Generic early warning indicators for vector-borne infections

My project aims at developing new methods to anticipate vector-borne disease outbreaks. I investigate if ecological theories such as the theory of critical slowing down and resilience indicators could be applied to epidemiological data. They could improve epidemics preparedness by predicting when and where an outbreak is about to occur, alerting Public Health authorities and thus improving timely response. I use epidemiological compartmental models to generate data and study how resilience indicators behave prior to an outbreak. I also want to integrate case studies based on real-world data into my work. Additionally, I am interested in exploring patterns in alternative data sources such as social media data, Google trends or sequence data. In my project I focus on several aspects of outbreaks anticipation: - In regards to critical slowing down indicators, I study the allocation of monitoring resources to yield the highest performance - As various sources of data can be combined when studying vector-borne disease, I aim to develop multivariate indicators taking advantage of this diversity of data - I am investigating artificial intelligence algorithms to anticipate upcoming epidemics - I want to explore alternative sources of data such as genetic data to anticipate spillover events