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Spatial modelling and simulation for disease surveillance

RAJABI GURANDANI, MOHAMMADREZA LU (2017)
Abstract
This thesis addresses the application of spatial approaches to disease surveillance and control from three different aspects. First, temporal trends, geographical distribution and spatial pattern of cardiovascular disease admission rates were explored on a national level in Sweden. Global Moran’s I was used to explore the structure of patterns and Anselin’s local Moran’s I was applied to explore the geographical patterns of admission rates. Second, the impact of landscape structure and environmental and socioeconomic conditions on disease elements were used to create susceptibility maps. Machine learning algorithms such as artificial neural networks, logistic regression and fuzzy operators were applied to model Visceral Lesihmaiasis (VL),... (More)
This thesis addresses the application of spatial approaches to disease surveillance and control from three different aspects. First, temporal trends, geographical distribution and spatial pattern of cardiovascular disease admission rates were explored on a national level in Sweden. Global Moran’s I was used to explore the structure of patterns and Anselin’s local Moran’s I was applied to explore the geographical patterns of admission rates. Second, the impact of landscape structure and environmental and socioeconomic conditions on disease elements were used to create susceptibility maps. Machine learning algorithms such as artificial neural networks, logistic regression and fuzzy operators were applied to model Visceral Lesihmaiasis (VL), and generate predictive risk maps for two endemic areas in northwestern Iran and southern Caucasus. Third, the dynamicity of a complex vector-borne disease, Cutaneous Leishmaniasis (CL), were simulated based on the interactions between vectors, hosts and reservoirs using agent-based modeling approaches. A Susceptible-Exposed-Infected-Recovered (SEIR) approach together with Bayesian modeling has been applied in the model to explore the spread of CL. The model is adapted locally for Isfahan Province, an endemic area in central Iran. The results from this thesis demonstrate how spatial approaches facilitate creating road maps for policy making and resource planning processes of healthcare authorities. (Less)
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author
supervisor
opponent
  • Professor Hansen, Henning Sten, Aalborg University Copenhagen, Denmark
organization
publishing date
type
Thesis
publication status
published
subject
keywords
GISgeografiska informationssystem, Agent Based Modeling, spatial epidemiology, machine learning
pages
65 pages
publisher
Lund University, Faculty of Science, Department of Physical Geography and Ecosystem Science
defense location
Lecture hall “Pangea”, Geocentrum II, Sölvegatan 12, Lund
defense date
2017-11-29 10:00
ISBN
978-91-85793-88-4
978-91-85793-87-7
language
English
LU publication?
yes
id
aaf4bd2e-2009-4df4-a03d-5262f05f4f01
date added to LUP
2017-11-02 14:57:49
date last changed
2017-11-23 10:09:01
@phdthesis{aaf4bd2e-2009-4df4-a03d-5262f05f4f01,
  abstract     = {This thesis addresses the application of spatial approaches to disease surveillance and control from three different aspects. First, temporal trends, geographical distribution and spatial pattern of cardiovascular disease admission rates were explored on a national level in Sweden. Global Moran’s I was used to explore the structure of patterns and Anselin’s local Moran’s I was applied to explore the geographical patterns of admission rates. Second, the impact of landscape structure and environmental and socioeconomic conditions on disease elements were used to create susceptibility maps. Machine learning algorithms such as artificial neural networks, logistic regression and fuzzy operators were applied to model Visceral Lesihmaiasis (VL), and generate predictive risk maps for two endemic areas in northwestern Iran and southern Caucasus. Third, the dynamicity of a complex vector-borne disease, Cutaneous Leishmaniasis (CL), were simulated based on the interactions between vectors, hosts and reservoirs using agent-based modeling approaches. A Susceptible-Exposed-Infected-Recovered (SEIR) approach together with Bayesian modeling has been applied in the model to explore the spread of CL. The model is adapted locally for Isfahan Province, an endemic area in central Iran. The results from this thesis demonstrate how spatial approaches facilitate creating road maps for policy making and resource planning processes of healthcare authorities.},
  author       = {RAJABI GURANDANI, MOHAMMADREZA},
  isbn         = {978-91-85793-88-4},
  keyword      = {GISgeografiska informationssystem,Agent Based Modeling,spatial epidemiology,machine learning},
  language     = {eng},
  pages        = {65},
  publisher    = {Lund University, Faculty of Science, Department of Physical Geography and Ecosystem Science},
  school       = {Lund University},
  title        = {Spatial modelling and simulation for disease surveillance},
  year         = {2017},
}