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Project
Spatio-temporal modelling of the epidemiology of nephropathia epidemica and Lyme borrelosis
The incidence of vector-borne diseases can be understood as the output of a complex interplay among three components: the vector/host, the pathogen and the environment. The environmental factors that determine disease risk are those affecting the habitat conditions for vectors/hosts, the presence and prevalence of pathogens and the human exposure to pathogens. Monitoring these factors is a major task in epidemiology that, giventhe complexity of the underlying mechanisms, often demands interdisciplinary approaches.
Nephropathia epidemica (NE) and Lyme borreliosis (LB) are vector-borne diseases for which awareness has increased in Western Europe as remarkable outbreaks for both diseases have been reported in recent years. NE is caused by the Puumala</> virus (PUUV) hosted by the bank vole Myodes glareolus</>. Humans get in contact with the virus by inhalation of aerosolized dry excreta. LB is caused by the spirochaete Borrelia burgdorferi</> that can be harbored by rodents (like Myodes glareolus</>), birds, reptiles, amongst other. This pathogen reaches humans by means of bites of the tick Ixodes ricinus</>.
The life cycle and demography of these vector/hosts organisms is tightly related to the physical characteristics andthe dynamics of the vegetative systems supporting their populations. The spatial attributes (location, size, adjacency to urban centres) of these vegetated areas is also an important determinant of the spatial spread of the diseases.
The framework of this research was an interdisciplinary initiative aimed at assessing the potential use of methods anddatasets from geomatics engineering in modelling the epidemiology of NEand LB. The prominent role of vegetative systems in NE and LB suggests that considering the spatial spread of vegetative systems and monitoringvegetation processes in time can support NE and LB epidemiologic modelling. In this regard, spaceborne remote sensing of vegetation is particularly interesting as it delivers georeferenced datasets on vegetation-related phenomena at regular time steps.
This dissertation is organized in seven chapters. The introductory chapter gives a general description of NE and LB, the organisms involved in the transmission of the pathogens and the determinants of disease risk. It offers also some considerations on the computation of disease risk and basic principles on the use of remote sensing to study vegetation-related phenomena.
Chapter 2 is based on evidences on the role of vegetation phenology in the demography of rodents. In this Chapter, phenology metrics of forested areasin southern Belgium were derived from time series of vegetation indicescomputed from remotely sensed reflectance values. These metrics were contrasted to reported NE occurrences in the neighboring areas. The focus in this chapter is broad-leaved forest patches located in the part of the country with the highest incidence values. The obtained results verified correspondence between the trend in seasonal indicators of vegetationactivity as derived fromremote sensing observations and the reported NE number of cases in the studied area.
Chapter 3 was based on the known fact that humidity is a major factor in tick ecology. This chapter presents an analysis of time series of two remotely sensed indices with sensitivity to vegetation greenness and moisture as explanatory variables of LB incidence. The study was conducted in two high incidence areas (northern and southern Belgium) where landscape conditions are different and evaluates the performance of two moisture-sensitive vegetation indices. These vegetation indices rely on the sensitivity of the short-wave infrared region to humidity. The difference between the two indices was the utilized specific wavelength range on the short-wave infrared region.The results suggested that
conducting multiresolution analysis on time series of moisture sensitive vegetation indices can reveal seasonal or annual patterns that may have impacted disease incidence. Moreover, the resulted also showed that the performance of the two evaluated indicesdiffered in function of characteristics of the vegetated areas.
Chapter 4 highlights the connection between NE and LB disease risk and land cover classes and landscape features. In this Chapter, tree-structured regression was implemented to investigate the associations between the spatial spread of NE and LB and landscape attributes. The studied was based on the CORINE land cover map and assessed the impact of different sampling settings in the derived associations.
Chapter 5 proposedan adaptation of one of the most commonly used spatial interaction models: the gravity model. This spatial interaction model has been applied in different fields where the response variable is a function of the attraction (or an analogous measure) between two bodies. For our application, the tested hypothesis was that distance between vegetated and urban areas in combination with the size of the areas could model disease risk by means of a gravity model analysis.
Given the existence of occupational risk in the diseases under study, an indicator of exposure through the conduction of outdoor labor activity was used as well. The results were satisfactory and show the suitability of both land cover maps andspaceborne datasets to derive information on vegetated areas for implementing the model.
Chapter 6 focused on the identification of seasonal indicators of vegetation conditions as potential predictors to be used in spatio-temporaldisease risk models. Based on the results of Chapters 2 and 3, a number of annual indicators of vegetation activity were derived; additionally, the notion of Growing Degree Days (GDD) was applied to obtain seasonal GDD values. By means of multivariate analysis, the set of more suited indicators was chosen for northern and southern Belgium. The results showed that the best set of indicators was obtained whenindicators based solely on meteorologic datasets (GDD) were combined with indi-
cators obtained from remote sensing signals.
Finally, general conclusions, main constraints as well as perspective for further research are summarized in Chapter 7.
Nephropathia epidemica (NE) and Lyme borreliosis (LB) are vector-borne diseases for which awareness has increased in Western Europe as remarkable outbreaks for both diseases have been reported in recent years. NE is caused by the Puumala</> virus (PUUV) hosted by the bank vole Myodes glareolus</>. Humans get in contact with the virus by inhalation of aerosolized dry excreta. LB is caused by the spirochaete Borrelia burgdorferi</> that can be harbored by rodents (like Myodes glareolus</>), birds, reptiles, amongst other. This pathogen reaches humans by means of bites of the tick Ixodes ricinus</>.
The life cycle and demography of these vector/hosts organisms is tightly related to the physical characteristics andthe dynamics of the vegetative systems supporting their populations. The spatial attributes (location, size, adjacency to urban centres) of these vegetated areas is also an important determinant of the spatial spread of the diseases.
The framework of this research was an interdisciplinary initiative aimed at assessing the potential use of methods anddatasets from geomatics engineering in modelling the epidemiology of NEand LB. The prominent role of vegetative systems in NE and LB suggests that considering the spatial spread of vegetative systems and monitoringvegetation processes in time can support NE and LB epidemiologic modelling. In this regard, spaceborne remote sensing of vegetation is particularly interesting as it delivers georeferenced datasets on vegetation-related phenomena at regular time steps.
This dissertation is organized in seven chapters. The introductory chapter gives a general description of NE and LB, the organisms involved in the transmission of the pathogens and the determinants of disease risk. It offers also some considerations on the computation of disease risk and basic principles on the use of remote sensing to study vegetation-related phenomena.
Chapter 2 is based on evidences on the role of vegetation phenology in the demography of rodents. In this Chapter, phenology metrics of forested areasin southern Belgium were derived from time series of vegetation indicescomputed from remotely sensed reflectance values. These metrics were contrasted to reported NE occurrences in the neighboring areas. The focus in this chapter is broad-leaved forest patches located in the part of the country with the highest incidence values. The obtained results verified correspondence between the trend in seasonal indicators of vegetationactivity as derived fromremote sensing observations and the reported NE number of cases in the studied area.
Chapter 3 was based on the known fact that humidity is a major factor in tick ecology. This chapter presents an analysis of time series of two remotely sensed indices with sensitivity to vegetation greenness and moisture as explanatory variables of LB incidence. The study was conducted in two high incidence areas (northern and southern Belgium) where landscape conditions are different and evaluates the performance of two moisture-sensitive vegetation indices. These vegetation indices rely on the sensitivity of the short-wave infrared region to humidity. The difference between the two indices was the utilized specific wavelength range on the short-wave infrared region.The results suggested that
conducting multiresolution analysis on time series of moisture sensitive vegetation indices can reveal seasonal or annual patterns that may have impacted disease incidence. Moreover, the resulted also showed that the performance of the two evaluated indicesdiffered in function of characteristics of the vegetated areas.
Chapter 4 highlights the connection between NE and LB disease risk and land cover classes and landscape features. In this Chapter, tree-structured regression was implemented to investigate the associations between the spatial spread of NE and LB and landscape attributes. The studied was based on the CORINE land cover map and assessed the impact of different sampling settings in the derived associations.
Chapter 5 proposedan adaptation of one of the most commonly used spatial interaction models: the gravity model. This spatial interaction model has been applied in different fields where the response variable is a function of the attraction (or an analogous measure) between two bodies. For our application, the tested hypothesis was that distance between vegetated and urban areas in combination with the size of the areas could model disease risk by means of a gravity model analysis.
Given the existence of occupational risk in the diseases under study, an indicator of exposure through the conduction of outdoor labor activity was used as well. The results were satisfactory and show the suitability of both land cover maps andspaceborne datasets to derive information on vegetated areas for implementing the model.
Chapter 6 focused on the identification of seasonal indicators of vegetation conditions as potential predictors to be used in spatio-temporaldisease risk models. Based on the results of Chapters 2 and 3, a number of annual indicators of vegetation activity were derived; additionally, the notion of Growing Degree Days (GDD) was applied to obtain seasonal GDD values. By means of multivariate analysis, the set of more suited indicators was chosen for northern and southern Belgium. The results showed that the best set of indicators was obtained whenindicators based solely on meteorologic datasets (GDD) were combined with indi-
cators obtained from remote sensing signals.
Finally, general conclusions, main constraints as well as perspective for further research are summarized in Chapter 7.
Date:1 Oct 2008 → 3 Sep 2013
Keywords:Lyme disease
Disciplines:Forestry sciences
Project type:PhD project