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Books on the topic 'Spatial disease'

Author: Grafiati
Published: 11 March 2023

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1

J, Jeger Michael, ed. Spatial components of plant disease epidemics. Englewood Cliffs, N.J: Prentice Hall, 1989.

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2

Thomas, R. W. Some spatial representation problems in disease modelling. Manchester: University of Manchester, Centre for Urban Policy Studies, 1988.

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3

Lai, Poh C. Spatial epidemiological approaches in disease mapping and analysis. Boca Raton: Taylor & Francis, 2009.

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4

Bhunia, Gouri Sankar, and Pravat Kumar Shit. Spatial Mapping and Modelling for Kala-azar Disease. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41227-2.

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5

Lai, Poh C. Spatial epidemiological approaches in disease mapping and analysis. Boca Raton: Taylor & Francis, 2009.

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6

Lawson, Andrew. Bayesian disease mapping: Hierarchical modeling in spatial epidemiology. Boca Raton: Taylor & Francis, 2008.

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7

Montana, Livia. Spatial modeling of HIV prevalence in Kenya. Calverton, MD: Macro International, 2007.

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8

Peter, Haggett, and Ord J. K, eds. Spatial aspects of influenza epidemics. London: Pion Ltd., 1986.

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9

Cliff, A. D. Spatial aspects of influenza epidemics. London, England: Pion Ltd., 1986.

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10

C, Gatrell Anthony, and North West Regional Research Laboratory., eds. Tests for spatial clustering in epidemiology: With special reference to Motor Neurone disease. Lancaster: North West Regional Research Laboratory, 1991.

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11

Nuttal, Pat, ed. Climate, ticks and disease. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789249637.0000.

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Abstract This book is a collection of 77 expert opinions arranged in three sections. Section 1 on "Climate" sets the scene, including predictions of future climate change, how climate change affects ecosystems, and how to model projections of the spatial distribution of ticks and tick-borne infections under different climate change scenarios. Section 2 on "Ticks" focuses on ticks (although tick-borne pathogens creep in) and whether or not changes in climate affect the tick biosphere, from physiology to ecology. Section 3 on "Disease" focuses on the tick-host-pathogen biosphere, ranging from the triangle of tick-host-pathogen molecular interactions to disease ecology in various regions and ecosystems of the world. Each of these three sections ends with a synopsis that aims to give a brief overview of all the expert opinions within the section. The book concludes with Section 4 (Final Synopsis and Future Predictions). This synopsis attempts to summarize evidence provided by the experts of tangible impacts of climate change on ticks and tick-borne infections. In constructing their expert opinions, contributors give their views on what the future might hold. The final synopsis provides a snapshot of their expert thoughts on the future.
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12

Zaremba, Jeffrey Donald. Spatial memory in health and disease: Hippocampal stability deficits in the Df(16)A+/- mouse model of schizophrenia. [New York, N.Y.?]: [publisher not identified], 2017.

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13

Page, Deaglan. Spatial learning deficits in rats carrying the Swedish mutation for amyloid precursor protein overexpression as a model for Alzheimer's disease. (s.l: The Author), 2001.

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14

Jessica, White, United Nations Development Programme. South East Asia HIV and Development Programme., and CICRED, eds. Development, spatial mobility and HIV/AIDS: A workshop on interrelations and programmatic responses, 1-3 September 2004, Paris, France. Bangkok: UNDP South East Asia HIV and Development Programme, 2004.

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15

Ölgen, M. Kirami. Spatial clustering of notifiable communicable diseases in Turkey. Istanbul: Kriter, 2020.

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16

Chen, Dongmei, Bernard Moulin, and Jianhong Wu, eds. Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118630013.

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17

Bosch, Frank van den. The velocity of spatial population expansion. [Amsterdam, Netherlands]: F. van den Bosch, 1990.

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18

Beirne, Raymond Oliver. Spatial vision for short-wavelength stimuli in normal and diseased eyes. [S.l: The Author], 2005.

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19

1938-, Majumdar Shyamal K., ed. Wildlife diseases: Landscape epidemiology, spatial distribution and utilization of remote sensing technology. Easton, PA: Pennsylvania Academy of Science, 2005.

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20

1965-, Sullivan Thomas, ed. Resource-constrained spatial hot spot identification. Santa Monica, CA: RAND, 2011.

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21

Nelson, Trisalyn Anna-Lisa. Large-area mountain pine beetle infestations: Spatial data representation and accuracy. Victoria, British Columbia: Pacific Forestry Centre, 2006.

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22

Nelson, Trisalyn Anna-Lisa. Large-area mountain pine beetle infestations: Spatial data representation and accuracy. Victoria, British Columbia: Pacific Forestry Centre, 2006.

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23

M, Liebhold Andrew, Barrett Hope R, United States Forest Service, Northeastern Forest Experiment Station (Radnor, Pa.), and Virginia Polytechnic Institute and State University, eds. Proceedings, spatial analysis and forest pest management: April 27-30, 1992, Mountain Lakes, Virginia. Radnor, Pa. (5 Radnor Corporate Center, 100 Matsonford Rd., Suite 200, Radnor 19087-8775): U.S. Dept. of Agriculture, Forest Service, Northeastern Forest Experiment Station, 1993.

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24

McKee, Gregory J. Modeling the effect of spatial externalities on invasive species management. Fargo, N.D: Dept. of Agribusiness and Applied Economics, Agricultural Experiment Station, North Dakota State University, 2006.

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25

Nelson, Trisalyn. Using conditional spatial randomization to identify insect infestation hot spots. Victoria, B.C: Pacific Forestry Centre, 2007.

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26

National Council on Radiation Protection and Measurements. Potential impact of individual genetic susceptibility and previous radiation exposure on radiation risk for astronauts. Bethesda, Md: National Council on Radiation Protection and Measurements, 2011.

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27

Joanne, White, Mountain Pine Beetle Initiative (Canada), and Pacific Forestry Centre, eds. Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery. Victoria, B.C: Canadian Forest Service, Pacific Forestry Centre, 2005.

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28

Bentz, Barbara J. Localized spatial and temporal attack dynamics of the mountain pine beetle in lodgepole pine. Ogden, UT (324 25th St., Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1996.

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29

Bentz, Barbara J. Localized spatial and temporal attack dynamics of the mountain pine beetle in lodgepole pine. Ogden, UT (324 25th St., Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1996.

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30

Bentz, Barbara J. Localized spatial and temporal attack dynamics of the mountain pine beetle in lodgepole pine. Ogden, UT (324 25th St., Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1996.

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31

George, Christakos, ed. Interdisciplinary public health reasoning and epidemic modelling: The case of Black Death. Berlin: Springer, 2005.

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32

Aukema, Brian H. Landscape-level analysis of mountain pine beetle in British Columbia: Spatiotemporal development and spatial synchrony within the present outbreak. Victoria, B.C: Pacific Forestry Centre, 2009.

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33

National Council on Radiation Protection and Measurements., ed. Information needed to make radiation protection recommendations for space missions beyond low-earth orbit. Bethesda, MD: National Council on Radiation Protection and Measurements, 2006.

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34

Isabel, Dyck, ed. Women, body, illness: Space and identity in the everyday lives of women with chronic illness. Lanham, MD: Rowman & Littlefield, 2002.

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35

Lai, Poh-Chin, Fun-Mun So, and Ka Wing Chan. Spatial Epidemiological Approaches in Disease Mapping and Analysis. Taylor & Francis Group, 2008.

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36

Bayesian Disease Mapping Hierarchical Modeling In Spatial Epidemiology. Taylor & Francis Inc, 2013.

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37

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Taylor & Francis Group, 2013.

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38

Spatial Epidemiological Approaches in Disease Mapping and Analysis. CRC, 2008.

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39

Keiding, Niels, Andrew B. Lawson, Terry Speed, Byron J. Morgan, and Peter Van Der Heijden. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Taylor & Francis Group, 2008.

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40

Shit, Pravat Kumar, and Gouri Sankar Bhunia. Spatial Mapping and Modelling for Kala-azar Disease. Springer, 2020.

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41

Lai, Poh Chin, Fun Mun So, and Ka Wing Chan. Spatial Epidemiological Approaches in Disease Mapping and Analysis. Taylor & Francis Group, 2009.

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42

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Taylor & Francis Group, 2008.

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43

Lai, Poh Chin, Fun Mun So, and Ka Wing Chan. Spatial Epidemiological Approaches in Disease Mapping and Analysis. Taylor & Francis Group, 2008.

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44

Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition. Chapman and Hall/CRC, 2018.

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45

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition. Taylor & Francis Group, 2018.

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46

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition. Taylor & Francis Group, 2018.

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47

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition. Taylor & Francis Group, 2013.

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48

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition. Taylor & Francis Group, 2018.

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49

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition. Taylor & Francis Group, 2018.

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50

Viboud, Cécile, Hélène Broutin, and Gerardo Chowell. Spatial-temporal transmission dynamics and control of infectious diseases: Ebola virus disease (EVD) as a case study. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789833.003.0004.

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Disentangling the spatial-temporal dynamics of infectious disease transmission is important to address issues of disease persistence, epidemic growth and optimal control. In this chapter, we review key concepts relating to the spatial-temporal dynamics of infectious diseases in meta-populations, whereby geographically separate subpopulations are connected by migration or mobility rates. We review the dynamics of colonization, persistence and extinction of emerging and recurrent pathogens in meta-populations; the role of demographic and environmental factors; and geographic heterogeneity in epidemic growth rate. We illustrate theoretical concepts by reviewing the spatial dynamics of childhood diseases and other acute infections in low- and middle-income countries, and provide a detailed description of the spatial-temporal dynamics of the 2014–16 Ebola epidemic in West Africa. We further discuss how increased availability of empirical data and recent methodological developments provide a deeper mechanistic understanding of transmission processes in space and time, and make recommendations for future work.
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