1,843 research outputs found

    Using scenario modelling for adapting to urbanization and water scarcity: towards a sustainable city in semi-arid areas

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    Sustainable development on a global scale has been hindered by urbanization and water scarcity, but the greatest threat is from decision-makers ignoring these challenges, particularly in developing countries. In addition, urbanization is spreading at an alarming rate across the globe, affecting the environment and society in profound ways. This study reviews previous studies that examined future scenarios of urban areas under the challenges of rapid population growth, urban sprawl and water scarcity, in order to improve supported decision-making (SDM). Scholars expected that the rapid development of the urbanization scenario would cause resource sustainability to continually be threatened as a result of excessive use of natural resources. In contrast, a sustainable development scenario is an ambitious plan that relies on optimal land use, which views land as a limited and non-renewable resource. In consequence, estimating these threats together could be crucial for planning sustainable strategies for the long term. In light of this review, the SDM tool could be improved by combining the cellular automata model, water evolution and planning model coupled with geographic information systems, remote sensing and criteria analytic hierarchical process modelling. Urban planners could optimize, simulate and visualize the dynamic processes of land-use change and urban water, using them to overcome critical conditions

    Capacity-building activities related to climate change vulnerability and adaptation assessment and economic valuation for Fiji

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    The Terms of Reference for this work specified three objectives to the Fiji component: Objective 1a: to provide a prototype FIJICLIM model (covered under PICCAP funding) Objective 1b: to provide training and transfer of FIJICLIM Objective 1c: to present and evaluate World Bank study findings and to identify future directions for development and use of FIJICLIM (2-day workshop) Proceedings of the training course and workshop were prepared by the Fiji Department of Environment. The summaries from these proceedings reflect a very high degree of success with the contracted activities

    GIS in Healthcare

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    The landscape of healthcare is dynamic, gradually becoming more complicated with factors beyond simple supply and demand. Similar to the diversity of social, political and economic contexts, the practical utilization of healthcare resources also varies around the world. However, the spatial components of these contexts, along with aspects of supply and demand, can reveal a common theme among these factors. This book presents advancements in GIS applications that reveal the complexity of and solutions for a dynamic healthcare landscape

    Japanese Encephalitis: Assessing disease risk due to landscape factors at multiple scales

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    Japanese Encephalitis is a mosquito-borne disease and is the leading cause of viral encephalitis in Asia. In many Asian countries, the geographical distribution of JE is dependent on a variety of human-environment interactions that can be conceptualized as a complex social-ecological system. The JE transmission cycle is influenced by a few primary human-landscape factors; the abundance and the spatial configuration of rice paddy fields (which provide habitat for the vector), the distribution of pig farms (which position the virus\u27 amplifying host), and the location of a susceptible human population. Our models integrate population dynamics, landscape characteristics, and weather variables that influence the spatiotemporal risk of contracting the JE virus. At a geographically small scale, we highlight regions within the geographic distribution of the disease that are of high-risk in the near future. An individual-level model was also developed to assess disease risk at a larger geographic scale. Model output reproduced the spatial and temporal dynamics of Japanese Surveillance data obtained from the World Health Organization. Such a model can be used to assess various scenarios that examine the spatial epidemiology of Japanese Encephalitis

    Understanding the Spatial Clustering of Severe Acute Respiratory Syndrome (SARS) in Hong Kong

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    We applied cartographic and geostatistical methods in analyzing the patterns of disease spread during the 2003 severe acute respiratory syndrome (SARS) outbreak in Hong Kong using geographic information system (GIS) technology. We analyzed an integrated database that contained clinical and personal details on all 1,755 patients confirmed to have SARS from 15 February to 22 June 2003. Elementary mapping of disease occurrences in space and time simultaneously revealed the geographic extent of spread throughout the territory. Statistical surfaces created by the kernel method confirmed that SARS cases were highly clustered and identified distinct disease “hot spots.” Contextual analysis of mean and standard deviation of different density classes indicated that the period from day 1 (18 February) through day 16 (6 March) was the prodrome of the epidemic, whereas days 86 (15 May) to 106 (4 June) marked the declining phase of the outbreak. Origin-and-destination plots showed the directional bias and radius of spread of superspreading events. Integration of GIS technology into routine field epidemiologic surveillance can offer a real-time quantitative method for identifying and tracking the geospatial spread of infectious diseases, as our experience with SARS has demonstrated

    Combining contact tracing with targeted indoor residual spraying significantly reduces dengue transmission

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    The widespread transmission of dengue viruses (DENV), coupled with the alarming increase of birth defects and neurological disorders associated with Zika virus, has put the world in dire need of more efficacious tools for Aedes aegypti–borne disease mitigation. We quantitatively investigated the epidemiological value of location-based contact tracing (identifying potential out-of-home exposure locations by phone interviews) to infer transmission foci where high-quality insecticide applications can be targeted. Space-time statistical modeling of data from a large epidemic affecting Cairns, Australia, in 2008–2009 revealed a complex pattern of transmission driven primarily by human mobility (Cairns accounted for ~60% of virus transmission to and from residents of satellite towns, and 57% of all potential exposure locations were nonresidential). Targeted indoor residual spraying with insecticides in potential exposure locations reduced the probability of future DENV transmission by 86 to 96%, compared to unsprayed premises. Our findings provide strong evidence for the effectiveness of combining contact tracing with residual spraying within a developed urban center, and should be directly applicable to areas with similar characteristics (for example, southern USA, Europe, or Caribbean countries) that need to control localized Aedes-borne virus transmission or to protect pregnant women’s homes in areas with active Zika transmission. Future theoretical and empirical research should focus on evaluation of the applicability and scalability of this approach to endemic areas with variable population size and force of DENV infection

    Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

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    Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models
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