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Lin CH, Wen TH. Assessing the impact of emergency measures in varied population density areas during a large dengue outbreak. Heliyon 2024; 10:e27931. [PMID: 38509971 PMCID: PMC10950701 DOI: 10.1016/j.heliyon.2024.e27931] [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: 07/25/2023] [Revised: 02/15/2024] [Accepted: 03/08/2024] [Indexed: 03/22/2024] Open
Abstract
Background The patterns of dengue are affected by many factors, including population density and climate factors. Densely populated areas could play a role in dengue transmission due to increased human-mosquito contacts, the presence of more diverse and suitable vector habitats and breeding sites, and changes in land use. In addition to population densities, climatic factors such as temperature, relative humidity, and precipitation have been demonstrated to predict dengue patterns. To control dengue, emergency measures should focus on vector management. Most approaches to assessing emergency responses to dengue risks involve applying simulation models or describing emergency activities and the results of implementing those responses. Research using real-world data with analytical methods to evaluate emergency responses to dengue has been limited. This study investigated emergency control measures associated with dengue risks in areas with high and low population densities, considering their different control capacities. Methodology Data from the 2015 dengue outbreak in Kaohsiung City, Taiwan, were utilized. The government database provided information on confirmed dengue cases, emergency control measures, and climatic data. The study employed a distributed lag non-linear model (DLNM) to assess the effect of emergency control measures and their time lags on dengue risk. Principal findings The findings revealed that in areas with high population density, the absence of emergency measures significantly elevated the risks of dengue. However, implementing emergency measures, especially a higher number, was associated with lower risks. In contrast, in areas with low population density, the risks of dengue were only significantly elevated at the 1st week lag if no emergency control measures were implemented. When emergency activities were carried out, the risks of dengue significantly decreased only for the 1st week lag. Conclusions Our findings reveal distinct exposure-lag-response patterns in the associations between emergency control measures and dengue in areas with high and low population density. In regions with a high population density, implementing emergency activities during a significant dengue outbreak is crucial for reducing the risk. Conversely, in areas of low population density, the necessity of applying emergency activities may be less pronounced. The implications of this study on dengue management could provide valuable insights for health authorities dealing with limited resources.
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Affiliation(s)
- Chia-Hsien Lin
- Department of Health Promotion and Health Education, National Taiwan Normal University, Taipei City, Taiwan
| | - Tzai-Hung Wen
- Department of Geography, National Taiwan University, Taipei City, Taiwan
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Gibb R, Colón-González FJ, Lan PT, Huong PT, Nam VS, Duoc VT, Hung DT, Dong NT, Chien VC, Trang LTT, Kien Quoc D, Hoa TM, Tai NH, Hang TT, Tsarouchi G, Ainscoe E, Harpham Q, Hofmann B, Lumbroso D, Brady OJ, Lowe R. Interactions between climate change, urban infrastructure and mobility are driving dengue emergence in Vietnam. Nat Commun 2023; 14:8179. [PMID: 38081831 PMCID: PMC10713571 DOI: 10.1038/s41467-023-43954-0] [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: 07/24/2023] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
Dengue is expanding globally, but how dengue emergence is shaped locally by interactions between climatic and socio-environmental factors is not well understood. Here, we investigate the drivers of dengue incidence and emergence in Vietnam, through analysing 23 years of district-level case data spanning a period of significant socioeconomic change (1998-2020). We show that urban infrastructure factors (sanitation, water supply, long-term urban growth) predict local spatial patterns of dengue incidence, while human mobility is a more influential driver in subtropical northern regions than the endemic south. Temperature is the dominant factor shaping dengue's distribution and dynamics, and using long-term reanalysis temperature data we show that warming since 1950 has expanded transmission risk throughout Vietnam, and most strongly in current dengue emergence hotspots (e.g., southern central regions, Ha Noi). In contrast, effects of hydrometeorology are complex, multi-scalar and dependent on local context: risk increases under either short-term precipitation excess or long-term drought, but improvements in water supply mitigate drought-associated risks except under extreme conditions. Our findings challenge the assumption that dengue is an urban disease, instead suggesting that incidence peaks in transitional landscapes with intermediate infrastructure provision, and provide evidence that interactions between recent climate change and mobility are contributing to dengue's expansion throughout Vietnam.
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Affiliation(s)
- Rory Gibb
- Department of Infectious Disease Epidemiology & Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
- Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, UK.
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution & Environment, University College London, London, UK.
| | - Felipe J Colón-González
- Department of Infectious Disease Epidemiology & Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, UK
- Data for Science and Health, Wellcome Trust, London, UK
| | - Phan Trong Lan
- General Department of Preventative Medicine (GDPM), Ministry of Health, Hanoi, Vietnam
| | - Phan Thi Huong
- General Department of Preventative Medicine (GDPM), Ministry of Health, Hanoi, Vietnam
| | - Vu Sinh Nam
- National Institute of Hygiene and Epidemiology (NIHE), Hanoi, Vietnam
| | - Vu Trong Duoc
- National Institute of Hygiene and Epidemiology (NIHE), Hanoi, Vietnam
| | - Do Thai Hung
- Pasteur Institute Nha Trang, Nha Trang, Khanh Hoa Province, Vietnam
| | | | - Vien Chinh Chien
- Tay Nguyen Institute of Hygiene and Epidemiology (TIHE), Buon Ma Thuot, Dak Lak Province, Vietnam
| | - Ly Thi Thuy Trang
- Tay Nguyen Institute of Hygiene and Epidemiology (TIHE), Buon Ma Thuot, Dak Lak Province, Vietnam
| | - Do Kien Quoc
- Pasteur Institute Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Tran Minh Hoa
- Center for Disease Control, Dong Nai Province, Vietnam
| | | | | | | | | | | | | | | | - Oliver J Brady
- Department of Infectious Disease Epidemiology & Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Rachel Lowe
- Department of Infectious Disease Epidemiology & Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, UK
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
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Pakaya R, Daniel D, Widayani P, Utarini A. Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review. BMC Public Health 2023; 23:2448. [PMID: 38062404 PMCID: PMC10701958 DOI: 10.1186/s12889-023-17185-3] [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: 05/28/2023] [Accepted: 11/08/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Creating a spatial model of dengue fever risk is challenging duet to many interrelated factors that could affect dengue. Therefore, it is crucial to understand how these critical factors interact and to create reliable predictive models that can be used to mitigate and control the spread of dengue. METHODS This scoping review aims to provide a comprehensive overview of the important predictors, and spatial modelling tools capable of producing Dengue Haemorrhagic Fever (DHF) risk maps. We conducted a methodical exploration utilizing diverse sources, i.e., PubMed, Scopus, Science Direct, and Google Scholar. The following data were extracted from articles published between January 2011 to August 2022: country, region, administrative level, type of scale, spatial model, dengue data use, and categories of predictors. Applying the eligibility criteria, 45 out of 1,349 articles were selected. RESULTS A variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and machine learning technique. We found that there was no pattern of predictor use associated with particular approaches. Instead, a wide range of predictors was used to create the DHF risk maps. These predictors may include climatology factors (e.g., temperature, rainfall, humidity), epidemiological factors (population, demographics, socio-economic, previous DHF cases), environmental factors (land-use, elevation), and relevant factors. CONCLUSIONS DHF risk spatial models are useful tools for detecting high-risk locations and driving proactive public health initiatives. Relying on geographical and environmental elements, these models ignored the impact of human behaviour and social dynamics. To improve the prediction accuracy, there is a need for a more comprehensive approach to understand DHF transmission dynamics.
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Affiliation(s)
- Ririn Pakaya
- Doctoral Program in Public Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.
- Department of Public Health, Public Health Faculty, Universitas Gorontalo, Gorontalo, Indonesia.
| | - D Daniel
- Department of Health Behaviour, Environment and Social Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Prima Widayani
- Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Adi Utarini
- Doctoral Program in Public Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
- Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
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Mhana KH, Norhisham SB, Katman HYB, Yaseen ZM. Environmental impact assessment of transportation and land alteration using Earth observational datasets: Comparative study between cities in Asia and Europe. Heliyon 2023; 9:e19413. [PMID: 37809986 PMCID: PMC10558544 DOI: 10.1016/j.heliyon.2023.e19413] [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: 05/23/2023] [Revised: 07/29/2023] [Accepted: 08/22/2023] [Indexed: 10/10/2023] Open
Abstract
Developments in the transportation field are emerging because of the growing worldwide demand and upgrading requirements. This study measured the transportation development, shortage distance, and decadal land transformation of Kuala Lumpur and Madrid using various remote sensing and GIS approaches. The kernel density estimation (KDE) tool was applied for road and railway density analysis, and hotspot information increased the knowledge about assessable areas. Landsat datasets were used (1991-2021) for land transformation and related analyses. The built-up land increased by 1327.27 and 404.09 km2 in Kuala Lumpur and Madrid, respectively. In the last thirty years, the temperature increased 6.45 °C in Kuala Lumpur and 4.15 °C in Madrid owing to urban expansion and road construction. Chamberi, Retiro, Moratalaz, Salama, Wangsa Maju, Titiwangsa, Bukit Bintang, and Seputeh have very high road densities. KDE measurements showed that the road densities in Kuala Lumpur (4498.34) and Madrid (9099.15) were high in the central parts of the city, and the railway densities were 348.872 and 2197.87, respectively. The observed P values were 0.99 and 0.96 for traffic signals and 0.98 and 0.99 for bus stops, respectively. The information provided by this study can support local planners, administrators, scientists, and researchers in understanding the global transportation issues that require implementation strategies for ensuring sustainable livelihoods.
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Affiliation(s)
- Khalid Hardan Mhana
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia
- Civil Engineering Department, College of Engineering, University Of Anbar, Iraq
| | - Shuhairy Bin Norhisham
- Institute of Energy Infrastructure (IEI) and Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia
| | - Herda Yati Binti Katman
- Institute of Energy Infrastructure (IEI) and Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia
| | - Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
- Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
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