Vector-borne diseases are a rising threat in the Netherlands due to its water dominated landscape, dense humans and livestock population in addition to climate change which makes it a suitable habitat for mosquitoes. Vector-borne disease outbreaks are most probably tipping points, shifting to the endemic state because of a gradual change in its properties.
When a complex system approaches a tipping point, it displays generic dynamical symptoms such as critical slowing down. We want to examine how those fundamental principles may apply to the dynamics of vector-borne disease outbreaks. We will explore how to detect such signals in simulated data in the first place, and then in real surveillance data. If detected effectively, such indicators could be used to anticipate future vector-borne disease outbreaks.