Model-based prediction of outbreak dynamics of nephropathia epidemica using climate and vegetation data

Wildlife-originated zoonotic diseases in general are a major contributor to emerging infectious diseases. Fifteen emerging zoonotic or vector-borne infections with increasing impact on humans in Europe were identified during the period 2000-2006. Global climate change may be a major contributor to the spread of these zoonotic diseases. Rodent borne hantavirus infections are part of this list. Puumala virus (PUUV), hosted by the bank vole (Myodes glareolus), is such a hantavirus. It is common over vast areas of Europe and causes a general mild form hemorrhagic fever with renal syndrome (HFRS) called nephropathia epidemica (NE).

It is well established that climate is an important determinant of the spatial and temporal distribution of vectors (in epidemiology, a vector is any agent -person, animal or microorganism- that carries and transmits an infectious pathogen into another living organism) and pathogens. Therefore a change in climate is expected to cause changes in the geographical range, seasonality (inter annual variability) and in the incidence rate (with or without changes in geographical or seasonal patterns) of NE outbreaks.

The main aim of this dissertation is in developing modelling approaches for monitoring and predicting NE outbreaks by taking into account the most significant environmental factors which affect the temporal and spatial pattern of NE cases by using compact model structures that take into account climate and vegetation data.

In the chapters 2 to 6 of this dissertation we discussed in detail how data-based (mechanistic) models can be used to model and predict outbreaks of nephropatia epidemica (NE) as a basis for the development of disease prevention and control strategy. In contrast with the mechanistic modelling approach, data-based modelling techniques identify the dynamic characteristics of processes based on measured data and are as such (initially) not based on a priori process knowledge.

In this dissertation, we discussed how knowledge obtained from mechanistic epidemiological population models can be used to improve the data-based model structures. In chapter 2, we discussed the importance of the carrying capacity for modelling the NE prevalence. Furthermore, we discussed the link between carrying capacity and the forest phenology which explains the possibility of predicting NE outbreaks based only on the climatological and vegetation data, without any knowledge of the bank vole’s population dynamics (chapter 3). In the second part of this thesis we described a modelling approach to predict the NE outbreaks by taking into account measured population dynamics of the bank voles only and knowledge from a mechanistic epidemiological model (Chapter 3 and 4).

Human hantavirus epidemics have often been explained by bank vole abundance. Therefore in order to be able to control and prevent the occurrence of the NE cases (as an example of zoonotic disease), it is important to detect and monitor the environmental factors that affect the spatial and temporal variations of the bank vole. A method was described to produce maps of potential geographical distribution of bank voles in Western Europe based on occurrence data points of bank voles and climate information and land cover maps (chapter 5) and in chapter 6 we modelled the bank vole population dynamics in Belgium and Finland using a data-based modelling approach.

The results of the current study help to define significant environmental factors on the spread of the disease. Developing a dynamic data-based mechanistic modelling approach for NE may form the basis of an expert tool to predict and prevent the incidence of NE cases by making use of remote sensing tools for measuring broad leaves forest phenology and monitoring the vegetation dynamics together with climatological data.