Grant: New Investigator Award-PA:Ontario HIV Treatment Network-Health Serv/ Pop Health

Epidemics of HIV and other sexually transmitted infections (STIs) are costly - they cost lives, health, and money. With shrinking funds, difficult choices are being made about how best to protect the most vulnerable from HIV/ STIs. Mathematical models can help decision-makers by reproducing epidemics with computer simulations and letting us run what-if experiments to (a) characterize local epidemics; (b) to compare the potential effectiveness and cost-effectiveness (health benefit per additional cost to the health system) of different HIV/STI prevention strategies; and (c) to identify the most important data we should collect to better understand and monitor local epidemics. Over the next 5 years, I will work with public health decision-makers, community-researchers, and data scientists, and use the best available local data to build models of HIV and STI epidemics and address the following specific objectives: (1) to characterize the syphilis and HIV epidemic among men who have sex with men (MSM) and estimate the potential effectiveness and cost-effectiveness of different syphilis control strategies in reducing syphilis and HIV among MSM in Canada; (2) to characterize and understand the role of early HIV risk in fueling HIV epidemics among Canadians born in Africa or the Caribbean, and among female sex workers in low- and middle-income countries; and (3) to examine the influence of individual and health system characteristics and uncertainties in the data on model projections in order to optimize HIV/STI surveillance. Potential impact: This research program will provide an important, transparent, and reliable evidence base for policy-decisions and for the design of efficient and sustainable HIV and STI programs in Canada and other settings. Research results have the potential to dramatically improve how we currently deliver these programs, and thereby improve the health and well-being of people with and at risk of HIV and STIs.