Habitat models are increasingly used not only for modeling the spread of disease vectors and
hosts, but also of complex disease transmission systems in terms of a black box. In this thesis
an overview of habitat models for geographical spread of infectious diseases by means of a
literature research is given. Thereby 137 studies were selected, dealing with 51 different
pathogens. These studies include a total of 174 habitat models developed with nine different
model algorithms. Machine learning methods were most frequently applied (76%), the second
most used approaches were regression models (21%) and profile methods and BIOMOD
account for the remaining 3%. The Maxent algorithmus was the most common application out
of the machine learning methods (39% of all models). Over the years, the proportion of
machine learning methods has increased markedly compared to regression models. The most
models were created for zoonotic pathogens (total of 123), for human pathogens there are 16
models and for veterinary pathogens 35 models. With a total of 20 models avian influenza
viruses (Avian influenza virus, H1N1, H5N1, H7N9 virus) were the most commonly modeled
pathogens. Second place is taken by
Batrachochytrium dendrobatidis
, a fungal disease of
amphibians (17 models) and in third place by the Hantavirus (14 models). In 13 studies
projections for future distribution of pathogens using climate change scenarios were
examined. By far the most frequently cited paper (803 citations) describes the global spread of
dengue.