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Perofsky AC, Huddleston J, Hansen CL, Barnes JR, Rowe T, Xu X, Kondor R, Wentworth DE, Lewis N, Whittaker L, Ermetal B, Harvey R, Galiano M, Daniels RS, McCauley JW, Fujisaki S, Nakamura K, Kishida N, Watanabe S, Hasegawa H, Sullivan SG, Barr IG, Subbarao K, Krammer F, Bedford T, Viboud C. Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States. eLife 2024; 13:RP91849. [PMID: 39319780 PMCID: PMC11424097 DOI: 10.7554/elife.91849] [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] [Indexed: 09/26/2024] Open
Abstract
Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.
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MESH Headings
- Influenza A Virus, H3N2 Subtype/genetics
- Influenza A Virus, H3N2 Subtype/immunology
- United States/epidemiology
- Influenza, Human/epidemiology
- Influenza, Human/virology
- Influenza, Human/immunology
- Humans
- Hemagglutinin Glycoproteins, Influenza Virus/genetics
- Hemagglutinin Glycoproteins, Influenza Virus/immunology
- Epidemics
- Antigenic Drift and Shift/genetics
- Child
- Adult
- Neuraminidase/genetics
- Neuraminidase/immunology
- Adolescent
- Child, Preschool
- Antigens, Viral/immunology
- Antigens, Viral/genetics
- Young Adult
- Evolution, Molecular
- Seasons
- Middle Aged
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Affiliation(s)
- Amanda C Perofsky
- Fogarty International Center, National Institutes of Health, Bethesda, United States
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, United States
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, United States
| | - Chelsea L Hansen
- Fogarty International Center, National Institutes of Health, Bethesda, United States
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, United States
| | - John R Barnes
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), Atlanta, United States
| | - Thomas Rowe
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), Atlanta, United States
| | - Xiyan Xu
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), Atlanta, United States
| | - Rebecca Kondor
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), Atlanta, United States
| | - David E Wentworth
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), Atlanta, United States
| | - Nicola Lewis
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Lynne Whittaker
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Burcu Ermetal
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Ruth Harvey
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Monica Galiano
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Rodney Stuart Daniels
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - John W McCauley
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Seiichiro Fujisaki
- Influenza Virus Research Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Kazuya Nakamura
- Influenza Virus Research Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Noriko Kishida
- Influenza Virus Research Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Shinji Watanabe
- Influenza Virus Research Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Hideki Hasegawa
- Influenza Virus Research Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Sheena G Sullivan
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Ian G Barr
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Kanta Subbarao
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Florian Krammer
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, United States
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Trevor Bedford
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, United States
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, United States
- Department of Genome Sciences, University of Washington, Seattle, United States
- Howard Hughes Medical Institute, Seattle, United States
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, United States
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de Jong SP, Conlan A, Han AX, Russell CA. Commuting-driven competition between transmission chains shapes seasonal influenza virus epidemics in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.09.24311720. [PMID: 39148829 PMCID: PMC11326338 DOI: 10.1101/2024.08.09.24311720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Despite intensive study, much remains unknown about the dynamics of seasonal influenza virus epidemic establishment and spread in the United States (US) each season. By reconstructing transmission lineages from seasonal influenza virus genomes collected in the US from 2014 to 2023, we show that most epidemics consisted of multiple distinct transmission lineages. Spread of these lineages exhibited strong spatiotemporal hierarchies and lineage size was correlated with timing of lineage establishment in the US. Mechanistic epidemic simulations suggest that mobility-driven competition between lineages determined the extent of individual lineages' geographical spread. Based on phylogeographic analyses and epidemic simulations, lineage-specific movement patterns were dominated by human commuting behavior. These results suggest that given the locations of early-season epidemic sparks, the topology of inter-state human mobility yields repeatable patterns of which influenza viruses will circulate where, but the importance of short-term processes limits predictability of regional and national epidemics.
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Affiliation(s)
- Simon P.J. de Jong
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam; Amsterdam, The Netherlands
| | - Andrew Conlan
- Department of Veterinary Medicine, University of Cambridge; Cambridge, United Kingdom
| | - Alvin X. Han
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam; Amsterdam, The Netherlands
| | - Colin A. Russell
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam; Amsterdam, The Netherlands
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3
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Wang L, Xu C, Hu M, Wang J, Qiao J, Chen W, Zhu Q, Wang Z. Modeling tuberculosis transmission flow in China, 2010-2012. BMC Infect Dis 2024; 24:784. [PMID: 39103752 DOI: 10.1186/s12879-024-09649-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 07/23/2024] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND China has the third largest number of TB cases in the world, and the average annual floating population in China is more than 200 million, the increasing floating population across regions has a tremendous potential for spreading infectious diseases, however, the role of increasing massive floating population in tuberculosis transmission is yet unclear in China. METHODS 29,667 tuberculosis flow data were derived from the new smear-positive pulmonary tuberculosis cases in China. Spatial variation of TB transmission was measured by geodetector q-statistic and spatial interaction model was used to model the tuberculosis flow and the regional socioeconomic factors. RESULTS Tuberculosis transmission flow presented spatial heterogeneity. The Pearl River Delta in southern China and the Yangtze River Delta along China's east coast presented as the largest destination and concentration areas of tuberculosis inflows. Socioeconomic factors were determinants of tuberculosis flow. Some impact factors showed different spatial associations with tuberculosis transmission flow. A 10% increase in per capita GDP was associated with 10.2% in 2010 or 2.1% in 2012 decrease in tuberculosis outflows from the provinces of origin, and 1.2% in 2010 or 0.5% increase in tuberculosis inflows to the destinations and 18.9% increase in intraprovincial flow in 2012. Per capita net income of rural households and per capita disposable income of urban households were positively associated with tuberculosis flows. A 10% increase in per capita net income corresponded to 14.0% in 2010 or 3.6% in 2012 increase in outflows from the origin, 44.2% in 2010 or 12.8% increase in inflows to the destinations and 47.9% increase in intraprovincial flows in 2012. Tuberculosis incidence had positive impacts on tuberculosis flows. A 10% increase in the number of tuberculosis cases corresponded to 2.2% in 2010 or 1.1% in 2012 increase in tuberculosis inflows to the destinations, 5.2% in 2010 or 2.0% in 2012 increase in outflows from the origins, 11.5% in 2010 or 2.2% in 2012 increase in intraprovincial flows. CONCLUSIONS Tuberculosis flows had clear spatial stratified heterogeneity and spatial autocorrelation, regional socio-economic characteristics had diverse and statistically significant effects on tuberculosis flows in the origin and destination, and income factor played an important role among the determinants.
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Affiliation(s)
- Li Wang
- College of Geography and Environmental Science, Henan University, KaiFeng, 475001, China
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, KaiFeng, 475001, China
| | - Chengdong Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Maogui Hu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jinfeng Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jiajun Qiao
- College of Geography and Environmental Science, Henan University, KaiFeng, 475001, China.
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, KaiFeng, 475001, China.
| | - Wei Chen
- Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Qiankun Zhu
- College of Geography and Environmental Science, Henan University, KaiFeng, 475001, China
| | - Zhipeng Wang
- College of Geography and Environmental Science, Henan University, KaiFeng, 475001, China
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4
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Xia C, Delei W. Urban resilience governance mechanism: Insights from COVID-19 prevention and control in 30 Chinese cities. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2024. [PMID: 38922992 DOI: 10.1111/risa.14615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 06/02/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024]
Abstract
Due to the pervasive uncertainty in human society, super large and megacities are increasingly prone to becoming high-risk areas. However, the construction of urban resilience in this new era lacks sufficient research on the core conditions and complex interactive mechanisms governing it. Hence, this study proposes a specialized event-oriented framework for governing urban resilience in China based on the pressure-state-response (PSR) theory. We examined COVID-19 cases in 30 cities across China and analyzed the distribution of prevention and control achievements between high-level and non-high-level conditions. Our findings reveal the following key points: (1) High-level achievements in COVID-19 prevention and control rely on three condition configurations: non-pressure-responsive type, pressure-state type, and pressure-responsive type. (2) High economic resilience may indicate a robust state of urban systems amid demographic pressures. In cities experiencing fewer event pressure factors, the application of digital technology plays a crucial role in daily urban management. (3) The implementation of flexible policies proves beneficial in mitigating the impact of objective pressure conditions, such as environmental factors, on urban resilience.
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Affiliation(s)
- Cao Xia
- School of Economics and Management, Harbin Engineering University, Harbin, China
| | - Wang Delei
- School of Economics and Management, Harbin Engineering University, Harbin, China
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5
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Perofsky AC, Huddleston J, Hansen C, Barnes JR, Rowe T, Xu X, Kondor R, Wentworth DE, Lewis N, Whittaker L, Ermetal B, Harvey R, Galiano M, Daniels RS, McCauley JW, Fujisaki S, Nakamura K, Kishida N, Watanabe S, Hasegawa H, Sullivan SG, Barr IG, Subbarao K, Krammer F, Bedford T, Viboud C. Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.10.02.23296453. [PMID: 37873362 PMCID: PMC10593063 DOI: 10.1101/2023.10.02.23296453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection dynamics, presumably via heterosubtypic cross-immunity.
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Affiliation(s)
- Amanda C Perofsky
- Fogarty International Center, National Institutes of Health, United States
- Brotman Baty Institute for Precision Medicine, University of Washington, United States
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, United States
| | - Chelsea Hansen
- Fogarty International Center, National Institutes of Health, United States
- Brotman Baty Institute for Precision Medicine, University of Washington, United States
| | - John R Barnes
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - Thomas Rowe
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - Xiyan Xu
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - Rebecca Kondor
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - David E Wentworth
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - Nicola Lewis
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Lynne Whittaker
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Burcu Ermetal
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Ruth Harvey
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Monica Galiano
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Rodney Stuart Daniels
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - John W McCauley
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Seiichiro Fujisaki
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Kazuya Nakamura
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Noriko Kishida
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Shinji Watanabe
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Hideki Hasegawa
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Sheena G Sullivan
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Australia
| | - Ian G Barr
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Australia
| | - Kanta Subbarao
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Australia
| | - Florian Krammer
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, United States
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, United States
| | - Trevor Bedford
- Brotman Baty Institute for Precision Medicine, University of Washington, United States
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, United States
- Department of Genome Sciences, University of Washington, United States
- Howard Hughes Medical Institute, Seattle, United States
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, United States
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6
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Lyu M, Liu K, Hall RW. Spatial Interaction Analysis of Infectious Disease Import and Export between Regions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:643. [PMID: 38791857 PMCID: PMC11120745 DOI: 10.3390/ijerph21050643] [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: 03/19/2024] [Revised: 05/07/2024] [Accepted: 05/12/2024] [Indexed: 05/26/2024]
Abstract
Human travel plays a crucial role in the spread of infectious disease between regions. Travel of infected individuals from one region to another can transport a virus to places that were previously unaffected or may accelerate the spread of disease in places where the disease is not yet well established. We develop and apply models and metrics to analyze the role of inter-regional travel relative to the spread of disease, drawing from data on COVID-19 in the United States. To better understand how transportation affects disease transmission, we established a multi-regional time-varying compartmental disease model with spatial interaction. The compartmental model was integrated with statistical estimates of travel between regions. From the integrated model, we derived a transmission import index to assess the risk of COVID-19 transmission between states. Based on the index, we determined states with high risk for disease spreading to other states at the scale of months, and we analyzed how the index changed over time during 2020. Our model provides a tool for policymakers to evaluate the influence of travel between regions on disease transmission in support of strategies for epidemic control.
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Affiliation(s)
- Mingdong Lyu
- National Renewable Energy Laboratory, Mobility, Behavior, and Advanced Powertrains Department, Denver, CO 80401, USA
| | - Kuofu Liu
- Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089, USA; (K.L.); (R.W.H.)
| | - Randolph W. Hall
- Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089, USA; (K.L.); (R.W.H.)
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7
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Kline MC, Kissler SM, Whittles LK, Barnett ML, Grad YH. Spatiotemporal Trends in Group A Streptococcal Pharyngitis in the United States. Clin Infect Dis 2024; 78:1345-1351. [PMID: 38373257 DOI: 10.1093/cid/ciae083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/03/2024] [Accepted: 02/12/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Group A Streptococcus (GAS) causes an estimated 5.2 million outpatient visits for pharyngitis annually in the United States, with incidence peaking in winter, but the annual spatiotemporal pattern of GAS pharyngitis across the United States is poorly characterized. METHODS We used outpatient claims data from individuals with private medical insurance between 2010 and 2018 to quantify GAS pharyngitis visit rates across U.S. census regions, subregions, and states. We evaluated seasonal and age-based patterns of geographic spread and the association between school start dates and the summertime upward inflection in GAS visits. RESULTS The South had the most visits per person (yearly average, 39.11 visits per 1000 people; 95% confidence interval, 36.21-42.01) and the West had the fewest (yearly average, 17.63 visits per 1000 people; 95% confidence interval, 16.76-18.49). Visits increased earliest in the South and in school-age children. Differences in visits between the South and other regions were most pronounced in the late summer through early winter. Visits peaked earliest in central southern states, in December to January, and latest on the coasts, in March. The onset of the rise in GAS pharyngitis visits correlated with, but preceded, average school start times. CONCLUSIONS The burden and timing of GAS pharyngitis varied across the continental United States, with the South experiencing the highest overall rates and earliest onset and peak in outpatient visits. Understanding the drivers of these regional differences in GAS pharyngitis will help in identifying and targeting prevention measures.
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Affiliation(s)
- Madeleine C Kline
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Stephen M Kissler
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Computer Science, University of Colorado Boulder, Boulder, Colorado, USA
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, School of Public Health, Imperial College London, Norfolk Place, London, United Kingdom
| | - Michael L Barnett
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yonatan H Grad
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Division of Infectious Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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8
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Kummer A, Zhang J, Jiang C, Litvinova M, Ventura P, Garcia M, Vespignani A, Wu H, Yu H, Ajelli M. Evaluating Seasonal Variations in Human Contact Patterns and Their Impact on the Transmission of Respiratory Infectious Diseases. Influenza Other Respir Viruses 2024; 18:e13301. [PMID: 38733199 PMCID: PMC11087848 DOI: 10.1111/irv.13301] [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: 11/07/2023] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Human contact patterns are a key determinant driving the spread of respiratory infectious diseases. However, the relationship between contact patterns and seasonality as well as their possible association with the seasonality of respiratory diseases is yet to be clarified. METHODS We investigated the association between temperature and human contact patterns using data collected through a cross-sectional diary-based contact survey in Shanghai, China, between December 24, 2017, and May 30, 2018. We then developed a compartmental model of influenza transmission informed by the derived seasonal trends in the number of contacts and validated it against A(H1N1)pdm09 influenza data collected in Shanghai during the same period. RESULTS We identified a significant inverse relationship between the number of contacts and the seasonal temperature trend defined as a spline interpolation of temperature data (p = 0.003). We estimated an average of 16.4 (95% PrI: 15.1-17.5) contacts per day in December 2017 that increased to an average of 17.6 contacts (95% PrI: 16.5-19.3) in January 2018 and then declined to an average of 10.3 (95% PrI: 9.4-10.8) in May 2018. Estimates of influenza incidence obtained by the compartmental model comply with the observed epidemiological data. The reproduction number was estimated to increase from 1.24 (95% CI: 1.21-1.27) in December to a peak of 1.34 (95% CI: 1.31-1.37) in January. The estimated median infection attack rate at the end of the season was 27.4% (95% CI: 23.7-30.5%). CONCLUSIONS Our findings support a relationship between temperature and contact patterns, which can contribute to deepen the understanding of the relationship between social interactions and the epidemiology of respiratory infectious diseases.
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Affiliation(s)
- Allisandra G. Kummer
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and BiostatisticsIndiana University School of Public HealthBloomingtonIndianaUSA
| | - Juanjuan Zhang
- Shanghai Institute of Infectious Disease and Biosecurity, Department of Epidemiology, School of Public HealthFudan UniversityShanghaiChina
- Department of Epidemiology, School of Public HealthFudan University, Key Laboratory of Public Health Safety, Ministry of EducationShanghaiChina
| | - Chenyan Jiang
- Shanghai Municipal Center for Disease Control and PreventionShanghaiChina
| | - Maria Litvinova
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and BiostatisticsIndiana University School of Public HealthBloomingtonIndianaUSA
| | - Paulo C. Ventura
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and BiostatisticsIndiana University School of Public HealthBloomingtonIndianaUSA
| | - Marc A. Garcia
- Lerner Center for Public Health Promotion, Aging Studies Institute, Department of Sociology, and Maxwell School of Citizenship & Public AffairsSyracuse UniversitySyracuseNew YorkUSA
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio‐technical SystemsNortheastern UniversityBostonMassachusettsUSA
| | - Huanyu Wu
- Shanghai Municipal Center for Disease Control and PreventionShanghaiChina
| | - Hongjie Yu
- Shanghai Institute of Infectious Disease and Biosecurity, Department of Epidemiology, School of Public HealthFudan UniversityShanghaiChina
- Department of Epidemiology, School of Public HealthFudan University, Key Laboratory of Public Health Safety, Ministry of EducationShanghaiChina
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and BiostatisticsIndiana University School of Public HealthBloomingtonIndianaUSA
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Hay JA, Zhu H, Jiang CQ, Kwok KO, Shen R, Kucharski A, Yang B, Read JM, Lessler J, Cummings DAT, Riley S. Reconstructed influenza A/H3N2 infection histories reveal variation in incidence and antibody dynamics over the life course. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.18.24304371. [PMID: 38562868 PMCID: PMC10984066 DOI: 10.1101/2024.03.18.24304371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Humans experience many influenza infections over their lives, resulting in complex and varied immunological histories. Although experimental and quantitative analyses have improved our understanding of the immunological processes defining an individual's antibody repertoire, how these within-host processes are linked to population-level influenza epidemiology remains unclear. Here, we used a multi-level mathematical model to jointly infer antibody dynamics and individual-level lifetime influenza A/H3N2 infection histories for 1,130 individuals in Guangzhou, China, using 67,683 haemagglutination inhibition (HI) assay measurements against 20 A/H3N2 strains from repeat serum samples collected between 2009 and 2015. These estimated infection histories allowed us to reconstruct historical seasonal influenza patterns and to investigate how influenza incidence varies over time, space and age in this population. We estimated median annual influenza infection rates to be approximately 18% from 1968 to 2015, but with substantial variation between years. 88% of individuals were estimated to have been infected at least once during the study period (2009-2015), and 20% were estimated to have three or more infections in that time. We inferred decreasing infection rates with increasing age, and found that annual attack rates were highly correlated across all locations, regardless of their distance, suggesting that age has a stronger impact than fine-scale spatial effects in determining an individual's antibody profile. Finally, we reconstructed each individual's expected antibody profile over their lifetime and inferred an age-stratified relationship between probability of infection and HI titre. Our analyses show how multi-strain serological panels provide rich information on long term, epidemiological trends, within-host processes and immunity when analyzed using appropriate inference methods, and adds to our understanding of the life course epidemiology of influenza A/H3N2.
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Affiliation(s)
- James A. Hay
- Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, Imperial College London
| | - Huachen Zhu
- Guangdong-Hong Kong Joint Laboratory of Emerging Infectious Diseases/MOE Joint Laboratory for International Collaboration in Virology and Emerging Infectious Diseases, Joint Institute of Virology (Shantou University/The University of Hong Kong), Shantou University, Shantou, China
- State Key Laboratory of Emerging Infectious Diseases / World Health Organization Influenza Reference Laboratory, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- 5EKIH (Gewuzhikang) Pathogen Research Institute, Guangdong, China
| | | | - Kin On Kwok
- The Jockey Club School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Hong Kong Institute of Asia-Pacific Studies, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Ruiyin Shen
- Guangzhou No.12 Hospital, Guangzhou, Guangdong, China
| | - Adam Kucharski
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, United Kingdom
| | - Bingyi Yang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jonathan M. Read
- Centre for Health Informatics Computing and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
- Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, United States
- UNC Carolina Population Center, Chapel Hill, United States
| | - Derek A. T. Cummings
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Imperial College London
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Nasution YN, Sitorus MY, Sukandar K, Nuraini N, Apri M, Salama N. The epidemic forest reveals the spatial pattern of the spread of acute respiratory infections in Jakarta, Indonesia. Sci Rep 2024; 14:7619. [PMID: 38556584 PMCID: PMC10982301 DOI: 10.1038/s41598-024-58390-3] [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: 07/16/2023] [Accepted: 03/28/2024] [Indexed: 04/02/2024] Open
Abstract
Acute respiratory infection (ARI) is a communicable disease of the respiratory tract that implies impaired breathing. The infection can expand from one to the neighboring areas at a region-scale level through a human mobility network. Specific to this study, we leverage a record of ARI incidences in four periods of outbreaks for 42 regions in Jakarta to study its spatio-temporal spread using the concept of the epidemic forest. This framework generates a forest-like graph representing an explicit spread of disease that takes the onset time, spatio-temporal distance, and case prevalence into account. To support this framework, we use logistic curves to infer the onset time of the outbreak for each region. The result shows that regions with earlier onset dates tend to have a higher burden of cases, leading to the idea that the culprits of the disease spread are those with a high load of cases. To justify this, we generate the epidemic forest for the four periods of ARI outbreaks and identify the implied dominant trees (that with the most children cases). We find that the primary infected city of the dominant tree has a relatively higher burden of cases than other trees. In addition, we can investigate the timely ( R t ) and spatial reproduction number ( R c ) by directly evaluating them from the inferred graphs. We find that R t for dominant trees are significantly higher than non-dominant trees across all periods, with regions in western Jakarta tend to have higher values of R c . Lastly, we provide simulated-implied graphs by suppressing 50% load of cases of the primary infected city in the dominant tree that results in a reduced R c , suggesting a potential target of intervention to depress the overall ARI spread.
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Affiliation(s)
- Yuki Novia Nasution
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung, 40132, Indonesia
| | - Marli Yehezkiel Sitorus
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung, 40132, Indonesia
| | - Kamal Sukandar
- Department of Mathematics, Imperial College London, London, SW7 2RH, United Kingdom
| | - Nuning Nuraini
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung, 40132, Indonesia.
| | - Mochamad Apri
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung, 40132, Indonesia
| | - Ngabila Salama
- DKI Jakarta Provincial Health Office, Jakarta, Indonesia
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11
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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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Parino F, Gustani-Buss E, Bedford T, Suchard MA, Trovão NS, Rambaut A, Colizza V, Poletto C, Lemey P. Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.14.24303719. [PMID: 38559244 PMCID: PMC10980132 DOI: 10.1101/2024.03.14.24303719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological and virological data, integrating different data sources. We propose a novel combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across global macro-regions simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn-winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn-winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power. The multiscale approach enables model selection yielding a novel computational framework for describing global influenza dynamics at different scales - local transmission and national epidemics vs. international coupling through mobility and imported cases. Our findings have important implications to improve preparedness against seasonal influenza epidemics. The approach can be generalized to other epidemic contexts, such as emerging disease outbreaks to improve the flexibility and predictive power of modeling.
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Affiliation(s)
- Francesco Parino
- Sorbonne Université, INSERM, Institut Pierre Louis d’Epidemiologie et de Santé Publique (IPLESP), Paris, France
| | - Emanuele Gustani-Buss
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, 3000 Leuven, Belgium
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington 98109, USA
- Howard Hughes Medical Institute, Seattle, Washington 98109, USA
| | - Marc A. Suchard
- Departments of Biomathematics and Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, 90095, USA
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA
| | | | - Andrew Rambaut
- Institute of Ecology and Evolution, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d’Epidemiologie et de Santé Publique (IPLESP), Paris, France
- Department of Biology, Georgetown University, Washington, DC, USA
| | - Chiara Poletto
- Department of Molecular Medicine, University of Padova, 35121 Padova, Italy
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, 3000 Leuven, Belgium
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Chen X, Jiang Z, Chen R, Zhu Z, Wu Y, Sun Z, Chen L. Nosocomial outbreak of colistin-resistant, carbapenemase-producing Klebsiella pneumoniae ST11 in a medical intensive care unit. J Glob Antimicrob Resist 2024; 36:436-443. [PMID: 37931688 DOI: 10.1016/j.jgar.2023.10.013] [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: 04/01/2023] [Revised: 10/07/2023] [Accepted: 10/22/2023] [Indexed: 11/08/2023] Open
Abstract
OBJECTIVES Klebsiella pneumoniae is an important opportunistic Gram-negative pathogen. This study describes an outbreak due to colistin-resistant and carbapenem-resistant Klebsiella pneumoniae (ColR-CRKP) in a tertiary hospital related to six patients successively admitted to the department of medical intensive care unit (MICU) between March 11 and April 29, 2021. METHODS Phenotypic characterization was conducted on 16 ColR-CRKP strains obtained from six infected patients and five ColR-CRKP strains isolated from 48 environmental samples, followed by whole-genome sequencing (WGS) and polymerase chain reaction (PCR) analysis. RESULTS All ColR-CRKP strains showed resistance to commonly used antibiotics. Whole-genome sequencing revealed a variety of resistance genes such as blaKPC-2, blaCTX-M-65, and blaTEM-4 present in all strains, which is consistent with their antimicrobial resistance profile. All isolates were identified as the high-risk sequence type 11 (ST11) clonal lineage by multilocus sequencing typing (MLST) and subsequently clustered into a single clonal type by core genome MLST (cgMLST). IS5-like element ISKpn26 family transposase insertion mutations at positions 74 nucleotides in the mgrB gene were the main cause of colistin resistance in these ColR-CRKP. The variations of genes were verified by PCR. SCOTTI analysis demonstrated the transmission pathway of the ColR-CRKP between the patients. CONCLUSION Our study highlights the importance of coordinated efforts between clinical microbiologists and infection control teams to implement aggressive surveillance cultures and proper bacterial genotyping to diagnose nosocomial infections and take control measures. Routine surveillance and the use of advanced sequencing technologies should be implemented to enhance nosocomial infection control and prevention measures.
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Affiliation(s)
- Xi Chen
- Department of Laboratory Medicine, General Hospital of Southern Theater Command, Guangzhou, China
| | - Zhihui Jiang
- Department of Pharmacy, General Hospital of Southern Theater Command, Guangzhou, China; School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, China
| | - Rui Chen
- Department of Medical Intensive Care Unit, General Hospital of Southern Theater Command, Guangzhou, China
| | - Zijing Zhu
- Department of Laboratory Medicine, General Hospital of Southern Theater Command, Guangzhou, China
| | - Yixue Wu
- Department of Laboratory Medicine, General Hospital of Southern Theater Command, Guangzhou, China
| | - Zhaohui Sun
- Department of Laboratory Medicine, General Hospital of Southern Theater Command, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China.
| | - Lidan Chen
- Department of Laboratory Medicine, General Hospital of Southern Theater Command, Guangzhou, China; Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China.
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14
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Yang M, Gong S, Huang S, Huo X, Wang W. Geographical characteristics and influencing factors of the influenza epidemic in Hubei, China, from 2009 to 2019. PLoS One 2023; 18:e0280617. [PMID: 38011126 PMCID: PMC10681244 DOI: 10.1371/journal.pone.0280617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 09/13/2023] [Indexed: 11/29/2023] Open
Abstract
Influenza is an acute respiratory infectious disease that commonly affects people and has an important impact on public health. Based on influenza incidence data from 103 counties in Hubei Province from 2009 to 2019, this study used time series analysis and geospatial analysis to analyze the spatial and temporal distribution characteristics of the influenza epidemic and its influencing factors. The results reveal significant spatial-temporal clustering of the influenza epidemic in Hubei Province. Influenza mainly occurs in winter and spring of each year (from December to March of the next year), with the highest incidence rate observed in 2019 and an overall upward trend in recent years. There were significant spatial and urban-rural differences in influenza prevalence in Hubei Province, with the eastern region being more seriously affected than the central and western regions, and the urban regions more seriously affected than the rural region. Hubei's influenza epidemic showed an obvious spatial agglomeration distribution from 2009 to 2019, with the strongest clustering in winter. The hot spot areas of interannual variation in influenza were mainly distributed in eastern and western Hubei, and the cold spot areas were distributed in north-central Hubei. In addition, the cold hot spot areas of influenza epidemics varied from season to season. The seasonal changes in influenza prevalence in Hubei Province are mainly governed by meteorological factors, such as temperature, sunshine, precipitation, humidity, and wind speed. Low temperature, less rain, less sunshine, low wind speed and humid weather will increase the risk of contracting influenza; the interannual changes and spatial differentiation of influenza are mainly influenced by socioeconomic factors, such as road density, number of health technicians per 1,000 population, urbanization rate and population density. The strength of influenza's influencing factors in Hubei Province exhibits significant spatial variation, but in general, the formation of spatial variation of influenza in Hubei Province is still the result of the joint action of socioeconomic factors and natural meteorological factors. Understanding the temporal and spatial distribution characteristics of influenza in Hubei Province and its influencing factors can provide a reasonable decision-making basis for influenza prevention and control and public health development in Hubei Province and can also effectively improve the scientific understanding of the public with respect to influenza and other respiratory infectious diseases to reduce the influenza incidence, which also has reference significance for the prevention and control of influenza and other respiratory infectious diseases in other countries or regions.
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Affiliation(s)
- Mengmeng Yang
- College of Urban and Environmental Sciences, Central China Normal University, Wuhan, China
| | - Shengsheng Gong
- College of Urban and Environmental Sciences, Central China Normal University, Wuhan, China
| | - Shuqiong Huang
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, China
| | - Xixiang Huo
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, China
| | - Wuwei Wang
- College of Urban and Environmental Sciences, Central China Normal University, Wuhan, China
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15
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Kline MC, Kissler SM, Whittles LK, Barnett ML, Grad YH. Spatiotemporal Trends in Group A Streptococcal Pharyngitis in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.16.23298647. [PMID: 38014331 PMCID: PMC10680878 DOI: 10.1101/2023.11.16.23298647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Background Group A Streptococcus (GAS) causes an estimated 5.2 million outpatient visits for pharyngitis annually in the United States (U.S.) with incidence peaking in winter, but the annual spatiotemporal pattern of GAS pharyngitis across the U.S. is poorly characterized. Methods We used outpatient claims data from individuals with private medical insurance between 2010-2018 to quantify GAS pharyngitis visit rates across U.S. census regions, subregions, and states. We evaluated seasonal and age-based patterns of geographic spread and the association between school start dates and the summertime upward inflection in GAS visits. Results The South had the most visits per person (yearly average 39.11 visits per 1000 people, 95% CI: 36.21-42.01), and the West had the fewest (yearly average 17.63 visits per 1000 people, 95% CI: 16.76-18.49). Visits increased earliest in the South and in school-age children. Differences in visits between the South and other regions were most pronounced in the late summer through early winter. Visits peaked earliest in central southern states, in December to January, and latest on the coasts, in March. The onset of the rise in GAS pharyngitis visits correlated with, but preceded, average school start times. Conclusions The burden and timing of GAS pharyngitis varied across the continental U.S., with the South experiencing the highest overall rates and earliest onset and peak in outpatient visits. Understanding the drivers of these regional differences in GAS pharyngitis will help in identifying and targeting prevention measures.
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Affiliation(s)
- Madeleine C. Kline
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Stephen M. Kissler
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA
| | - Lilith K. Whittles
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Michael L. Barnett
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health
- Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Yonatan H. Grad
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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16
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Delussu F, Tizzoni M, Gauvin L. The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach. PNAS NEXUS 2023; 2:pgad302. [PMID: 37811338 PMCID: PMC10558401 DOI: 10.1093/pnasnexus/pgad302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 08/17/2023] [Indexed: 10/10/2023]
Abstract
Mobile phone data have been widely used to model the spread of COVID-19; however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. Using multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on COVID-19 spread. Our approach allows us to determine the best metric for predicting disease incidence in a particular location, at different spatial scales. Additionally, we identify geographic and demographic factors, such as users' coverage and commuting patterns, that explain the (non)observed relationship between mobility and epidemic patterns. Our work provides epidemiologists and public health officials with a general-not limited to COVID-19-framework to evaluate the usefulness of human mobility data in responding to epidemics.
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Affiliation(s)
- Federico Delussu
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- Department of Applied Mathematics and Computer Science, DTU, Richard Petersens Plads, DK-2800 Copenhagen, Denmark
| | - Michele Tizzoni
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- Department of Sociology and Social Research, University of Trento, via Verdi 26, I-38122 Trento, Italy
| | - Laetitia Gauvin
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- UMR 215 PRODIG, Institute for Research on Sustainable Development - IRD, 5 cours des Humanités, F-93 322 Aubervilliers Cedex, France
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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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18
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Heine C, O'Keeffe KP, Santi P, Yan L, Ratti C. Travel distance, frequency of return, and the spread of disease. Sci Rep 2023; 13:14064. [PMID: 37640718 PMCID: PMC10462643 DOI: 10.1038/s41598-023-38840-0] [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: 01/17/2023] [Accepted: 07/16/2023] [Indexed: 08/31/2023] Open
Abstract
Human mobility is a key driver of infectious disease spread. Recent literature has uncovered a clear pattern underlying the complexity of human mobility in cities: [Formula: see text], the product of distance traveled r and frequency of return f per user to a given location, is invariant across space. This paper asks whether the invariant [Formula: see text] also serves as a driver for epidemic spread, so that the risk associated with human movement can be modeled by a unifying variable [Formula: see text]. We use two large-scale datasets of individual human mobility to show that there is in fact a simple relation between r and f and both speed and spatial dispersion of disease spread. This discovery could assist in modeling spread of disease and inform travel policies in future epidemics-based not only on travel distance r but also on frequency of return f.
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Affiliation(s)
- Cate Heine
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Kevin P O'Keeffe
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Paolo Santi
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Istituto di Informatica e Telematica del CNR, Pisa, Italy
| | - Li Yan
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Carlo Ratti
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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19
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Jiao J, Shi L, Yang M, Yang J, Liu M, Sun G. The impact of containment policy and mobility on COVID-19 cases through structural equation model in Chile, Singapore, South Korea and Israel. PeerJ 2023; 11:e15769. [PMID: 37547719 PMCID: PMC10402700 DOI: 10.7717/peerj.15769] [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: 02/16/2023] [Accepted: 06/28/2023] [Indexed: 08/08/2023] Open
Abstract
Objectives The study aims to understand the impact of containment policy and mobility on COVID-19 cases in Chile, Singapore, South Korea and Israel. To provide experience in epidemic prevention and control. Methods Structural equation modeling (SEM) of containment policies, mobility, and COVID-19 cases were used to test and analyze the proposed hypotheses. Results Chile, Israel and Singapore adopted containment strategies, focusing on closure measures. South Korea adopted a mitigation strategy with fewer closure measures, focusing on vaccination and severe case management. There was a significant negative relationship among containment policies, mobility, and COVID-19 cases. Conclusion To control the COVID-19 and slow down the increase of COVID-19 cases, countries can increase the stringency of containment policies when COVID-19 epidemic is more severe. Thus, countries can take measures from the following three aspects: strengthen the risk monitoring, and keep abreast of the COVID-19 risk; adjust closure measures in time and reduce mobility; and strengthen public education on COVID-19 prevention to motivate citizen to consciously adhere to preventive measures.
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Affiliation(s)
- Jun Jiao
- Department of Health Management, School of Health Management, Southern Medical University, Guangzhou, Guangdong, China
- School of Sociology and Population Studies, Renmin University of China, Beijing, China
| | - Leiyu Shi
- Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America
| | - Manfei Yang
- Department of Health Management, School of Health Management, Southern Medical University, Guangzhou, Guangdong, China
| | - Junyan Yang
- Department of Health Management, School of Health Management, Southern Medical University, Guangzhou, Guangdong, China
| | - Meiheng Liu
- Department of Health Management, School of Health Management, Southern Medical University, Guangzhou, Guangdong, China
| | - Gang Sun
- Department of Health Management, School of Health Management, Southern Medical University, Guangzhou, Guangdong, China
- Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America
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20
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Tsui JLH, McCrone JT, Lambert B, Bajaj S, Inward RP, Bosetti P, Tegally H, Hill V, Pena RE, Zarebski AE, Peacock TP, Liu L, Wu N, Davis M, Bogoch II, Khan K, Kall M, Abdul Aziz NIB, Colquhoun R, O’Toole Á, Jackson B, Dasgupta A, Wilkinson E, de Oliveira T, Connor TR, Loman NJ, Colizza V, Fraser C, Volz E, Ji X, Gutierrez B, Chand M, Dellicour S, Cauchemez S, Raghwani J, Suchard MA, Lemey P, Rambaut A, Pybus OG, Kraemer MU. Genomic assessment of invasion dynamics of SARS-CoV-2 Omicron BA.1. Science 2023; 381:336-343. [PMID: 37471538 PMCID: PMC10866301 DOI: 10.1126/science.adg6605] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 06/15/2023] [Indexed: 07/22/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) now arise in the context of heterogeneous human connectivity and population immunity. Through a large-scale phylodynamic analysis of 115,622 Omicron BA.1 genomes, we identified >6,000 introductions of the antigenically distinct VOC into England and analyzed their local transmission and dispersal history. We find that six of the eight largest English Omicron lineages were already transmitting when Omicron was first reported in southern Africa (22 November 2021). Multiple datasets show that importation of Omicron continued despite subsequent restrictions on travel from southern Africa as a result of export from well-connected secondary locations. Initiation and dispersal of Omicron transmission lineages in England was a two-stage process that can be explained by models of the country's human geography and hierarchical travel network. Our results enable a comparison of the processes that drive the invasion of Omicron and other VOCs across multiple spatial scales.
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Affiliation(s)
| | - John T. McCrone
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- Helix, San Mateo, USA
| | - Ben Lambert
- Institute of Ecology and Evolution, University of Edinburgh, Edinburgh, UK
| | - Sumali Bajaj
- Department of Biology, University of Oxford, Oxford, UK
| | | | - Paolo Bosetti
- Institut Pasteur, Université Paris Cité, CNRS, Paris, France
| | - Houriiyah Tegally
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Verity Hill
- Helix, San Mateo, USA
- Yale University, New Haven, USA
| | | | | | - Thomas P. Peacock
- Department of Infectious Disease, Imperial College London, London, UK
- UK Health Security Agency, London, UK
| | | | - Neo Wu
- Google Research, Mountain View, USA
| | | | - Isaac I. Bogoch
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | - Kamran Khan
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | | | | | | | | | | | | | - Eduan Wilkinson
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | | | - Thomas R. Connor
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
- School of Biosciences, The Sir Martin Evans Building, Cardiff University, UK
- Quadram Institute, Norwich, UK
| | - Nicholas J. Loman
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP), Paris, France
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, UK
- Pandemic Sciences Institute, University of Oxford, UK
| | - Erik Volz
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Xiang Ji
- Department of Mathematics, Tulane University, New Orleans, USA
| | | | | | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Simon Cauchemez
- Institut Pasteur, Université Paris Cité, CNRS, Paris, France
| | - Jayna Raghwani
- Department of Biology, University of Oxford, Oxford, UK
- Department of Pathobiology and Population Science, Royal Veterinary College, London, UK
| | - Marc A. Suchard
- Departments of Biostatistics, Biomathematics and Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | | | - Oliver G. Pybus
- Department of Biology, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, UK
- Department of Pathobiology and Population Science, Royal Veterinary College, London, UK
| | - Moritz U.G. Kraemer
- Department of Biology, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, UK
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21
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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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22
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Servadio JL, Thai PQ, Choisy M, Boni MF. Repeatability and timing of tropical influenza epidemics. PLoS Comput Biol 2023; 19:e1011317. [PMID: 37467254 PMCID: PMC10389745 DOI: 10.1371/journal.pcbi.1011317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 06/29/2023] [Indexed: 07/21/2023] Open
Abstract
Much of the world experiences influenza in yearly recurring seasons, particularly in temperate areas. These patterns can be considered repeatable if they occur predictably and consistently at the same time of year. In tropical areas, including southeast Asia, timing of influenza epidemics is less consistent, leading to a lack of consensus regarding whether influenza is repeatable. This study aimed to assess repeatability of influenza in Vietnam, with repeatability defined as seasonality that occurs at a consistent time of year with low variation. We developed a mathematical model incorporating parameters to represent periods of increased transmission and then fitted the model to data collected from sentinel hospitals throughout Vietnam as well as four temperate locations. We fitted the model for individual (sub)types of influenza as well as all combined influenza throughout northern, central, and southern Vietnam. Repeatability was evaluated through the variance of the timings of peak transmission. Model fits from Vietnam show high variance (sd = 64-179 days) in peak transmission timing, with peaks occurring at irregular intervals and throughout different times of year. Fits from temperate locations showed regular, annual epidemics in winter months, with low variance in peak timings (sd = 32-57 days). This suggests that influenza patterns are not repeatable or seasonal in Vietnam. Influenza prevention in Vietnam therefore cannot rely on anticipation of regularly occurring outbreaks.
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Affiliation(s)
- Joseph L Servadio
- Center for Infectious Disease Dynamics and Department of Biology, Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Pham Quang Thai
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
- School of Preventative Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam
| | - Marc Choisy
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Maciej F Boni
- Center for Infectious Disease Dynamics and Department of Biology, Pennsylvania State University, University Park, Pennsylvania, United States of America
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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23
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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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24
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Brett TS, Bansal S, Rohani P. Charting the spatial dynamics of early SARS-CoV-2 transmission in Washington state. PLoS Comput Biol 2023; 19:e1011263. [PMID: 37379328 PMCID: PMC10335681 DOI: 10.1371/journal.pcbi.1011263] [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: 08/24/2022] [Revised: 07/11/2023] [Accepted: 06/12/2023] [Indexed: 06/30/2023] Open
Abstract
The spread of SARS-CoV-2 has been geographically uneven. To understand the drivers of this spatial variation in SARS-CoV-2 transmission, in particular the role of stochasticity, we used the early stages of the SARS-CoV-2 invasion in Washington state as a case study. We analysed spatially-resolved COVID-19 epidemiological data using two distinct statistical analyses. The first analysis involved using hierarchical clustering on the matrix of correlations between county-level case report time series to identify geographical patterns in the spread of SARS-CoV-2 across the state. In the second analysis, we used a stochastic transmission model to perform likelihood-based inference on hospitalised cases from five counties in the Puget Sound region. Our clustering analysis identifies five distinct clusters and clear spatial patterning. Four of the clusters correspond to different geographical regions, with the final cluster spanning the state. Our inferential analysis suggests that a high degree of connectivity across the region is necessary for the model to explain the rapid inter-county spread observed early in the pandemic. In addition, our approach allows us to quantify the impact of stochastic events in determining the subsequent epidemic. We find that atypically rapid transmission during January and February 2020 is necessary to explain the observed epidemic trajectories in King and Snohomish counties, demonstrating a persisting impact of stochastic events. Our results highlight the limited utility of epidemiological measures calculated over broad spatial scales. Furthermore, our results make clear the challenges with predicting epidemic spread within spatially extensive metropolitan areas, and indicate the need for high-resolution mobility and epidemiological data.
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Affiliation(s)
- Tobias S. Brett
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, D.C., United States of America
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia, United States of America
- Center for Influenza Disease & Emergence Research (CIDER), Athens, Georgia, United States of America
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25
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Zhong S, Ma F, Gao J, Bian L. Who Gets the Flu? Individualized Validation of Influenza-like Illness in Urban Spaces. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20105865. [PMID: 37239591 DOI: 10.3390/ijerph20105865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/27/2023] [Accepted: 05/15/2023] [Indexed: 05/28/2023]
Abstract
Urban dwellers are exposed to communicable diseases, such as influenza, in various urban spaces. Current disease models are able to predict health outcomes at the individual scale but are mostly validated at coarse scales due to the lack of fine-scaled ground truth data. Further, a large number of transmission-driving factors have been considered in these models. Because of the lack of individual-scaled validations, the effectiveness of factors at their intended scale is not substantiated. These gaps significantly undermine the efficacy of the models in assessing the vulnerability of individuals, communities, and urban society. The objectives of this study are twofold. First, we aim to model and, most importantly, validate influenza-like illness (ILI) symptoms at the individual scale based on four sets of transmission-driving factors pertinent to home-work space, service space, ambient environment, and demographics. The effort is supported by an ensemble approach. For the second objective, we investigate the effectiveness of the factor sets through an impact analysis. The validation accuracy reaches 73.2-95.1%. The validation substantiates the effectiveness of factors pertinent to urban spaces and unveils the underlying mechanism that connects urban spaces and population health. With more fine-scaled health data becoming available, the findings of this study may see increasing value in informing policies that improve population health and urban livability.
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Affiliation(s)
- Shiran Zhong
- Department of Geography, University of Western Ontario, London, ON N6A 3K7, Canada
| | - Fenglong Ma
- College of Information Sciences and Technology, Pennsylvania State University, University Park, State College, PA 16802, USA
| | - Jing Gao
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Ling Bian
- Department of Geography, University at Buffalo, The State University of New York, Buffalo, NY 14261, USA
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26
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Kim S, Carrel M, Kitchen A. Spatial genetic structure of 2009 H1N1 pandemic influenza established as a result of interaction with human populations in mainland China. PLoS One 2023; 18:e0284716. [PMID: 37196010 PMCID: PMC10191359 DOI: 10.1371/journal.pone.0284716] [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: 06/03/2022] [Accepted: 04/06/2023] [Indexed: 05/19/2023] Open
Abstract
Identifying the spatial patterns of genetic structure of influenza A viruses is a key factor for understanding their spread and evolutionary dynamics. In this study, we used phylogenetic and Bayesian clustering analyses of genetic sequences of the A/H1N1pdm09 virus with district-level locations in mainland China to investigate the spatial genetic structure of the A/H1N1pdm09 virus across human population landscapes. Positive correlation between geographic and genetic distances indicates high degrees of genetic similarity among viruses within small geographic regions but broad-scale genetic differentiation, implying that local viral circulation was a more important driver in the formation of the spatial genetic structure of the A/H1N1pdm09 virus than even, countrywide viral mixing and gene flow. Geographic heterogeneity in the distribution of genetic subpopulations of A/H1N1pdm09 virus in mainland China indicates both local to local transmission as well as broad-range viral migration. This combination of both local and global structure suggests that both small-scale and large-scale population circulation in China is responsible for viral genetic structure. Our study provides implications for understanding the evolution and spread of A/H1N1pdm09 virus across the population landscape of mainland China, which can inform disease control strategies for future pandemics.
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Affiliation(s)
- Seungwon Kim
- Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, Iowa, United States of America
| | - Margaret Carrel
- Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, Iowa, United States of America
- Department of Epidemiology, University of Iowa, Iowa City, Iowa, United States of America
| | - Andrew Kitchen
- Department of Anthropology, University of Iowa, Iowa City, Iowa, United States of America
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27
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Tiu A, Bansal S. Estimating county-level flu vaccination in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.10.23289756. [PMID: 37214921 PMCID: PMC10197794 DOI: 10.1101/2023.05.10.23289756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
In the United States, influenza vaccines are an important part of public health efforts to blunt the effects of seasonal influenza epidemics. This in turn emphasizes the importance of understanding the spatial distribution of influenza vaccination coverage. Despite this, high quality data at a fine spatial scale and spanning a multitude of recent flu seasons are not readily available. To address this gap, we develop county-level counts of vaccination across five recent, consecutive flu seasons and fit a series of regression models to these data that account for bias. We find that the spatial distribution of our bias-corrected vaccination coverage estimates is generally consistent from season to season, with the highest coverage in the Northeast and Midwest but is spatially heterogeneous within states. We also observe a negative relationship between a county's vaccination coverage and social vulnerability. Our findings stress the importance of quantifying flu vaccination coverage at a fine spatial scale, as relying on state or region-level estimates misses key heterogeneities.
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Affiliation(s)
- Andrew Tiu
- Department of Biology, Georgetown University, Washington, DC, USA
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, DC, USA
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28
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Han SM, Robert A, Masuda S, Yasaka T, Kanda S, Komori K, Saito N, Suzuki M, Endo A, Baguelin M, Ariyoshi K. Transmission dynamics of seasonal influenza in a remote island population. Sci Rep 2023; 13:5393. [PMID: 37012350 PMCID: PMC10068240 DOI: 10.1038/s41598-023-32537-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/29/2023] [Indexed: 04/05/2023] Open
Abstract
Seasonal influenza outbreaks remain an important public health concern, causing large numbers of hospitalizations and deaths among high-risk groups. Understanding the dynamics of individual transmission is crucial to design effective control measures and ultimately reduce the burden caused by influenza outbreaks. In this study, we analyzed surveillance data from Kamigoto Island, Japan, a semi-isolated island population, to identify the drivers of influenza transmission during outbreaks. We used rapid influenza diagnostic test (RDT)-confirmed surveillance data from Kamigoto island, Japan and estimated age-specific influenza relative illness ratios (RIRs) over eight epidemic seasons (2010/11 to 2017/18). We reconstructed the probabilistic transmission trees (i.e., a network of who-infected-whom) using Bayesian inference with Markov-chain Monte Carlo method and then performed a negative binomial regression on the inferred transmission trees to identify the factors associated with onwards transmission risk. Pre-school and school-aged children were most at risk of getting infected with influenza, with RIRs values consistently above one. The maximal RIR values were 5.99 (95% CI 5.23, 6.78) in the 7-12 aged-group and 5.68 (95%CI 4.59, 6.99) in the 4-6 aged-group in 2011/12. The transmission tree reconstruction suggested that the number of imported cases were consistently higher in the most populated and busy districts (Tainoura-go and Arikawa-go) ranged from 10-20 to 30-36 imported cases per season. The number of secondary cases generated by each case were also higher in these districts, which had the highest individual reproduction number (Reff: 1.2-1.7) across the seasons. Across all inferred transmission trees, the regression analysis showed that cases reported in districts with lower local vaccination coverage (incidence rate ratio IRR = 1.45 (95% CI 1.02, 2.05)) or higher number of inhabitants (IRR = 2.00 (95% CI 1.89, 2.12)) caused more secondary transmissions. Being younger than 18 years old (IRR = 1.38 (95%CI 1.21, 1.57) among 4-6 years old and 1.45 (95% CI 1.33, 1.59) 7-12 years old) and infection with influenza type A (type B IRR = 0.83 (95% CI 0.77, 0.90)) were also associated with higher numbers of onwards transmissions. However, conditional on being infected, we did not find any association between individual vaccination status and onwards transmissibility. Our study showed the importance of focusing public health efforts on achieving high vaccine coverage throughout the island, especially in more populated districts. The strong association between local vaccine coverage (including neighboring regions), and the risk of transmission indicate the importance of achieving homogeneously high vaccine coverage. The individual vaccine status may not prevent onwards transmission, though it may reduce the severity of infection.
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Affiliation(s)
- Su Myat Han
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
| | - Alexis Robert
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London, UK
| | - Shingo Masuda
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
- Department of Internal Medicine, Kamigoto Hospital, Kamigoto, Japan
| | - Takahiro Yasaka
- Department of Internal Medicine, Kamigoto Hospital, Kamigoto, Japan
| | - Satoshi Kanda
- Department of Internal Medicine, Kamigoto Hospital, Kamigoto, Japan
| | - Kazuhiri Komori
- Department of Internal Medicine, Kamigoto Hospital, Kamigoto, Japan
| | - Nobuo Saito
- Department of Microbiology, Faculty of Medicine, Oita University, Yufu, Japan
- Department of Clinical Medicine, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Japan
| | - Motoi Suzuki
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
- Infectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Akira Endo
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London, UK
| | - Marc Baguelin
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease, London, UK
| | - Koya Ariyoshi
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
- Department of Clinical Medicine, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Japan
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Deng X, Chen Z, Zhao Z, Chen J, Li M, Yang J, Yu H. Regional characteristics of influenza seasonality patterns in mainland China, 2005-2017: a statistical modeling study. Int J Infect Dis 2023; 128:91-97. [PMID: 36581188 DOI: 10.1016/j.ijid.2022.12.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/06/2022] [Accepted: 12/21/2022] [Indexed: 12/27/2022] Open
Abstract
OBJECTIVES To quantify the seasonal and antigenic characteristics of influenza to help understand influenza activity and inform vaccine recommendations. METHODS We employed a generalized linear model with harmonic terms to quantify the seasonal pattern of influenza in China from 2005-2017, including amplitude (circulatory intensity), semiannual periodicity (given two peaks a year), annual peak time, and epidemic duration. The antigenic differences were distinguished as antigenic similarity between 2009 and 2020. We categorized regions above 33° N, between 27° N and 33° N, and below 27° N as the north, central, and south regions, respectively. RESULTS We estimated that the amplitude in the north region (median: 0.019, 95% CI: 0.018-0.021) was significantly higher than that in the central region (median: 0.011, 95% CI: 0.01-0.012, P <0.001) and south region (median: 0.008, 95% CI: 0.007-0.008, P <0.001) for influenza A virus subtype H3N2 (A/H3N2). The A/H3N2 in the central region had a semiannual periodicity (median: 0.548, 95% CI: 0.517-0.577), while no semiannual pattern was found in other regions or subtypes/lineages. The antigenic similarity was low (below 50% in the 2009-2010, 2014-2015, 2016-2018, and 2019-2020 seasons) for A/H3N2. CONCLUSION Our study depicted the seasonal pattern differences and antigenic differences of influenza in China, which provides information for vaccination strategies.
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Affiliation(s)
- Xiaowei Deng
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Zhiyuan Chen
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Zeyao Zhao
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Junbo Chen
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Mei Li
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Juan Yang
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Hongjie Yu
- Department of Infectious Diseases, Huashan Hospital, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China; National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
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Wardle J, Bhatia S, Kraemer MUG, Nouvellet P, Cori A. Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study. Epidemics 2023; 42:100666. [PMID: 36689876 DOI: 10.1016/j.epidem.2023.100666] [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: 03/07/2022] [Revised: 11/18/2022] [Accepted: 01/06/2023] [Indexed: 01/13/2023] Open
Abstract
Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases. We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest. Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.
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Affiliation(s)
- Jack Wardle
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK
| | | | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK; School of Life Sciences, University of Sussex, Brighton, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK.
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31
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Del-Águila-Mejía J, García-García D, Rojas-Benedicto A, Rosillo N, Guerrero-Vadillo M, Peñuelas M, Ramis R, Gómez-Barroso D, Donado-Campos JDM. Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4356. [PMID: 36901366 PMCID: PMC10001675 DOI: 10.3390/ijerph20054356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
Human mobility drives the geographical diffusion of infectious diseases at different scales, but few studies focus on mobility itself. Using publicly available data from Spain, we define a Mobility Matrix that captures constant flows between provinces by using a distance-like measure of effective distance to build a network model with the 52 provinces and 135 relevant edges. Madrid, Valladolid and Araba/Álaba are the most relevant nodes in terms of degree and strength. The shortest routes (most likely path between two points) between all provinces are calculated. A total of 7 mobility communities were found with a modularity of 63%, and a relationship was established with a cumulative incidence of COVID-19 in 14 days (CI14) during the study period. In conclusion, mobility patterns in Spain are governed by a small number of high-flow connections that remain constant in time and seem unaffected by seasonality or restrictions. Most of the travels happen within communities that do not completely represent political borders, and a wave-like spreading pattern with occasional long-distance jumps (small-world properties) can be identified. This information can be incorporated into preparedness and response plans targeting locations that are at risk of contagion preventively, underscoring the importance of coordination between administrations when addressing health emergencies.
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Affiliation(s)
- Javier Del-Águila-Mejía
- Departamento de Medicina Preventiva y Salud Pública y Microbiología, Facultad de Medicina, Universidad Autónoma de Madrid. C. Arzobispo Morcillo 4, 28029 Madrid, Spain
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Servicio de Medicina Preventiva, Hospital Universitario de Móstoles, Calle Río Júcar s/n, 28935 Móstoles, Spain
| | - David García-García
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Ayelén Rojas-Benedicto
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
- Universidad Nacional de Educación a Distancia (UNED), Calle de Bravo Murillo 38, 28015 Madrid, Spain
| | - Nicolás Rosillo
- Servicio de Medicina Preventiva, Hospital Universitario 12 de Octubre, Avenida de Córdoba s/n, 28041 Madrid, Spain
| | - María Guerrero-Vadillo
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Marina Peñuelas
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Rebeca Ramis
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Diana Gómez-Barroso
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Juan de Mata Donado-Campos
- Departamento de Medicina Preventiva y Salud Pública y Microbiología, Facultad de Medicina, Universidad Autónoma de Madrid. C. Arzobispo Morcillo 4, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
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Fofana AM, Moultrie H, Scott L, Jacobson KR, Shapiro AN, Dor G, Crankshaw B, Silva PD, Jenkins HE, Bor J, Stevens WS. Cross-municipality migration and spread of tuberculosis in South Africa. Sci Rep 2023; 13:2674. [PMID: 36792792 PMCID: PMC9930008 DOI: 10.1038/s41598-023-29804-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
Human migration facilitates the spread of infectious disease. However, little is known about the contribution of migration to the spread of tuberculosis in South Africa. We analyzed longitudinal data on all tuberculosis test results recorded by South Africa's National Health Laboratory Service (NHLS), January 2011-July 2017, alongside municipality-level migration flows estimated from the 2016 South African Community Survey. We first assessed migration patterns in people with laboratory-diagnosed tuberculosis and analyzed demographic predictors. We then quantified the impact of cross-municipality migration on tuberculosis incidence in municipality-level regression models. The NHLS database included 921,888 patients with multiple clinic visits with TB tests. Of these, 147,513 (16%) had tests in different municipalities. The median (IQR) distance travelled was 304 (163 to 536) km. Migration was most common at ages 20-39 years and rates were similar for men and women. In municipality-level regression models, each 1% increase in migration-adjusted tuberculosis prevalence was associated with a 0.47% (95% CI: 0.03% to 0.90%) increase in the incidence of drug-susceptible tuberculosis two years later, even after controlling for baseline prevalence. Similar results were found for rifampicin-resistant tuberculosis. Accounting for migration improved our ability to predict future incidence of tuberculosis.
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Affiliation(s)
- Abdou M Fofana
- Institute for Health System Innovation & Policy, Boston University, Questrom School of Business, Boston, USA.
- Boston University School of Public Health, Boston, USA.
| | - Harry Moultrie
- Centre for Tuberculosis, National Institute for Communicable Diseases, a division of the National Health Laboratory Services, Johannesburg, South Africa
| | - Lesley Scott
- Wits Diagnostic Innovation Hub, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Karen R Jacobson
- Section of Infectious Diseases, Boston University School of Medicine and Boston Medical Center, Boston, USA
| | | | - Graeme Dor
- Wits Diagnostic Innovation Hub, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Beth Crankshaw
- Centre for Tuberculosis, National Institute for Communicable Diseases, a division of the National Health Laboratory Services, Johannesburg, South Africa
| | - Pedro Da Silva
- National Health Laboratory Service, Johannesburg, South Africa
| | | | - Jacob Bor
- Health Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Boston University School of Public Health, Boston, USA
| | - Wendy S Stevens
- Wits Diagnostic Innovation Hub, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- National Health Laboratory Service, Johannesburg, South Africa
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33
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Tegally H, Khan K, Huber C, de Oliveira T, Kraemer MUG. Shifts in global mobility dictate the synchrony of SARS-CoV-2 epidemic waves. J Travel Med 2022; 29:taac134. [PMID: 36367200 PMCID: PMC9793401 DOI: 10.1093/jtm/taac134] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 09/20/2022] [Accepted: 09/23/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND Human mobility changed in unprecedented ways during the SARS-CoV-2 pandemic. In March and April 2020, when lockdowns and large travel restrictions began in most countries, global air-travel almost entirely halted (92% decrease in commercial global air travel in the months between February and April 2020). Initial recovery in global air travel started around July 2020 and subsequently nearly tripled between May and July 2021. Here, we aim to establish a preliminary link between global mobility patterns and the synchrony of SARS-CoV-2 epidemic waves across the world. METHODS We compare epidemic peaks and human global mobility in two time periods: November 2020 to February 2021 (when just over 70 million passengers travelled) and November 2021 to February 2022 (when more than 200 million passengers travelled). We calculate the time interval during which continental epidemic peaks occurred for both of these time periods, and we calculate the pairwise correlations of epidemic waves between all pairs of countries for the same time periods. RESULTS We find that as air travel increases at the end of 2021, epidemic peaks around the world are more synchronous with one another, both globally and regionally. Continental epidemic peaks occur globally within a 20 day interval at the end of 2021 compared with 73 days at the end of 2020, and epidemic waves globally are more correlated with one another at the end of 2021. CONCLUSIONS This suggests that the rebound in human mobility dictates the synchrony of global and regional epidemic waves. In line with theoretical work, we show that in a more connected world, epidemic dynamics are more synchronized.
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Affiliation(s)
- Houriiyah Tegally
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Kamran Khan
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | | | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Moritz U G Kraemer
- Department of Biology, University of Oxford, Oxford, United Kingdom
- Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
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Strzelecki A. The Apple Mobility Trends Data in Human Mobility Patterns during Restrictions and Prediction of COVID-19: A Systematic Review and Meta-Analysis. Healthcare (Basel) 2022; 10:2425. [PMID: 36553949 PMCID: PMC9778143 DOI: 10.3390/healthcare10122425] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 11/24/2022] [Accepted: 11/29/2022] [Indexed: 12/03/2022] Open
Abstract
The objective of this systematic review with PRISMA guidelines is to discover how population movement information has epidemiological implications for the spread of COVID-19. In November 2022, the Web of Science and Scopus databases were searched for relevant reports for the review. The inclusion criteria are: (1) the study uses data from Apple Mobility Trends Reports, (2) the context of the study is about COVID-19 mobility patterns, and (3) the report is published in a peer-reviewed venue in the form of an article or conference paper in English. The review included 35 studies in the period of 2020-2022. The main strategy used for data extraction in this review is a matrix proposal to present each study from a perspective of research objective and outcome, study context, country, time span, and conducted research method. We conclude by pointing out that these data are not often used in studies and it is better to study a single country instead of doing multiple-country research. We propose topic classifications for the context of the studies as transmission rate, transport policy, air quality, re-increased activities, economic activities, and financial markets.
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Affiliation(s)
- Artur Strzelecki
- Department of Informatics, University of Economics in Katowice, 40-287 Katowice, Poland
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35
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The Impact of Urbanization and Human Mobility on Seasonal Influenza in Northern China. Viruses 2022; 14:v14112563. [PMID: 36423173 PMCID: PMC9697484 DOI: 10.3390/v14112563] [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: 10/19/2022] [Revised: 11/14/2022] [Accepted: 11/17/2022] [Indexed: 11/22/2022] Open
Abstract
The intensity of influenza epidemics varies significantly from year to year among regions with similar climatic conditions and populations. However, the underlying mechanisms of the temporal and spatial variations remain unclear. We investigated the impact of urbanization and public transportation size on influenza activity. We used 6-year weekly provincial-level surveillance data of influenza-like disease incidence (ILI) and viral activity in northern China. We derived the transmission potential of influenza for each epidemic season using the susceptible-exposed-infectious-removed-susceptible (SEIRS) model and estimated the transmissibility in the peak period via the instantaneous reproduction number (Rt). Public transport was found to explain approximately 28% of the variance in the seasonal transmission potential. Urbanization and public transportation size explained approximately 10% and 21% of the variance in maximum Rt in the peak period, respectively. For the mean Rt during the peak period, urbanization and public transportation accounted for 9% and 16% of the variance in Rt, respectively. Our results indicated that the differences in the intensity of influenza epidemics among the northern provinces of China were partially driven by urbanization and public transport size. These findings are beneficial for predicting influenza intensity and developing preparedness strategies for the early stages of epidemics.
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Guldemir HH, Kayali Sevim M, Eti S. Clinical characteristics of bus drivers and field officers infected with COVID-19: A cross-sectional study from Istanbul. Work 2022; 74:823-830. [PMID: 36404566 DOI: 10.3233/wor-220292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND: In metropolitans, where public transportation is used extensively, bus drivers are one of the occupational groups with a high risk of contracting COVID-19. OBJECTIVE: This study aimed to assess the difference between the clinical status of a group of bus drivers and field officers with COVID-19 on public transportation lines in Istanbul. METHODS: The study was conducted with 477 male volunteer participants. COVID-19 was confirmed through a positive nasopharyngeal culture sample using the real-time PCR test. Demographic information, biochemical parameters, clinical status, and the use of nutritional supplements were compared between those who recovered from COVID-19 at home or in the hospital. RESULTS: The body mass indexes (BMI) of 83.9% of individuals was above normal and 75.4% were treated for the disease at home. There were significant differences in terms of age, BMI, weight loss, smoking, use of nutritional supplements, blood glucose levels and vitamin B12 values. However, there was no significant difference between the types of nutritional supplements used or other biochemical parameters. CONCLUSION: It was determined that those who survived the disease at home were younger and had a lower BMI. It is important for both individuals and for general public health to create healthy working environments, especially for bus drivers, who have a high risk of COVID-19 contamination and transmission due to their long exposure time.
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Affiliation(s)
- Hilal Hizli Guldemir
- Department of Nutrition and Dietetics, Faculty of Health Sciences, Anadolu University, Eskisehir, Turkey
| | - Merve Kayali Sevim
- Institute of Health Sciences, Istanbul Medipol University, Istanbul, Turkey
| | - Serkan Eti
- Department of Computer Programming, Istanbul Medipol University, Istanbul, Turkey
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Schaber KL, Kobayashi T, Hast M, Searle KM, Shields TM, Hamapumbu H, Lubinda J, Thuma PE, Lupiya J, Chaponda M, Munyati S, Gwanzura L, Mharakurwa S, Moss WJ, Wesolowski A. What Heterogeneities in Individual-level Mobility Are Lost During Aggregation? Leveraging GPS Logger Data to Understand Fine-scale and Aggregated Patterns of Mobility. Am J Trop Med Hyg 2022; 107:1145-1153. [PMID: 36252797 PMCID: PMC9709031 DOI: 10.4269/ajtmh.22-0202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 08/18/2022] [Indexed: 01/25/2023] Open
Abstract
Human movement drives spatial transmission patterns of infectious diseases. Population-level mobility patterns are often quantified using aggregated data sets, such as census migration surveys or mobile phone data. These data are often unable to quantify individual-level travel patterns and lack the information needed to discern how mobility varies by demographic groups. Individual-level datasets can capture additional, more precise, aspects of mobility that may impact disease risk or transmission patterns and determine how mobility differs across cohorts; however, these data are rare, particularly in locations such as sub-Saharan Africa. Using detailed GPS logger data collected from three sites in southern Africa, we explore metrics of mobility such as percent time spent outside home, number of locations visited, distance of locations, and time spent at locations to determine whether they vary by demographic, geographic, or temporal factors. We further create a composite mobility score to identify how well aggregated summary measures would capture the full extent of mobility patterns. Although sites had significant differences in all mobility metrics, no site had the highest mobility for every metric, a distinction that was not captured by the composite mobility score. Further, the effects of sex, age, and season on mobility were all dependent on site. No factor significantly influenced the number of trips to locations, a common way to aggregate datasets. When collecting and analyzing human mobility data, it is difficult to account for all the nuances; however, these analyses can help determine which metrics are most helpful and what underlying differences may be present.
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Affiliation(s)
- Kathryn L. Schaber
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Tamaki Kobayashi
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Marisa Hast
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Kelly M. Searle
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Timothy M. Shields
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Jailos Lubinda
- Telethon Kids Institute, Malaria Atlas Project, Nedlands, Australia
| | - Philip E. Thuma
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - James Lupiya
- The Tropical Diseases Research Centre, Ndola, Zambia
| | - Mike Chaponda
- The Tropical Diseases Research Centre, Ndola, Zambia
| | - Shungu Munyati
- Biomedical Research and Training Institute, Harare, Zimbabwe
| | - Lovemore Gwanzura
- Biomedical Research and Training Institute, Harare, Zimbabwe
- College of Health Sciences, University of Zimbabwe, Harare, Zimbabwe
| | - Sungano Mharakurwa
- Biomedical Research and Training Institute, Harare, Zimbabwe
- College of Health, Agriculture and Natural Sciences, Africa University, Mutare, Zimbabwe
| | - William J. Moss
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Arambepola R, Schaber KL, Schluth C, Huang AT, Labrique AB, Mehta SH, Solomon SS, Cummings DAT, Wesolowski A. Fine scale human mobility changes in 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.11.04.22281943. [PMID: 36380765 PMCID: PMC9665343 DOI: 10.1101/2022.11.04.22281943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
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 mobility data from 26 US cities between February 2 â€" 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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Transmission Patterns of Seasonal Influenza in China between 2010 and 2018. Viruses 2022; 14:v14092063. [PMID: 36146868 PMCID: PMC9501233 DOI: 10.3390/v14092063] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/06/2022] [Accepted: 09/14/2022] [Indexed: 11/21/2022] Open
Abstract
Background Understanding the transmission source, pattern, and mechanism of infectious diseases is essential for targeted prevention and control. Though it has been studied for many years, the detailed transmission patterns and drivers for the seasonal influenza epidemics in China remain elusive. Methods In this study, utilizing a suite of epidemiological and genetic approaches, we analyzed the updated province-level weekly influenza surveillance, sequence, climate, and demographic data between 1 April 2010 and 31 March 2018 from continental China, to characterize detailed transmission patterns and explore the potential initiating region and drivers of the seasonal influenza epidemics in China. Results An annual cycle for influenza A(H1N1)pdm09 and B and a semi-annual cycle for influenza A(H3N2) were confirmed. Overall, the seasonal influenza A(H3N2) virus caused more infection in China and dominated the summer season in the south. The summer season epidemics in southern China were likely initiated in the “Lingnan” region, which includes the three most southern provinces of Hainan, Guangxi, and Guangdong. Additionally, the regions in the south play more important seeding roles in maintaining the circulation of seasonal influenza in China. Though intense human mobility plays a role in the province-level transmission of influenza epidemics on a temporal scale, climate factors drive the spread of influenza epidemics on both the spatial and temporal scales. Conclusion The surveillance of seasonal influenza in the south, especially the “Lingnan” region in the summer, should be strengthened. More broadly, both the socioeconomic and climate factors contribute to the transmission of seasonal influenza in China. The patterns and mechanisms revealed in this study shed light on the precise forecasting, prevention, and control of seasonal influenza in China and worldwide.
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Bedoya-Maya F, Calatayud A, Giraldez F, Sánchez González S. Urban mobility patterns and the spatial distribution of infections in Santiago de Chile. TRANSPORTATION RESEARCH. PART A, POLICY AND PRACTICE 2022; 163:43-54. [PMID: 35845317 PMCID: PMC9270950 DOI: 10.1016/j.tra.2022.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The process of a virus spread is inherently spatial. Even though Latin America became the epicenter of the COVID-19 pandemic in May 2020, there is still little evidence of the relationship between urban mobility and virus propagation in the region. This paper combines network analysis of mobility patterns in public transportation with a spatial error correction model for Santiago de Chile. Results indicate that a 10% higher number of daily public transportation trips received by an administrative unit in the city was associated with a 1.3% higher number of confirmed COVID-19 cases per 100,000 inhabitants. Following these findings, we propose an empirical method to identify and classify neighborhoods according to the level and type of risk for COVID-19-like disease propagation, helping policymakers manage mobility during the initial stages of an epidemic outbreak.
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Gupta A, Katarya R. Possibility of the COVID-19 third wave in India: mapping from second wave to third wave. INDIAN JOURNAL OF PHYSICS AND PROCEEDINGS OF THE INDIAN ASSOCIATION FOR THE CULTIVATION OF SCIENCE (2004) 2022; 97:389-399. [PMID: 35855730 PMCID: PMC9281261 DOI: 10.1007/s12648-022-02425-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 06/21/2022] [Indexed: 06/15/2023]
Abstract
After a consistent drop in daily new coronavirus cases during the second wave of COVID-19 in India, there is speculation about the possibility of a future third wave of the virus. The pandemic is returning in different waves; therefore, it is necessary to determine the factors or conditions at the initial stage under which a severe third wave could occur. Therefore, first, we examine the effect of related multi-source data, including social mobility patterns, meteorological indicators, and air pollutants, on the COVID-19 cases during the initial phase of the second wave so as to predict the plausibility of the third wave. Next, based on the multi-source data, we proposed a simple short-term fixed-effect multiple regression model to predict daily confirmed cases. The study area findings suggest that the coronavirus dissemination can be well explained by social mobility. Furthermore, compared with benchmark models, the proposed model improves prediction R 2 by 33.6%, 10.8%, 27.4%, and 19.8% for Maharashtra, Kerala, Karnataka, and Tamil Nadu, respectively. Thus, the simplicity and interpretability of the model are a meaningful contribution to determining the possibility of upcoming waves and direct pandemic prevention and control decisions at a local level in India.
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Affiliation(s)
- Aakansha Gupta
- Big Data Analytics and Web Intelligence Laboratory, Department of Computer Science and Engineering, Delhi Technological University, New Delhi, India
| | - Rahul Katarya
- Big Data Analytics and Web Intelligence Laboratory, Department of Computer Science and Engineering, Delhi Technological University, New Delhi, India
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Manlove K, Wilber M, White L, Bastille‐Rousseau G, Yang A, Gilbertson MLJ, Craft ME, Cross PC, Wittemyer G, Pepin KM. Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace‐of‐life. Ecol Lett 2022; 25:1760-1782. [DOI: 10.1111/ele.14032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/21/2022] [Accepted: 05/03/2022] [Indexed: 12/20/2022]
Affiliation(s)
- Kezia Manlove
- Department of Wildland Resources and Ecology Center Utah State University Logan Utah USA
| | - Mark Wilber
- Department of Forestry, Wildlife, and Fisheries University of Tennessee Institute of Agriculture Knoxville Tennessee USA
| | - Lauren White
- National Socio‐Environmental Synthesis Center University of Maryland Annapolis Maryland USA
| | | | - Anni Yang
- Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado USA
- National Wildlife Research Center, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services National Wildlife Research Center Fort Collins Colorado USA
- Department of Geography and Environmental Sustainability University of Oklahoma Norman Oklahoma USA
| | - Marie L. J. Gilbertson
- Department of Veterinary Population Medicine University of Minnesota St. Paul Minnesota USA
- Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology University of Wisconsin–Madison Madison Wisconsin USA
| | - Meggan E. Craft
- Department of Ecology, Evolution, and Behavior University of Minnesota St. Paul Minnesota USA
| | - Paul C. Cross
- U.S. Geological Survey Northern Rocky Mountain Science Center Bozeman Montana USA
| | - George Wittemyer
- Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado USA
| | - Kim M. Pepin
- National Wildlife Research Center, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services National Wildlife Research Center Fort Collins Colorado USA
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Days of Flooding Associated with Increased Risk of Influenza. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2022; 2022:8777594. [PMID: 35692665 PMCID: PMC9187473 DOI: 10.1155/2022/8777594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 05/10/2022] [Indexed: 11/18/2022]
Abstract
Influenza typically causes mild infection but can lead to severe outcomes for those with compromised lung health. Flooding, a seasonal problem in Iowa, can expose many Iowans to molds and allergens shown to alter lung inflammation, leading to asthma attacks and decreased viral clearance. Based on this, the hypothesis for this research was that there would be geographically specific positive associations in locations with flooding with influenza diagnosis. An ecological study was performed using influenza diagnoses and positive influenza polymerase chain reaction tests from a de-identified large private insurance database and Iowa State Hygienic Lab. After adjustment for multiple confounding factors, Poisson regression analysis resulted in a consistent 1% associated increase in influenza diagnoses per day above flood stage (95% confidence interval: 1.00–1.04). This relationship remained after removal of the 2009–2010 influenza pandemic year. There was no associated risk between flooding and influenza-like illness as a nonspecific diagnosis. Associated risks between flooding and increased influenza diagnoses were geographically specific, with the greatest risk in the most densely populated areas. This study indicates that populations who live, work, or volunteer in flooded environments should consider preventative measures to avoid environmental exposures to mitigate illness from influenza in the following year.
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McCulley EM, Mullachery PH, Ortigoza AF, Rodríguez DA, Diez Roux AV, Bilal U. Urban Scaling of Health Outcomes: a Scoping Review. J Urban Health 2022; 99:409-426. [PMID: 35513600 PMCID: PMC9070109 DOI: 10.1007/s11524-021-00577-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/09/2021] [Indexed: 11/04/2022]
Abstract
Urban scaling is a framework that describes how city-level characteristics scale with variations in city size. This scoping review mapped the existing evidence on the urban scaling of health outcomes to identify gaps and inform future research. Using a structured search strategy, we identified and reviewed a total of 102 studies, a majority set in high-income countries using diverse city definitions. We found several historical studies that examined the dynamic relationships between city size and mortality occurring during the nineteenth and early twentieth centuries. In more recent years, we documented heterogeneity in the relation between city size and health. Measles and influenza are influenced by city size in conjunction with other factors like geographic proximity, while STIs, HIV, and dengue tend to occur more frequently in larger cities. NCDs showed a heterogeneous pattern that depends on the specific outcome and context. Homicides and other crimes are more common in larger cities, suicides are more common in smaller cities, and traffic-related injuries show a less clear pattern that differs by context and type of injury. Future research should aim to understand the consequences of urban growth on health outcomes in low- and middle-income countries, capitalize on longitudinal designs, systematically adjust for covariates, and examine the implications of using different city definitions.
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Affiliation(s)
- Edwin M McCulley
- Urban Health Collaborative, Drexel University Dornsife School of Public Health, 3600 Market St, 7th floor, Philadelphia, PA, 19104, USA
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - Pricila H Mullachery
- Urban Health Collaborative, Drexel University Dornsife School of Public Health, 3600 Market St, 7th floor, Philadelphia, PA, 19104, USA
| | - Ana F Ortigoza
- Urban Health Collaborative, Drexel University Dornsife School of Public Health, 3600 Market St, 7th floor, Philadelphia, PA, 19104, USA
| | - Daniel A Rodríguez
- Department of City and Regional Planning, University of California Berkeley, Berkeley, CA, USA
| | - Ana V Diez Roux
- Urban Health Collaborative, Drexel University Dornsife School of Public Health, 3600 Market St, 7th floor, Philadelphia, PA, 19104, USA
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - Usama Bilal
- Urban Health Collaborative, Drexel University Dornsife School of Public Health, 3600 Market St, 7th floor, Philadelphia, PA, 19104, USA.
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA.
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Jahja M, Chin A, Tibshirani RJ. Real-Time Estimation of COVID-19 Infections: Deconvolution and Sensor Fusion. Stat Sci 2022. [DOI: 10.1214/22-sts856] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Maria Jahja
- Maria Jahja is Ph.D. Candidate, Department of Statistics & Data Science, Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Andrew Chin
- Andrew Chin is Statistical Developer, Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Ryan J. Tibshirani
- Ryan J. Tibshirani is Professor, Department of Statistics & Data Science, Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
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Zhu P, Tan X. Evaluating the effectiveness of Hong Kong's border restriction policy in reducing COVID-19 infections. BMC Public Health 2022; 22:803. [PMID: 35449094 PMCID: PMC9023047 DOI: 10.1186/s12889-022-13234-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
This study evaluates the effectiveness of Hong Kong's strict border restrictions with mainland China in curbing the transmission of COVID-19. Combining big data from Baidu Population Migration with traditional meteorological data and census data for over 200 Chinese cities, we utilize an advanced quantitative approach, namely synthetic control modeling, to produce a counterfactual "synthetic Hong Kong" without a strict border restriction policy. We then simulate infection trends under the hypothetical scenarios and compare them to actual infection numbers. Our counterfactual synthetic control model demonstrates a lower number of COVID-19 infections than the actual scenario, where strict border restrictions with mainland China were implemented from February 8 to March 6, 2020. Moreover, the second synthetic control model, which assumes a border reopen on 7 May 2020 demonstrates nonpositive effects of extending the border restriction policy on preventing and controlling infections. We conclude that the border restriction policy and its further extension may not be useful in containing the spread of COVID-19 when the virus is already circulating in the local community. Given the substantial economic and social costs, and as precautionary measures against COVID-19 becomes the new normal, countries can consider reopening borders with neighbors who have COVID-19 under control. Governments also need to closely monitor the changing epidemic situations in other countries in order to make prompt and sensible amendments to their border restriction policies.
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Affiliation(s)
- Pengyu Zhu
- Associate Professor Division of Public Policy, Hong Kong University of Science and Technology, Hong Kong SAR, China.
| | - Xinying Tan
- PhD Student in Public Policy, Division of Public Policy, Hong Kong University of Science and Technology, Hong Kong SAR, China
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Revealing Dynamic Spatial Structures of Urban Mobility Networks and the Underlying Evolutionary Patterns. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11040237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Urban space exhibits rich and diverse organizational structures, which is difficult to characterize and interpret. Modelling urban spatial structures in the context of mobility and revealing their underlying patterns in dynamic networks are key to understanding urban spatial structures and how urban systems work. Most existing methods overlook its temporal dimension and oversimplify its spatial heterogeneity, and it is challenging to address these complex properties using one single method. Therefore, we propose a framework based on temporal networks for modeling dynamic urban mobility structures. First, we cast aggregated traffic flows into a compact and informative temporal network for structure representation. Then, we explore spatial cluster substructures and temporal evolution patterns to acquire evolution regularities. Last, the capability of the proposed framework is examined by an empirical analysis based on taxi mobility networks. The experiment results enable to quantitatively depict urban space dynamics and effectively detect spatiotemporal heterogeneity in mobility networks.
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Becchetti L, Conzo G, Conzo P, Salustri F. Understanding the heterogeneity of COVID-19 deaths and contagions: The role of air pollution and lockdown decisions. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 305:114316. [PMID: 34998067 PMCID: PMC8714297 DOI: 10.1016/j.jenvman.2021.114316] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 11/02/2021] [Accepted: 12/14/2021] [Indexed: 05/26/2023]
Abstract
The uneven geographical distribution of the novel coronavirus epidemic (COVID-19) in Italy is a puzzle given the intense flow of movements among the different geographical areas before lockdown decisions. To shed light on it, we test the effect of the quality of air (as measured by particulate matter and nitrogen dioxide) and lockdown restrictions on daily adverse COVID-19 outcomes during the first pandemic wave in the country. We find that air pollution is positively correlated with adverse outcomes of the pandemic, with lockdown being strongly significant and more effective in reducing deceases in more polluted areas. Results are robust to different methods including cross-section, pooled and fixed-effect panel regressions (controlling for spatial correlation), instrumental variable regressions, and difference-in-differences estimates of lockdown decisions through predicted counterfactual trends. They are consistent with the consolidated body of literature in previous medical studies suggesting that poor quality of air creates chronic exposure to adverse outcomes from respiratory diseases. The estimated correlation does not change when accounting for other factors such as temperature, commuting flows, quality of regional health systems, share of public transport users, population density, the presence of Chinese community, and proxies for industry breakdown such as the share of small (artisan) firms. Our findings provide suggestions for investigating uneven geographical distribution patterns in other countries, and have implications for environmental and lockdown policies.
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Huang Y, Li R. The lockdown, mobility, and spatial health disparities in COVID-19 pandemic: A case study of New York City. CITIES (LONDON, ENGLAND) 2022; 122:103549. [PMID: 35125596 PMCID: PMC8806179 DOI: 10.1016/j.cities.2021.103549] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 11/26/2021] [Accepted: 12/28/2021] [Indexed: 05/04/2023]
Abstract
The world has adopted unprecedented lockdown as the key method to mitigate COVID-19; yet its effect on pandemic outcomes and health disparities remains largely unknown. Adopting a multilevel conceptual framework, this research investigates how city-level lockdown policy and public transit system shape mobility and thus intra-city health disparities, using New York City as a case study. With a spatial method and multiple sources of data, this research demonstrates the uneven impact of the lockdown policy and public transit system in shaping local pandemic outcomes. Census tracts with people spending more time at home have lower infection and death rates, while those with a higher density of transit stations have higher infection and death rates. Residential profile matters and census tracts with a higher concentration of disadvantaged population, such as Blacks, Hispanics, poor and elderly people, and people with no health insurance, have higher infection and death rates. Spatial analyses identify clusters where the lockdown policy was not effective and census tracts that share similar pandemic characteristics. Through the lens of mobility, this research advances knowledge of health disparities by focusing on institutional causes for health disparities and the role of the government through intervention policy and public transit system.
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Affiliation(s)
- Youqin Huang
- Department of Geography and Planning, Center for Social and Demographic Analysis, University at Albany, SUNY, United States of America
| | - Rui Li
- Department of Geography and Planning, University at Albany, SUNY, United States of America
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50
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Ou C, Hu S, Luo K, Yang H, Hang J, Cheng P, Hai Z, Xiao S, Qian H, Xiao S, Jing X, Xie Z, Ling H, Liu L, Gao L, Deng Q, Cowling BJ, Li Y. Insufficient ventilation led to a probable long-range airborne transmission of SARS-CoV-2 on two buses. BUILDING AND ENVIRONMENT 2022; 207:108414. [PMID: 34629689 PMCID: PMC8487323 DOI: 10.1016/j.buildenv.2021.108414] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 05/02/2023]
Abstract
Uncertainty remains on the threshold of ventilation rate in airborne transmission of SARS-CoV-2. We analyzed a COVID-19 outbreak in January 2020 in Hunan Province, China, involving an infected 24-year-old man, Mr. X, taking two subsequent buses, B1 and B2, in the same afternoon. We investigated the possibility of airborne transmission and the ventilation conditions for its occurrence. The ventilation rates on the buses were measured using a tracer-concentration decay method with the original driver on the original route. We measured and calculated the spread of the exhaled virus-laden droplet tracer from the suspected index case. Ten additional passengers were found to be infected, with seven of them (including one asymptomatic) on B1 and two on B2 when Mr. X was present, and one passenger infected on the subsequent B1 trip. B1 and B2 had time-averaged ventilation rates of approximately 1.7 and 3.2 L/s per person, respectively. The difference in ventilation rates and exposure time could explain why B1 had a higher attack rate than B2. Airborne transmission due to poor ventilation below 3.2 L/s played a role in this two-bus outbreak of COVID-19.
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Affiliation(s)
- Cuiyun Ou
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Shixiong Hu
- Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Kaiwei Luo
- Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Hongyu Yang
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Jian Hang
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Pan Cheng
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China
| | - Zheng Hai
- Shaodong County Center for Disease Control and Prevention, Shaodong, China
| | - Shanliang Xiao
- Shaoyang City Center for Disease Control and Prevention, Shaoyang, China
| | - Hua Qian
- School of Energy and Environment, Southeast University, Nanjing, China
| | - Shenglan Xiao
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China
| | - Xinping Jing
- Shaodong County Center for Disease Control and Prevention, Shaodong, China
| | - Zhengshen Xie
- Shaodong County Center for Disease Control and Prevention, Shaodong, China
| | - Hong Ling
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Li Liu
- School of Architecture, Tsinghua University, Beijing, China
| | - Lidong Gao
- Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Qihong Deng
- XiangYa School of Public Health, Central South University, Changsha, China
| | | | - Yuguo Li
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China
- School of Public Health, The University of Hong Kong, Hong Kong, China
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