My research program focuses on three main areas to address evaluation challenges in the field of health:

1. Documenting Evaluation Innovation:
This area focuses on addressing the lack of empirical data on the effectiveness of emerging evaluation methods, such as realistic evaluation or arts-based evaluation. It aims to provide practical recommendations for the application of these methods to assist analysts and managers in choosing the most suitable approaches for their specific needs.

2. Understanding Sector-Specific Evaluation Capacities:
In this area, we explore evaluation capacities within specific sectors, such as aging or LGBTQ+ health, taking into account interactions between organizations. The goal is to develop a comprehensive understanding of these capacities to better address the unique evaluation needs of each sector.

3. Strengthening Evaluation Structures and Policies:
Building on the insights from the second area, this part investigates strategies to enhance the adoption and sustainability of evaluation practices within health organizations. It also explores the potential of artificial intelligence as a facilitative tool while carefully considering its ethical boundaries and implications.

My interdisciplinary training allows me to use tools from epidemiology, public health, informatics, and statistics to untangle the causes, forecast future burdens, and evaluate intervention effectiveness of vector-borne diseases. I am also interested in climate change implications for vectorborne diseases. Specifically, my research is focused on malaria, arboviruses (dengue, chikungunya, Zika), and most recently, Lyme disease. 

1. Evaluation of large-scale vector-borne disease interventions

I have been involved with evaluating the effectiveness of large-scale malaria interventions and programs including indoor residual spraying and universal bednet coverage in Uganda. I have recently begun to evaluate a community mobilization approach for arbovirus control in Fortaleza, Brazil with various partners. 

2. Infectious disease forecasting and spatiotemporal modelling

I am interested in applying different forecasting methods and data streams for disease burden estimations, and most recently exploring machine learning methods. I also use spatiotemporal methods to understand the patterns of disease emergence, identifying at-risk locations and time periods, and disease determinants.

3. Estimating the impact of climate change on vector-borne diseases (VBD)

Climate change will have important implications for future VBD and using different scenarios, we forecast future disease burdens using various methods.  We also consider sociodemographic changes and intervention scenarios in our work.

4. Improving disease surveillance

I am involved with various malaria surveillance projects which aim to integrate fragmented data sources and improve data harmonization. Most recently, we are evaluating the biases in reported arboviral cases in the national surveillance system in Colombia.