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Journal articles on the topic 'Spatial disease'

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Author: Grafiati

Published: 11 March 2023

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1

Natesan, Balaji, Anandakumar Singaravelan, Jia-Lien Hsu, Yi-Hsien Lin, Baiying Lei, and Chuan-Ming Liu. "Channel–Spatial Segmentation Network for Classifying Leaf Diseases." Agriculture 12, no. 11 (November 9, 2022): 1886. http://dx.doi.org/10.3390/agriculture12111886.

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Agriculture is an important resource for the global economy, while plant disease causes devastating yield loss. To control plant disease, every country around the world spends trillions of dollars on disease management. Some of the recent solutions are based on the utilization of computer vision techniques in plant science which helps to monitor crop industries such as tomato, maize, grape, citrus, potato and cassava, and other crops. The attention-based CNN network has become effective in plant disease prediction. However, existing approaches are less precise in detecting minute-scale disease in the leaves. Our proposed Channel–Spatial segmentation network will help to determine the disease in the leaf, and it consists of two main stages: (a) channel attention discriminates diseased and healthy parts as well as channel-focused features, and (b) spatial attention consumes channel-focused features and highlights the diseased part for the final prediction process. This investigation forms a channel and spatial attention in a sequential way to identify diseased and healthy leaves. Finally, identified leaf diseases are divided into Mild, Medium, Severe, and Healthy. Our model successfully predicts the diseased leaves with the highest accuracy of 99.76%. Our research study shows evaluation metrics, comparison studies, and expert analysis to comprehend the network performance. This concludes that the Channel–Spatial segmentation network can be used effectively to diagnose different disease degrees based on a combination of image processing and statistical calculation.

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2

Levin, Bonnie E. "Spatial Cognition in Parkinson Disease." Alzheimer Disease & Associated Disorders 4, no. 3 (1990): 161–70. http://dx.doi.org/10.1097/00002093-199040300-00004.

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3

Abellan, J. J., S. Richardson, and N. Best. "Spatial Versus Spatiotemporal Disease Mapping." Epidemiology 18, Suppl (September 2007): S111. http://dx.doi.org/10.1097/01.ede.0000288446.95319.0a.

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4

Horan, Richard, Christopher A. Wolf, Eli P. Fenichel, and Kenneth H. Mathews. "Spatial Management of Wildlife Disease*." Review of Agricultural Economics 27, no. 3 (September 2005): 483–90. http://dx.doi.org/10.1111/j.1467-9353.2005.00248.x.

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5

Nissen, Mary Jo. "Spatial Vision in Alzheimer's Disease." Archives of Neurology 42, no. 7 (July 1, 1985): 667. http://dx.doi.org/10.1001/archneur.1985.04060070057015.

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6

Henderson, V. W., W. Mack, and B. W. Williams. "Spatial Disorientation in Alzheimer's Disease." Archives of Neurology 46, no. 4 (April 1, 1989): 391–94. http://dx.doi.org/10.1001/archneur.1989.00520400045018.

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7

Irvine, Michael A., James C. Bull, and Matthew J. Keeling. "Disease transmission promotes evolution of host spatial patterns." Journal of The Royal Society Interface 13, no. 122 (September 2016): 20160463. http://dx.doi.org/10.1098/rsif.2016.0463.

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Ecological dynamics can produce a variety of striking patterns. On ecological time scales, pattern formation has been hypothesized to be due to the interaction between a species and its local environment. On longer time scales, evolutionary factors must be taken into account. To examine the evolutionary robustness of spatial pattern formation, we construct a spatially explicit model of vegetation in the presence of a pathogen. Initially, we compare the dynamics for vegetation parameters that lead to competition induced spatial patterns and those that do not. Over ecological time scales, banded spatial patterns dramatically reduced the ability of the pathogen to spread, lowered its endemic density and hence increased the persistence of the vegetation. To gain an evolutionary understanding, each plant was given a heritable trait defining its resilience to competition; greater competition leads to lower vegetation density but stronger spatial patterns. When a disease is introduced, the selective pressure on the plant's resilience to the competition parameter is determined by the transmission of the disease. For high transmission, vegetation that has low resilience to competition and hence strong spatial patterning is an evolutionarily stable strategy. This demonstrates a novel mechanism by which striking spatial patterns can be maintained by disease-driven selection.

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Valdebenito-Maturana, Braulio, Cristina Guatimosim, Mónica Alejandra Carrasco, and Juan Carlos Tapia. "Spatially Resolved Expression of Transposable Elements in Disease and Somatic Tissue with SpatialTE." International Journal of Molecular Sciences 22, no. 24 (December 20, 2021): 13623. http://dx.doi.org/10.3390/ijms222413623.

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Spatial transcriptomics (ST) is transforming the way we can study gene expression and its regulation through position-specific resolution within tissues. However, as in bulk RNA-Seq, transposable elements (TEs) are not being studied due to their highly repetitive nature. In recent years, TEs have been recognized as important regulators of gene expression, and thus, TE expression analysis in a spatially resolved manner could further help to understand their role in gene regulation within tissues. We present SpatialTE, a tool to analyze TE expression from ST datasets and show its application in somatic and diseased tissues. The results indicate that TEs have spatially regulated expression patterns and that their expression profiles are spatially altered in ALS disease, indicating that TEs might perform differential regulatory functions within tissue organs. We have made SpatialTE publicly available as open-source software under an MIT license.

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9

Dimovski, E. B., J. C. Stout, S. A. Wylie, and E. R. Siemers. "Spatial cognitive shifting in huntington's disease and parkinson's disease." Archives of Clinical Neuropsychology 14, no. 1 (January 1, 1999): 126. http://dx.doi.org/10.1093/arclin/14.1.126.

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10

Allison, Samantha L., Anne M. Fagan, John C. Morris, and Denise Head. "Spatial Navigation in Preclinical Alzheimer’s Disease." Journal of Alzheimer's Disease 52, no. 1 (April 26, 2016): 77–90. http://dx.doi.org/10.3233/jad-150855.

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11

Golden, Hannah L., Jennifer M. Nicholas, Keir X. X. Yong, Laura E. Downey, Jonathan M. Schott, Catherine J. Mummery, Sebastian J. Crutch, and Jason D. Warren. "Auditory spatial processing in Alzheimer’s disease." Brain 138, no. 1 (December 1, 2014): 189–202. http://dx.doi.org/10.1093/brain/awu337.

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12

Kelsall, Julia, and Jonathan Wakefield. "Modeling Spatial Variation in Disease Risk." Journal of the American Statistical Association 97, no. 459 (September 2002): 692–701. http://dx.doi.org/10.1198/016214502388618438.

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13

Drago, Valeria, Paul S. Foster, Frank Skidmore, Daniel Trifiletti, and Kenneth M. Heilman. "Spatial Emotional Akinesia in Parkinson Disease." Cognitive and Behavioral Neurology 21, no. 2 (June 2008): 92–97. http://dx.doi.org/10.1097/wnn.0b013e31816bdfb2.

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14

Payne, Robert J. H., and David C. Krakauer. "The spatial dynamics of prion disease." Proceedings of the Royal Society of London. Series B: Biological Sciences 265, no. 1412 (December 7, 1998): 2341–46. http://dx.doi.org/10.1098/rspb.1998.0581.

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15

Wang, Wendi, and Xiao-Qiang Zhao. "Spatial Invasion Threshold of Lyme Disease." SIAM Journal on Applied Mathematics 75, no. 3 (January 2015): 1142–70. http://dx.doi.org/10.1137/140981769.

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16

Possin, Katherine L. "Visual spatial cognition in neurodegenerative disease." Neurocase 16, no. 6 (November 30, 2010): 466–87. http://dx.doi.org/10.1080/13554791003730600.

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17

Congdon, Peter. "Spatial heterogeneity in Bayesian disease mapping." GeoJournal 84, no. 5 (September 4, 2018): 1303–16. http://dx.doi.org/10.1007/s10708-018-9920-1.

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18

Laczó, Jan, Martina Parizkova, and Scott D. Moffat. "Spatial navigation, aging and Alzheimer’s disease." Aging 10, no. 11 (November 4, 2018): 3050–51. http://dx.doi.org/10.18632/aging.101634.

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19

Crucian, Gregory P., Sheyan Armaghani, Avan Armaghani, Paul S. Foster, David W. Burks, Barry Skoblar, Valeria Drago, and Kenneth M. Heilman. "Visual–spatial disembedding in Parkinson's disease." Journal of Clinical and Experimental Neuropsychology 32, no. 2 (May 29, 2009): 190–200. http://dx.doi.org/10.1080/13803390902902441.

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20

Montgomery, P., P. Silverstein, R. Wichmann, K. Fleischaker, and M. Andberg. "Spatial Updating in Parkinson′s Disease." Brain and Cognition 23, no. 2 (November 1993): 113–26. http://dx.doi.org/10.1006/brcg.1993.1050.

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21

Walter, S. D. "Assessing spatial patterns in disease rates." Statistics in Medicine 12, no. 19-20 (October 1993): 1885–94. http://dx.doi.org/10.1002/sim.4780121914.

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22

Kulldorff, Martin, and Neville Nagarwalla. "Spatial disease clusters: Detection and inference." Statistics in Medicine 14, no. 8 (April 30, 1995): 799–810. http://dx.doi.org/10.1002/sim.4780140809.

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23

Saleem, Sheikh Mohd, Chaitnya Aggarwal, Om Prakash Bera, Radhika Rana, Gurmandeep Singh, and Sudip Bhattacharya. "Non-communicable disease surveillance in India using Geographical Information System-An experience from Punjab." Indian Journal of Community Health 33, no. 3 (September 30, 2021): 506–11. http://dx.doi.org/10.47203/ijch.2021.v33i03.017.

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"Geographic information system (GIS) collects various kinds of data based on the geographic relationship across space." Data in GIS is stored to visualize, analyze, and interpret geographic data to learn about an area, an ongoing project, site planning, business, health economics and health-related surveys and information. GIS has evolved from ancient disease maps to 3D digital maps and continues to grow even today. The visual-spatial mapping of the data has given us an insight into different diseases ranging from diarrhea, pneumonia to non-communicable diseases like diabetes mellitus, hypertension, cardiovascular diseases, or risk factors like obesity, being overweight, etc. All in a while, this information has highlighted health-related issues and knowledge about these in a contemporary manner worldwide. Researchers, scientists, and administrators use GIS for research project planning, execution, and disease management. Cases of diseases in a specific area or region, the number of hospitals, roads, waterways, and health catchment areas are examples of spatially referenced data that can be captured and easily presented using GIS. Currently, we are facing an epidemic of non-communicable diseases, and a powerful tool like GIS can be used efficiently in such a situation. GIS can provide a powerful and robust framework for effectively monitoring and identifying the leading cause behind such diseases. GIS, which provides a spatial viewpoint regarding the disease spectrum, pattern, and distribution, is of particular importance in this area and helps better understand disease transmission dynamics and spatial determinants. The use of GIS in public health will be a practical approach for surveillance, monitoring, planning, optimization, and service delivery of health resources to the people at large. The GIS platform can link environmental and spatial information with the disease itself, which makes it an asset in disease control progression all over the globe.

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24

Liu, Yue, Yuwei Su, and Xiaoyu Li. "Analyzing the Spatial Equity of Walking-Based Chronic Disease Pharmacies: A Case Study in Wuhan, China." International Journal of Environmental Research and Public Health 20, no. 1 (December 24, 2022): 278. http://dx.doi.org/10.3390/ijerph20010278.

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Chronic diseases place a substantial financial burden on both the patient and the state. As chronic diseases become increasingly prevalent with urbanization and aging, primary chronic disease pharmacies should be planned to ensure that patients receive an equitable distribution of resources. Here, the spatial equity of chronic disease pharmacies is investigated. In this study, planning radiuses and Web mapping are used to assess the walkability and accessibility of planned chronic disease pharmacies; Lorenz curves are used to evaluate the match between the service area of the pharmacies and population; location quotients are used to identify the spatial differences of the allocation of chronic disease pharmacies based on residents. Results show that chronic disease pharmacies have a planned service coverage of 38.09%, an overlap rate of 58.34%, and actual service coverage of 28.05% in Wuhan. Specifically, chronic disease pharmacies are spatially dispersed inconsistently with the population, especially the elderly. The allocation of chronic disease pharmacies is directly related to the standard of patients’ livelihood. Despite this, urban development does not adequately address this group’s equity in access to medication. Based on a case study in Wuhan, China, this study aims to fill this gap by investigating the spatial equity of chronic disease medication purchases.

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25

Platz, M., J. Rapp, M. Groessler, E. Niehaus, A. Babu, and B. Soman. "Mathematical Modeling of spatial disease variables by Spatial Fuzzy Logic for Spatial Decision Support Systems." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 27, 2014): 213–20. http://dx.doi.org/10.5194/isprsarchives-xl-8-213-2014.

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A Spatial Decision Support System (SDSS) provides support for decision makers and should not be viewed as replacing human intelligence with machines. Therefore it is reasonable that decision makers are able to use a feature to analyze the provided spatial decision support in detail to crosscheck the digital support of the SDSS with their own expertise. Spatial decision support is based on risk and resource maps in a Geographic Information System (GIS) with relevant layers e.g. environmental, health and socio-economic data. Spatial fuzzy logic allows the representation of spatial properties with a value of truth in the range between 0 and 1. Decision makers can refer to the visualization of the spatial truth of single risk variables of a disease. Spatial fuzzy logic rules that support the allocation of limited resources according to risk can be evaluated with measure theory on topological spaces, which allows to visualize the applicability of this rules as well in a map. Our paper is based on the concept of a spatial fuzzy logic on topological spaces that contributes to the development of an adaptive Early Warning And Response System (EWARS) providing decision support for the current or future spatial distribution of a disease. It supports the decision maker in testing interventions based on available resources and apply risk mitigation strategies and provide guidance tailored to the geo-location of the user via mobile devices. The software component of the system would be based on open source software and the software developed during this project will also be in the open source domain, so that an open community can build on the results and tailor further work to regional or international requirements and constraints. A freely available <i>EWARS Spatial Fuzzy Logic Demo</i> was developed wich enables a user to visualize risk and resource maps based on individual data in several data formats.

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26

Schumaker, Nathan H., and Sydney M. Watkins. "Adding Space to Disease Models: A Case Study with COVID-19 in Oregon, USA." Land 10, no. 4 (April 20, 2021): 438. http://dx.doi.org/10.3390/land10040438.

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We selected the COVID-19 outbreak in the state of Oregon, USA as a system for developing a general geographically nuanced epidemiological forecasting model that balances simplicity, realism, and accessibility. Using the life history simulator HexSim, we inserted a mathematical SIRD disease model into a spatially explicit framework, creating a distributed array of linked compartment models. Our spatial model introduced few additional parameters, but casting the SIRD equations into a geographic setting significantly altered the system’s emergent dynamics. Relative to the non-spatial model, our simple spatial model better replicated the record of observed infection rates in Oregon. We also observed that estimates of vaccination efficacy drawn from the non-spatial model tended to be higher than those obtained from models that incorporate geographic variation. Our spatially explicit SIRD simulations of COVID-19 in Oregon suggest that modest additions of spatial complexity can bring considerable realism to a traditional disease model.

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27

Ojiambo, P. S., and E. L. Kang. "Modeling Spatial Frailties in Survival Analysis of Cucurbit Downy Mildew Epidemics." Phytopathology® 103, no. 3 (March 2013): 216–27. http://dx.doi.org/10.1094/phyto-07-12-0152-r.

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Cucurbit downy mildew caused by Pseudoperonospora cubensis is economically the most important disease of cucurbits globally, and the pathogen is disseminated aerially over a large spatial scale. Spatio-temporal spread of the disease was characterized during phase I (low and sporadic disease outbreaks) and II (rapid increase in disease outbreaks) of the epidemic using records collected from sentinel plots from 2008 to 2009 in 23 states in the eastern United States as part of the United States Department of Agriculture Cucurbit Downy Mildew ipmPIPE network. A substantive goal of this study was to explain the pattern of time to disease outbreak using important covariates while accounting for spatially correlated differences in risk of disease outbreak among the states. Survival analyses that accounts for spatial dependence were performed on time to disease outbreak, and posterior median frailties (or random effects) were mapped to identify states with high or low risk for disease outbreak. From February to October, disease occurred in 195 and 172 out of 413 and 556 cases monitored in 2008 and 2009, respectively. Disease outbreaks were spatially aggregated, with a spatial dependence of up to ≈1,025 km where clustering of outbreaks in phase I and II of the epidemic were similar. However, unlike in phase I of the epidemic, space–time point pattern analysis was significant (P < 0.0001) for outbreaks in phase II, during which the highest risk window as estimated by the space–time function was within 1.5 months and 500 km of the initial outbreak. The risk of disease outbreak peaked around July and decreased thereafter until the end of the study period. Spatially correlated analysis of time to disease outbreak indicated the need to incorporate spatial frailties in standard survival analysis models. Evaluation of alternative formulations of the spatial models demonstrated that a Bayesian hierarchical spatially structured frailty model best described time to disease outbreak. This frailty model showed clustering of outbreaks at the state level and indicated that states in the mid-Atlantic region have high spatial frailties and a high risk of downy mildew outbreak.

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Verma, S., and R. D. Gupta. "Spatial and Temporal Variation of Japanese encephalitis Disease and Detection of Disease Hotspots: a Case Study of Gorakhpur District, Uttar Pradesh, India." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 1–7. http://dx.doi.org/10.5194/isprsannals-ii-8-1-2014.

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In recent times, Japanese Encephalitis (JE) has emerged as a serious public health problem. In India, JE outbreaks were recently reported in Uttar Pradesh, Gorakhpur. The present study presents an approach to use GIS for analyzing the reported cases of JE in the Gorakhpur district based on spatial analysis to bring out the spatial and temporal dynamics of the JE epidemic. The study investigates spatiotemporal pattern of the occurrence of disease and detection of the JE hotspot. Spatial patterns of the JE disease can provide an understanding of geographical changes. Geospatial distribution of the JE disease outbreak is being investigated since 2005 in this study. The JE incidence data for the years 2005 to 2010 is used. The data is then geo-coded at block level. Spatial analysis is used to evaluate autocorrelation in JE distribution and to test the cases that are clustered or dispersed in space. The Inverse Distance Weighting interpolation technique is used to predict the pattern of JE incidence distribution prevalent across the study area. Moran's I Index (Moran's I) statistics is used to evaluate autocorrelation in spatial distribution. The Getis-Ord Gi*(d) is used to identify the disease areas. The results represent spatial disease patterns from 2005 to 2010, depicting spatially clustered patterns with significant differences between the blocks. It is observed that the blocks on the built up areas reported higher incidences.

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Lin, Chia-Hsien, and Tzai-Hung Wen. "How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission." Tropical Medicine and Infectious Disease 7, no. 8 (August 2, 2022): 164. http://dx.doi.org/10.3390/tropicalmed7080164.

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Both directly and indirectly transmitted infectious diseases in humans are spatial-related. Spatial dimensions include: distances between susceptible humans and the environments shared by people, contaminated materials, and infectious animal species. Therefore, spatial concepts in managing and understanding emerging infectious diseases are crucial. Recently, due to the improvements in computing performance and statistical approaches, there are new possibilities regarding the visualization and analysis of disease spatial data. This review provides commonly used spatial or spatial-temporal approaches in managing infectious diseases. It covers four sections, namely: visualization, overall clustering, hot spot detection, and risk factor identification. The first three sections provide methods and epidemiological applications for both point data (i.e., individual data) and aggregate data (i.e., summaries of individual points). The last section focuses on the spatial regression methods adjusted for neighbour effects or spatial heterogeneity and their implementation. Understanding spatial-temporal variations in the spread of infectious diseases have three positive impacts on the management of diseases. These are: surveillance system improvements, the generation of hypotheses and approvals, and the establishment of prevention and control strategies. Notably, ethics and data quality have to be considered before applying spatial-temporal methods. Developing differential global positioning system methods and optimizing Bayesian estimations are future directions.

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Murad, Abdulkader, and Bandar Fuad Khashoggi. "Using GIS for Disease Mapping and Clustering in Jeddah, Saudi Arabia." ISPRS International Journal of Geo-Information 9, no. 5 (May 18, 2020): 328. http://dx.doi.org/10.3390/ijgi9050328.

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Geographic information systems (GIS) can be used to map the geographical distribution of the prevalence of disease, trends in disease transmission, and to spatially model environmental aspects of disease occurrence. The aim of this study is to discuss a GIS application created to produce mapping and cluster modeling of three diseases in Jeddah, Saudi Arabia: diabetes, asthma, and hypertension. Data about these diseases were obtained from health centers’ registered patient records. These data were spatially evaluated using several spatial–statistical analytical models, including kernel and hotspot models. These models were created to explore and display the disparate patterns of the selected diseases and to illustrate areas of high concentration, and may be invaluable in understanding local patterns of diseases and their geographical associations.

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Premashthira, Sith, Mo D. Salman, Ashley E. Hill, Robin M. Reich, and Bruce A. Wagner. "Epidemiological simulation modeling and spatial analysis for foot-and-mouth disease control strategies: a comprehensive review." Animal Health Research Reviews 12, no. 2 (December 2011): 225–34. http://dx.doi.org/10.1017/s146625231100017x.

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AbstractFoot-and-mouth disease (FMD) is one of the most serious transboundary, contagious viral diseases of cloven-hoofed livestock, because it can spread rapidly with high morbidity rates when introduced into disease-free herds or areas. Epidemiological simulation modeling can be developed to study the hypothetical spread of FMD and to evaluate potential disease control strategies that can be implemented to decrease the impact of an outbreak or to eradicate the virus from an area. Spatial analysis, a study of the distributions of events in space, can be applied to an area to investigate the spread of animal disease. Hypothetical FMD outbreaks can be spatially analyzed to evaluate the effect of the event under different control strategies. The main objective of this paper is to review FMD-related articles on FMD epidemiology, epidemiological simulation modeling and spatial analysis with the focus on disease control. This review will contribute to the development of models used to simulate FMD outbreaks under various control strategies, and to the application of spatial analysis to assess the outcome of FMD spread and its control.

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32

Frank, Steven A. "Puzzles in modern biology. IV. Neurodegeneration, localized origin and widespread decay." F1000Research 5 (October 19, 2016): 2537. http://dx.doi.org/10.12688/f1000research.9790.1.

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The motor neuron disease amyotrophic lateral sclerosis (ALS) typically begins with localized muscle weakness. Progressive, widespread paralysis often follows over a few years. Does the disease begin with local changes in a small piece of neural tissue and then spread? Or does neural decay happen independently across diverse spatial locations? The distinction matters, because local initiation may arise by local changes in a tissue microenvironment, by somatic mutation, or by various epigenetic or regulatory fluctuations in a few cells. A local trigger must be coupled with a mechanism for spread. By contrast, independent decay across spatial locations cannot begin by a local change, but must depend on some global predisposition or spatially distributed change that leads to approximately synchronous decay. This article outlines the conceptual frame by which one contrasts local triggers and spread versus parallel spatially distributed decay. Various neurodegenerative diseases differ in their mechanistic details, but all can usefully be understood as falling along a continuum of interacting local and global processes. Cancer provides an example of disease progression by local triggers and spatial spread, setting a conceptual basis for clarifying puzzles in neurodegeneration. Heart disease also has crucial interactions between global processes, such as circulating lipid levels, and local processes in the development of atherosclerotic plaques. The distinction between local and global processes helps to understand these various age-related diseases.

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Belan, Leônidas Leoni, Edson Ampélio Pozza, Marcelo de Carvalho Alves, and Marcelo Loran de Oliveira Freitas. "Geostatistical analysis of bacterial blight in coffee tree seedlings in the nursery." Summa Phytopathologica 44, no. 4 (December 2018): 317–25. http://dx.doi.org/10.1590/0100-5405/179559.

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ABSTRACT In nurseries of coffee tree seedlings (Coffea arabica), there are favorable conditions for bacterial blight epidemics (Pseudomonas syringae pv. garcae). Studying the spatial distribution of diseased plants can help in the adoption of management strategies. Likewise, geostatistics has been applied to shape the spatial distribution and study epidemiological aspects of plant diseases. Thus, this study was developed to characterize the spatial distribution pattern of bacterial blight in a nursery of coffee tree seedlings. The disease progress was monitored over time in 704 seedlings organized in lines and columns in a nursery. Considering the mean diameter of the pots used for producing seedlings, georeferencing was carried out in Cartesian coordinate system for the seedlings in the nursery. The disease incidence data were subjected to non-spatial exploratory analysis and geostatistical analysis. The spherical isotropic semivariogram model was adjusted to the data and data interpolation was performed by ordinary kriging to visualize the spatial distribution of symptomatic seedlings. Bacterial blight epidemic was detected in the nursery during the experimental period, and there was variability and spatial dependence in relation to the distribution of diseased seedlings. As the epidemic progressed, the population of diseased plants increased, as well as the number and the size of the foci and their coalescence. Besides, there was an increase in the range value, sill and nugget effect. The kriging maps showed the disease progress and its variance. The bacterial blight epidemic of coffee tree started with a random spatial distribution pattern, progressing to an aggregate pattern.

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Smith, Daniel T., Soazig Casteau, and Neil Archibald. "Spatial attention and spatial short term memory in PSP and Parkinson's disease." Cortex 137 (April 2021): 49–60. http://dx.doi.org/10.1016/j.cortex.2020.12.019.

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35

Owen, Adrian M., Joanna L. Iddon, John R. Hodges, Beatrice A. Summers, and Trevor W. Robbins. "Spatial and non-spatial working memory at different stages of Parkinson's disease." Neuropsychologia 35, no. 4 (February 1997): 519–32. http://dx.doi.org/10.1016/s0028-3932(96)00101-7.

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36

Porter, Aaron T., and Jacob J. Oleson. "A spatial epidemic model for disease spread over a heterogeneous spatial support." Statistics in Medicine 35, no. 5 (September 13, 2015): 721–33. http://dx.doi.org/10.1002/sim.6730.

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37

Aswi, Aswi, Andi Mauliyana, Muhammad Arif Tiro, and Muhammad Nadjib Bustan. "RELATIVE RISK OF CORONAVIRUS DISEASE (COVID-19) IN SOUTH SULAWESI PROVINCE, INDONESIA: BAYESIAN SPATIAL MODELING." MEDIA STATISTIKA 14, no. 2 (December 12, 2021): 158–69. http://dx.doi.org/10.14710/medstat.14.2.158-169.

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The Covid-19 has exploded in the world since late 2019. South Sulawesi Province has the highest number of Covid-19 cases outside Java Island in Indonesia. This paper aims to determine the most suitable Bayesian spatial conditional autoregressive (CAR) localised models in modeling the relative risk (RR) of Covid-19 in South Sulawesi Province, Indonesia. Bayesian spatial CAR localised models with different hyperpriors were performed adopting a Poisson distribution for the confirmed Covid-19 counts to examine the grouping of Covid-19 cases. All confirmed cases of Covid-19 (19 March 2020-18 February 2021) for each district were included. Overall, Bayesian CAR localised model with G = 5 with a hyperprior IG (1, 0.1) is the preferred model to estimate the RR based on the two criteria used. Makassar and Toraja Utara have the highest and the lowest RR, respectively. The group formed in the localised model is influenced by the magnitude of the mean and variance in the count data between areas. Using suitable Bayesian spatial CAR localised models enables the identification of high-risk areas of Covid-19 cases. This localised model could be applied in other case studies.

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38

De Cola, Lee. "Spatial Forecasting of Disease Risk and Uncertainty." Cartography and Geographic Information Science 29, no. 4 (January 2002): 363–80. http://dx.doi.org/10.1559/152304002782008413.

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Parizkova, Martina, Ondrej Lerch, Ross Andel, Jana Kalinova, Hana Markova, Martin Vyhnalek, Jakub Hort, and Jan Laczó. "Spatial Pattern Separation in Early Alzheimer’s Disease." Journal of Alzheimer's Disease 76, no. 1 (June 30, 2020): 121–38. http://dx.doi.org/10.3233/jad-200093.

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Jacguez, G. M., and Bio Medware. "201 SPATIAL DISEASE CLUSTERING FOR INEXACT LOCATIONS." Epidemiology 6, no. 2 (March 1995): S41. http://dx.doi.org/10.1097/00001648-199503000-00231.

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Corberán-Vallet, A. "Prospective surveillance of multivariate spatial disease data." Statistical Methods in Medical Research 21, no. 5 (April 25, 2012): 457–77. http://dx.doi.org/10.1177/0962280212446319.

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Lessler, Justin, Henrik Salje, M. Kate Grabowski, and Derek A. T. Cummings. "Measuring Spatial Dependence for Infectious Disease Epidemiology." PLOS ONE 11, no. 5 (May 19, 2016): e0155249. http://dx.doi.org/10.1371/journal.pone.0155249.

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Couette, Maryline, Anne-Catherine Bachoud-Levi, Pierre Brugieres, Eric Sieroff, and Paolo Bartolomeo. "Orienting of spatial attention in Huntington's Disease." Neuropsychologia 46, no. 5 (2008): 1391–400. http://dx.doi.org/10.1016/j.neuropsychologia.2007.12.017.

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Davidsdottir, Sigurros, Alice Cronin-Golomb, and Alison Lee. "Visual and spatial symptoms in Parkinson’s disease." Vision Research 45, no. 10 (May 2005): 1285–96. http://dx.doi.org/10.1016/j.visres.2004.11.006.

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Jia, Peng, Weihua Dong, Shujuan Yang, Zhicheng Zhan, La Tu, and Shengjie Lai. "Spatial Lifecourse Epidemiology and Infectious Disease Research." Trends in Parasitology 36, no. 3 (March 2020): 235–38. http://dx.doi.org/10.1016/j.pt.2019.12.012.

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Thomas, R. W. "Some Spatial Representation Problems in Disease Modeling." Geographical Analysis 22, no. 3 (September 3, 2010): 209–23. http://dx.doi.org/10.1111/j.1538-4632.1990.tb00206.x.

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Deardon, Rob, Babak Habibzadeh, and Hau Yi Chung. "Spatial measurement error in infectious disease models." Journal of Applied Statistics 39, no. 5 (May 2012): 1139–50. http://dx.doi.org/10.1080/02664763.2011.644522.

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Leroux, Brian G. "Modelling spatial disease rates using maximum likelihood." Statistics in Medicine 19, no. 17-18 (2000): 2321–32. http://dx.doi.org/10.1002/1097-0258(20000915/30)19:17/18<2321::aid-sim572>3.0.co;2-#.

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Lawson, Andrew B., and Fiona L. R. Williams. "Spatial competing risk models in disease mapping." Statistics in Medicine 19, no. 17-18 (2000): 2451–67. http://dx.doi.org/10.1002/1097-0258(20000915/30)19:17/18<2451::aid-sim581>3.0.co;2-w.

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Steindorf, Vanessa, and Norberto Aníbal Maidana. "Modeling the Spatial Spread of Chagas Disease." Bulletin of Mathematical Biology 81, no. 6 (February 25, 2019): 1687–730. http://dx.doi.org/10.1007/s11538-019-00581-5.

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