Improving genomic epidemiology methodologies and practice through interdisciplinary data integration and analysis

Infectious diseases as shown by the COVID-19 pandemic, remains a serious threat. Genomic sequencing has revolutionized the detection and characterization of pathogens for surveillance and outbreak investigation, creating the new field of genomic epidemiology. During this ongoing pandemic, we have witnessed several gaps in establishing effective global responses that require coordinated action such as our ability to quickly adapt analytical methods to new pathogens and the ability to integrate several data sources to generate knowledge for enabling evidence-informed decision making. In this proposed research, I aim to further this field of genomic epidemiology by developing advanced data analysis methods. Additionally, I aim to optimize these methods to be capable of adapting to datasets from various pathogens, saving time to develop again for every outbreak. Finally, I want to combine genomics and advanced data analysis (bioinformatics) to establish a method of integrating epidemiological, political, and other contextual information with genomic data to improve public health preventive measures. This project will develop a program to use intersectoral genomic epidemiology for countering infectious diseases.