In the pursuit of innovative solutions to some of the world’s most pressing health challenges, my research revolves around constructing advanced statistical models for predicting infection outbreaks. My PhD and post-doctoral research focused primarily on understanding the evolution of epidemic and pandemic viruses. Currently, these models are tailored to forecast Dengue outbreaks in tropical countries and Influenza outbreaks in the US. My work is not just theoretical; along with the team in the Santillana lab, the Dengue forecasts I produce play a crucial role in supporting clinical trial programs conducted by Johnson and Johnson. Similarly, the Influenza models I’ve helped to develop are instrumental in contributing to the CDC’s Flusight project. The complexities of this research demand a multifaceted approach. I employ a diverse range of methods, bridging the domains of machine learning, infectious disease epidemiology, and time series forecasting. I use classic time series and multivariate methods as well as both custom-produced supervised and unsupervised learning techniques. Subsequent stages involve error analysis and the visualization of our findings. Collaboration is central to the success of these projects. I routinely interact with both governmental and non-governmental stakeholders. In addition, I am a physician and routinely communicate with clinical teams regarding the application of public health data to clinical problems.
