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Chen Y, Tang F, Cao Z, Zeng J, Qiu Z, Zhang C, Long H, Cheng P, Sun Q, Han W, Tang K, Tang J, Zhao Y, Tian D, Du X. Global pattern and determinant for interaction of seasonal influenza viruses. J Infect Public Health 2024; 17:1086-1094. [PMID: 38705061 DOI: 10.1016/j.jiph.2024.04.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/22/2024] [Accepted: 04/25/2024] [Indexed: 05/07/2024] Open
Abstract
BACKGROUND The prevalence of different types/subtypes varies across seasons and countries for seasonal influenza viruses, indicating underlying interactions between types/subtypes. The global interaction patterns and determinants for seasonal influenza types/subtypes need to be explored. METHODS Influenza epidemiological surveillance data, as well as multidimensional data that include population-related, environment-related, and virus-related factors from 55 countries worldwide were used to explore type/subtype interactions based on Spearman correlation coefficient. The machine learning method Extreme Gradient Boosting (XGBoost) and interpretable framework SHapley Additive exPlanation (SHAP) were utilized to quantify contributing factors and their effects on interactions among influenza types/subtypes. Additionally, causal relationships between types/subtypes were also explored based on Convergent Cross-mapping (CCM). RESULTS A consistent globally negative correlation exists between influenza A/H3N2 and A/H1N1. Meanwhile, interactions between influenza A (A/H3N2, A/H1N1) and B show significant differences across countries, primarily influenced by population-related factors. Influenza A has a stronger driving force than influenza B, and A/H3N2 has a stronger driving force than A/H1N1. CONCLUSION The research elucidated the globally complex and heterogeneous interaction patterns among influenza type/subtypes, identifying key factors shaping their interactions. This sheds light on better seasonal influenza prediction and model construction, informing targeted prevention strategies and ultimately reducing the global burden of seasonal influenza.
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Affiliation(s)
- Yilin Chen
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Feng Tang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Foshan Center for Disease Control and Prevention, Foshan 528000, PR China
| | - Zicheng Cao
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; School of Public Health, Shantou University, Shantou 515000, PR China
| | - Jinfeng Zeng
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Zekai Qiu
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Chi Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Haoyu Long
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Peiwen Cheng
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Qianru Sun
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Wenjie Han
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Kang Tang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Jing Tang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Yang Zhao
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Shenzhen Key Laboratory of Pathogenic Microbes & Biosafety, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Dechao Tian
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Shenzhen Key Laboratory of Pathogenic Microbes & Biosafety, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Xiangjun Du
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Shenzhen Key Laboratory of Pathogenic Microbes & Biosafety, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou 510030, PR China.
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2
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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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3
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Menhat M, Ariffin EH, Dong WS, Zakaria J, Ismailluddin A, Shafril HAM, Muhammad M, Othman AR, Kanesan T, Ramli SP, Akhir MF, Ratnayake AS. Rain, rain, go away, come again another day: do climate variations enhance the spread of COVID-19? Global Health 2024; 20:43. [PMID: 38745248 PMCID: PMC11092248 DOI: 10.1186/s12992-024-01044-w] [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: 07/27/2023] [Accepted: 04/22/2024] [Indexed: 05/16/2024] Open
Abstract
The spread of infectious diseases was further promoted due to busy cities, increased travel, and climate change, which led to outbreaks, epidemics, and even pandemics. The world experienced the severity of the 125 nm virus called the coronavirus disease 2019 (COVID-19), a pandemic declared by the World Health Organization (WHO) in 2019. Many investigations revealed a strong correlation between humidity and temperature relative to the kinetics of the virus's spread into the hosts. This study aimed to solve the riddle of the correlation between environmental factors and COVID-19 by applying RepOrting standards for Systematic Evidence Syntheses (ROSES) with the designed research question. Five temperature and humidity-related themes were deduced via the review processes, namely 1) The link between solar activity and pandemic outbreaks, 2) Regional area, 3) Climate and weather, 4) Relationship between temperature and humidity, and 5) the Governmental disinfection actions and guidelines. A significant relationship between solar activities and pandemic outbreaks was reported throughout the review of past studies. The grand solar minima (1450-1830) and solar minima (1975-2020) coincided with the global pandemic. Meanwhile, the cooler, lower humidity, and low wind movement environment reported higher severity of cases. Moreover, COVID-19 confirmed cases and death cases were higher in countries located within the Northern Hemisphere. The Blackbox of COVID-19 was revealed through the work conducted in this paper that the virus thrives in cooler and low-humidity environments, with emphasis on potential treatments and government measures relative to temperature and humidity. HIGHLIGHTS: • The coronavirus disease 2019 (COIVD-19) is spreading faster in low temperatures and humid area. • Weather and climate serve as environmental drivers in propagating COVID-19. • Solar radiation influences the spreading of COVID-19. • The correlation between weather and population as the factor in spreading of COVID-19.
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Affiliation(s)
- Masha Menhat
- Faculty of Maritime Studies, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia
| | - Effi Helmy Ariffin
- Institute of Oceanography and Environment, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia.
| | - Wan Shiao Dong
- Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia
| | - Junainah Zakaria
- Institute of Oceanography and Environment, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia
| | - Aminah Ismailluddin
- Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia
| | | | - Mahazan Muhammad
- Social, Environmental and Developmental Sustainability Research Center, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
| | - Ahmad Rosli Othman
- Institute of Geology Malaysia, Board of Geologists, 62100, Putrajaya, Malaysia
| | - Thavamaran Kanesan
- Executive Office, Proofreading By A UK PhD, 51-1, Biz Avenue II, 63000, Cyberjaya, Malaysia
| | - Suzana Pil Ramli
- Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Mohd Fadzil Akhir
- Institute of Oceanography and Environment, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia
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4
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Lei H, Zhang N, Xiao S, Zhuang L, Yang X, Chen T, Yang L, Wang D, Li Y, Shu Y. Relative Role of Age Groups and Indoor Environments in Influenza Transmission Under Different Urbanization Rates in China. Am J Epidemiol 2024; 193:596-605. [PMID: 37946322 DOI: 10.1093/aje/kwad218] [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: 08/23/2022] [Revised: 06/20/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
Exploring the relative role of different indoor environments in respiratory infections transmission remains unclear, which is crucial for developing targeted nonpharmaceutical interventions. In this study, a total of 2,583,441 influenza-like illness cases tested from 2010 to 2017 in China were identified. An agent-based model was built and calibrated with the surveillance data, to assess the roles of 3 age groups (children <19 years, younger adults 19-60 years, older adults >60 years) and 4 types of indoor environments (home, schools, workplaces, and community areas) in influenza transmission by province with varying urbanization rates. When the urbanization rates increased from 35% to 90%, the proportion of children aged <19 years among influenza cases decreased from 76% to 45%. Additionally, we estimated that infections originating from children decreased from 95.1% (95% confidence interval (CI): 92.7, 97.5) to 59.3% (95% CI: 49.8, 68.7). Influenza transmission in schools decreased from 80.4% (95% CI: 76.5, 84.3) to 36.6% (95% CI: 20.6, 52.5), while transmission in the community increased from 2.4% (95% CI: 1.9, 2.8) to 45.4% (95% CI: 35.9, 54.8). With increasing urbanization rates, community areas and younger adults contributed more to infection transmission. These findings could help the development of targeted public health policies. This article is part of a Special Collection on Environmental Epidemiology. This article is part of a Special Collection on Environmental Epidemiology.
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5
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Nagasawa Y, Nakayama M, Kato Y, Ogawa Y, Aribam SD, Tsugami Y, Iwata T, Mikami O, Sugiyama A, Onishi M, Hayashi T, Eguchi M. A novel vaccine strategy using quick and easy conversion of bacterial pathogens to unnatural amino acid-auxotrophic suicide derivatives. Microbiol Spectr 2024; 12:e0355723. [PMID: 38385737 PMCID: PMC10986568 DOI: 10.1128/spectrum.03557-23] [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: 10/04/2023] [Accepted: 01/24/2024] [Indexed: 02/23/2024] Open
Abstract
We propose a novel strategy for quick and easy preparation of suicide live vaccine candidates against bacterial pathogens. This method requires only the transformation of one or more plasmids carrying genes encoding for two types of biological devices, an unnatural amino acid (uAA) incorporation system and toxin-antitoxin systems in which translation of the antitoxins requires the uAA incorporation. Escherichia coli BL21-AI laboratory strains carrying the plasmids were viable in the presence of the uAA, whereas the free toxins killed these strains after the removal of the uAA. The survival time after uAA removal could be controlled by the choice of the uAA incorporation system and toxin-antitoxin systems. Multilayered toxin-antitoxin systems suppressed escape frequency to less than 1 escape per 109 generations in the best case. This conditional suicide system also worked in Salmonella enterica and E. coli clinical isolates. The S. enterica vaccine strains were attenuated with a >105 fold lethal dose. Serum IgG response and protection against the parental pathogenic strain were confirmed. In addition, the live E. coli vaccine strain was significantly more immunogenic and provided greater protection than a formalin-inactivated vaccine. The live E. coli vaccine was not detected after inoculation, presumably because the uAA is not present in the host animals or the natural environment. These results suggest that this strategy provides a novel way to rapidly produce safe and highly immunogenic live bacterial vaccine candidates. IMPORTANCE Live vaccines are the oldest vaccines with a history of more than 200 years. Due to their strong immunogenicity, live vaccines are still an important category of vaccines today. However, the development of live vaccines has been challenging due to the difficulties in achieving a balance between safety and immunogenicity. In recent decades, the frequent emergence of various new and old pathogens at risk of causing pandemics has highlighted the need for rapid vaccine development processes. We have pioneered the use of uAAs to control gene expression and to conditionally kill host bacteria as a biological containment system. This report proposes a quick and easy conversion of bacterial pathogens into live vaccine candidates using this containment system. The balance between safety and immunogenicity can be modulated by the selection of the genetic devices used. Moreover, the uAA-auxotrophy can prevent the vaccine from infecting other individuals or establishing the environment.
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Affiliation(s)
- Yuya Nagasawa
- National Institute of Animal Health, National Agriculture and Food Research Organization (NARO), Sapporo, Hokkaido, Japan
| | - Momoko Nakayama
- National Institute of Animal Health, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
| | - Yusuke Kato
- Institute of Agrobiological Sciences, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
| | - Yohsuke Ogawa
- National Institute of Animal Health, National Agriculture and Food Research Organization (NARO), Sapporo, Hokkaido, Japan
| | - Swarmistha Devi Aribam
- National Institute of Animal Health, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
| | - Yusaku Tsugami
- National Institute of Animal Health, National Agriculture and Food Research Organization (NARO), Sapporo, Hokkaido, Japan
| | - Taketoshi Iwata
- National Institute of Animal Health, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
| | - Osamu Mikami
- National Institute of Animal Health, National Agriculture and Food Research Organization (NARO), Sapporo, Hokkaido, Japan
| | - Aoi Sugiyama
- National Institute of Animal Health, National Agriculture and Food Research Organization (NARO), Sapporo, Hokkaido, Japan
| | - Megumi Onishi
- National Institute of Animal Health, National Agriculture and Food Research Organization (NARO), Sapporo, Hokkaido, Japan
| | - Tomohito Hayashi
- National Institute of Animal Health, National Agriculture and Food Research Organization (NARO), Sapporo, Hokkaido, Japan
| | - Masahiro Eguchi
- National Institute of Animal Health, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
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6
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Noppert GA, Clarke P, Hoover A, Kubale J, Melendez R, Duchowny K, Hegde ST. State variation in neighborhood COVID-19 burden across the United States. COMMUNICATIONS MEDICINE 2024; 4:36. [PMID: 38429552 PMCID: PMC10907669 DOI: 10.1038/s43856-024-00459-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 02/12/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 infection burden has been distributed across neighborhoods, a key determinant of both risk and resilience. Without more spatially resolute data, efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities will remain difficult to quantify and intervene on. METHODS We leveraged spatially-referenced data from 21 states collated through the COVID Neighborhood Project to examine the distribution of COVID-19 cases across neighborhoods and states in the U.S. We also linked the COVID-19 case data with data on the neighborhood social environment from the National Neighborhood Data Archive. We then estimated correlations between neighborhood COVID-19 burden and features of the neighborhood social environment. RESULTS We find that the distribution of COVID-19 at the neighborhood-level varies within and between states. The median case count per neighborhood (coefficient of variation (CV)) in Wisconsin is 3078.52 (0.17) per 10,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (CV) is 810.98 (0.84) per 10,000 population. We also find that correlations between features of the neighborhood social environment and burden vary in magnitude and direction by state. CONCLUSIONS Our findings underscore the importance that local contexts may play when addressing the long-term social and economic fallout communities will face from COVID-19.
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Affiliation(s)
- Grace A Noppert
- Institute for Social Research, University of Michigan, Ann Arbor, USA.
| | - Philippa Clarke
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - Andrew Hoover
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - John Kubale
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - Robert Melendez
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - Kate Duchowny
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - Sonia T Hegde
- Department of Epidemiology, Johns Hopkins University, Baltimore, USA
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7
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Ullah S, Khattak SR, Ullah R, Fayaz M, Han H, Yoo S, Ariza-Montes A, Raposo A. Unveiling the global nexus: Pandemic fear, government responses, and climate change-an empirical study. Heliyon 2024; 10:e23815. [PMID: 38261913 PMCID: PMC10797138 DOI: 10.1016/j.heliyon.2023.e23815] [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: 08/21/2023] [Revised: 12/10/2023] [Accepted: 12/13/2023] [Indexed: 01/25/2024] Open
Abstract
This study examined the relationships between pandemic fear, government responses, and climate change using a time-series dataset from January 1, 2020, to December 31, 2020. By employing an auto-regressive distributed lag (ARDL) approach, the results revealed that pandemic fear significantly impacts climate change, while government responses to COVID-19 negatively influence climate change in the long run. Climate change and government responses significantly positively affect pandemic fear in the long run. Moreover, we found a bidirectional causality between government responses and climate change, unidirectional causality from government responses to pandemic fear, and no Granger causality between pandemic fear and climate change. Our findings have some important policy implications. Governments must encourage coordination, enhance crisis responses, and consider revising economic metrics to maintain environmental sustainability. The COVID-19 experience can inform strategies for reducing CO2 emissions and investing in green economies and healthcare to prepare for future challenges.
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Affiliation(s)
- Sabeeh Ullah
- Institute of Business and Management Sciences, The University of Agriculture, Peshawar, Pakistan
| | - Sajid Rahman Khattak
- Institute of Business and Management Sciences, The University of Agriculture, Peshawar, Pakistan
| | - Rezwan Ullah
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
| | - Mohammad Fayaz
- Institute of Business and Management Sciences, The University of Agriculture, Peshawar, Pakistan
| | - Heesup Han
- College of Hospitality and Tourism Management, Sejong University, Seoul 05006, South Korea
| | - Sunghoon Yoo
- Audit Team, Hanmoo Convention (Oakwood Premier), 49, Teheran-ro 87-gil, Gangnam-gu, Seoul 06164, South Korea
| | - Antonio Ariza-Montes
- Social Matters Research Group, Universidad Loyola Andalucía, C/Escritor Castilla Aguayo, 4, 14004 Córdoba, Spain
| | - António Raposo
- CBIOS (Research Center for Biosciences and Health Technologies), Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, Portugal
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8
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Fu JX, Liu Y, Chen LH, Han WK, Liu X, Shao JX, Yan X, Gu ZG. Positional Isomers of Covalent Organic Frameworks for Indoor Humidity Regulation. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2303897. [PMID: 37533408 DOI: 10.1002/smll.202303897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 07/22/2023] [Indexed: 08/04/2023]
Abstract
Humidity is one of the most important indicators affecting human health. Here, a pair of covalent organic frameworks (COFs) of positional isomers (p-COF and o-COF) for indoor humidity regulation is reported. Although p-COF and o-COF have the same sql topology and pore size, they exhibit different water adsorption behaviors due to the subtle differences in water adsorption sites. Particularly, o-COF exhibits a steep adsorption isotherm in the range of 45-65% RH with a hysteresis loop, which is perfectly suitable for indoor humidity regulation. In the laboratory experiment, when the humidity of the external environment is 20-75% RH, o-COF can control the humidity of the room in the range of 45-60% RH. o-COF has shown great potential as a dual humidification/dehumidification adsorbent for indoor humidity regulation.
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Affiliation(s)
- Jia-Xing Fu
- Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi, 214122, P. R. China
| | - Yong Liu
- Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi, 214122, P. R. China
| | - Liang-Hui Chen
- Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi, 214122, P. R. China
| | - Wang-Kang Han
- Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi, 214122, P. R. China
| | - Xin Liu
- Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi, 214122, P. R. China
| | - Jun-Xiang Shao
- Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi, 214122, P. R. China
| | - Xiaodong Yan
- Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi, 214122, P. R. China
| | - Zhi-Guo Gu
- Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi, 214122, P. R. China
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9
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Guan C, Tan J, Li Y, Cheng T, Yang J, Liu C, Keith M. How do density, employment and transit affect the prevalence of COVID-19 pandemic? A study of 3,141 counties across the United States. Health Place 2023; 84:103117. [PMID: 37769578 DOI: 10.1016/j.healthplace.2023.103117] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 09/01/2023] [Accepted: 09/06/2023] [Indexed: 10/03/2023]
Abstract
Previous research has explored the effect of the built environment on the spread of the coronavirus disease (COVID-19) pandemic. This study extends the existing literature by examining the relationship between pandemic prevalence and density, employment, and transit factors at the county level. Using multilinear spatial-lag regressions and time series clustering analyses on the Smart Location Database encompassing 3141 counties in the United States, our findings reveal the following: (1) Density, employment, and transit variables yield heterogeneous effects to infection rate, death rate, and mortality rate. (2) Pedestrian-oriented road density is positively correlated to the prevalence of COVID-19, every 0.011 miles/acre increase is associated with 1% increase in the infection rate. (3) A consistent negative correlation is observed between jobs per household and infection rate, while a decrease in unemployment rate leads to an increase in the death rate. (4) The results from time series analysis suggest that areas characterized by low auto-oriented intersection density but high pedestrian-oriented road density are more susceptible to the impacts of pandemics. This highlights the need to prioritize pandemic prevention efforts in the suburban and rural areas with low population density, as emphasized in existing literature emphasized.
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Affiliation(s)
- ChengHe Guan
- Shanghai Key Laboratory of Urban Design and Urban Science, NYU Shanghai, Shanghai, China; Division of Arts and Sciences, NYU Shanghai, Shanghai, China.
| | - Junjie Tan
- Shanghai Key Laboratory of Urban Design and Urban Science, NYU Shanghai, Shanghai, China; PEAK Urban Programme, University of Oxford, Oxford, UK
| | - Ying Li
- Shanghai Key Laboratory of Urban Design and Urban Science, NYU Shanghai, Shanghai, China; Division of Arts and Sciences, NYU Shanghai, Shanghai, China.
| | - Tong Cheng
- Shanghai Key Laboratory of Urban Design and Urban Science, NYU Shanghai, Shanghai, China
| | - Junyan Yang
- School of Architecture and Planning, Southeast University, Nanjing, China
| | - Chao Liu
- Department of Urban Planning, College of Architecture and Urban Planning, Tongji University, Shanghai, China
| | - Michael Keith
- PEAK Urban Programme, University of Oxford, Oxford, UK
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10
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Cazelles B, Cazelles K, Tian H, Chavez M, Pascual M. Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections. SCIENCE ADVANCES 2023; 9:eadf7202. [PMID: 37756402 PMCID: PMC10530079 DOI: 10.1126/sciadv.adf7202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 08/21/2023] [Indexed: 09/29/2023]
Abstract
Identifying climate drivers is essential to understand and predict epidemics of mosquito-borne infections whose population dynamics typically exhibit seasonality and multiannual cycles. Which climate covariates to consider varies across studies, from local factors such as temperature to remote drivers such as the El Niño-Southern Oscillation. With partial wavelet coherence, we present a systematic investigation of nonstationary associations between mosquito-borne disease incidence and a given climate factor while controlling for another. Analysis of almost 200 time series of dengue and malaria around the globe at different geographical scales shows a systematic effect of global climate drivers on interannual variability and of local ones on seasonality. This clear separation of time scales of action enhances detection of climate drivers and indicates those best suited for building early-warning systems.
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Affiliation(s)
- Bernard Cazelles
- UMMISCO, Sorbonne Université, Paris, France
- Eco-Evolution Mathématique, IBENS, CNRS UMR-8197, Ecole Normale Supérieure, Paris, France
| | - Kévin Cazelles
- Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada
- inSileco Inc., 2-775 Avenue Monk, Québec, Québec, Canada
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Mario Chavez
- Hôpital de la Pitié-Salpêtrière, CNRS UMR-7225, Paris, France
| | - Mercedes Pascual
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
- The Santa Fe Institute, Santa Fe, NM, USA
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11
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Suresh S, Meraj G, Kumar P, Singh D, Khan ID, Gupta A, Yadav TK, Kouser A, Avtar R. Interactions of urbanisation, climate variability, and infectious disease dynamics: insights from the Coimbatore district of Tamil Nadu. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1226. [PMID: 37725204 DOI: 10.1007/s10661-023-11856-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/07/2023] [Indexed: 09/21/2023]
Abstract
Climate change and shifts in land use/land cover (LULC) are critical factors affecting the environmental, societal, and health landscapes, notably influencing the spread of infectious diseases. This study delves into the intricate relationships between climate change, LULC alterations, and the prevalence of vector-borne and waterborne diseases in Coimbatore district, Tamil Nadu, India, between 1985 and 2015. The research utilised Landsat-4, Landsat-5, and Landsat-8 data to generate LULC maps, applying the maximum likelihood algorithm to highlight significant transitions over the years. This study revealed that built-up areas have increased by 67%, primarily at the expense of agricultural land, which was reduced by 51%. Temperature and rainfall data were obtained from APHRODITE Water Resources, and with a statistical analysis of the time series data revealed an annual average temperature increase of 1.8 °C and a minor but statistically significant rainfall increase during the study period. Disease data was obtained from multiple national health programmes, revealing an increasing trend in dengue and diarrhoeal diseases over the study period. In particular, dengue cases surged, correlating strongly with the increase in built-up areas and temperature. This research is instrumental for policy decisions in public health, urban planning, and climate change mitigation. Amidst limited research on the interconnections among infectious diseases, climate change, and LULC changes in India, our study serves as a significant precursor for future management strategies in Coimbatore and analogous regions.
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Affiliation(s)
- Sudha Suresh
- Graduate School of Environmental Science, Hokkaido University, Sapporo, 060-0810, Japan
| | - Gowhar Meraj
- Department of Ecosystem Studies, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Tokyo, 113-8654, Japan
| | - Pankaj Kumar
- Institute for Global Environmental Strategies, Hayama, 240-0115, Japan
| | - Deepak Singh
- Research Institute for Humanity and Nature (RIHN), 457-4 MotoyamaKita-Ku, KamigamoKyoto, 603-8047, Japan
| | - Inam Danish Khan
- Department of Clinical Microbiology, Army Base Hospital, Delhi Cantonment, New Delhi, 110010, India
| | - Ankita Gupta
- Graduate School of Environmental Science, Hokkaido University, Sapporo, 060-0810, Japan
| | - Tarun Kumar Yadav
- Centre of Environmental Science, University of Allahabad, Prayagraj, Uttar Pradesh, 211002, India
| | - Asma Kouser
- Department of Economics, Bengaluru City University, Bengaluru, Karnataka, 560001, India
- Faculty of Environmental Earth Science, Hokkaido University, Sapporo, 060-0810, Japan
| | - Ram Avtar
- Graduate School of Environmental Science, Hokkaido University, Sapporo, 060-0810, Japan.
- Faculty of Environmental Earth Science, Hokkaido University, Sapporo, 060-0810, Japan.
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12
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Zhu PP, Gao Y, Zhou GZ, Liu R, Li XB, Fu XX, Fu J, Lin F, Zhou YP, Li L. Short-term effects of high-resolution (1-km) ambient PM 2.5 and PM 10 on hospital admission for pulmonary tuberculosis: a case-crossover study in Hainan, China. Front Public Health 2023; 11:1252741. [PMID: 37736088 PMCID: PMC10509552 DOI: 10.3389/fpubh.2023.1252741] [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: 07/04/2023] [Accepted: 08/16/2023] [Indexed: 09/23/2023] Open
Abstract
Introduction There is limited evidence regarding particulate matter (PM)'s short-term effects on pulmonary tuberculosis (PTB) hospital admission. Our study aimed to determine the short-term associations of the exposure to ambient PM with aerodynamic diameters <2.5 μm (PM2.5) and < 10 μm (PM10) with hospital admission for PTB in Hainan, a tropical province in China. Methods We collected individual data on patients hospitalized with PTB, PM2.5, PM10, and meteorological data from 2016 to 2019 in Hainan Province, China. Conditional logistic regression models with a time-stratified case-crossover design were used to assess the short-term effects of PM2.5 and PM10 on hospital admission for PTB at a spatial resolution of 1 km × 1 km. Stratified analyses were performed according to age at admission, sex, marital status, administrative division, and season of admission. Results Each interquartile range (IQR) increases in the concentrations of PM2.5 and PM10 were associated with 1.155 (95% confidence interval [CI]: 1.041-1.282) and 1.142 (95% CI: 1.033-1.263) hospital admission risks for PTB at lag 0-8 days, respectively. The stratified analyses showed that the effects of PM2.5 and PM10 were statistically significant for patients aged ≥65 years, males, married, and those residing in prefecture-level cities. Regarding seasonal differences, the associations between PM and hospital admission for PTB were statistically significant in the warm season but not in the cold season. The effect of PM2.5 was consistently stronger than that of PM10 in most subgroups. Conclusion Short-term exposure to PM increases the risk of hospital admission for PTB. The potential impact of PM with smaller aerodynamic diameter is more detrimental. Our findings highlight the importance of reducing ambient PM level to alleviate the burden of PTB.
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Affiliation(s)
- Pan-Pan Zhu
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
| | - Yi Gao
- Department of Infectious Disease and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
- Department of Infectious Disease, Hainan General Hospital, Hainan Medical University, Haikou, Hainan, China
| | - Gui-Zhong Zhou
- Department of Infectious Disease, The Second Affiliated Hospital, Hainan Medical University, Haikou, Hainan, China
| | - Rui Liu
- Department of Infectious Disease, The Second Affiliated Hospital, Hainan Medical University, Haikou, Hainan, China
| | - Xiao-Bo Li
- Department of Neurosurgery, Haikou Municipal People’s Hospital and Central South University Xiangya Medical College Affiliated Hospital, Haikou, Hainan, China
| | - Xian-Xian Fu
- Clinical Lab, Haikou Municipal People’s Hospital and Central South University Xiangya Medical College Affiliated Hospital, Haikou, Hainan, China
| | - Jian Fu
- Department of Infectious Disease, Hainan General Hospital, Hainan Medical University, Haikou, Hainan, China
| | - Feng Lin
- Department of Infectious Disease, Hainan General Hospital, Hainan Medical University, Haikou, Hainan, China
| | - Yuan-Ping Zhou
- Department of Infectious Disease and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Li Li
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
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13
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Mahmud AS, Martinez PP, Baker RE. The impact of current and future climates on spatiotemporal dynamics of influenza in a tropical setting. PNAS NEXUS 2023; 2:pgad307. [PMID: 38741656 PMCID: PMC11089418 DOI: 10.1093/pnasnexus/pgad307] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 07/25/2023] [Accepted: 09/11/2023] [Indexed: 05/16/2024]
Abstract
Although the drivers of influenza have been well studied in high-income settings in temperate regions, many open questions remain about the burden, seasonality, and drivers of influenza dynamics in the tropics. In temperate climates, the inverse relationship between specific humidity and transmission can explain much of the observed temporal and spatial patterns of influenza outbreaks. Yet, this relationship fails to explain seasonality, or lack there-of, in tropical and subtropical countries. Here, we analyzed eight years of influenza surveillance data from 12 locations in Bangladesh to quantify the role of climate in driving disease dynamics in a tropical setting with a distinct rainy season. We find strong evidence for a nonlinear bimodal relationship between specific humidity and influenza transmission in Bangladesh, with highest transmission occurring for relatively low and high specific humidity values. We simulated influenza burden under current and future climate in Bangladesh using a mathematical model with a bimodal relationship between humidity and transmission, and decreased transmission at very high temperatures, while accounting for changes in population immunity. The climate-driven mechanistic model can accurately capture both the temporal and spatial variation in influenza activity observed across Bangladesh, highlighting the usefulness of mechanistic models for low-income countries with inadequate surveillance. By using climate model projections, we also highlight the potential impact of climate change on influenza dynamics in the tropics and the public health consequences.
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Affiliation(s)
- Ayesha S Mahmud
- Department of Demography, University of California, Berkeley, Berkeley, CA, USA
| | - Pamela P Martinez
- Department of Microbiology, University of Illinois Urbana-Champaign, Champaign, IL, USA
- Department of Statistics, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Rachel E Baker
- Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
- Institute at Brown for Environment and Society, Brown University, Providence, RI, USA
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14
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Zahid RA, Ali Q, Saleem A, Sági J. Impact of geographical, meteorological, demographic, and economic indicators on the trend of COVID-19: A global evidence from 202 affected countries. Heliyon 2023; 9:e19365. [PMID: 37810034 PMCID: PMC10558342 DOI: 10.1016/j.heliyon.2023.e19365] [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: 11/25/2022] [Revised: 07/30/2023] [Accepted: 08/21/2023] [Indexed: 10/10/2023] Open
Abstract
Research problem Public health and the economy face immense problems because of pathogens in history globally. The outbreak of novel SARS-CoV-2 emerged in the form of coronavirus (COVID-19), which affected global health and the economy in almost all countries of the world. Study design The objective of this research is to examine the trend of COVID-19, deaths, and transmission rates in 202 affected countries. The virus-affected countries were grouped according to their continent, meteorological indicators, demography, and income. This is quantitative research in which we have applied the Poisson regression method to assess how temperature, precipitation, population density, and income level impact COVID-19 cases and fatalities. This has been done by using a semi-parametric and additive polynomial model. Findings The trend analysis depicts that COVID-19 cases per million were comparatively higher for two groups of countries i.e., (a) average temperature below 7.5 °C and (b) average temperature between 7.5 °C and 15 °C, up to the 729th day of the outbreak. However, COVID-19 cases per million were comparatively low in the countries having an average temperature between 22.5 °C and 30 °C. The day-wise trend was comparatively higher for the countries having average precipitation between (a) 1 mm and 750 mm and (b) 750 mm and 1500 mm up to the 729th day of the outbreak. The day-wise trend was comparatively higher for the countries having more than 1000 people per sq. km. Discussing the COVID-19 cases per million, the day-wise trend was higher for the HICs, followed by UMICs, LMICs, and LIC. Conclusion The study highlights the need for targeted interventions and responses based on the specific circumstances and factors affecting each country, including their geographical location, temperature, precipitation levels, population density, and per capita income.
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Affiliation(s)
- R.M. Ammar Zahid
- School of Accounting, Yunnan Technology and Business University, Yunnan, PR China
| | - Qamar Ali
- Department of Economics, Virtual University of Pakistan, Faisalabad Campus 38000, Pakistan
| | - Adil Saleem
- Doctoral School of Economics and Regional Studies, Hungarian University of Agriculture and Life Sciences, H-2100 Gödöllő, Hungary
| | - Judit Sági
- Faculty of Finance and Accountancy, Budapest Business University — University of Applied Sciences, H-1149 Budapest, Hungary
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15
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Liang Y, Sun Z, Hua W, Li D, Han L, Liu J, Huo L, Zhang H, Zhang S, Zhao Y, He X. Spatiotemporal effects of meteorological conditions on global influenza peaks. ENVIRONMENTAL RESEARCH 2023; 231:116171. [PMID: 37230217 DOI: 10.1016/j.envres.2023.116171] [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: 03/27/2023] [Revised: 05/01/2023] [Accepted: 05/15/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND Numerous studies have suggested that meteorological conditions such as temperature and absolute humidity are highly indicative of influenza outbreaks. However, the explanatory power of meteorological factors on the seasonal influenza peaks varied widely between countries at different latitudes. OBJECTIVES We aimed to explore the modification effects of meteorological factors on the seasonal influenza peaks in multi-countries. METHODS Data on influenza positive rate (IPR) were collected across 57 countries and data on meteorological factors were collected from ECMWF Reanalysis v5 (ERA5). We used linear regression and generalized additive models to investigate the spatiotemporal associations between meteorological conditions and influenza peaks in cold and warm seasons. RESULTS Influenza peaks were significantly correlated with months with both lower and higher temperatures. In temperate countries, the average intensity of cold season peaks was stronger than that of warm season peaks. However, the average intensity of warm season peaks was stronfger than of cold season peaks in tropical countries. Temperature and specific humidity had synergistic effects on influenza peaks at different latitudes, stronger in temperate countries (cold season: R2=0.90; warm season: R2=0.84) and weaker in tropical countries (cold season: R2=0.64; warm season: R2=0.03). Furthermore, the effects could be divided into cold-dry and warm-humid modes. The temperature transition threshold between the two modes was 16.5-19.5 °C. During the transition from cold-dry mode to warm-humid mode, the average 2 m specific humidity increased by 2.15 times, illustrating that transporting a large amount of water vapor may compensate for the negative effect of rising temperatures on the spread of the influenza virus. CONCLUSION Differences in the global influenza peaks were related to the synergistic influence of temperature and specific humidity. The global influenza peaks could be divided into cold-dry and warm-humid modes, and specific thresholds of meteorological conditions were needed for the transition of the two modes.
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Affiliation(s)
- Yinglin Liang
- School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, 610225, China; State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing, 100081, China; Key Laboratory of Urban Meteorology, China Meteorological Administration, Beijing, 100089, China
| | - Zhaobin Sun
- State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing, 100081, China; Key Laboratory of Urban Meteorology, China Meteorological Administration, Beijing, 100089, China.
| | - Wei Hua
- School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, 610225, China.
| | - Demin Li
- National Clinical Research Center for Respiratory Diseases, China-Japan Friendship Hospital, 100192, China
| | - Ling Han
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Jian Liu
- Cardiology Department, Peking University People's Hospital, Beijing, 100044, China
| | - Liming Huo
- Cardiology Department, Peking University People's Hospital, Beijing, 100044, China
| | - Hongchun Zhang
- National Clinical Research Center for Respiratory Diseases, China-Japan Friendship Hospital, 100192, China
| | - Shuwen Zhang
- State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing, 100081, China
| | - Yuxin Zhao
- State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing, 100081, China
| | - Xiaonan He
- Emergency Critical Care Center, Beijing AnZhen Hospital, Capital Medical University, Beijing, 100029, China
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16
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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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17
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Lei H, Zhang N, Niu B, Wang X, Xiao S, Du X, Chen T, Yang L, Wang D, Cowling B, Li Y, Shu Y. Effect of Rapid Urbanization in Mainland China on the Seasonal Influenza Epidemic: Spatiotemporal Analysis of Surveillance Data From 2010 to 2017. JMIR Public Health Surveill 2023; 9:e41435. [PMID: 37418298 PMCID: PMC10362421 DOI: 10.2196/41435] [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: 10/08/2022] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND The world is undergoing an unprecedented wave of urbanization. However, the effect of rapid urbanization during the early or middle stages of urbanization on seasonal influenza transmission remains unknown. Since about 70% of the world population live in low-income countries, exploring the impact of urbanization on influenza transmission in urbanized countries is significant for global infection prediction and prevention. OBJECTIVE The aim of this study was to explore the effect of rapid urbanization on influenza transmission in China. METHODS We performed spatiotemporal analyses of province-level influenza surveillance data collected in Mainland China from April 1, 2010, to March 31, 2017. An agent-based model based on hourly human contact-related behaviors was built to simulate the influenza transmission dynamics and to explore the potential mechanism of the impact of urbanization on influenza transmission. RESULTS We observed persistent differences in the influenza epidemic attack rates among the provinces of Mainland China across the 7-year study period, and the attack rate in the winter waves exhibited a U-shaped relationship with the urbanization rates, with a turning point at 50%-60% urbanization across Mainland China. Rapid Chinese urbanization has led to increases in the urban population density and percentage of the workforce but decreases in household size and the percentage of student population. The net effect of increased influenza transmission in the community and workplaces but decreased transmission in households and schools yielded the observed U-shaped relationship. CONCLUSIONS Our results highlight the complicated effects of urbanization on the seasonal influenza epidemic in China. As the current urbanization rate in China is approximately 59%, further urbanization with no relevant interventions suggests a worrisome increasing future trend in the influenza epidemic attack rate.
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Affiliation(s)
- Hao Lei
- School of Public Health, Zhejiang University, Hangzhou, China
| | - Nan Zhang
- Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China
| | - Beidi Niu
- School of Public Health, Zhejiang University, Hangzhou, China
| | - Xiao Wang
- School of Public Health, Shenzhen Campus, Sun Yat-sen University, Shenzhen, China
| | - Shenglan Xiao
- School of Public Health, Shenzhen Campus, Sun Yat-sen University, Shenzhen, China
| | - Xiangjun Du
- School of Public Health, Shenzhen Campus, Sun Yat-sen University, Shenzhen, China
| | - Tao Chen
- Key Laboratory for Medical Virology, Chinese Center for Disease Control and Prevention, National Health Commission, Beijing, China
| | - Lei Yang
- Key Laboratory for Medical Virology, Chinese Center for Disease Control and Prevention, National Health Commission, Beijing, China
| | - Dayan Wang
- Key Laboratory for Medical Virology, Chinese Center for Disease Control and Prevention, National Health Commission, Beijing, China
| | - Benjamin Cowling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Yuguo Li
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Yuelong Shu
- School of Public Health, Shenzhen Campus, Sun Yat-sen University, Shenzhen, China
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18
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Luca M, Campedelli GM, Centellegher S, Tizzoni M, Lepri B. Crime, inequality and public health: a survey of emerging trends in urban data science. Front Big Data 2023; 6:1124526. [PMID: 37303974 PMCID: PMC10248183 DOI: 10.3389/fdata.2023.1124526] [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: 12/15/2022] [Accepted: 05/10/2023] [Indexed: 06/13/2023] Open
Abstract
Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.
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Affiliation(s)
- Massimiliano Luca
- Mobile and Social Computing Lab, Bruno Kessler Foundation, Trento, Italy
- Faculty of Computer Science, Free University of Bolzano, Bolzano, Italy
| | | | | | - Michele Tizzoni
- Department of Sociology and Social Research, University of Trento, Trento, Italy
| | - Bruno Lepri
- Mobile and Social Computing Lab, Bruno Kessler Foundation, Trento, Italy
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19
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Noppert GA, Clarke P, Hoover A, Kubale J, Melendez R, Duchowny K, Hegde ST. State Variation in Neighborhood COVID-19 Burden: Findings from the COVID Neighborhood Project. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.19.23290222. [PMID: 37293100 PMCID: PMC10246150 DOI: 10.1101/2023.05.19.23290222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 burden has been distributed across neighborhoods, a known geographic unit of both risk and resilience, and is hampering efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities. Using spatially-referenced data from 21 states at the ZIP code or census tract level, we documented how the distribution of COVID-19 at the neighborhood-level varies significantly within and between states. The median case count per neighborhood (IQR) in Oregon was 3,608 (2,487) per 100,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (IQR) was 8,142 (11,031) per 100,000. We also found that the association between features of the neighborhood social environment and burden varied in magnitude and direction by state. Our findings underscore the importance of local contexts when addressing the long-term social and economic fallout communities will face from COVID-19.
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Affiliation(s)
| | | | - Andrew Hoover
- Institute for Social Research, University of Michigan
| | - John Kubale
- Institute for Social Research, University of Michigan
| | | | - Kate Duchowny
- Institute for Social Research, University of Michigan
| | - Sonia T Hegde
- Department of Epidemiology, Johns Hopkins University
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Mantilla Caicedo GC, Rusticucci M, Suli S, Dankiewicz V, Ayala S, Caiman Peñarete A, Díaz M, Fontán S, Chesini F, Jiménez-Buitrago D, Barreto Pedraza LR, Barrera F. Spatio-temporal multidisciplinary analysis of socio-environmental conditions to explore the COVID-19 early evolution in urban sites in South America. Heliyon 2023; 9:e16056. [PMID: 37200576 PMCID: PMC10162854 DOI: 10.1016/j.heliyon.2023.e16056] [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: 12/01/2021] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 05/20/2023] Open
Abstract
This study aimed to analyse how socio-environmental conditions affected the early evolution of COVID-19 in 14 urban sites in South America based on a spatio-temporal multidisciplinary approach. The daily incidence rate of new COVID-19 cases with symptoms as the dependent variable and meteorological-climatic data (mean, maximum, and minimum temperature, precipitation, and relative humidity) as the independent variables were analysed. The study period was from March to November of 2020. We inquired associations of these variables with COVID-19 data using Spearman's non-parametric correlation test, and a principal component analysis considering socio economic and demographic variables, new cases, and rates of COVID-19 new cases. Finally, an analysis using non-metric multidimensional scale ordering by the Bray-Curtis similarity matrix of meteorological data, socio economic and demographic variables, and COVID-19 was performed. Our findings revealed that the average, maximum, and minimum temperatures and relative humidity were significantly associated with rates of COVID-19 new cases in most of the sites, while precipitation was significantly associated only in four sites. Additionally, demographic variables such as the number of inhabitants, the percentage of the population aged 60 years and above, the masculinity index, and the GINI index showed a significant correlation with COVID-19 cases. Due to the rapid evolution of the COVID-19 pandemic, these findings provide strong evidence that biomedical, social, and physical sciences should join forces in truly multidisciplinary research that is critically needed in the current state of our region.
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Affiliation(s)
| | - Matilde Rusticucci
- Universidad de Buenos Aires, Departamento de Ciencias de la Atmósfera y los Océanos, CONICET, Argentina
| | - Solange Suli
- Universidad de Buenos Aires, Departamento de Ciencias de la Atmósfera y los Océanos, CONICET, Argentina
| | - Verónica Dankiewicz
- Universidad de Buenos Aires, Departamento de Ciencias de la Atmósfera y los Océanos, CONICET, Argentina
| | - Salvador Ayala
- Universidad de Chile, Programa de Doctorado en Salud Pública, Instituto de Salud Pública de Chile, Chile
| | - Alexandra Caiman Peñarete
- Subred Integrada de Servicios Hospitalarios Centro Oriente ESE, Red Hospitalaria Bogotá Distrito Capital, Colombia
| | - Martín Díaz
- Universidad Nacional de La Matanza, Departamento de Ciencias de la Salud, Argentina
| | - Silvia Fontán
- Universidad Nacional de La Matanza, Departamento de Ciencias de la Salud, Argentina
| | | | - Diana Jiménez-Buitrago
- Ministerio de Salud y Protección Social, Mesa de Variabilidad y Cambio Climático de la CONASA, Colombia
| | - Luis R. Barreto Pedraza
- Instituto de Hidrología, Meteorología y Estudios Ambientales - IDEAM, Subdirección de Meteorología, Mesa de Variabilidad y Cambio Climático de la CONASA, Miembro del grupo QuASAR UPN, Colombia
| | - Facundo Barrera
- Centro Austral de Investigaciones Científicas (CADIC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ushuaia, Argentina
- Centro i∼mar, Universidad de Los Lagos, Chile and Centre for Climate and Resilience Research (CR)2, Casilla 557, Puerto Montt Chile
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21
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Loo BPY, Tsoi KH, Axhausen KW, Cao M, Lee Y, Koh KP. Spatial risk for a superspreading environment: Insights from six urban facilities in six global cities across four continents. Front Public Health 2023; 11:1128889. [PMID: 37089495 PMCID: PMC10113652 DOI: 10.3389/fpubh.2023.1128889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/20/2023] [Indexed: 04/08/2023] Open
Abstract
IntroductionThis study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment.MethodsFocusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich. Then, considering facility agglomeration, visitors' profile and the density of the population, facilities are classified into four potential spatial risk (PSR) classes. Finally, a kernel density function is employed to derive the risk surface in each city based on the spatial risk class and nature of activities.ResultsResults of the human mobility analysis reflect the geographical and cultural context of various facilities, transport characteristics and people's lifestyle across cities. Consistent across the six global cities, geographical agglomeration is a risk factor for bars. For other urban facilities, the lack of agglomeration is a risk factor. Based on the spatial risk maps, some high-risk areas of superspreading are identified and discussed in each city.DiscussionIntegrating activity-travel patterns in risk models can help identify areas that attract highly mobile visitors and are conducive to superspreading. Based on the findings, this study proposes a place-based strategy of non-pharmaceutical interventions that balance the control of the pandemic and the daily life of the urban population.
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Affiliation(s)
- Becky P. Y. Loo
- Department of Geography, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- School of Geography and Environment, Jiangxi Normal University, Nanchang, China
| | - Ka Ho Tsoi
- Department of Geography, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- *Correspondence: Ka Ho Tsoi
| | - Kay W. Axhausen
- Department of Civil, Environment and Geomatic Engineering, ETH Zürich, Zürich, Switzerland
| | - Mengqiu Cao
- School of Architecture and Cities, University of Westminster, London, United Kingdom
| | - Yongsung Lee
- Department of Geography, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Keumseok Peter Koh
- Department of Geography, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
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22
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Susswein Z, Rest EC, Bansal S. Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data. eLife 2023; 12:e80466. [PMID: 37014055 PMCID: PMC10118388 DOI: 10.7554/elife.80466] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Background Since the outset of the COVID-19 pandemic, substantial public attention has focused on the role of seasonality in impacting transmission. Misconceptions have relied on seasonal mediation of respiratory diseases driven solely by environmental variables. However, seasonality is expected to be driven by host social behavior, particularly in highly susceptible populations. A key gap in understanding the role of social behavior in respiratory disease seasonality is our incomplete understanding of the seasonality of indoor human activity. Methods We leverage a novel data stream on human mobility to characterize activity in indoor versus outdoor environments in the United States. We use an observational mobile app-based location dataset encompassing over 5 million locations nationally. We classify locations as primarily indoor (e.g. stores, offices) or outdoor (e.g. playgrounds, farmers markets), disentangling location-specific visits into indoor and outdoor, to arrive at a fine-scale measure of indoor to outdoor human activity across time and space. Results We find the proportion of indoor to outdoor activity during a baseline year is seasonal, peaking in winter months. The measure displays a latitudinal gradient with stronger seasonality at northern latitudes and an additional summer peak in southern latitudes. We statistically fit this baseline indoor-outdoor activity measure to inform the incorporation of this complex empirical pattern into infectious disease dynamic models. However, we find that the disruption of the COVID-19 pandemic caused these patterns to shift significantly from baseline and the empirical patterns are necessary to predict spatiotemporal heterogeneity in disease dynamics. Conclusions Our work empirically characterizes, for the first time, the seasonality of human social behavior at a large scale with a high spatiotemporal resolutio and provides a parsimonious parameterization of seasonal behavior that can be included in infectious disease dynamics models. We provide critical evidence and methods necessary to inform the public health of seasonal and pandemic respiratory pathogens and improve our understanding of the relationship between the physical environment and infection risk in the context of global change. Funding Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM123007.
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Affiliation(s)
- Zachary Susswein
- Department of Biology, Georgetown UniversityWashington, DCUnited States
| | - Eva C Rest
- Department of Biology, Georgetown UniversityWashington, DCUnited States
| | - Shweta Bansal
- Department of Biology, Georgetown UniversityWashington, DCUnited States
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23
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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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24
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Altuntas G, Cetin M, Canakci ME, Yazıcı MM. The Effect of Meteorological Factors on the COVID-19 Pandemic in Northeast Turkiye. Cureus 2023; 15:e36934. [PMID: 37131559 PMCID: PMC10148944 DOI: 10.7759/cureus.36934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
Introduction Although various studies have been conducted on the relationship between meteorological factors and coronavirus disease 2019 (COVID-19), this issue has not been sufficiently clarified. In particular, there are a limited number of studies on the course of COVID-19 in the warmer-humidity seasons. Methods Patients presenting to the emergency departments of health institutions and to clinics set aside for cases of suspected COVID-19 in the province of Rize between 1 June and 31 August 2021 and who met the case definition based on the Turkish COVID-19 epidemiological guideline were included in this retrospective study. The effect of meteorological factors on case numbers throughout the study was investigated. Results During the study period, 80,490 tests were performed on patients presenting to emergency departments and clinics dedicated to patients with suspected COVID-19. The total case number was 16,270, with a median daily number of 64 (range 43-328). The total number of deaths was 103, with a median daily figure of 1.00 (range 0.00-1.25). According to the Poisson distribution analysis, it is found that the number of cases tended to increase at temperatures between 20.8 and 27.2°C. Conclusion It is predicted that the number of COVID-19 cases will not decrease with the increase in temperature in temperate regions with high rainfall. Therefore, unlike influenza, there may not be seasonal variation in the prevalence of COVID-19. The requisite measures should be adopted in health systems and hospitals to manage increases in case numbers associated with changes in meteorological factors.
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25
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Joshua BW, Fuwape I, Rabiu B, Pires EES, Sa'id RS, Ogunro TT, Awe OF, Osunmakinwa OO, Ogunjo S. The Impact of the First and Second Waves of COVID-19 Pandemic in Nigeria. GEOHEALTH 2023; 7:e2022GH000722. [PMID: 36968154 PMCID: PMC10030272 DOI: 10.1029/2022gh000722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 06/18/2023]
Abstract
In recent times, the COVID-19 pandemic has been the subject of global concern. It has so far claimed over 5.4 million lives globally, with over 291 million cases recorded worldwide as of 5 January 2022. It is known to have different waves and variants, thus making it difficult to handle/manage. This study investigates the impact of the first and second waves of COVID-19 in Nigeria, West Africa. The data used is for the 36 states of Nigeria archived at the National Centre for Disease Control from February 2020 to April 2021. Results from the study reveal that the highest number of COVID-19 cases during the first/second wave was recorded at Lagos (23,238/34,616), followed by the Federal Capital Territory (FCT) (6,770/12,911) and alternates between Plateau (3,858/5,170) and Kaduna (3,064/5,908). Similarly, the highest number of deaths (during the first/second wave) was also recorded in Lagos (220/219), followed by Edo (112/73), and then FCT (83/81). The Case Fatality Ratio (CFR) was observed to be higher mostly in northern Nigeria during the first wave and the southeast during the second wave of the pandemic. On the average, the number of cases/deaths recorded during the second wave was higher than those of the first wave, but a decrease in the CFR values was observed during the second wave. Higher values of COVID-19 cases/death were mostly recorded in Nigeria during; maximum relative humidity (RH) (>70%) with minimum Temperatures (<25°C), Low temperatures, and low RH which is mostly observed during the cold/dusty periods.
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Affiliation(s)
- Benjamin Wisdom Joshua
- Department of PhysicsKebbi State University of Science and Technology AlieroKauraNigeria
- Physics UnitDepartment of Physical and Natural SciencesUniversity of the GambiaSerrekundaNigeria
| | - Ibiyinka Fuwape
- Department of PhysicsMichael and Cecilia Ibru UniversityEriem FieldsNigeria
- Department of PhysicsFederal University of Technology AkureGagaNigeria
| | - Babatunde Rabiu
- African Regional Centre for Space Science and Technology Education ‐ EnglishIle‐IfeNigeria
- Atmospheric & Space Weather Research LaboratoryARCSSTE‐ENASRDAOsun State UniversityOsogboNigeria
| | - Evanilton E. S. Pires
- Centro de Estudos e Pesquisa do TundavalaEngineering DepartmentISPTundavalaLubangoAngola
| | | | | | - Oluwayomi Funmilola Awe
- Atmospheric & Space Weather Research LaboratoryARCSSTE‐ENASRDAOsun State UniversityOsogboNigeria
| | | | - Samuel Ogunjo
- Department of PhysicsFederal University of Technology AkureGagaNigeria
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26
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Han S, Zhang T, Lyu Y, Lai S, Dai P, Zheng J, Yang W, Zhou XH, Feng L. Influenza's Plummeting During the COVID-19 Pandemic: The Roles of Mask-Wearing, Mobility Change, and SARS-CoV-2 Interference. ENGINEERING (BEIJING, CHINA) 2023. [PMID: 35127196 DOI: 10.1016/j.eng.2022.05.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Seasonal influenza activity typically peaks in the winter months but plummeted globally during the current coronavirus disease 2019 (COVID-19) pandemic. Unraveling lessons from influenza's unprecedented low profile is critical in informing preparedness for incoming influenza seasons. Here, we explored a country-specific inference model to estimate the effects of mask-wearing, mobility changes (international and domestic), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interference in China, England, and the United States. We found that a one-week increase in mask-wearing intervention had a percent reduction of 11.3%-35.2% in influenza activity in these areas. The one-week mobility mitigation had smaller effects for the international (1.7%-6.5%) and the domestic community (1.6%-2.8%). In 2020-2021, the mask-wearing intervention alone could decline percent positivity by 13.3-19.8. The mobility change alone could reduce percent positivity by 5.2-14.0, of which 79.8%-98.2% were attributed to the deflected international travel. Only in 2019-2020, SARS-CoV-2 interference had statistically significant effects. There was a reduction in percent positivity of 7.6 (2.4-14.4) and 10.2 (7.2-13.6) in northern China and England, respectively. Our results have implications for understanding how influenza evolves under non-pharmaceutical interventions and other respiratory diseases and will inform health policy and the design of tailored public health measures.
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Affiliation(s)
- Shasha Han
- Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China
- Harvard Medical School, Harvard University, Boston, MA 02115, USA
| | - Ting Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Yan Lyu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Peixi Dai
- Division for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Jiandong Zheng
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100871, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Xiao-Hua Zhou
- Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100871, China
- National Engineering Laboratory of Big Data Analysis and Applied Technology, Peking University, Beijing 100871, China
| | - Luzhao Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
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27
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Han S, Zhang T, Lyu Y, Lai S, Dai P, Zheng J, Yang W, Zhou XH, Feng L. Influenza's Plummeting During the COVID-19 Pandemic: The Roles of Mask-Wearing, Mobility Change, and SARS-CoV-2 Interference. ENGINEERING (BEIJING, CHINA) 2023; 21:195-202. [PMID: 35127196 PMCID: PMC8808434 DOI: 10.1016/j.eng.2021.12.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/13/2021] [Accepted: 12/26/2021] [Indexed: 05/09/2023]
Abstract
Seasonal influenza activity typically peaks in the winter months but plummeted globally during the current coronavirus disease 2019 (COVID-19) pandemic. Unraveling lessons from influenza's unprecedented low profile is critical in informing preparedness for incoming influenza seasons. Here, we explored a country-specific inference model to estimate the effects of mask-wearing, mobility changes (international and domestic), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interference in China, England, and the United States. We found that a one-week increase in mask-wearing intervention had a percent reduction of 11.3%-35.2% in influenza activity in these areas. The one-week mobility mitigation had smaller effects for the international (1.7%-6.5%) and the domestic community (1.6%-2.8%). In 2020-2021, the mask-wearing intervention alone could decline percent positivity by 13.3-19.8. The mobility change alone could reduce percent positivity by 5.2-14.0, of which 79.8%-98.2% were attributed to the deflected international travel. Only in 2019-2020, SARS-CoV-2 interference had statistically significant effects. There was a reduction in percent positivity of 7.6 (2.4-14.4) and 10.2 (7.2-13.6) in northern China and England, respectively. Our results have implications for understanding how influenza evolves under non-pharmaceutical interventions and other respiratory diseases and will inform health policy and the design of tailored public health measures.
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Affiliation(s)
- Shasha Han
- Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China
- Harvard Medical School, Harvard University, Boston, MA 02115, USA
| | - Ting Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Yan Lyu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Peixi Dai
- Division for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Jiandong Zheng
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100871, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Xiao-Hua Zhou
- Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100871, China
- National Engineering Laboratory of Big Data Analysis and Applied Technology, Peking University, Beijing 100871, China
| | - Luzhao Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
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28
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Gu Y, Hsu ACY, Zuo X, Guo X, Zhou Z, Jiang S, Ouyang Z, Wang F. Chronic exposure to low-level lipopolysaccharide dampens influenza-mediated inflammatory response via A20 and PPAR network. Front Immunol 2023; 14:1119473. [PMID: 36726689 PMCID: PMC9886269 DOI: 10.3389/fimmu.2023.1119473] [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: 12/08/2022] [Accepted: 01/02/2023] [Indexed: 01/18/2023] Open
Abstract
Influenza A virus (IAV) infection leads to severe inflammation, and while epithelial-driven inflammatory responses occur via activation of NF-κB, the factors that modulate inflammation, particularly the negative regulators are less well-defined. In this study we show that A20 is a crucial molecular switch that dampens IAV-induced inflammatory responses. Chronic exposure to low-dose LPS environment can restrict this excessive inflammation. The mechanisms that this environment provides to suppress inflammation remain elusive. Here, our evidences show that chronic exposure to low-dose LPS suppressed IAV infection or LPS stimulation-induced inflammation in vitro and in vivo. Chronic low-dose LPS environment increases A20 expression, which in turn positively regulates PPAR-α and -γ, thus dampens the NF-κB signaling pathway and NLRP3 inflammasome activation. Knockout of A20 abolished the inhibitory effect on inflammation. Thus, A20 and its induced PPAR-α and -γ play a key role in suppressing excessive inflammatory responses in the chronic low-dose LPS environment.
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Affiliation(s)
- Yinuo Gu
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Alan Chen-Yu Hsu
- Signature Research Program in Emerging Infectious Diseases, Duke - National University of Singapore (NUS) Graduate Medical School, Singapore, Singapore,School of Medicine and Public Health, The University of Newcastle, Newcastle, NSW, Australia,Viruses, Infections/Immunity, Vaccines and Asthma, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Xu Zuo
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Xiaoping Guo
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Zhengjie Zhou
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Shengyu Jiang
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Zhuoer Ouyang
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Fang Wang
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun, China,*Correspondence: Fang Wang,
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Chakrabortty R, Pal SC, Ghosh M, Arabameri A, Saha A, Roy P, Pradhan B, Mondal A, Ngo PTT, Chowdhuri I, Yunus AP, Sahana M, Malik S, Das B. Weather indicators and improving air quality in association with COVID-19 pandemic in India. Soft comput 2023; 27:3367-3388. [PMID: 34276248 PMCID: PMC8276232 DOI: 10.1007/s00500-021-06012-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2021] [Indexed: 12/13/2022]
Abstract
The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad. Supplementary Information The online version contains supplementary material available at 10.1007/s00500-021-06012-9.
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Affiliation(s)
- Rabin Chakrabortty
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
| | - Subodh Chandra Pal
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
| | - Manoranjan Ghosh
- Centre for Rural Development and Sustainable Innovative Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal India
| | - Alireza Arabameri
- Department of Geomorphology, Tarbiat Modares University, 14117-13116 Tehran, Iran
| | - Asish Saha
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
| | - Paramita Roy
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007 Australia ,Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjin-gu, Seoul, 05006 Korea ,Center of Excellence for Climate Change Research, King Abdulaziz University, P.O. Box 80234, Jeddah, 21589 Saudi Arabia ,Earth Observation Center, Institute of Climate Change, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Malaysia
| | - Ayan Mondal
- Ecology and Environmental Modelling Laboratory, Department of Environmental Science, The University of Burdwan, Burdwan, West Bengal India
| | - Phuong Thao Thi Ngo
- Institute of Research and Development, Duy Tan University, Da Nang, 550000 Vietnam
| | - Indrajit Chowdhuri
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
| | - Ali P. Yunus
- Centre for Climate Change Adaptation, National Institute for Environmental Studies, Ibaraki, 305-8506 Japan
| | - Mehebub Sahana
- School of Environment, Education and Development, University of Manchester, Oxford Road, Manchester, M13 9PL UK
| | - Sadhan Malik
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
| | - Biswajit Das
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
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Zhang ZS, Xi L, Yang LL, Lian XY, Du J, Cui Y, Li HJ, Zhang WX, Wang C, Liu B, Yang YN, Cui F, Lu QB. Impact of air pollutants on influenza-like illness outpatient visits under urbanization process in the sub-center of Beijing, China. Int J Hyg Environ Health 2023; 247:114076. [PMID: 36427387 DOI: 10.1016/j.ijheh.2022.114076] [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: 07/26/2022] [Revised: 11/01/2022] [Accepted: 11/15/2022] [Indexed: 11/23/2022]
Abstract
Air pollutants can cause serious harm to human health and a variety of respiratory diseases. This study aimed to explore the associations between air pollutants and outpatient visits for influenza-like illness (ILI) under urbanization process in the sub-center of Beijing. The data of ILI in sub-center of Beijing from April 1, 2014 to December 31, 2020 were obtained from Beijing Influenza Surveillance Network. A generalized additive Poisson model was applied to examine the associations between the concentrations of air pollutants and daily outpatient visits for ILI when controlling meteorological factors and holidays. A total of 322,559 patients with ILI were included. The results showed that in the early urbanization period, the effects of PM2.5, PM10, SO2, O3, and CO on lag0 day, and PM2.5, PM10, O3, and CO on lag1 day were not significant. In the later urbanization period, AQI and the concentrations of PM2.5, PM10, SO2, NO2 and CO on lag1 day were all significantly associated with an increased risk of outpatient visits for ILI, which increased by 0.34% (95%CI 0.23%, 0.45%), 0.42% (95%CI 0.29%, 0.56%), 0.44% (95%CI 0.33%, 0.55%), 0.36% (95%CI 0.24%, 0.49%), 0.91% (95%CI 0.62%, 1.21%) and 0.38% (95%CI 0.26%, 0.49%). The concentration of O3 on lag1 day was significantly associated with a decreased risk of outpatient visits for ILI, which decreased by 0.21% (95%CI 0.04%, 0.39%). We found that the urbanization process had significantly aggravated the impact of air pollutants on ILI outpatient visits. These findings expand the current knowledge of ILI outpatient visits correlated with air pollutants under urbanization process.
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Affiliation(s)
- Zhong-Song Zhang
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, 100191, PR China
| | - Lu Xi
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, 101100, PR China
| | - Li-Li Yang
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, 101100, PR China
| | - Xin-Yao Lian
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, 100191, PR China
| | - Juan Du
- Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing, 100191, PR China
| | - Yan Cui
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, 101100, PR China
| | - Hong-Jun Li
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, 101100, PR China
| | - Wan-Xue Zhang
- Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing, 100191, PR China
| | - Chao Wang
- Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing, 100191, PR China
| | - Bei Liu
- Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing, 100191, PR China
| | - Yan-Na Yang
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, 101100, PR China
| | - Fuqiang Cui
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, 100191, PR China; Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing, 100191, PR China.
| | - Qing-Bin Lu
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, 100191, PR China; Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing, 100191, PR China.
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Zhang B, Huang W, Pei S, Zeng J, Shen W, Wang D, Wang G, Chen T, Yang L, Cheng P, Wang D, Shu Y, Du X. Mechanisms for the circulation of influenza A(H3N2) in China: A spatiotemporal modelling study. PLoS Pathog 2022; 18:e1011046. [PMID: 36525468 PMCID: PMC9803318 DOI: 10.1371/journal.ppat.1011046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 12/30/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022] Open
Abstract
Circulation of seasonal influenza is the product of complex interplay among multiple drivers, yet characterizing the underlying mechanism remains challenging. Leveraging the diverse seasonality of A(H3N2) virus and abundant climatic space across regions in China, we quantitatively investigated the relative importance of population susceptibility, climatic factors, and antigenic change on the dynamics of influenza A(H3N2) through an integrative modelling framework. Specifically, an absolute humidity driven multiscale transmission model was constructed for the 2013/2014, 2014/2015 and 2016/2017 influenza seasons that were dominated by influenza A(H3N2). We revealed the variable impact of absolute humidity on influenza transmission and differences in the occurring timing and magnitude of antigenic change for those three seasons. Overall, the initial population susceptibility, climatic factors, and antigenic change explained nearly 55% of variations in the dynamics of influenza A(H3N2). Specifically, the additional variation explained by the initial population susceptibility, climatic factors, and antigenic change were at 33%, 26%, and 48%, respectively. The vaccination program alone failed to fully eliminate the summer epidemics of influenza A(H3N2) and non-pharmacological interventions were needed to suppress the summer circulation. The quantitative understanding of the interplay among driving factors on the circulation of influenza A(H3N2) highlights the importance of simultaneous monitoring of fluctuations for related factors, which is crucial for precise and targeted prevention and control of seasonal influenza.
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Affiliation(s)
- Bing Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
- Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, People’s Republic of China
| | - Weijuan Huang
- National Institute for Viral Disease Control and Prevention, Collaboration Innovation Center for Diagnosis and Treatment of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, People’s Republic of China
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, United States of America
| | - Jinfeng Zeng
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Wei Shen
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
- Department of Rheumatology and Immunology, Drum Tower Clinic Medical College of Nanjing Medical University, Nanjing, People’s Republic of China
| | - Daoze Wang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Gang Wang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Tao Chen
- National Institute for Viral Disease Control and Prevention, Collaboration Innovation Center for Diagnosis and Treatment of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, People’s Republic of China
| | - Lei Yang
- National Institute for Viral Disease Control and Prevention, Collaboration Innovation Center for Diagnosis and Treatment of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, People’s Republic of China
| | - Peiwen Cheng
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Dayan Wang
- National Institute for Viral Disease Control and Prevention, Collaboration Innovation Center for Diagnosis and Treatment of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, People’s Republic of China
- * E-mail: (DW); (YS); (XD)
| | - Yuelong Shu
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
- Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, People’s Republic of China
- Institute of Pathogen Biology of Chinese Academy of Medical Science (CAMS)/ Peking Union Medical College (PUMC), Beijing, People’s Republic of China
- * E-mail: (DW); (YS); (XD)
| | - Xiangjun Du
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
- Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, People’s Republic of China
- * E-mail: (DW); (YS); (XD)
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Long-term benefits of nonpharmaceutical interventions for endemic infections are shaped by respiratory pathogen dynamics. Proc Natl Acad Sci U S A 2022; 119:e2208895119. [PMID: 36445971 PMCID: PMC9894244 DOI: 10.1073/pnas.2208895119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
COVID-19 nonpharmaceutical interventions (NPIs), including mask wearing, have proved highly effective at reducing the transmission of endemic infections. A key public health question is whether NPIs could continue to be implemented long term to reduce the ongoing burden from endemic pathogens. Here, we use epidemiological models to explore the impact of long-term NPIs on the dynamics of endemic infections. We find that the introduction of NPIs leads to a strong initial reduction in incidence, but this effect is transient: As susceptibility increases, epidemics return while NPIs are in place. For low R0 infections, these return epidemics are of reduced equilibrium incidence and epidemic peak size. For high R0 infections, return epidemics are of similar magnitude to pre-NPI outbreaks. Our results underline that managing ongoing susceptible buildup, e.g., with vaccination, remains an important long-term goal.
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Naudé W, Nagler P. COVID-19 and the city: Did urbanized countries suffer more fatalities? CITIES (LONDON, ENGLAND) 2022; 131:103909. [PMID: 35966968 PMCID: PMC9359513 DOI: 10.1016/j.cities.2022.103909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 04/22/2022] [Accepted: 08/02/2022] [Indexed: 06/15/2023]
Abstract
In this paper we derive a theoretical model of the spread of a viral infection which we use as basis for an estimation strategy to test four interrelated hypotheses on the relationship between country-level COVID-19 mortality rates and the extent of urban development. Using data covering 81 countries we find evidence that countries with a higher population density, a higher share of the urban population living in the largest city, and countries with a higher urbanization rate had on average the same or fewer COVID-19 fatalities compared to less urbanized countries in 2020. Even though COVID-19 spreads faster in cities, fatalities may be lower, conditional on economic development, trust in government, and a well-functioning health care system. Generally, urbanization and city development are associated with economic development: with the resources urbanized countries have, it is easier for them to manage and maintain stricter lockdowns, and to roll out effective pharmaceutical interventions.
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Affiliation(s)
- Wim Naudé
- Department of Economics, University College Cork, Ireland
- RWTH Aachen University, Germany
| | - Paula Nagler
- Institute for Housing and Urban Development Studies, Erasmus University Rotterdam, the Netherlands
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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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Tunnicliffe L, Warren‐Gash C. Investigating the effects of population density of residence and rural/urban classification on rate of influenza-like illness symptoms in England and Wales. Influenza Other Respir Viruses 2022; 16:1183-1190. [PMID: 35922884 PMCID: PMC9530544 DOI: 10.1111/irv.13032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/15/2022] [Accepted: 07/21/2022] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND Better understanding of risk factors for influenza could help improve seasonal and pandemic planning. There is a dearth of literature on area-level risk factors such as population density and rural/urban living. METHODS We used data from Flusurvey, an online community-based cohort that records influenza events. The study outcome was symptoms of influenza-like illness (ILI). Multivariable Poisson regression analysis was used to explore associations of both population density and rural/urban status with rate of ILI symptoms and whether these effects differed by vaccination status. RESULTS Of the 6177 study participants, the median age was 45 (IQR 32-57), 65.73% were female, and 66% reported at least one episode of ILI symptoms between 2011 and 2016. We found no evidence to suggest that the rate of ILI symptoms was higher in the medium [RR 1.02 (95% CI 0.95-1.09)] or high [RR 1.02 (95% CI 0.96-1.09)] population density group versus the low population density group. This was the same for the effect of urban living [RR 0.96 (95% CI 0.90-1.03)] versus rural living on symptom rate. There was weak evidence to suggest that the ILI symptom rate was lower in urban areas compared with rural areas among unvaccinated individuals only [RR 0.90 (95% CI 0.83-0.99)], whereas no difference was seen among vaccinated individuals [1.04 (95% CI 0.94-1.16)]. CONCLUSIONS Although neither population density nor rural/urban status was associated with ILI symptom rate in this community cohort, future research that incorporates activity and contact patterns will help to elucidate this relationship further.
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Affiliation(s)
- Louis Tunnicliffe
- Department of Non‐communicable Disease Epidemiology, Faculty of Epidemiology and Population HealthLondon School of Hygiene and Tropical MedicineLondonUK
| | - Charlotte Warren‐Gash
- Department of Non‐communicable Disease Epidemiology, Faculty of Epidemiology and Population HealthLondon School of Hygiene and Tropical MedicineLondonUK
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Karim R, Akter N. Effects of climate variables on the COVID-19 mortality in Bangladesh. THEORETICAL AND APPLIED CLIMATOLOGY 2022; 150:1463-1475. [PMID: 36276261 PMCID: PMC9579573 DOI: 10.1007/s00704-022-04211-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/12/2022] [Indexed: 06/16/2023]
Abstract
Infectious diseases such as severe acute respiratory syndrome (SARS) and influenza are influenced by weather conditions. Climate variables, for example, temperature and humidity, are two important factors in the severity of COVID-19's impact on the human respiratory system. This study aims to examine the effects of these climate variables on COVID-19 mortality. The data are collected from March 08, 2020, to April 30, 2022. The parametric regression under GAM and semiparametric regression under GAMLSS frameworks are used to analyze the daily number of death due to COVID-19. Our findings revealed that temperature and relative humidity are commencing to daily deaths due to COVID-19. A positive association with COVID-19 daily death counts was observed for temperature range and a positive association for humidity. In addition, one-unit increase in daily temperature range was only associated with a 1.08% (95% CI: 1.06%, 1.10%), and humidity range was only associated with a 1.03% (95% CI: 1.02%, 1.03%) decrease in COVID-19 deaths. A flexible regression model within the framework of Generalized Additive Models for Location Scale and Shape is used to analyze the data by adjusting the time effect. We used two adaptable predictor models, such as (i) the Fractional polynomial model and (ii) the B-spline smoothing model, to estimate the systematic component of the GAMLSS model. According to both models, high humidity and temperature significantly (and drastically) lessened the severity of COVID-19 death. The findings on the epidemiological trends of the COVID-19 pandemic and weather changes may interest policymakers and health officials.
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Affiliation(s)
- Rezaul Karim
- Department of Statistics, Jahangirnagar University, Savar Union, Bangladesh
| | - Nazmin Akter
- Department of Statistics, Jahangirnagar University, Savar Union, Bangladesh
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Influence of weather factors on the incidence of COVID-19 in Spain. MEDICINA CLÍNICA (ENGLISH EDITION) 2022; 159:255-261. [PMID: 36060101 PMCID: PMC9425111 DOI: 10.1016/j.medcle.2021.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 10/28/2021] [Indexed: 11/22/2022]
Abstract
Introduction Several studies have analyzed the influence of meteorological and geographical factors on the incidence of COVID-19. Seasonality could be important in the transmission of SARS-CoV-2. This study aims to evaluate the geographical pattern of COVID-19 in Spain and its relationship with different meteorological variables. Methods A provincial ecological study analyzing the influence of meteorological and geographical factors on the cumulative incidence of COVID-19 in the 52 (24 coastal and 28 inland) Spanish provinces during the first three waves was carried out. The cumulative incidence was calculated with data from the National Statistical Institute (INE) and the National Epidemiological Surveillance Network (RENAVE), while the meteorological variables were obtained from the Spanish Meteorological Agency (AEMET). Results The total cumulative incidence, in all three waves, was lower in the coastal provinces than in the inland ones (566 ± 181 vs. 782 ± 154; P = 2.5 × 10−5). The cumulative incidence correlated negatively with mean air temperature (r = −0.49; P = 2.2 × 10−4) and rainfall (r = −0.33; P = .01), and positively with altitude (r = 0.56; P = 1.4 × 10−5). The Spanish provinces with an average temperature <10 °C had almost twice the cumulative incidence than the provinces with temperatures >16 °C. The mean air temperature and rainfall were associated with the cumulative incidence of COVID-19, regardless of other factors (Beta Coefficient of −0.62; P = 3.7 × 10−7 and −0.47; P = 4.2 × 10−5 respectively) Conclusions Meteorological and geographical factors could influence the evolution of the pandemic in Spain. Knowledge regarding the seasonality of the virus would help to predict new waves of COVID-19 infections
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Iqbal N, Rafiq M, Shah M, Tareen S, Ahmad M, Nawaz F, Khan S, Riaz R, Yang T, Fatima A, Jamal M, Mansoor S, Liu X, Ahmed N. The SARS-CoV-2 differential genomic adaptation in response to varying UVindex reveals potential genomic resources for better COVID-19 diagnosis and prevention. Front Microbiol 2022; 13:922393. [PMID: 36016784 PMCID: PMC9396647 DOI: 10.3389/fmicb.2022.922393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 06/27/2022] [Indexed: 01/08/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) has been a pandemic disease reported in almost every country and causes life-threatening, severe respiratory symptoms. Recent studies showed that various environmental selection pressures challenge the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infectivity and, in response, the virus engenders new mutations, leading to the emergence of more virulent strains of WHO concern. Advance prediction of the forthcoming virulent SARS-CoV-2 strains in response to the principal environmental selection pressures like temperature and solar UV radiation is indispensable to overcome COVID-19. To discover the UV-solar radiation-driven genomic adaption of SARS-CoV-2, a curated dataset of 2,500 full-grade genomes from five different UVindex regions (25 countries) was subjected to in-depth downstream genome-wide analysis. The recurrent variants that best respond to UV-solar radiations were extracted and extensively annotated to determine their possible effects and impacts on gene functions. This study revealed 515 recurrent single nucleotide variants (rcntSNVs) as SARS-CoV-2 genomic responses to UV-solar radiation, of which 380 were found to be distinct. For all discovered rcntSNVs, 596 functional effects (rcntEffs) were detected, containing 290 missense, 194 synonymous, 81 regulatory, and 31 in the intergenic region. The highest counts of missense rcntSNVs in spike (27) and nucleocapsid (26) genes explain the SARS-CoV-2 genomic adjustment to escape immunity and prevent UV-induced DNA damage, respectively. Among all, the most commonly observed rcntEffs were four missenses (RdRp-Pro327Leu, N-Arg203Lys, N-Gly204Arg, and Spike-Asp614Gly) and one synonymous (ORF1ab-Phe924Phe) functional effects. The highest number of rcntSNVs found distinct and were uniquely attributed to the specific UVindex regions, proposing solar-UV radiation as one of the driving forces for SARS-CoV-2 differential genomic adaptation. The phylogenetic relationship indicated the high UVindex region populating SARS-CoV-2 as the recent progenitor of all included samples. Altogether, these results provide baseline genomic data that may need to be included for preparing UVindex region-specific future diagnostic and vaccine formulations.
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Namdar-Khojasteh D, Yeghaneh B, Maher A, Namdar-Khojasteh F, Tu J. Assessment of the relationship between exposure to air pollutants and COVID-19 pandemic in Tehran city, Iran. ATMOSPHERIC POLLUTION RESEARCH 2022; 13:101474. [PMID: 35721792 PMCID: PMC9187902 DOI: 10.1016/j.apr.2022.101474] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/18/2022] [Accepted: 05/29/2022] [Indexed: 05/31/2023]
Abstract
The COVID-19 disease caused by the SARS-CoV-2 virus first identified in December 2019 has resulted in millions of deaths so far around the world. Controlling the spread of the disease requires a good understanding of the factors (e.g. air pollutants) that influence virus transmission and the conditions under which it spreads. This study analyzed the relationships between COVID-19 cases and both short-term (6-month) and long-term (60-month) exposures to eight air pollutants (NO, NO2, NOx, CO, SO2, O3, PM2.5 and PM10) in Tehran city, Iran, by integrating geostatistical interpolation models, regression analysis, and an innovated COVID-19 incidence rate calculation (Q-index) that considered the spatial distributions of both population and air pollution. The results show that the higher COVID-19 incidence rate was significantly associated with the exposure to higher concentrations of CO, NO, and NOx during the short-term period; the higher COVID-19 incidence rate was significantly related to the exposure to higher concentrations of PM2.5 during the long-term period; while COVID-19 incidence rate was not significantly associated with the concentrations of O3, SO2, PM10 and NO2 in either period. This study indicates that exposure to air pollutants can effect an increase in the number of infected people by transmitting the virus through the air or by predisposing people to the disease over time. The Q-index calculation method developed in this study can be also used by other studies to calculate more accurate disease rates that consider the spatial distribution of both population and air pollution.
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Affiliation(s)
- Davood Namdar-Khojasteh
- Department of Soil Science, Soil and Health Group, Faculty of Agriculture, Shahed University, P.O.Box 18155/159, 3319118651, Tehran, Iran
| | - Bijan Yeghaneh
- Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
| | - Ali Maher
- Department of Health Services Management, School of Virtual, Medical Education and Management, Shahid Beheshti Medical University, Tehran, Iran
| | | | - Jun Tu
- Department of Geography and Anthropology, Kennesaw State University, 1000 Chastain Road, Kennesaw, GA, 30144, USA
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Lin S, Rui J, Xie F, Zhan M, Chen Q, Zhao B, Zhu Y, Li Z, Deng B, Yu S, Li A, Ke Y, Zeng W, Su Y, Chiang YC, Chen T. Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations. Front Public Health 2022; 10:920312. [PMID: 35844849 PMCID: PMC9284004 DOI: 10.3389/fpubh.2022.920312] [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] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 06/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Meteorological factors have been proven to affect pathogens; both the transmission routes and other intermediate. Many studies have worked on assessing how those meteorological factors would influence the transmissibility of COVID-19. In this study, we used generalized estimating equations to evaluate the impact of meteorological factors on Coronavirus disease 2019 (COVID-19) by using three outcome variables, which are transmissibility, incidence rate, and the number of reported cases. Methods In this study, the data on the daily number of new cases and deaths of COVID-19 in 30 provinces and cities nationwide were obtained from the provincial and municipal health committees, while the data from 682 conventional weather stations in the selected provinces and cities were obtained from the website of the China Meteorological Administration. We built a Susceptible-Exposed-Symptomatic-Asymptomatic-Recovered/Removed (SEIAR) model to fit the data, then we calculated the transmissibility of COVID-19 using an indicator of the effective reproduction number (Reff ). To quantify the different impacts of meteorological factors on several outcome variables including transmissibility, incidence rate, and the number of reported cases of COVID-19, we collected panel data and used generalized estimating equations. We also explored whether there is a lag effect and the different times of meteorological factors on the three outcome variables. Results Precipitation and wind speed had a negative effect on transmissibility, incidence rate, and the number of reported cases, while humidity had a positive effect on them. The higher the temperature, the lower the transmissibility. The temperature had a lag effect on the incidence rate, while the remaining five meteorological factors had immediate and lag effects on the incidence rate and the number of reported cases. Conclusion Meteorological factors had similar effects on incidence rate and number of reported cases, but different effects on transmissibility. Temperature, relative humidity, precipitation, sunshine hours, and wind speed had immediate and lag effects on transmissibility, but with different lag times. An increase in temperature may first cause a decrease in virus transmissibility and then lead to a decrease in incidence rate. Also, the mechanism of the role of meteorological factors in the process of transmissibility to incidence rate needs to be further explored.
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Affiliation(s)
- Shengnan Lin
- School of Public Health, Xiamen University, Xiamen, China
| | - Jia Rui
- School of Public Health, Xiamen University, Xiamen, China
- Cirad, UMR 17, Intertryp, Université de Montpellier, Montpellier, France
| | - Fang Xie
- School of Public Health, Xiamen University, Xiamen, China
| | - Meirong Zhan
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Qiuping Chen
- School of Public Health, Xiamen University, Xiamen, China
- Cirad, UMR 17, Intertryp, Université de Montpellier, Montpellier, France
| | - Bin Zhao
- Clinical Medical Laboratory, Xiang'an Hospital of Xiamen University, Xiamen, China
| | - Yuanzhao Zhu
- School of Public Health, Xiamen University, Xiamen, China
| | - Zhuoyang Li
- School of Public Health, Xiamen University, Xiamen, China
| | - Bin Deng
- School of Public Health, Xiamen University, Xiamen, China
| | - Shanshan Yu
- School of Public Health, Xiamen University, Xiamen, China
| | - An Li
- School of Public Health, Xiamen University, Xiamen, China
| | - Yanshu Ke
- School of Public Health, Xiamen University, Xiamen, China
| | - Wenwen Zeng
- School of Public Health, Xiamen University, Xiamen, China
| | - Yanhua Su
- School of Public Health, Xiamen University, Xiamen, China
| | - Yi-Chen Chiang
- School of Public Health, Xiamen University, Xiamen, China
| | - Tianmu Chen
- School of Public Health, Xiamen University, Xiamen, China
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Zhang R, Lai KY, Liu W, Liu Y, Lu J, Tian L, Webster C, Luo L, Sarkar C. Community-level ambient fine particulate matter and seasonal influenza among children in Guangzhou, China: A Bayesian spatiotemporal analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 826:154135. [PMID: 35227720 DOI: 10.1016/j.scitotenv.2022.154135] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/21/2022] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Influenza is a major preventable infectious respiratory disease. However, there is little detailed long-term evidence of its associations with PM2.5 among children. We examined the community-level associations between exposure to ambient PM2.5 and incident influenza in Guangzhou, China. METHODS We used data from the city-wide influenza surveillance system collected by Guangzhou Centre for Disease Control and Prevention (GZCDC) over the period 2013 and 2019. Incident influenza was defined as daily new influenza (both clinically diagnosed and laboratory confirmed) cases as per standard diagnostic criteria. A 200-meter city-wide grid of daily ambient PM2.5 exposure was generated using a random forest model. We developed spatiotemporal Bayesian hierarchical models to examine the community-level associations between PM2.5 and the influenza adjusting for meteorological and socioeconomic variables and accounting for spatial autocorrelation. We also calculated community-wide influenza cases attributable to PM2.5 levels exceeding the China Grade 1 and World Health Organization (WHO) regulatory thresholds. RESULTS Our study comprised N = 191,846 children from Guangzhou aged ≤19 years and diagnosed with influenza between January 1, 2013 and December 31, 2019. Each 10 μg/m3 increment in community-level PM2.5 measured on the day of case confirmation (lag 0) and over a 6-day moving average (lag 0-5 days) was associated with higher risks of influenza (RR = 1.05, 95% CI: 1.05-1.06 for lag 0 and RR = 1.15, 95% CI: 1.14-1.16 for lag 05). We estimated that 8.10% (95%CI: 7.23%-8.57%) and 20.11% (95%CI: 17.64%-21.48%) influenza cases respectively were attributable to daily PM2.5 exposure exceeding the China Grade I (35 μg/m3) and the WHO limits (25 μg/m3). The risks associated with PM2.5 exposures were more pronounced among children of the age-group 10-14 compared to other age groups. CONCLUSIONS More targeted non-pharmaceutical interventions aimed at reducing PM2.5 exposures at home, school and during commutes among children may constitute additional influenza prevention and control polices.
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Affiliation(s)
- Rong Zhang
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Ka Yan Lai
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Wenhui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Yanhui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Jianyun Lu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Linwei Tian
- School of Public Health, The University of Hong Kong, Patrick Mason Building, Sassoon Road, Pokfulam, Hong Kong, China
| | - Chris Webster
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Lei Luo
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China.
| | - Chinmoy Sarkar
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China.
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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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Understanding the spatial diffusion dynamics of the COVID-19 pandemic in the city system in China. Soc Sci Med 2022; 302:114988. [PMID: 35512611 PMCID: PMC9046135 DOI: 10.1016/j.socscimed.2022.114988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 11/22/2021] [Accepted: 04/21/2022] [Indexed: 01/17/2023]
Abstract
Investigating the spatial epidemic dynamics of COVID-19 is crucial in understanding the routine of spatial diffusion and in surveillance, prediction, identification and prevention of another potential outbreak. However, previous studies attempting to evaluate these spatial diffusion dynamics are limited. Using city as the research unit and spatial association analysis as the primary strategy, this study explored the changing primary risk factors impacting the spatial spread of COVID-19 across Chinese cities under various diffusion assumptions and throughout the epidemic stage. Moreover, this study investigated the characteristics and geographical distributions of high-risk areas in different epidemic stages. The results empirically indicated rapid intercity diffusion at the early stage and primarily intracity diffusion thereafter. Before countermeasures took effect, proximity, GDP per capita, medical resources, outflows from Wuhan and intercity mobility significantly affected early diffusion. With speedily effective countermeasures, outflows from the epicenter, proximity, and intracity outflows played an important role. At the early stage, high-risk areas were mainly cities adjacent to the epicenter, with higher GDP per capita, or a combination of higher GDP per capita and better medical resources, with more outflow from the epicenter, or more intercity mobility. After countermeasures were effected, cities adjacent to the epicenter, or with more outflow from the epicenter or more intracity mobility became high-risk areas. This study provides an insightful understanding of the spatial diffusion of COVID-19 across cities. The findings are informative for effectively handling the potential recurrence of COVID-19 in various settings.
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Duncan NA, L'Her GF, Osborne AG, Sawyer SL, Deinert MR. Estimating the effect of non-pharmaceutical interventions on US SARS-CoV-2 infections in the first year of the pandemic. ROYAL SOCIETY OPEN SCIENCE 2022; 9:210875. [PMID: 35774134 PMCID: PMC9240671 DOI: 10.1098/rsos.210875] [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: 05/18/2021] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
SARS-CoV-2 emerged in late 2019 as a zoonotic infection of humans, and proceeded to cause a worldwide pandemic of historic magnitude. Here, we use a simple epidemiological model and consider the full range of initial estimates from published studies for infection and recovery rates, seasonality, changes in mobility, the effectiveness of masks and the fraction of people wearing them. Monte Carlo simulations are used to simulate the progression of possible pandemics and we show a match for the real progression of the pandemic during 2020 with an R 2 of 0.91. The results show that the combination of masks and changes in mobility avoided approximately 248.3 million (σ = 31.2 million) infections in the US before vaccinations became available.
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Affiliation(s)
- N. A. Duncan
- Mechanical Engineering, The Colorado School of Mines, Golden, CO 10996, USA
| | - G. F. L'Her
- Mechanical Engineering, The Colorado School of Mines, Golden, CO 10996, USA
| | - A. G. Osborne
- Mechanical Engineering, The Colorado School of Mines, Golden, CO 10996, USA
| | - S. L. Sawyer
- Molecular Biology, University of Colorado at Boulder, Boulder, CO, USA
| | - M. R. Deinert
- Mechanical Engineering, The Colorado School of Mines, Golden, CO 10996, USA
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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] [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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Sun C, Chao L, Li H, Hu Z, Zheng H, Li Q. Modeling and Preliminary Analysis of the Impact of Meteorological Conditions on the COVID-19 Epidemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:6125. [PMID: 35627661 PMCID: PMC9140896 DOI: 10.3390/ijerph19106125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 01/27/2023]
Abstract
Since the COVID-19 epidemic outbreak at the end of 2019, many studies regarding the impact of meteorological factors on the attack have been carried out, and inconsistent conclusions have been reached, indicating the issue's complexity. To more accurately identify the effects and patterns of meteorological factors on the epidemic, we used a combination of logistic regression (LgR) and partial least squares regression (PLSR) modeling to investigate the possible effects of common meteorological factors, including air temperature, relative humidity, wind speed, and surface pressure, on the transmission of the COVID-19 epidemic. Our analysis shows that: (1) Different countries and regions show spatial heterogeneity in the number of diagnosed patients of the epidemic, but this can be roughly classified into three types: "continuous growth", "staged shock", and "finished"; (2) Air temperature is the most significant meteorological factor influencing the transmission of the COVID-19 epidemic. Except for a few areas, regional air temperature changes and the transmission of the epidemic show a significant positive correlation, i.e., an increase in air temperature is conducive to the spread of the epidemic; (3) In different countries and regions studied, wind speed, relative humidity, and surface pressure show inconsistent correlation (and significance) with the number of diagnosed cases but show some regularity.
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Affiliation(s)
- Chenglong Sun
- School of Atmospheric Sciences and Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Sun Yat-Sen University, Zhuhai 519082, China; (C.S.); (L.C.); (H.L.)
| | - Liya Chao
- School of Atmospheric Sciences and Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Sun Yat-Sen University, Zhuhai 519082, China; (C.S.); (L.C.); (H.L.)
| | - Haiyan Li
- School of Atmospheric Sciences and Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Sun Yat-Sen University, Zhuhai 519082, China; (C.S.); (L.C.); (H.L.)
| | - Zengyun Hu
- Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
| | - Hehui Zheng
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Qingxiang Li
- School of Atmospheric Sciences and Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Sun Yat-Sen University, Zhuhai 519082, China; (C.S.); (L.C.); (H.L.)
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Abstract
PURPOSE OF REVIEW Understanding the association between urbanization and Internet addiction is essential to the design and implementation of Internet addiction prevention measures in urban areas. This epidemiological review explores the urbanization-Internet addiction association and its potential underlying factors. RECENT FINDINGS Nine studies have reported that Internet addiction prevalence is higher in urban areas, but three studies have noted the opposite. Psychiatric disorders and stress are the most commonly mentioned factors underlying the association. The effects of urbanization on Internet availability, Internet cafes, online gaming, outdoor or interactive activities, and family regulation and monitoring have been suggested to lead to higher Internet addiction risk. The ongoing COVID-19 pandemic, obesity, sleep problems, and the migration of parents to urban areas in search of work have strengthened the effect of urbanization on Internet addiction. SUMMARY Early assessment and treatment provided by mental health services are crucial for mitigating the effect of urbanization on Internet addiction risk. Cities should be designed to provide adequate space for physical and interactive activities. To promote outdoor activities, air pollution, traffic congestion, and crime should be controlled. Prospective face-to-face studies involving analysis of data on pollution, traffic, and Internet addiction could provide evidence to elucidate the urbanization- Internet addiction association.
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Data-driven multiscale modelling and analysis of COVID-19 spatiotemporal evolution using explainable AI. SUSTAINABLE CITIES AND SOCIETY 2022; 80:103772. [PMID: 35186668 PMCID: PMC8832881 DOI: 10.1016/j.scs.2022.103772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 01/27/2022] [Accepted: 02/10/2022] [Indexed: 05/21/2023]
Abstract
To quantificationally identify the optimal control measures for regulators to best minimize COVID-19′s growth (G-rate) and death (D-rate) rates in today's context, this paper develops a top-down multiscale engineering approach which encompasses a series of systematic analyses, namely: (global scale) predictive modelling of G-rate and D-rate due to COVID-19 globally, followed by determining the most effective control factors which can best minimize both parameters over time via explainable Artificial Intelligence (AI) with SHAP (SHapley Additive exPlanations) method; (continental scale) same predictive forecasting of G-rate and D-rate in all continents, followed by performing explainable SHAP analysis to determine the most effective control factors for the respective continents; and (country scale) clustering the different countries (> 150 in total) into 3 main clusters to identify the universal set of effective control measures. By using the historical period between 2 May 2020 and 1 Oct 2021, the average MAPE scores for forecasting G-rate and D-rate are within 10%, or less on average, at the global and continental scales. Systematically, we have quantificationally demonstrated that the top 3 most effective control measures for regulators to best minimize G-rate universally are COVID-CONTACT-TRACING, PUBLIC-GATHERING-RULES, and COVID-STRINGENCY-INDEX, while the control factors relating to D-rate depend on the modelling scenario.
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Pramanik M, Chowdhury K, Rana MJ, Bisht P, Pal R, Szabo S, Pal I, Behera B, Liang Q, Padmadas SS, Udmale P. Climatic influence on the magnitude of COVID-19 outbreak: a stochastic model-based global analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2022; 32:1095-1110. [PMID: 33090891 DOI: 10.1080/09603123.2020.1831446] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 09/28/2020] [Indexed: 05/25/2023]
Abstract
We investigate the climatic influence on COVID-19 transmission risks in 228 cities globally across three climatic zones. The results, based on the application of a Boosted Regression Tree algorithm method, show that average temperature and average relative humidity explain significant variations in COVID-19 transmission across temperate and subtropical regions, whereas in the tropical region, the average diurnal temperature range and temperature seasonality significantly predict the infection outbreak. The number of positive cases showed a decrease sharply above an average temperature of 10°C in the cities of France, Turkey, the US, the UK, and Germany. Among the tropical countries, COVID-19 in Indian cities is most affected by mean diurnal temperature, and those in Brazil by temperature seasonality. The findings have implications on public health interventions, and contribute to the ongoing scientific and policy discourse on the complex interplay of climatic factors determining the risks of COVID-19 transmission.
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Affiliation(s)
- Malay Pramanik
- Department of Development and Sustainability, School of Environment, Resources and Development, Asian Institute of Technology (AIT), PO. Box 4, Klong Luang, Pathumthani 12120, Thailand
- entre of International Politics, Organization, and Disarmament, School of International Studies, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Koushik Chowdhury
- Department of Humanities and Social Sciences, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Md Juel Rana
- Centre for the Study of Regional Development, School of Social Sciences, Jawaharlal Nehru University, New Delhi, 110067, India
- International Institute for Population Sciences, Govandi Station Road, Deonar, Mumbai, 400088, Maharashtra, India
| | - Praffulit Bisht
- entre of International Politics, Organization, and Disarmament, School of International Studies, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Raghunath Pal
- Centre for the Study of Regional Development, School of Social Sciences, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Sylvia Szabo
- Department of Social Welfare Counseling, College of Future Convergence, Dongguk University, Seoul 04620, South Korea
| | - Indrajit Pal
- Disaster Prevention, Mitigation, and Management, Asian Institute of Technology (AIT), PO. Box 4, Klong Luang, Pathumthani 12120, Thailand
| | - Bhagirath Behera
- Department of Humanities and Social Sciences, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Qiuhua Liang
- School of Architecture, Building and Civil Engineering, Loughborough University, Epinal Way, Loughborough LE11 3TU, United Kingdom
| | - Sabu S Padmadas
- Department of Social Statistics and Demography, Global Health Research Institute, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Parmeshwar Udmale
- Department of Development and Sustainability, School of Environment, Resources and Development, Asian Institute of Technology (AIT), PO. Box 4, Klong Luang, Pathumthani 12120, Thailand
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Wang C, Yang YN, Xi L, Yang LL, Du J, Zhang ZS, Lian XY, Cui Y, Li HJ, Zhang WX, Liu B, Cui F, Lu QB. Dynamics of influenza-like illness under urbanization procedure and COVID-19 pandemic in the sub-center of Beijing during 2013-2021. J Med Virol 2022; 94:3801-3810. [PMID: 35451054 PMCID: PMC9088387 DOI: 10.1002/jmv.27803] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/11/2022] [Accepted: 04/20/2022] [Indexed: 12/02/2022]
Abstract
Influenza‐like illness (ILI) varies in intensity year by year, generally keeping a stable pattern except for great changes of its epidemic pattern. Of the most impacting factors, urbanization has been suggested as shaping the intensity of influenza epidemics. Besides, growing evidence indicates the nonpharmaceutical interventions (NPIs) to severe acute respiratory syndrome coronavirus 2 offer great advantages in controlling infectious diseases. The present study aimed to evaluate the impact of urbanization and NPIs on the dynamic of ILI in Tongzhou, Beijing, during January 2013 to March 2021. ILI epidemiological surveillance data in Tongzhou district were obtained from Beijing Influenza Surveillance Network and separated into three periods of urbanization and four intervals of coronavirus disease 2019 pandemic. Standardized average incidence rates of ILI in each separate stages were calculated and compared by using Wilson method and time series model of seasonal ARIMA. Influenza seasonal outbreaks showed similar epidemic size and intensity before urbanization during 2013–2016. Increased ILI activity was found during the process of Tongzhou's urbanization during 2017–2019, with the rate difference of 2.48 (95% confidence interva [CI]: 2.44, 2.52) and the rate ratio of 1.75 (95% CI: 1.74, 1.76) of ILI incidence between preurbanization and urbanization periods. ILI activity abruptly decreased from the beginning of 2020 and kept at the bottom level almost in every epidemic interval. The top decrease in ILI activity by NPIs was shown in 5–14 years group in 2020–2021 influenza season, as 92.2% (95% CI: 78.3%, 95.2%). The results indicated that both urbanization and NPIs interrupted the epidemic pattern of ILI. We should pay more attention to public health when facing increasing population density, human contact, population mobility, and migration in the process of urbanization. NPIs and influenza vaccination should be implemented as necessary measures to protect people from common infectious diseases like ILI.
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Affiliation(s)
- Chao Wang
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Yan-Na Yang
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, People's Republic of China
| | - Lu Xi
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, People's Republic of China
| | - Li-Li Yang
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, People's Republic of China
| | - Juan Du
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Zhong-Song Zhang
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Xin-Yao Lian
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Yan Cui
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, People's Republic of China
| | - Hong-Jun Li
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, People's Republic of China
| | - Wan-Xue Zhang
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Bei Liu
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Fuqiang Cui
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Qing-Bin Lu
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
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