on developing and applying data-driven computational models of infectious diseases, particularly those with subcritical transmission (i.e. R < 1). A key goal has been to elucidate risk factors for disease emergence and quantify the impact of patient-specific or population-wide control interventions. I have applied my methods to neglected tropical diseases, zoonoses, vaccine-preventable diseases and antimicrobial resistance. My involvement in direct patient care provides a practical perspective of investigations and measurable metrics that can have immediate impact on clinical guidelines and public health. Current projects include:

Annual mass drug administration of azithromycin has proven remarkably effective in preventing blindness due to trachoma. Unfortunately, progress has fallen far short of the World Health Organization’s goal of achieving global control of trachoma by 2020. In countries such as Ethiopia, infection persists in problem districts after more than a decade of mass drug administration. Achievement of global control will require identification of problem districts, detection of hotspots within a district, and optimization of treatment strategies for hotspots. To enhance disease control, our team is using mathematical and statistical models to facilitate identification and control of trachoma hotspots.

Our group has developed methods for evaluating whether isolated clusters of zoonotic infection pose risk for endemic spread. Emerging zoonoses are unique in that the persistence of infection is dependent on repeated introductions of disease into the human population from an animal reservoir.  Subsequent human-to-human transmission may be limited, but superspreading and differential spread among certain high-risk groups can amplify the impact of animal-to-human spillover.  Understanding the degree of transmission heterogeneity and its root causes provides opportunities for targeted control.

 Our group is modeling the effectiveness of mitigation efforts to reduce spread infection in high-risk populations and developing tools for assessing the individual risk that a COVID-19 patient progresses to more severe disease. We are particularly interested in how socio-economic status and self-identified race impact clinical outcomes. We are also using models to evaluate the effectiveness of surveillance and preventive measures for reducing transmission of SARS-CoV-2 in congregate settings.

Our team is using mathematical modeling and machine learning approaches to build decision-making technologies that improve the risk assessment, prevention, and control of healthcare associated infections. Our proposed technologies account for spatial and temporal dynamics, provide continuous, real-time feedback to clinicians and are robust to changes in risk factors and disease prevalence over time. We concentrate our efforts on two of the most important healthcare-associated pathogens: methicillin-resistant Staphylococcus aureus and Clostridioides difficile infections.