Discrete-event simulation modeling for housing of homeless populations. - Dashi I Singham; Jennifer Lucky; Stephanie Reinauer (PLoS ONE, 2023)
The San Francisco Bay Area has experienced a rapid rise in homelessness over the past decade. There is a critical need for quantitative analysis to help determine how to increase the amount of housing to meet the needs of people experiencing homelessness. Noting that the shortage of housing available through the homelessness response system can be modeled as a queue, we propose a discrete-event simulation to model the long-term flow of people through the homelessness response system. The model takes as input the rate of additional housing and shelter available each year and delivers as output the predicted number of people housed, sheltered, or unsheltered in the system. We worked with a team of stakeholders to analyze the data and processes for Alameda County in California and use this information to build and calibrate two simulation models. One model looks at aggregate need for housing, while the other differentiates the housing needs of the population into eight different types. The model suggests that a large investment in permanent housing and an initial ramp up of shelter is needed to solve unsheltered homelessness and accommodate future inflow to the system.