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 demograph