Research
Ongoing Work
Susceptibility as a Continuous Function
A simulation study to explore the impact of modelling susceptibility as a continuous function, similar to modelling time-varying viral kinetics.
Critical Slowing Down for Outbreak Forecasting
Pre-Print
A simulation study of how the accuracy of outbreak Early Warning Systems developed using Critical Slowing Down metrics are impacted by uncertainty generated through imperfect diagnostic tests in the context of co-circulating infections.
Outbreak Detection and Surveillance
Pre-Print
A simulation study of the potential use and effectiveness of a rapid diagnostic test (RDT) in the surveillance of, and outbreak detection for, measles in near-elimination status regions. This project was conducted for Gavi and the WHO to assist in guiding the target product profile for a potential measles RDT for public health surveillance purposes.
Measles in the DRC
An ongoing collaboration with Médecin Sans Frontières’ (MSF) Epicentre unit to analyze measles seroprevalence in the ex-Katanga region of the Democratic Republic of Congo (DRC). The intial project examined the role of new laboratory testing facilities on the speed of diagnosis and outbreak response decisions. The second project examines the impact of Supplemental Immunization Activities in the ex-Katanga region; principally, characterizing the spatial and age-specific seronegativity, and examining the relationship between optical density (OD) distributions and seropositivity thresholds using finite mixture models and generalized additive models.
Prior Projects
Foot-and-Mouth Disease in India
Worked closely with Indian government partners to digitize serological survey data
Data4Action (COVID-19 at Penn State)
Paper 1
Paper 2
A longitudinal cohort study, comprising of two geographically related cohorts, that aims to examine the effect of Penn State University students on the SARS-CoV-2 incidence in the surrounding community. We have recently published a the interim serological results in Nature Scientific Reports, which can be found here. The second part of the project focuses on relating the differential exposures observed in the student cohort to latent risk profiles, examining the potential impact of interventions to reduce infections.
Waning Measles Immunity Among Infants in Canada
Paper 1
Paper 2
Paper 3
A prospective cross-sectional serology study of Canadian newborns and mothers assessing the rate of waning measles, varicella, and mumps antibodies in infants, including subgroup analysis by vaccination status. A retrospective analysis of waning measles antibody titers (PRNT) in stored sera from a separate cohort of Canadian newborns was published as part of the study, and can be found here.
Teaching
An Introduction to Git and GitHub
Designed and create a 2-hour workshop on the basics of Git and GitHub to graduate students, postdocs, and faculty at the Center for Infectious Disease Dynamics at Penn State University. Focusses on building a conceptual understanding of Git and GitHub, and how it can be used to improve research workflows by providing clear examples that are directly relevant to infectious disease researchers.
SISMID Modeling Infectious Diseases
Created the website and rewrote the teaching materials for the 2023 SISMID module 2 (Mathematical Models of Infectious Diseases), that introduced students to the basics of mathematical modeling of infectious diseases. Additionally, I wrote and delivered the lecture on understanding heterogeneity in models.
Julia for Epidemiologists
I am currently in the process of (slowly) writing a book on using Julia for epidemiological research. The book is aimed at epidemiologists with no prior programming experience, and will cover the basics of Julia, and how to use it for epidemiological research. The EpiRHandbook is an excellent resource for new and experienced epidemiologists, but Julia has many advantages over R (and certainly some disadvantages), and I hope this book will serve a similar purpose and help epidemiologists thinking about the transition to Julia.
AI for Epidemiologists
I am currently in the process of writing a book on how scientists and epidemiologists can integrate AI tools into their research process to improve the quality of their code and, therefore, research. The book is aimed at epidemiologists with limited exposure to software development best-practices, such as unit testing and documentation - a common set of measure often missing from academic software.