surveillance data to model the different COVID-19 variant-driven waves separately, and evaluate if there is a changing role of the potential drivers of health outcomes across waves. RESULTS Our study revealed a strong and statistically significant negative association between vaccine uptake and COVID-19 CHR across each variant wave, with boosters providing additional protection during the Omicron wave. Higher underlying population immunity is shown to be associated with reduced COVID-19 CHR. Additionally, more stringent government policies are generally associated with decreased CHR. However, the impact of activity-related engagement levels on COVID-19 health outcomes varied across different waves. Regarding static variables, the social vulnerability index consistently exhibits positive associations with CHR, while Medicaid spending per person consistently shows a negative association. However, the impacts of other static factors vary in magnitude and significance across different waves. CONCLUSIONS This study concludes that despite the emergence of new variants, vaccines remain highly correlated with reduced COVID-19 harm. Therefore, given the ongoing threat posed by COVID-19, vaccines remain a critical line of defense for protecting the public and reducing the burden on healthcare systems.
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Affiliation(s)
- Hongru Du
- Center for Systems Science and Engineering, Johns Hopkins University, 3400 N. Charles Street, Shaffer 4, Baltimore, MD, 21218, USA.
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Samee Saiyed
- Center for Systems Science and Engineering, Johns Hopkins University, 3400 N. Charles Street, Shaffer 4, Baltimore, MD, 21218, USA
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Lauren M Gardner
- Center for Systems Science and Engineering, Johns Hopkins University, 3400 N. Charles Street, Shaffer 4, Baltimore, MD, 21218, USA
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
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10
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Zuccarelli J, Seaman L, Rader K. Assessing the Impact of Non-Pharmaceutical Interventions on Consumer Mobility Patterns and COVID-19 Transmission in the US. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:67. [PMID: 38248532 PMCID: PMC10815148 DOI: 10.3390/ijerph21010067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/02/2024] [Accepted: 01/05/2024] [Indexed: 01/23/2024]
Abstract
The initial outbreak of COVID-19 during late December 2019 and the subsequent global pandemic markedly changed consumer mobility patterns worldwide, largely in response to government-ordered non-pharmaceutical interventions (NPIs). In this study, we investigate these changes as they relate to the initial spread of COVID-19 within two states-Massachusetts and Michigan. Specifically, we use linear and generalized linear mixed-effects models to quantify the relationship between four NPIs and individuals' point-of-sale (POS) credit card transactions, as well as the relationship between subsequent changes in POS transactions and county-level COVID-19 case growth rates. Our analysis reveals a significant negative association between NPIs and daily POS transactions, particularly a dose-response relationship, in which stringent workplace closures, stay-at-home requirements, and gathering restrictions were all associated with decreased POS transactions. We also uncover a significant positive association between 12-day lagged changes in POS transactions compared to pre-pandemic baselines and county-level COVID-19 case growth rates. Overall, our study supports previous findings that early NPIs reduced human mobility and COVID-19 transmission in the US, providing policymakers with quantitative evidence concerning the effectiveness of NPIs.
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Affiliation(s)
- Joseph Zuccarelli
- The Charles Stark Draper Laboratory, Cambridge, MA 02139, USA;
- Department of Statistics, Harvard University, Cambridge, MA 02139, USA;
| | - Laura Seaman
- The Charles Stark Draper Laboratory, Cambridge, MA 02139, USA;
| | - Kevin Rader
- Department of Statistics, Harvard University, Cambridge, MA 02139, USA;
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11
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Li R, Song Y, Qu H, Li M, Jiang GP. A data-driven epidemic model with human mobility and vaccination protection for COVID-19 prediction. J Biomed Inform 2024; 149:104571. [PMID: 38092247 DOI: 10.1016/j.jbi.2023.104571] [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: 08/13/2023] [Revised: 11/22/2023] [Accepted: 12/07/2023] [Indexed: 12/18/2023]
Abstract
Epidemiological models allow for quantifying the dynamic characteristics of large-scale outbreaks. However, capturing detailed and accurate epidemiological information often requires consideration of multiple kinetic mechanisms and parameters. Due to the uncertainty of pandemic evolution, such as pathogen variation, host immune response and changes in mitigation strategies, the parameter evaluation and state prediction of complex epidemiological models are challenging. Here, we develop a data-driven epidemic model with a generalized SEIR mechanistic structure that includes new compartments, human mobility and vaccination protection. To address the issue of model complexity, we embed the epidemiological model dynamics into physics-informed neural networks (PINN), taking the observed series of time instances as direct input of the network to simultaneously infer unknown parameters and unobserved dynamics of the underlying model. Using actual data during the COVID-19 outbreak in Australia, Israel, and Switzerland, our model framework demonstrates satisfactory performance in multi-step ahead predictions compared to several benchmark models. Moreover, our model infers time-varying parameters such as transmission rates, hospitalization ratios, and effective reproduction numbers, as well as calculates the latent period and asymptomatic infection count, which are typically unreported in public data. Finally, we employ the proposed data-driven model to analyze the impact of different mitigation strategies on COVID-19.
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Affiliation(s)
- Ruqi Li
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Yurong Song
- College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
| | - Hongbo Qu
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Min Li
- College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Guo-Ping Jiang
- College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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12
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Valdano E, Colombi D, Poletto C, Colizza V. Epidemic graph diagrams as analytics for epidemic control in the data-rich era. Nat Commun 2023; 14:8472. [PMID: 38123580 PMCID: PMC10733371 DOI: 10.1038/s41467-023-43856-1] [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: 01/18/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
COVID-19 highlighted modeling as a cornerstone of pandemic response. But it also revealed that current models may not fully exploit the high-resolution data on disease progression, epidemic surveillance and host behavior, now available. Take the epidemic threshold, which quantifies the spreading risk throughout epidemic emergence, mitigation, and control. Its use requires oversimplifying either disease or host contact dynamics. We introduce the epidemic graph diagrams to overcome this by computing the epidemic threshold directly from arbitrarily complex data on contacts, disease and interventions. A grammar of diagram operations allows to decompose, compare, simplify models with computational efficiency, extracting theoretical understanding. We use the diagrams to explain the emergence of resistant influenza variants in the 2007-2008 season, and demonstrate that neglecting non-infectious prodromic stages of sexually transmitted infections biases the predicted epidemic risk, compromising control. The diagrams are general, and improve our capacity to respond to present and future public health challenges.
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Affiliation(s)
- Eugenio Valdano
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012, Paris, France
| | | | - Chiara Poletto
- Department of Molecular Medicine, University of Padova, 35121, Padova, Italy
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012, Paris, France.
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13
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Althouse BM, Wallace B, Case BKM, Scarpino SV, Allard A, Berdahl AM, White ER, Hébert-Dufresne L. The unintended consequences of inconsistent closure policies and mobility restrictions during epidemics. BMC GLOBAL AND PUBLIC HEALTH 2023; 1:28. [PMID: 38798822 PMCID: PMC11116187 DOI: 10.1186/s44263-023-00028-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: 08/18/2023] [Accepted: 10/17/2023] [Indexed: 05/29/2024]
Abstract
Background Controlling the spread of infectious diseases-even when safe, transmission-blocking vaccines are available-may require the effective use of non-pharmaceutical interventions (NPIs), e.g., mask wearing, testing, limits on group sizes, venue closure. During the SARS-CoV-2 pandemic, many countries implemented NPIs inconsistently in space and time. This inconsistency was especially pronounced for policies in the United States of America (US) related to venue closure. Methods Here, we investigate the impact of inconsistent policies associated with venue closure using mathematical modeling and high-resolution human mobility, Google search, and county-level SARS-CoV-2 incidence data from the USA. Specifically, we look at high-resolution location data and perform a US-county-level analysis of nearly 8 million SARS-CoV-2 cases and 150 million location visits, including 120 million church visitors across 184,677 churches, 14 million grocery visitors across 7662 grocery stores, and 13.5 million gym visitors across 5483 gyms. Results Analyzing the interaction between venue closure and changing mobility using a mathematical model shows that, across a broad range of model parameters, inconsistent or partial closure can be worse in terms of disease transmission as compared to scenarios with no closures at all. Importantly, changes in mobility patterns due to epidemic control measures can lead to increase in the future number of cases. In the most severe cases, individuals traveling to neighboring jurisdictions with different closure policies can result in an outbreak that would otherwise have been contained. To motivate our mathematical models, we turn to mobility data and find that while stay-at-home orders and closures decreased contacts in most areas of the USA, some specific activities and venues saw an increase in attendance and an increase in the distance visitors traveled to attend. We support this finding using search query data, which clearly shows a shift in information seeking behavior concurrent with the changing mobility patterns. Conclusions While coarse-grained observations are not sufficient to validate our models, taken together, they highlight the potential unintended consequences of inconsistent epidemic control policies related to venue closure and stress the importance of balancing the societal needs of a population with the risk of an outbreak growing into a large epidemic. Supplementary Information The online version contains supplementary material available at 10.1186/s44263-023-00028-z.
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Affiliation(s)
- Benjamin M. Althouse
- University of Washington, Seattle, 98105 WA USA
- New Mexico State University, Las Cruces, 88003 NM USA
| | - Brendan Wallace
- Department of Applied Mathematics, University of Washington, Seattle, 98195 WA USA
- Present Address: Quantitative Ecology and Resource Management, University of Washington, Seattle, WA 98195 USA
- School of Aquatic & Fishery Sciences,, University of Washington, Seattle, WA 98195 USA
| | - B. K. M. Case
- Department of Computer Science, University of Vermont, Burlington, 05405 VT USA
- Vermont Complex Systems Center, University of Vermont, Burlington, 05405 VT USA
| | - Samuel V. Scarpino
- Vermont Complex Systems Center, University of Vermont, Burlington, 05405 VT USA
- Institute for Experiential AI, Northeastern University, Boston, Massachusetts USA
- Department of Health Sciences, Northeastern University, Boston, MA USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA USA
- Santa Fe Institute, Santa Fe, NM USA
| | - Antoine Allard
- Vermont Complex Systems Center, University of Vermont, Burlington, 05405 VT USA
- Département de physique, de génie physique et d’optique, Université Laval, Québec (Québec), G1V 0A6 Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec (Québec), G1V 0A6 Canada
| | - Andrew M. Berdahl
- School of Aquatic & Fishery Sciences, University of Washington, Seattle, 98195 WA USA
| | - Easton R. White
- Department of Biological Sciences, University of New Hampshire, Durham, 03824 NH USA
- Gund Institute for Environment, University of Vermont, Burlington, 05405 VT USA
| | - Laurent Hébert-Dufresne
- Department of Computer Science, University of Vermont, Burlington, 05405 VT USA
- Vermont Complex Systems Center, University of Vermont, Burlington, 05405 VT USA
- Département de physique, de génie physique et d’optique, Université Laval, Québec (Québec), G1V 0A6 Canada
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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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Drake JM, Handel A, Marty É, O’Dea EB, O’Sullivan T, Righi G, Tredennick AT. A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States. PLoS Comput Biol 2023; 19:e1011610. [PMID: 37939201 PMCID: PMC10659176 DOI: 10.1371/journal.pcbi.1011610] [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: 03/01/2023] [Revised: 11/20/2023] [Accepted: 10/17/2023] [Indexed: 11/10/2023] Open
Abstract
To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March-December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time.
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Affiliation(s)
- John M. Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Andreas Handel
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- College of Public Health, University of Georgia, Athens, Georgia, United States of America
| | - Éric Marty
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Eamon B. O’Dea
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Tierney O’Sullivan
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Giovanni Righi
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Andrew T. Tredennick
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Western EcoSystems Technology, Inc., Laramie, Wyoming, United States of America
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16
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Li H, Wei YD. COVID-19, Cities and Inequality. APPLIED GEOGRAPHY (SEVENOAKS, ENGLAND) 2023; 160:103059. [PMID: 37841058 PMCID: PMC10569256 DOI: 10.1016/j.apgeog.2023.103059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
COVID-19 has changed our lives and will likely leave a lasting imprint on our cities. This paper reviews how the pandemic has altered the way people commute, work, collaborate, and consume, especially its reflection on urban space and spatial inequality. We conceptualize these urban changes as structural transformation, accelerated transition, and temporal change. First, we have seen more structural transformation far exceeding scholars' earlier predictions, especially remote working and global supply chain restructuring. Second, COVID-19 has accelerated the processes of digitalization and sustainable transition. While COVID-19 has contributed to suburbanization and urban sprawl, it has also raised the significance of green spaces and the environment. Third, COVID-19 reduced human impact on the environment, which might be temporary. Last, the pandemic has also amplified the pre-existing inequalities in urban areas, created a more fragmented and segregated urban landscape, and expanded the scope of urban inequality research by connecting health inequality with environmental and socio-injustice. We further discuss the emergence of post-pandemic urban theories and identify research questions for future research.
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Affiliation(s)
- Han Li
- Department of Geography and Sustainable Development, University of Miami, Coral Gables, FL 33146, USA
| | - Yehua Dennis Wei
- Department of Geography, University of Utah, Salt Lake City, UT 84112-9155, USA
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17
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Wood AJ, Sanchez AR, Bessell PR, Wightman R, Kao RR. Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data. PLoS Comput Biol 2023; 19:e1011611. [PMID: 38011282 PMCID: PMC10703279 DOI: 10.1371/journal.pcbi.1011611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 12/07/2023] [Accepted: 10/17/2023] [Indexed: 11/29/2023] Open
Abstract
For the long term control of an infectious disease such as COVID-19, it is crucial to identify the most likely individuals to become infected and the role that differences in demographic characteristics play in the observed patterns of infection. As high-volume surveillance winds down, testing data from earlier periods are invaluable for studying risk factors for infection in detail. Observed changes in time during these periods may then inform how stable the pattern will be in the long term. To this end we analyse the distribution of cases of COVID-19 across Scotland in 2021, where the location (census areas of order 500-1,000 residents) and reporting date of cases are known. We consider over 450,000 individually recorded cases, in two infection waves triggered by different lineages: B.1.1.529 ("Omicron") and B.1.617.2 ("Delta"). We use random forests, informed by measures of geography, demography, testing and vaccination. We show that the distributions are only adequately explained when considering multiple explanatory variables, implying that case heterogeneity arose from a combination of individual behaviour, immunity, and testing frequency. Despite differences in virus lineage, time of year, and interventions in place, we find the risk factors remained broadly consistent between the two waves. Many of the observed smaller differences could be reasonably explained by changes in control measures.
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Affiliation(s)
- Anthony J. Wood
- Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Aeron R. Sanchez
- Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Paul R. Bessell
- Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Rebecca Wightman
- Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Rowland R. Kao
- Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
- Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, United Kingdom
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18
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Dang T, Spathis D, Ghosh A, Mascolo C. Human-centred artificial intelligence for mobile health sensing: challenges and opportunities. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230806. [PMID: 38026044 PMCID: PMC10646451 DOI: 10.1098/rsos.230806] [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: 06/09/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023]
Abstract
Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions.
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Affiliation(s)
- Ting Dang
- University of Cambridge, Cambridge, UK
- Nokia Bell Labs, Cambridge, UK
| | - Dimitris Spathis
- University of Cambridge, Cambridge, UK
- Nokia Bell Labs, Cambridge, UK
| | - Abhirup Ghosh
- University of Cambridge, Cambridge, UK
- University of Birmingham, Birmingham, UK
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19
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Murari A, Gelfusa M, Craciunescu T, Gelfusa C, Gaudio P, Bovesecchi G, Rossi R. Effects of environmental conditions on COVID-19 morbidity as an example of multicausality: a multi-city case study in Italy. Front Public Health 2023; 11:1222389. [PMID: 37965519 PMCID: PMC10642182 DOI: 10.3389/fpubh.2023.1222389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 10/06/2023] [Indexed: 11/16/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), broke out in December 2019 in Wuhan city, in the Hubei province of China. Since then, it has spread practically all over the world, disrupting many human activities. In temperate climates overwhelming evidence indicates that its incidence increases significantly during the cold season. Italy was one of the first nations, in which COVID-19 reached epidemic proportions, already at the beginning of 2020. There is therefore enough data to perform a systematic investigation of the correlation between the spread of the virus and the environmental conditions. The objective of this study is the investigation of the relationship between the virus diffusion and the weather, including temperature, wind, humidity and air quality, before the rollout of any vaccine and including rapid variation of the pollutants (not only their long term effects as reported in the literature). Regarding them methodology, given the complexity of the problem and the sparse data, robust statistical tools based on ranking (Spearman and Kendall correlation coefficients) and innovative dynamical system analysis techniques (recurrence plots) have been deployed to disentangle the different influences. In terms of results, the evidence indicates that, even if temperature plays a fundamental role, the morbidity of COVID-19 depends also on other factors. At the aggregate level of major cities, air pollution and the environmental quantities affecting it, particularly the wind intensity, have no negligible effect. This evidence should motivate a rethinking of the public policies related to the containment of this type of airborne infectious diseases, particularly information gathering and traffic management.
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Affiliation(s)
- Andrea Murari
- Consorzio RFX (CNR, ENEA, INFN, Università di Padova, Acciaierie Venete SpA), Padua, Italy
- Istituto per la Scienza e la Tecnologia dei Plasmi, CNR, Padua, Italy
| | - Michela Gelfusa
- Department of Industrial Engineering, University of Rome “Tor Vergata”, Rome, Italy
| | - Teddy Craciunescu
- National Institute for Laser, Plasma and Radiation Physics, Măgurele, Romania
| | - Claudio Gelfusa
- Department of Industrial Engineering, University of Rome “Tor Vergata”, Rome, Italy
| | - Pasquale Gaudio
- Department of Industrial Engineering, University of Rome “Tor Vergata”, Rome, Italy
| | - Gianluigi Bovesecchi
- Department of Enterprise Engineering, University of Rome “Tor Vergata”, Rome, Italy
| | - Riccardo Rossi
- Department of Industrial Engineering, University of Rome “Tor Vergata”, Rome, Italy
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20
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Zhang J, Tan S, Peng C, Xu X, Wang M, Lu W, Wu Y, Sai B, Cai M, Kummer AG, Chen Z, Zou J, Li W, Zheng W, Liang Y, Zhao Y, Vespignani A, Ajelli M, Lu X, Yu H. Heterogeneous changes in mobility in response to the SARS-CoV-2 Omicron BA.2 outbreak in Shanghai. Proc Natl Acad Sci U S A 2023; 120:e2306710120. [PMID: 37824525 PMCID: PMC10589641 DOI: 10.1073/pnas.2306710120] [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: 04/24/2023] [Accepted: 09/12/2023] [Indexed: 10/14/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic and the measures taken by authorities to control its spread have altered human behavior and mobility patterns in an unprecedented way. However, it remains unclear whether the population response to a COVID-19 outbreak varies within a city or among demographic groups. Here, we utilized passively recorded cellular signaling data at a spatial resolution of 1 km × 1 km for over 5 million users and epidemiological surveillance data collected during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BA.2 outbreak from February to June 2022 in Shanghai, China, to investigate the heterogeneous response of different segments of the population at the within-city level and examine its relationship with the actual risk of infection. Changes in behavior were spatially heterogenous within the city and population groups and associated with both the infection incidence and adopted interventions. We also found that males and individuals aged 30 to 59 y old traveled more frequently, traveled longer distances, and their communities were more connected; the same groups were also associated with the highest SARS-CoV-2 incidence. Our results highlight the heterogeneous behavioral change of the Shanghai population to the SARS-CoV-2 Omicron BA.2 outbreak and the effect of heterogenous behavior on the spread of COVID-19, both spatially and demographically. These findings could be instrumental for the design of targeted interventions for the control and mitigation of future outbreaks of COVID-19, and, more broadly, of respiratory pathogens.
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Affiliation(s)
- Juanjuan Zhang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Suoyi Tan
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Cheng Peng
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Xiangyanyu Xu
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Mengning Wang
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Wanying Lu
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Yanpeng Wu
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Bin Sai
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Mengsi Cai
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Allisandra G. Kummer
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN47405
| | - Zhiyuan Chen
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Junyi Zou
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Wenxin Li
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Wen Zheng
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Yuxia Liang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Yuchen Zhao
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA02115
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN47405
| | - Xin Lu
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Hongjie Yu
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
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21
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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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22
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Feltham E, Forastiere L, Alexander M, Christakis NA. Mass gatherings for political expression had no discernible association with the local course of the COVID-19 pandemic in the USA in 2020 and 2021. Nat Hum Behav 2023; 7:1708-1728. [PMID: 37524931 DOI: 10.1038/s41562-023-01654-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 06/14/2023] [Indexed: 08/02/2023]
Abstract
Epidemic disease can spread during mass gatherings. We assessed the impact of a type of mass gathering about which comprehensive data were available on the local-area trajectory of the COVID-19 epidemic. Here we examined five types of political event in 2020 and 2021: the US primary elections, the US Senate special election in Georgia, the gubernatorial elections in New Jersey and Virginia, Donald Trump's political rallies and the Black Lives Matter protests. Our study period encompassed over 700 such mass gatherings during multiple phases of the pandemic. We used data from the 48 contiguous states, representing 3,108 counties, and we implemented a novel extension of a recently developed non-parametric, generalized difference-in-difference estimator with a (high-quality) matching procedure for panel data to estimate the average effect of the gatherings on local mortality and other outcomes. There were no statistically significant increases in cases, deaths or a measure of epidemic transmissibility (Rt) in a 40-day period following large-scale political activities. We estimated small and statistically non-significant effects, corresponding to an average difference of -0.0567 deaths (95% CI = -0.319, 0.162) and 8.275 cases (95% CI = -1.383, 20.7) on each day for counties that held mass gatherings for political expression compared to matched control counties. In sum, there is no statistical evidence of a material increase in local COVID-19 deaths, cases or transmissibility after mass gatherings for political expression during the first 2 years of the pandemic in the USA. This may relate to the specific manner in which such activities are typically conducted.
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Affiliation(s)
- Eric Feltham
- Yale Institute for Network Science, Yale University, New Haven, CT, USA.
- Department of Sociology, Yale University, New Haven, CT, USA.
| | - Laura Forastiere
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Marcus Alexander
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Frank H. Netter MD School of Medicine, Quinnipiac University, North Haven, CT, USA
| | - Nicholas A Christakis
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Department of Sociology, Yale University, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
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23
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Al-Zubaidy N, Fernandez Crespo R, Jones S, Gould L, Leis M, Maheswaran H, Neves AL, Darzi A, Drikvandi R. Exploring the relationship between government stringency and preventative social behaviours during the COVID-19 pandemic in the United Kingdom. Health Informatics J 2023; 29:14604582231215867. [PMID: 37982397 DOI: 10.1177/14604582231215867] [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] [Indexed: 11/21/2023]
Abstract
We constructed a preventive social behaviours (PSB) Index using survey questions that were aligned with WHO recommendations, and used linear regression to assess the impact of reported COVID-19 deaths (RCD), people's confidence of government handling of the pandemic (CGH) and government stringency (GS) in the United Kingdom (UK) over time on the PSB index. We used repeated, nationally representative, cross-sectional surveys in the UK over the course of 41 weeks from 1st April 2020 to January 28th, 2021, including a total of 38,092 participants. The PSB index was positively correlated with the logarithm of RCD (R: 0.881, p < .001), CGH (R: 0.592, p < .001) and GS (R: 0.785, p < .001), but was not correlated with time (R: -0.118, p = .485). A multivariate linear regression analysis suggests that the log of RCD (coefficient: 0.125, p < .001), GS (coefficient: 0.010, p = .019), and CGH (coefficient: 0.0.009, p < .001) had a positive and significant impact on the PSB Index, while time did not affect it significantly. These findings suggest that people's behaviours could have been affected by multiple factors during the pandemic, with the number of COVID-19 deaths being the largest contributor towards an increase in protective behaviours in our model.
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Affiliation(s)
- Noor Al-Zubaidy
- Institute of Global Health Innovation, Imperial College London, London, UK
| | | | - Sarah Jones
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Lisa Gould
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Melanie Leis
- Institute of Global Health Innovation, Imperial College London, London, UK
| | | | - Ana Luisa Neves
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Ara Darzi
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Reza Drikvandi
- Department of Mathematical Sciences, Durham University, Durham, UK
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24
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Forsyth J, Wang L, Thomas-Bachli A. COVID-19 case rates, spatial mobility, and neighbourhood socioeconomic characteristics in Toronto: a spatial-temporal analysis. CANADIAN JOURNAL OF PUBLIC HEALTH = REVUE CANADIENNE DE SANTE PUBLIQUE 2023; 114:806-822. [PMID: 37526916 PMCID: PMC10486339 DOI: 10.17269/s41997-023-00791-4] [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: 11/21/2022] [Accepted: 05/29/2023] [Indexed: 08/02/2023]
Abstract
OBJECTIVES This study has two primary research objectives: (1) to investigate the spatial clustering pattern of mobility reductions and COVID-19 cases in Toronto and their relationships with marginalized populations, and (2) to identify the most relevant socioeconomic characteristics that relate to human mobility and COVID-19 case rates in Toronto's neighbourhoods during five distinct time periods of the pandemic. METHODS Using a spatial-quantitative approach, we combined hot spot analyses, Pearson correlation analyses, and Wilcoxon two-sample tests to analyze datasets including COVID-19 cases, a mobile device-derived indicator measuring neighbourhood-level time away from home (i.e., mobility), and socioeconomic data from 2016 census and Ontario Marginalization Index. Temporal variations among pandemic phases were examined as well. RESULTS The paper identified important spatial clustering patterns of mobility reductions and COVID-19 cases in Toronto, as well as their relationships with marginalized populations. COVID-19 hot spots were in more materially deprived neighbourhood clusters that had more essential workers and people who spent more time away from home. While the spatial pattern of clusters of COVID-19 cases and mobility shifted slightly over time, the group socioeconomic characteristics that clusters shared remained similar in all but the first time period. A series of maps and visualizations were created to highlight the dynamic spatiotemporal patterns. CONCLUSION Toronto's neighbourhoods have experienced the COVID-19 pandemic in significantly different ways, with hot spots of COVID-19 cases occurring in more materially and racially marginalized communities that are less likely to reduce their mobility. The study provides solid evidence in a Canadian context to enhance policy making and provide a deeper understanding of the social determinants of health in Toronto during the COVID-19 pandemic.
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Affiliation(s)
- Jack Forsyth
- Toronto Metropolitan University, Toronto, ON, Canada
- BlueDot, Toronto, ON, Canada
| | - Lu Wang
- Toronto Metropolitan University, Toronto, ON, Canada.
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25
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Nahian A, Huber VC, McFadden LM. Unique SARS-CoV-2 Variants, Tourism Metrics, and B.1.2 Emergence in Early COVID-19 Pandemic: A Correlation Analysis in South Dakota. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6748. [PMID: 37754608 PMCID: PMC10531005 DOI: 10.3390/ijerph20186748] [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: 06/05/2023] [Revised: 08/28/2023] [Accepted: 09/11/2023] [Indexed: 09/28/2023]
Abstract
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus, which is the source of the coronavirus disease 2019 (COVID-19), was declared a pandemic in the March of 2020. Travel and tourism were severely impacted as restrictions were imposed to help slow the disease spread, but some states took alternative approaches to travel restrictions. This study investigated the spread of COVID-19 in South Dakota during the early pandemic period to better understand how tourism affected the movement of the virus within the region. Sequences from the fall of 2020 were retrieved from public sources. CDC and other sources were used to determine infections, deaths, and tourism metrics during this time. The data were analyzed using correlation and logistic regression. This study found that the number of unique variants per month was positively correlated with hotel occupancy, but not with the number of cases or deaths. Interestingly, the emergence of the B.1.2 variant in South Dakota was positively correlated with increased case numbers and deaths. Data show that states with a shelter-in-place order were associated with a slower emergence of the B.1.2 variant compared to states without such an order, including South Dakota. Findings suggest complex relationships between tourism, SARS-CoV-2 infections, and mitigation strategies. The unique approach that South Dakota adopted provided insights into the spread of the disease in areas without state-wide restrictions. Our results suggest both positive and negative aspects of this approach. Finally, our data highlight the need for future surveillance efforts, including efforts focused on identifying variants with known increased transmission potential to produce effective population health management.
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Affiliation(s)
| | | | - Lisa M. McFadden
- Division of Basic Biomedical Sciences, University of South Dakota, 414 E. Clark St., Vermillion, SD 57069, USA
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26
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Kodera S, Ueta H, Unemi T, Nakata T, Hirata A. Population-Level Immunity for Transient Suppression of COVID-19 Waves in Japan from April 2021 to September 2022. Vaccines (Basel) 2023; 11:1457. [PMID: 37766133 PMCID: PMC10537865 DOI: 10.3390/vaccines11091457] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/24/2023] [Accepted: 09/02/2023] [Indexed: 09/29/2023] Open
Abstract
Multiple COVID-19 waves have been observed worldwide, with varying numbers of positive cases. Population-level immunity can partly explain a transient suppression of epidemic waves, including immunity acquired after vaccination strategies. In this study, we aimed to estimate population-level immunity in 47 Japanese prefectures during the three waves from April 2021 to September 2022. For each wave, characterized by the predominant variants, namely, Delta, Omicron, and BA.5, the estimated rates of population-level immunity in the 10-64-years age group, wherein the most positive cases were observed, were 20%, 35%, and 45%, respectively. The number of infected cases in the BA.5 wave was inversely associated with the vaccination rates for the second and third injections. We employed machine learning to replicate positive cases in three Japanese prefectures to validate the reliability of our model for population-level immunity. Using interpolation based on machine learning, we estimated the impact of behavioral factors and vaccination on the fifth wave of new positive cases that occurred during the Tokyo 2020 Olympic Games. Our computational results highlighted the critical role of population-level immunity, such as vaccination, in infection suppression. These findings underscore the importance of estimating and monitoring population-level immunity to predict the number of infected cases in future waves. Such estimations that combine numerical derivation and machine learning are of utmost significance for effective management of medical resources, including the vaccination strategy.
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Affiliation(s)
- Sachiko Kodera
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Haruto Ueta
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Tatsuo Unemi
- Glycan and Life Systems Integration Center, Soka University, Tokyo 192-8577, Japan
| | - Taisuke Nakata
- Graduate School of Economics, University of Tokyo, Tokyo 113-0033, Japan
- Graduate School of Public Policy, University of Tokyo, Tokyo 113-0033, Japan
| | - Akimasa Hirata
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
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27
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Rothstein AP, Jesser KJ, Feistel DJ, Konstantinidis KT, Trueba G, Levy K. Population genomics of diarrheagenic Escherichia coli uncovers high connectivity between urban and rural communities in Ecuador. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2023; 113:105476. [PMID: 37392822 PMCID: PMC10599324 DOI: 10.1016/j.meegid.2023.105476] [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: 05/07/2023] [Revised: 05/11/2023] [Accepted: 06/28/2023] [Indexed: 07/03/2023]
Abstract
Human movement may be an important driver of transmission dynamics for enteric pathogens but has largely been underappreciated except for international 'travelers' diarrhea or cholera. Phylodynamic methods, which combine genomic and epidemiological data, are used to examine rates and dynamics of disease matching underlying evolutionary history and biogeographic distributions, but these methods often are not applied to enteric bacterial pathogens. We used phylodynamics to explore the phylogeographic and evolutionary patterns of diarrheagenic E. coli in northern Ecuador to investigate the role of human travel in the geographic distribution of strains across the country. Using whole genome sequences of diarrheagenic E. coli isolates, we built a core genome phylogeny, reconstructed discrete ancestral states across urban and rural sites, and estimated migration rates between E. coli populations. We found minimal structuring based on site locations, urban vs. rural locality, pathotype, or clinical status. Ancestral states of phylogenomic nodes and tips were inferred to have 51% urban ancestry and 49% rural ancestry. Lack of structuring by location or pathotype E. coli isolates imply highly connected communities and extensive sharing of genomic characteristics across isolates. Using an approximate structured coalescent model, we estimated rates of migration among circulating isolates were 6.7 times larger for urban towards rural populations compared to rural towards urban populations. This suggests increased inferred migration rates of diarrheagenic E. coli from urban populations towards rural populations. Our results indicate that investments in water and sanitation prevention in urban areas could limit the spread of enteric bacterial pathogens among rural populations.
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Affiliation(s)
- Andrew P. Rothstein
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA
| | - Kelsey J. Jesser
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA
| | - Dorian J. Feistel
- School of a Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Konstantinos T. Konstantinidis
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- School of a Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Gabriel Trueba
- Instituto de Microbiología, Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito, Quito, Pichincha, Ecuador
| | - Karen Levy
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA
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28
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Ferro S, Serra C. The complex interplay between weather, social activity, and COVID-19 in the US. SSM Popul Health 2023; 23:101431. [PMID: 37287717 PMCID: PMC10225063 DOI: 10.1016/j.ssmph.2023.101431] [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: 04/05/2023] [Revised: 05/11/2023] [Accepted: 05/14/2023] [Indexed: 06/09/2023] Open
Abstract
Empirical studies on the impact of weather and policy interventions on Covid-19 infections have dedicated little attention to the mediation role of social activity. In this study, we combine mobile locations, weather, and COVID-19 data in a two-way fixed effects mediation model to estimate the impact of weather and policy interventions on the COVID-19 infection rate in the US before the availability of vaccines, disentangling their direct impact from the part of the effect that is mediated by the endogenous response of social activity. We show that, while temperature reduces viral infectiousness, it also increases the amount of time individuals spend out of home, which instead favours the spread of the virus. This second channel substantially attenuates the beneficial effect of temperature in curbing the spread of the virus, offsetting one-third of the potential seasonal fluctuations in the reproduction rate. The mediation role of social activity is particularly pronounced when viral incidence is low, and completely offsets the beneficial effect of temperature. Despite being significant predictors of social activity, wind speed and precipitation do not induce sufficient variation to affect infections. Our estimates also suggest that school closures and lockdowns are effective in reducing infections. We employ our estimates to quantify the seasonal variation in the reproduction rate stemming from weather seasonality in the US.
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29
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Kreslake JM, Aarvig K, Muller-Tabanera H, Vallone DM, Hair EC. Checkpoint Travel Numbers as a Proxy Variable in Population-Based Studies During the COVID-19 Pandemic: Validation Study. JMIR Public Health Surveill 2023; 9:e44950. [PMID: 37191643 PMCID: PMC10467631 DOI: 10.2196/44950] [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: 12/09/2022] [Revised: 03/30/2023] [Accepted: 05/16/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic had wide-ranging systemic impacts, with implications for social and behavioral factors in human health. The pandemic may introduce history bias in population-level research studies of other health topics during the COVID-19 period. OBJECTIVE We sought to identify and validate an accessible, flexible measure to serve as a covariate in research spanning the COVID-19 pandemic period. METHODS Transportation Security Administration checkpoint travel numbers were used to calculate a weekly sum of daily passengers and validated against two measures with strong face validity: (1) a self-reported item on social distancing practices drawn from a continuous tracking survey among a national sample of youths and young adults (15-24 years) in the United States (N=45,080, approximately 280 unique respondents each week); and (2) Google's Community Mobility Reports, which calculate daily values at the national level to represent rates of change in visits and length of stays to public spaces. For the self-reported survey data, an aggregated week-level variable was calculated as the proportion of respondents who did not practice social distancing that week (January 1, 2019, to May 31, 2022). For the community mobility data, a weekly estimate of change was calculated using daily values compared to a 5-week prepandemic baseline period (January 3, 2020, to February 6, 2020). Spearman rank correlation coefficients were calculated for each comparison. RESULTS Checkpoint travel data ranged from 668,719 travelers in the week of April 8, 2020, to nearly 15.5 million travelers in the week of May 18, 2022. The weekly proportion of survey respondents who did not practice social distancing ranged from 18.1% (n=42; week of April 15, 2020) to 70.9% (n=213; week of May 25, 2022). The measures were strongly correlated from January 2019 to May 2022 (ρ=0.90, P<.001) and March 2020 to May 2022 (ρ=0.87, P<.001). Strong correlations were observed when analyses were restricted to age groups (15-17 years: ρ=0.90; P<.001; 18-20 years: ρ=0.87; P<.001; 21-24 years: ρ=0.88; P<.001), racial or ethnic minorities (ρ=0.86, P<.001), and respondents with lower socioeconomic status (ρ=0.88, P<.001). There were also strong correlations between the weekly change from the baseline period for checkpoint travel data and community mobility data for transit stations (ρ=0.92, P<.001) and retail and recreation (ρ=0.89, P<.001), and moderate significant correlations for grocery and pharmacy (ρ=0.68, P<.001) and parks (ρ=0.62, P<.001). A strong negative correlation was observed for places of residence (ρ=-0.78, P<.001), and a weak but significant positive correlation was found for workplaces (ρ=0.24, P<.001). CONCLUSIONS The Transportation Security Administration's travel checkpoint data provide a publicly available flexible time-varying metric to control for history bias introduced by the pandemic in research studies spanning the COVID-19 period in the United States.
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Affiliation(s)
| | - Kathleen Aarvig
- Schroeder Institute, Truth Initiative, Washington, DC, United States
| | | | - Donna M Vallone
- Schroeder Institute, Truth Initiative, Washington, DC, United States
- Department of Health, Behavior and Society, Johnks Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
- College of Global Public Health, New York University, New York, NY, United States
| | - Elizabeth C Hair
- Schroeder Institute, Truth Initiative, Washington, DC, United States
- Department of Health, Behavior and Society, Johnks Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
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30
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Chamie JJ, Hibberd JA, Scheim DE. COVID-19 Excess Deaths in Peru's 25 States in 2020: Nationwide Trends, Confounding Factors, and Correlations With the Extent of Ivermectin Treatment by State. Cureus 2023; 15:e43168. [PMID: 37692571 PMCID: PMC10484241 DOI: 10.7759/cureus.43168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/07/2023] [Indexed: 09/12/2023] Open
Abstract
Introduction In 2020, nations hastened to contain an emerging COVID-19 pandemic by deploying diverse public health approaches, but conclusive appraisals of the efficacy of these approaches are elusive in most cases. One of the medicines deployed, ivermectin (IVM), a macrocyclic lactone having biochemical activity against SARS-CoV-2 through competitive binding to its spike protein, has yielded mixed results in randomized clinical trials (RCTs) for COVID-19 treatments. In Peru, an opportunity to track the efficacy of IVM with a close consideration of confounding factors was provided through data for excess deaths as correlated with IVM use in 2020, under semi-autonomous policies in its 25 states. Methods To evaluate possible IVM treatment effects, excess deaths as determined from Peruvian national health data were analyzed by state for ages ≥60 in Peru's 25 states. These data were compared with monthly summary data for excess deaths in Peru for the period 2020-2021 as published by the WHO in 2022. To identify potential confounding factors, Google mobility data, population densities, SARS-CoV-2 genetic variations, and seropositivity rates were also examined. Results Reductions in excess deaths over a period of 30 days after peak deaths averaged 74% in the 10 states with the most intensive IVM use. As determined across all 25 states, these reductions in excess deaths correlated closely with the extent of IVM use (p<0.002). During four months of IVM use in 2020, before a new president of Peru restricted its use, there was a 14-fold reduction in nationwide excess deaths and then a 13-fold increase in the two months following the restriction of IVM use. Notably, these trends in nationwide excess deaths align with WHO summary data for the same period in Peru. Conclusions The natural experiment that was put into motion with the authorization of IVM use for COVID-19 in Peru in May 2020, as analyzed using data on excess deaths by locality and by state from Peruvian national health sources, resulted in strong evidence for the drug's effectiveness. Several potential confounding factors, including effects of a social isolation mandate imposed in May 2020, variations in the genetic makeup of the SARS-CoV-2 virus, and differences in seropositivity rates and population densities across the 25 states, were considered but did not appear to have significantly influenced these outcomes.
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Affiliation(s)
- Juan J Chamie
- Data Analysis, Independent Data Analyst, Cambridge, USA
| | | | - David E Scheim
- Commissioned Corps, Inactive Reserve, United States Public Health Service, Blacksburg, USA
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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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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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Snook DW, Kaczkowski W, Fodeman AD. Mask On, Mask Off: Risk Perceptions for COVID-19 and Compliance with COVID-19 Safety Measures. Behav Med 2023; 49:246-257. [PMID: 35057698 DOI: 10.1080/08964289.2021.2021384] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 11/07/2021] [Accepted: 12/15/2021] [Indexed: 11/02/2022]
Abstract
Since early 2020, COVID-19 has spread throughout the United States (US), killing more than 700,000. Mask-wearing, social-distancing, and hand hygiene can curb the spread of COVID-19 and other infectious diseases. However, the adherence to COVID-19 safety measures varies considerably among the US public, likely due to disparate perceptions of COVID-19's risk. The current study examines risk perceptions for COVID-19 (RP-C) in a nationally representative sample of US residents (N = 512), as well as their political preferences, news media consumption, COVID-19 safety attitudes (SA-C) and reported COVID-19 safety behaviors (SB-C; e.g., mask-wearing and social-distancing). Using structural equation modeling, we tested a comprehensive measure for RP-C with a single latent factor, finding good model fit. We found that higher RP-C was associated with being more liberal, consuming more traditional news media, having attitudes that supported compliance with COVID-19 safety measures, and having greater reported compliance with COVID-19 safety measures. In addition, factor loadings for RP-C items indicate that people's RP-C was more strongly determined by personal and family, rather than collective or societal risk, which suggests risk communication may be improved by focusing on personal and family risk. Public health efforts to combat COVID-19 are only as good as compliance allows, and RP-C's strong relationship with SB-C indicates a potential means for risk communicators to increase compliance with COVID-19 safety measures. This finding will remain important as new COVID-19 variants, such as the Delta variant, emerge.
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Affiliation(s)
- Daniel W Snook
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | | | - Ari D Fodeman
- Department of Psychology, Georgia State University, Atlanta, GA, USA
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Nguyen MH, Nguyen THT, Molenberghs G, Abrams S, Hens N, Faes C. The impact of national and international travel on spatio-temporal transmission of SARS-CoV-2 in Belgium in 2021. BMC Infect Dis 2023; 23:428. [PMID: 37355572 DOI: 10.1186/s12879-023-08368-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 06/02/2023] [Indexed: 06/26/2023] Open
Abstract
BACKGROUND The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread over the world and caused tremendous impacts on global health. Understanding the mechanism responsible for the spread of this pathogen and the impact of specific factors, such as human mobility, will help authorities to tailor interventions for future SARS-CoV-2 waves or newly emerging airborne infections. In this study, we aim to analyze the spatio-temporal transmission of SARS-CoV-2 in Belgium at municipality level between January and December 2021 and explore the effect of different levels of human travel on disease incidence through the use of counterfactual scenarios. METHODS We applied the endemic-epidemic modelling framework, in which the disease incidence decomposes into endemic, autoregressive and neighbourhood components. The spatial dependencies among areas are adjusted based on actual connectivity through mobile network data. We also took into account other important factors such as international mobility, vaccination coverage, population size and the stringency of restriction measures. RESULTS The results demonstrate the aggravating effect of international travel on the incidence, and simulated counterfactual scenarios further stress the alleviating impact of a reduction in national and international travel on epidemic growth. It is also clear that local transmission contributed the most during 2021, and municipalities with a larger population tended to attract a higher number of cases from neighboring areas. CONCLUSIONS Although transmission between municipalities was observed, local transmission was dominant. We highlight the positive association between the mobility data and the infection spread over time. Our study provides insight to assist health authorities in decision-making, particularly when the disease is airborne and therefore likely influenced by human movement.
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Affiliation(s)
- Minh Hanh Nguyen
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium.
| | | | - Geert Molenberghs
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- I-BioStat, Katholieke Universiteit Leuven, BE-3000, Leuven, Belgium
| | - Steven Abrams
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- Global Health Institute, University of Antwerp, BE-2000, Antwerpen, Belgium
| | - Niel Hens
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- Global Health Institute, University of Antwerp, BE-2000, Antwerpen, Belgium
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, BE-2000, Antwerpen, Belgium
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- I-BioStat, Katholieke Universiteit Leuven, BE-3000, Leuven, Belgium
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Minato K, Shin JH, Kunisawa S, Fushimi K, Imanaka Y. The total number of patients with any of four major fragility fractures decreased during the first wave of the COVID-19 epidemic in Japan, commencing before the state of emergency declaration, which was not as enforceable as lockdown. Arch Osteoporos 2023; 18:86. [PMID: 37344710 DOI: 10.1007/s11657-023-01297-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/05/2023] [Indexed: 06/23/2023]
Abstract
Durin g the first wave of the COVID-19 epidemic, the total number of patients with any of the four major fragility fractures, including both inpatients and first-visit outpatients, began to decline shortly before the state of emergency was declared, rather than immediately after it was declared. PURPOSE This study aimed to investigate the impact of public health measures in the first wave of the COVID-19 epidemic on the occurrence of major fragility fractures (MFFs). METHODS Patients aged 50 years or older who were hospitalized or had an initial visit as an outpatient for an MFF, defined as a proximal femoral fracture (PFF), vertebral fragility fracture (VFF), distal radius fracture (DRF), or a proximal humeral fracture (PHF), were included in this study. Three-phase interrupted time-series analyses were performed to evaluate the impact of the voluntary event cancellation request in late February 2020 and the emergency declaration in early April 2020 on changes in the total number of patients, including inpatients and first-visit outpatients. RESULTS A total of 166,560 patients with MFFs were included (92,767 PFFs, 26,158 VFFs, 33,869 DRFs, and 13,766 PHFs). From the end of February, in seven prefectures with high proportions of urbanization, decreasing trends were estimated for level changes and slope changes in the total number of patients with any of the four MFFs (level change: PFF; point estimate; - 13.5 (95% CI; - 43.4, 16.5), VFF; - 15.3 (- 32.2, 1.5), DRF; - 16.1 (- 39.9, 7.6), PHF; - 1.9 (- 13.6, 9.8), slope change: PFF; - 4.8 (- 14.0, 4.4), VFF; - 3.0 (- 8.1, 2.2), DRF; - 0.6 (- 7.9, 6.7), PHF; - 2.4 (- 6.0, 1.2)). CONCLUSION The findings suggested that the total number of patients with any of the four MFFs did not begin to decline from early April 2020 after the state of emergency was declared but earlier, in late February 2020.
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Affiliation(s)
- Kenta Minato
- Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Yoshida Konoe-Cho, Sakyo-Ku, Kyoto City, Kyoto, 606-8501, Japan
| | - Jung-Ho Shin
- Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Yoshida Konoe-Cho, Sakyo-Ku, Kyoto City, Kyoto, 606-8501, Japan
| | - Susumu Kunisawa
- Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Yoshida Konoe-Cho, Sakyo-Ku, Kyoto City, Kyoto, 606-8501, Japan
| | - Kiyohide Fushimi
- Department of Health Policy and Informatics, Graduate School of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuichi Imanaka
- Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Yoshida Konoe-Cho, Sakyo-Ku, Kyoto City, Kyoto, 606-8501, Japan.
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Zeng Q, Yu X, Ni H, Xiao L, Xu T, Wu H, Chen Y, Deng H, Zhang Y, Pei S, Xiao J, Guo P. Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm. PLoS Negl Trop Dis 2023; 17:e0011418. [PMID: 37285385 DOI: 10.1371/journal.pntd.0011418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 05/24/2023] [Indexed: 06/09/2023] Open
Abstract
Predicting the specific magnitude and the temporal peak of the epidemic of individual local outbreaks is critical for infectious disease control. Previous studies have indicated that significant differences in spatial transmission and epidemic magnitude of dengue were influenced by multiple factors, such as mosquito population density, climatic conditions, and population movement patterns. However, there is a lack of studies that combine the above factors to explain their complex nonlinear relationships in dengue transmission and generate accurate predictions. Therefore, to study the complex spatial diffusion of dengue, this research combined the above factors and developed a network model for spatiotemporal transmission prediction of dengue fever using metapopulation networks based on human mobility. For improving the prediction accuracy of the epidemic model, the ensemble adjusted Kalman filter (EAKF), a data assimilation algorithm, was used to iteratively assimilate the observed case data and adjust the model and parameters. Our study demonstrated that the metapopulation network-EAKF system provided accurate predictions for city-level dengue transmission trajectories in retrospective forecasts of 12 cities in Guangdong province, China. Specifically, the system accurately predicts local dengue outbreak magnitude and the temporal peak of the epidemic up to 10 wk in advance. In addition, the system predicted the peak time, peak intensity, and total number of dengue cases more accurately than isolated city-specific forecasts. The general metapopulation assimilation framework presented in our study provides a methodological foundation for establishing an accurate system with finer temporal and spatial resolution for retrospectively forecasting the magnitude and temporal peak of dengue fever outbreaks. These forecasts based on the proposed method can be interoperated to better support intervention decisions and inform the public of potential risks of disease transmission.
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Affiliation(s)
- Qinghui Zeng
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Xiaolin Yu
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Haobo Ni
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Lina Xiao
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Ting Xu
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Haisheng Wu
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Yuliang Chen
- Department of Medical Quality Management, Nanfang Hospital, Guangzhou, China
| | - Hui Deng
- Institute of Vector Control, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Yingtao Zhang
- Institute of Infectious Disease Control and Prevention, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Pi Guo
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou, China
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Ly A, Davenport FV, Diffenbaugh NS. Exploring the Influence of Summer Temperature on Human Mobility During the COVID-19 Pandemic in the San Francisco Bay Area. GEOHEALTH 2023; 7:e2022GH000772. [PMID: 37287701 PMCID: PMC10243210 DOI: 10.1029/2022gh000772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 05/24/2023] [Accepted: 05/25/2023] [Indexed: 06/09/2023]
Abstract
Studies on the relationship between temperature and local, small scale mobility are limited, and sensitive to the region and time period of interest. We contribute to the growing mobility literature through a detailed characterization of the observed temperature-mobility relationship in the San Francisco Bay Area at fine spatial and temporal scale across two summers (2020-2021). We used anonymized cellphone data from SafeGraph's neighborhood patterns data set and gridded temperature data from gridMET, and analyzed the influence of incremental changes in temperature on mobility rate (i.e., visits per capita) using a panel regression with fixed effects. This strategy enabled us to control for spatial and temporal variability across the studied region. Our analysis suggested that all areas exhibited lower mobility rate in response to higher summer temperatures. We then explored how several additional variables altered these results. Extremely hot days resulted in faster mobility declines with increasing temperatures. Weekdays were often more resistant to temperature changes when compared to the weekend. In addition, the rate of decrease in mobility in response to high temperature was significantly greater among the wealthiest census block groups compared with the least wealthy. Further, the least mobile locations experienced significant differences in mobility response compared to the rest of the data set. Given the fundamental differences in the mobility response to temperature across most of our additive variables, our results are relevant for future mobility studies in the region.
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Affiliation(s)
- Amina Ly
- Department of Earth System ScienceStanford UniversityStanfordCAUSA
| | - Frances V. Davenport
- Department of Earth System ScienceStanford UniversityStanfordCAUSA
- Department of Atmospheric ScienceColorado State UniversityFort CollinsCOUSA
- Department of Civil and Environmental EngineeringColorado State UniversityFort CollinsCOUSA
| | - Noah S. Diffenbaugh
- Department of Earth System ScienceStanford UniversityStanfordCAUSA
- Doerr School of SustainabilityStanford UniversityStanfordCAUSA
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Seifert R, Pellicer-Chenoll M, Antón-González L, Pans M, Devís-Devís J, González LM. Who changed and who maintained their urban bike-sharing mobility after the COVID-19 outbreak? A within-subjects study. CITIES (LONDON, ENGLAND) 2023; 137:104343. [PMID: 37125007 PMCID: PMC10123356 DOI: 10.1016/j.cities.2023.104343] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 05/03/2023]
Abstract
The COVID-19 pandemic has remarkably impacted urban mobility. All non-essential movements were restricted in Valencia (Spain) to contain the virus. Thus, the transport usage patterns of Valencia's bike-sharing system (BSS) users changed during this emergency situation. The primary objective of this study was to analyse the behaviour patterns of BSS users in Valencia before and after the COVID-19 outbreak, specifically those who maintained or changed their transport routines. A within-subjects comparison design was developed using a group of BSS users before and after the onset of the pandemic. Data mining techniques were used on a sample of 4355 regular users and 25 variables were calculated to classify users by self-organising maps analysis. The results show a significant reduction (40 %) in BSS movements after the outbreak during the entire post-outbreak year. There was some recovery during the rest of 2020; however, this has yet to reach the pre-pandemic levels, with variations observed based on the activities performed in different areas of the city. Of the users, 63 % changed their BSS use patterns after the onset of the pandemic (LEAVE group), while 37 % maintained their patterns (REMAIN group). The user profile of the REMAIN group was characterised by a general reduction of approximately 35 % of journeys during 2020, with a slight increase in morning movements compared to those made in the evening. These users also presented an equivalent number of cycling days to those of the previous year, reduced the number of connections and increased the network's density and the travelling speed. These results can be useful in estimating the percentage of people who do not vary their usual behaviour during emergencies. Finally, several policy implications are outlined based on the findings.
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Affiliation(s)
- Rudi Seifert
- Departament d'Educació Física i Esportiva, Universitat de València, C/ Gascó Oliag, 3, 46010 València, Spain
| | - Maite Pellicer-Chenoll
- Departament d'Educació Física i Esportiva, Universitat de València, C/ Gascó Oliag, 3, 46010 València, Spain
| | - Laura Antón-González
- Departament d'Educació Física i Esportiva, Universitat de València, C/ Gascó Oliag, 3, 46010 València, Spain
| | - Miquel Pans
- Departament d'Educació Física i Esportiva, Universitat de València, C/ Gascó Oliag, 3, 46010 València, Spain
| | - José Devís-Devís
- Departament d'Educació Física i Esportiva, Universitat de València, C/ Gascó Oliag, 3, 46010 València, Spain
| | - Luis-Millán González
- Departament d'Educació Física i Esportiva, Universitat de València, C/ Gascó Oliag, 3, 46010 València, Spain
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Niu C, Zhang W. Causal effects of mobility intervention policies on intracity flows during the COVID-19 pandemic: The moderating role of zonal locations in the transportation networks. COMPUTERS, ENVIRONMENT AND URBAN SYSTEMS 2023; 102:101957. [PMID: 36938101 PMCID: PMC10011038 DOI: 10.1016/j.compenvurbsys.2023.101957] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 03/05/2023] [Accepted: 03/08/2023] [Indexed: 05/07/2023]
Abstract
Many studies have investigated the impact of mobility restriction policies on the change of intercity flows during the outbreak of COVID-19, whereas only a few have highlighted intracity flows. By using the mobile phone trajectory data of approximately three months, we develop an interrupted time series quasi-experimental design to estimate the abrupt and gradual effects of mobility intervention policies during the pandemic on intracity flows of 491 neighborhoods in Shenzhen, China, with a focus on the role of urban transport networks. The results show that the highest level of public health emergency response caused an abrupt decline by 4567 trips and a gradually increasing effect by 34 trips per day. The effectiveness of the second return-to-work order (RtW2) was found to be clearly larger than that of the first return-to-work order (RtW1) as a mobility restoration strategy. The causal effects of mobility intervention policies are heterogenous across zonal locations in varying urban transport networks. The declining effect of health emergency response and rebounding effect of RtW2 are considerably large in better-connected neighborhoods with metro transit, as well as in those close to the airport. These findings provide new insights into the identification of pandemic-vulnerable hotspots in the transport network inside the city, as well as of crucial neighborhoods with increased adaptability to mobility interventions during the onset and decline of COVID-19.
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Affiliation(s)
- Caicheng Niu
- School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Wenjia Zhang
- School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, Guangdong, China
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Schwarz E, Schwarz L, Teyton A, Crist K, Benmarhnia T. The role of the California tier system in controlling population mobility during the COVID-19 pandemic. BMC Public Health 2023; 23:905. [PMID: 37202789 DOI: 10.1186/s12889-023-15858-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 05/10/2023] [Indexed: 05/20/2023] Open
Abstract
BACKGROUND Policies to restrict population mobility are a commonly used strategy to limit the transmission of contagious diseases. Among measures implemented during the COVID-19 pandemic were dynamic stay-at-home orders informed by real-time, regional-level data. California was the first state in the U.S. to implement this novel approach; however, the effectiveness of California's four-tier system on population mobility has not been quantified. METHODS Utilizing data from mobile devices and county-level demographic data, we evaluated the impact of policy changes on population mobility and explored whether demographic characteristics explained variability in responsiveness to policy changes. For each California county, we calculated the proportion of people staying home and the average number of daily trips taken per 100 persons, across different trip distances and compared this to pre-COVID-19 levels. RESULTS We found that overall mobility decreased when counties moved to a more restrictive tier and increased when moving to a less restrictive tier, as the policy intended. When placed in a more restrictive tier, the greatest decrease in mobility was observed for shorter and medium-range trips, while there was an unexpected increase in the longer trips. The mobility response varied by geographic region, as well as county-level median income, gross domestic product, economic, social, and educational contexts, the prevalence of farms, and recent election results. CONCLUSIONS This analysis provides evidence of the effectiveness of the tier-based system in decreasing overall population mobility to ultimately reduce COVID-19 transmission. Results demonstrate that socio-political demographic indicators drive important variability in such patterns across counties.
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Affiliation(s)
- Emilie Schwarz
- École des Hautes Études en Santé Publique, Paris, France
| | - Lara Schwarz
- School of Public Health, San Diego State University, La Jolla, San Diego, CA, USA.
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, San Diego, CA, USA.
| | - Anaïs Teyton
- School of Public Health, San Diego State University, La Jolla, San Diego, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Katie Crist
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Tarik Benmarhnia
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, San Diego, CA, USA
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, San Diego, CA, USA
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41
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Das Swain V, Xie J, Madan M, Sargolzaei S, Cai J, De Choudhury M, Abowd GD, Steimle LN, Prakash BA. Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses. Front Digit Health 2023; 5:1060828. [PMID: 37260525 PMCID: PMC10227502 DOI: 10.3389/fdgth.2023.1060828] [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: 10/03/2022] [Accepted: 04/12/2023] [Indexed: 06/02/2023] Open
Abstract
Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students' learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility-a methodology we refer to as WiFi mobility models (WiMob). This approach enables policymakers to explore more granular policies like localized closures (LC). WiMob can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, WiMob enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from WiMob, we find that LC maintains the same reduction in cumulative infections as RI while showing greater reduction in peak infections and internal transmission. Moreover, LC reduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation. WiMob can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.
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Affiliation(s)
- Vedant Das Swain
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
| | - Jiajia Xie
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Maanit Madan
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
| | - Sonia Sargolzaei
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
| | - James Cai
- Department of Computer Science, Brown University, Providence, RI, United States
| | - Munmun De Choudhury
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
| | - Gregory D. Abowd
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
- College of Engineering, Northeastern University, Boston, MA, United States
| | - Lauren N. Steimle
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - B. Aditya Prakash
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
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42
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Rapti Z, Cuevas-Maraver J, Kontou E, Liu S, Drossinos Y, Kevrekidis PG, Barmann M, Chen QY, Kevrekidis GA. The Role of Mobility in the Dynamics of the COVID-19 Epidemic in Andalusia. Bull Math Biol 2023; 85:54. [PMID: 37166513 PMCID: PMC10173246 DOI: 10.1007/s11538-023-01152-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 03/28/2023] [Indexed: 05/12/2023]
Abstract
Metapopulation models have been a popular tool for the study of epidemic spread over a network of highly populated nodes (cities, provinces, countries) and have been extensively used in the context of the ongoing COVID-19 pandemic. In the present work, we revisit such a model, bearing a particular case example in mind, namely that of the region of Andalusia in Spain during the period of the summer-fall of 2020 (i.e., between the first and second pandemic waves). Our aim is to consider the possibility of incorporation of mobility across the province nodes focusing on mobile-phone time-dependent data, but also discussing the comparison for our case example with a gravity model, as well as with the dynamics in the absence of mobility. Our main finding is that mobility is key toward a quantitative understanding of the emergence of the second wave of the pandemic and that the most accurate way to capture it involves dynamic (rather than static) inclusion of time-dependent mobility matrices based on cell-phone data. Alternatives bearing no mobility are unable to capture the trends revealed by the data in the context of the metapopulation model considered herein.
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Affiliation(s)
- Z Rapti
- Department of Mathematics and Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Champaign, IL, USA.
| | - J Cuevas-Maraver
- Grupo de Física No Lineal, Departamento de Física Aplicada I, Universidad de Sevilla, Escuela Politécnica Superior, C/ Virgen de Africa, 7, 41011, Seville, Spain
- Instituto de Matemáticas de la Universidad de Sevilla (IMUS), Edificio Celestino Mutis, Avda. Reina Mercedes s/n, 41012, Seville, Spain
| | - E Kontou
- Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - S Liu
- Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Y Drossinos
- Thermal Hydraulics and Multiphase Flow Laboratory, Institute of Nuclear and Radiological Sciences and Technology, Energy and Safety, N.C.S.R. "Demokritos", 15341, Agia Paraskevi, Greece
| | - P G Kevrekidis
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, 01003-4515, USA
| | - M Barmann
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, 01003-4515, USA
| | - Q-Y Chen
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, 01003-4515, USA
| | - G A Kevrekidis
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21218, USA
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43
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Modeling the spread dynamics of multiple-variant coronavirus disease under public health interventions: A general framework. Inf Sci (N Y) 2023; 628:469-487. [PMID: 36777698 PMCID: PMC9901228 DOI: 10.1016/j.ins.2023.02.001] [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: 10/18/2022] [Revised: 01/11/2023] [Accepted: 02/01/2023] [Indexed: 02/09/2023]
Abstract
The COVID-19 pandemic was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is a single-stranded positive-stranded RNA virus with a high multi-directional mutation rate. Many new variants even have an immune-evading property, which means that some individuals with antibodies against one variant can be reinfected by other variants. As a result, the realistic is still suffering from new waves of COVID-19 by its new variants. How to control the transmission or even eradicate the COVID-19 pandemic remains a critical issue for the whole world. This work presents an epidemiological framework for mimicking the multi-directional mutation process of SARS-CoV-2 and the epidemic spread of COVID-19 under realistic scenarios considering multiple variants. The proposed framework is used to evaluate single and combined public health interventions, which include non-pharmaceutical interventions, pharmaceutical interventions, and vaccine interventions under the existence of multi-directional mutations of SARS-CoV-2. The results suggest that several combined intervention strategies give optimal results and are feasible, requiring only moderate levels of individual interventions. Furthermore, the results indicate that even if the mutation rate of SARS-CoV-2 decreased 100 times, the pandemic would still not be eradicated without appropriate public health interventions.
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44
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Lee KS, Eom JK. Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility. TRANSPORTATION 2023:1-55. [PMID: 37363373 PMCID: PMC10126540 DOI: 10.1007/s11116-023-10392-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
The unprecedented COVID-19 outbreak has significantly influenced our daily life, and COVID-19's spread is inevitably associated with human mobility. Given the pandemic's severity and extent of spread, a timely and comprehensive synthesis of the current state of research is needed to understand the pandemic's impact on human mobility and corresponding government measures. This study examined the relevant literature published to the present (March 2023), identified research trends, and conducted a systematic review of evidence regarding transport's response to COVID-19. We identified key research agendas and synthesized the results, examining: (1) mobility changes by transport modes analyzed regardless of government policy implementation, using empirical data and survey data; (2) the effect of diverse government interventions to reduce mobility and limit COVID-19 spread, and controversial issues on travel restriction policy effects; and (3) future research issues. The findings showed a strong relationship between the pandemic and mobility, with significant impacts on decreased overall mobility, a remarkable drop in transit ridership, changes in travel behavior, and improved traffic safety. Government implemented various non-pharmaceutical countermeasures, such as city lockdowns, travel restrictions, and social distancing. Many studies showed such interventions were effective. However, some researchers reported inconsistent outcomes. This review provides urban and transport planners with valuable insights to facilitate better preparation for future health emergencies that affect transportation. Supplementary Information The online version contains supplementary material available at 10.1007/s11116-023-10392-2.
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Affiliation(s)
- Kwang-Sub Lee
- Railroad Policy Research Department, Korea Railroad Research Institute, 176 Railroad Museum Road, Uiwang-Si, 16105 Gyeonggi-Do Korea
| | - Jin Ki Eom
- Railroad Policy Research Department, Korea Railroad Research Institute, 176 Railroad Museum Road, Uiwang-Si, 16105 Gyeonggi-Do Korea
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45
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Deb Nath N, Khan MM, Schmidt M, Njau G, Odoi A. Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States. BMC Public Health 2023; 23:720. [PMID: 37081453 PMCID: PMC10116449 DOI: 10.1186/s12889-023-15571-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 03/30/2023] [Indexed: 04/22/2023] Open
Abstract
BACKGROUND COVID-19 is an important public health concern due to its high morbidity, mortality and socioeconomic impact. Its burden varies by geographic location affecting some communities more than others. Identifying these disparities is important for guiding health planning and service provision. Therefore, this study investigated geographical disparities and temporal changes of the percentage of positive COVID-19 tests and COVID-19 incidence risk in North Dakota. METHODS COVID-19 retrospective data on total number of tests and confirmed cases reported in North Dakota from March 2020 to September 2021 were obtained from the North Dakota COVID-19 Dashboard and Department of Health, respectively. Monthly incidence risks of the disease were calculated and reported as number of cases per 100,000 persons. To adjust for geographic autocorrelation and the small number problem, Spatial Empirical Bayesian (SEB) smoothing was performed using queen spatial weights. Identification of high-risk geographic clusters of percentages of positive tests and COVID-19 incidence risks were accomplished using Tango's flexible spatial scan statistic. ArcGIS was used to display and visiualize the geographic distribution of percentages of positive tests, COVID-19 incidence risks, and high-risk clusters. RESULTS County-level percentages of positive tests and SEB incidence risks varied by geographic location ranging from 0.11% to 13.67% and 122 to 16,443 cases per 100,000 persons, respectively. Clusters of high percentages of positive tests were consistently detected in the western part of the state. High incidence risks were identified in the central and south-western parts of the state, where significant high-risk spatial clusters were reported. Additionally, two peaks (August 2020-December 2020 and August 2021-September 2021) and two non-peak periods of COVID-19 incidence risk (March 2020-July 2020 and January 2021-July 2021) were observed. CONCLUSION Geographic disparities in COVID incidence risks exist in North Dakota with high-risk clusters being identified in the rural central and southwest parts of the state. These findings are useful for guiding intervention strategies by identifying high risk communities so that resources for disease control can be better allocated to communities in need based on empirical evidence. Future studies will investigate predictors of the identified disparities so as to guide planning, disease control and health policy.
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Affiliation(s)
- Nirmalendu Deb Nath
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA
| | - Md Marufuzzaman Khan
- Department of Public Health, College of Education, Health, and Human Sciences, University of Tennessee, Knoxville, TN, USA
| | - Matthew Schmidt
- North Dakota Department of Health and Human Services, Special Projects and Health Analytics, Bismarck, ND, USA
| | - Grace Njau
- North Dakota Department of Health and Human Services, Special Projects and Health Analytics, Bismarck, ND, USA
| | - Agricola Odoi
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA.
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46
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Araújo JLB, Oliveira EA, Lima Neto AS, Andrade JS, Furtado V. Unveiling the paths of COVID-19 in a large city based on public transportation data. Sci Rep 2023; 13:5761. [PMID: 37031258 PMCID: PMC10082688 DOI: 10.1038/s41598-023-32786-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 04/02/2023] [Indexed: 04/10/2023] Open
Abstract
Human mobility plays a key role in the dissemination of infectious diseases around the world. However, the complexity introduced by commuting patterns in the daily life of cities makes such a role unclear, especially at the intracity scale. Here, we propose a multiplex network fed with 9 months of mobility data with more than 107 million public bus validations in order to understand the relation between urban mobility and the spreading of COVID-19 within a large city, namely, Fortaleza in the northeast of Brazil. Our results suggest that the shortest bus rides in Fortaleza, measured in the number of daily rides among all neighborhoods, decreased [Formula: see text]% more than the longest ones after an epidemic wave. Such a result is the opposite of what has been observed at the intercity scale. We also find that mobility changes among the neighborhoods are synchronous and geographically homogeneous. Furthermore, we find that the most central neighborhoods in mobility are the first targets for infectious disease outbreaks, which is quantified here in terms of the positive linear relation between the disease arrival time and the average of the closeness centrality ranking. These central neighborhoods are also the top neighborhoods in the number of reported cases at the end of an epidemic wave as indicated by the exponential decay behavior of the disease arrival time in relation to the number of accumulated reported cases with decay constant [Formula: see text] days. We believe that these results can help in the development of new strategies to impose restriction measures in the cities guiding decision-makers with smart actions in public health policies, as well as supporting future research on urban mobility and epidemiology.
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Affiliation(s)
- Jorge L B Araújo
- Laboratório de Ciência de Dados e Inteligência Artificial Universidade de Fortaleza, Fortaleza, Ceará, 60811-905, Brazil.
| | - Erneson A Oliveira
- Laboratório de Ciência de Dados e Inteligência Artificial Universidade de Fortaleza, Fortaleza, Ceará, 60811-905, Brazil
- Programa de Pós Graduação em Informática Aplicada Universidade de Fortaleza, Fortaleza, Ceará, 60811-905, Brazil
- Mestrado Profissional em Ciências da Cidade Universidade de Fortaleza, Fortaleza, Ceará, 60811-905, Brazil
| | - Antonio S Lima Neto
- Célula de Vigilância Epidemiológica Secretaria Municipal da Saúde, Fortaleza, Ceará, 60810-670, Brazil
- Centro de Ciências da Saúde Universidade de Fortaleza, Fortaleza, Ceará, 60811-905, Brazil
| | - José S Andrade
- Departamento de Física, Universidade Federal do Ceará, Fortaleza, Ceará, 60455-760, Brazil
| | - Vasco Furtado
- Laboratório de Ciência de Dados e Inteligência Artificial Universidade de Fortaleza, Fortaleza, Ceará, 60811-905, Brazil
- Programa de Pós Graduação em Informática Aplicada Universidade de Fortaleza, Fortaleza, Ceará, 60811-905, Brazil
- Empresa de Tecnologia da Informação do Ceará Governo do Estado do Ceará, Fortaleza, Ceará, 60130-240, Brazil
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47
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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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48
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Truong D, Truong MD. Impacts of Daily Travel by Distances on the Spread of COVID-19: An Artificial Neural Network Model. TRANSPORTATION RESEARCH RECORD 2023; 2677:934-945. [PMID: 37153208 PMCID: PMC10149352 DOI: 10.1177/03611981211066899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
The continued spread of COVID-19 poses significant threats to the safety of the community. Since it is still uncertain when the pandemic will end, it is vital to understand the factors contributing to new cases of COVID-19, especially from the transportation perspective. This paper examines the effect of the United States residents’ daily trips by distances on the spread of COVID-19 in the community. The artificial neural network method is used to construct and test the predictive model using data collected from two sources: Bureau of Transportation Statistics and the COVID-19 Tracking Project. The dataset uses ten daily travel variables by distances and new tests from March to September 2020, with a sample size of 10,914. The results indicate the importance of daily trips at different distances in predicting the spread of COVID-19. More specifically, trips shorter than 3 mi and trips between 250 and 500 mi contribute most to predicting daily new cases of COVID-19. Additionally, daily new tests and trips between 10 and 25 mi are among the variables with the lowest effects. This study’s findings can help governmental authorities evaluate the risk of COVID-19 infection based on residents’ daily travel behaviors and form necessary strategies to mitigate the risks. The developed neural network can be used to predict the infection rate and construct various scenarios for risk assessment and control.
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Affiliation(s)
- Dothang Truong
- School of Graduate Studies, Embry-Riddle Aeronautical University, Daytona Beach, FL
- Dothang Truong,
| | - My D. Truong
- College of Business, University of Central Florida, Orlando, FL
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49
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Gurbuz O, Aldrete RM, Salgado D, Gurbuz TM. Transportation as a Disease Vector in COVID-19: Border Mobility and Disease Spread. TRANSPORTATION RESEARCH RECORD 2023; 2677:826-838. [PMID: 38602941 PMCID: PMC10008995 DOI: 10.1177/03611981231156588] [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
More than a year after COVID-19 was declared a pandemic by the World Health Organization, the U.S.A. and Mexico rank first and fourth, respectively, with regard to the number of deaths. From March 2020, nonessential travelers were not allowed to cross the border into the U.S.A. from Mexico via international land ports of entry, which resulted in a more than 50% decrease in the number of people crossing the border. However, border communities still face a higher number of cases and faster community spread compared with those without international land ports of entry. This paper established an econometric model to understand the effects of cross-border mobility and other socioeconomic parameters on the speed of spread. The model was developed at the U.S. county level using data from all 3,141 counties in the U.S.A. Additionally, a follow-up U.S. county comparative analysis was developed to examine the significance of having a border crossing between the U.S.A. and Mexico for U.S. counties. The findings of the analysis revealed that the variables having a significant effect are as follows: population density; number of people per household; population in the 15-65 age group; median household income; mask use; number of visits to transit stations; number of visits to workplace; overall mobility; and having a border crossing to Mexico within county limits. The comparative analysis found that U.S. counties with border crossings have an average of 123 cases per 1,000 population whereas their counterparts without border crossings only have 90 cases per 1,000 population.
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Affiliation(s)
- Okan Gurbuz
- Texas A&M Transportation Institute, El Paso, TX
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50
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Chakraborty M, Shakir Mahmud M, Gates TJ, Sinha S. Analysis and Prediction of Human Mobility in the United States during the Early Stages of the COVID-19 Pandemic using Regularized Linear Models. TRANSPORTATION RESEARCH RECORD 2023; 2677:380-395. [PMID: 37153191 PMCID: PMC10149351 DOI: 10.1177/03611981211067794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Since the United States started grappling with the COVID-19 pandemic, with the highest number of confirmed cases and deaths in the world as of August 2020, most states have enforced travel restrictions resulting in drastic reductions in mobility and travel. However, the long-term implications of this crisis to mobility still remain uncertain. To this end, this study proposes an analytical framework that determines the most significant factors affecting human mobility in the United States during the early days of the pandemic. Particularly, the study uses least absolute shrinkage and selection operator (LASSO) regularization to identify the most significant variables influencing human mobility and uses linear regularization algorithms, including ridge, LASSO, and elastic net modeling techniques, to predict human mobility. State-level data were obtained from various sources from January 1, 2020 to June 13, 2020. The entire data set was divided into a training and a test data set, and the variables selected by LASSO were used to train models by the linear regularization algorithms, using the training data set. Finally, the prediction accuracy of the developed models was examined on the test data. The results indicate that several factors, including the number of new cases, social distancing, stay-at-home orders, domestic travel restrictions, mask-wearing policy, socioeconomic status, unemployment rate, transit mode share, percent of population working from home, and percent of older (60+ years) and African and Hispanic American populations, among others, significantly influence daily trips. Moreover, among all models, ridge regression provides the most superior performance with the least error, whereas both LASSO and elastic net performed better than the ordinary linear model.
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Affiliation(s)
- Meghna Chakraborty
- Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI
- Meghna Chakraborty,
| | - Md Shakir Mahmud
- Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI
| | - Timothy J. Gates
- Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI
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