Assessing spatio-temporal risks of vector-borne diseases: an interdisciplinary view integrating ecological and epidemiological models

Vector-borne diseases are infectious diseases that are transmitted among vertebrate hosts by (typically arthropod) vectors. Among the whole world’s population, 80% is at risk of one or more vector-borne diseases, leading to an annual death toll of 700 000. These striking numbers are calling for urgent actions to prevent vector-borne diseases from emerging further. However, to apply preventions, we need to know where a risk exists; and if possible, when the prevention should take place.
The key to those two primary questions are risk maps, which are typically generated with ecological niche models or epidemiological models. Ecological niche models require occurrence records of the transmissions and the respective environmental variables (mostly long-term-averaged) to build a correlative model. This correlative model can be projected to a different spatial extent, or into future climate scenarios, etc., showing the spatial outbreak risk. Epidemiological models, on the other hand, look into the transmission process and thus require a good understanding of the transmission cycle of the investigated vector-borne disease. Epidemiological models can work with time-series data, and produce spatio-temporal risk maps based on the basic reproduction number R0. In practice, both ecological niche models and epidemiological models have their respective strengths and drawbacks. In this thesis, I contribute to the improvement of both approaches by analyzing some of their drawbacks and making suggestions for new standards.
For ecological niche models, the correlative models are highly dependent on the quality of occurrence records. In this thesis, I investigate how positional error, i.e. substituting the geographical centroid of the respective administrative spatial unit for unknown occurrence records, affects model performance in the context of varying grain size of environmental data. I quantify the decrease of model performance caused by the use of geographical centroids and varying grain size, respectively. As a consequence, I suggest that special cautions should be given when geographical centroids are applied as substitutes; when possible, central tendency values should be preferred.
For epidemiological models, I review the common ways to generate risk maps and illustrate them with an example. I demonstrate that using different temporal aggregation methods affects the comparability and the quantity information of the resulting maps; and that via different visualization methods, two fundamentally different maps can appear very similar, and vice versa. Consequently, I highlight the importance of using appropriate temporal aggregations and visualizations and give suggestions for best practice. I recommend to show both intensity and duration of the risk, using small time-steps to show spatio-temporal dynamics when possible.
Pushing towards new standards for best practice in vector-borne disease risk mapping, I directly compare ecological niche models and epidemiological models, using Usutu virus as an example. The results from the parallel-model approach shows that relying on a single model for assessing vector-borne disease risk may lead to incomplete conclusions. For future research, it is crucial to realize this and aim to apply different modelling approaches for risk-assessment of under-studied emerging pathogens like Usutu virus.