Ingrid van de Leemput, Egbert van Nes
Description PhD project
Modelling complex systems such as the spread of vector borne diseases can help us understand the underlying mechanisms of transmission. The system can be analyzed and we can study its equilibria and the potential changes in the stability that occurs as a result of a perturbation close to a critical point. When a system approaches such a point it displays a critical slowing down resulting in an increase of variance or autocorrelation, which can be used to anticipate a shift in the system.
We will apply this theory to the dynamics of vector borne diseases. Detecting these indicators in real time can allow to anticipate outbreaks in advance and build a suitable response.
We will study various models and try to implement new ones, considering several aspects of the dynamics such as the space disparity, control mechanisms and variety of vectors. We will explore simulated data but also data from various sources such as social media data or google search behavior.
Research questions / objectives
The following objectives are preliminary and will be updated in the proposal.
- Can critical slowing down in the dynamics of vectors be used as resilience indicator?
Can these indicators improve predictions? Overview of other methods. Data from ecological models
- Can spatial explicit resilience indicators help prediction of vector borne diseases?
Model generated spatial data, when can we use spatial data for predictions
- Can we use social media data to predict the outbreak if infectious diseases?
Maybe not only vector borne diseases?
- Applying resilience indicators to data of vector borne diseases?
Use data from the project?
Tags matching with the contents of track 11
- Species distribution: We will use various models for analysis.
- Epidemiological modelling: We will use various models for analysis.
- Ecological modelling: We will use various models for analysis.
- Vector abundance: If data is available about vector abundance, it can be used to calibrate the model.
- Vector movement patterns, migration: We will try to use spatial explicit models in our analyses.
- Vector movement patterns, dispersal: We will try to use spatial explicit models in our analyses.
- Vector susceptibility: This is an important parameter to take into account in the models.
- Vector host-preference: This is an important parameter to take into account in the models.
- Vector competence: This is an important parameter to take into account in the models.
- Host abundance: If data is available about host abundance, it can be used to calibrate the model.
- Host movement patterns: This is an important aspect to take into account in the models.
- Host susceptibility: This is an important parameter to take into account in the models.
- Climate: It could be interesting to include seasonality in the models.
- Improved surveillance( response surveillance): Early warning indicators can be used as a signal to detect the start of an outbreak.