Current Research and Scholarly Interests
Our research group is interested in studying the transmission of infectious diseases and impact of public health interventions with an ultimate goal of informing public health policy. We study a diverse set of pathogens, both domestically and internationally, including vaccine-preventable infections (including COVID-19) and neglected parasitic diseases (such as schistosomiasis). Our group applies diverse computational methodologies, including tools from fields of epidemiology, mathematical and statistical modeling, simulation, and policy analysis.
A large emphasis of our work is translating scientific evidence into public health policy. Our track record includes multiple studies that have changed policy in the fields of neglected parasitic diseases and COVID-19. We work closely with policy organizations like the World Health Organization and the California Department of Public Health. Nathan was the lead writer of the World Health Organization guidelines on schistosomiasis (2022) and strongyloidiasis (2024).
Our current research focuses on the following areas:
(1) Vaccine-preventable infectious diseases (including COVID-19) in the United States, with a focus on studying vaccines and transmission dynamics
(2) Public health strategies for control and elimination of globally important neglected infectious diseases, such as helminths infections (schistosomiasis, strongyloidiasis) and typhoid fever
Our currently funded NIH projects include:
(1) Real-time predictive modeling for public health departments to control infectious diseases (DP2 AI170485)
(2) Precision mapping of Schistosoma mansoni risk for targeted public health control and elimination (R01 AI179771)
Hiring
We are seeking to fill multiple research positions at all levels. Candidates interested in working on computational public health research related to infectious diseases with a strong quantitative background are highly encouraged to apply. If you an interested, please submit a cover letter, CV, and names of two references to Nathan.Lo@stanford.edu.
