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The opioid epidemic is a crisis that has plagued the United States (US) fordecades. One central issue is inequitable access to treatment for opioid usedisorder (OUD), which puts certain populations at a higher risk of opioidoverdose. We integrate a predictive dynamical model and a prescriptiveoptimization problem to compute high-quality opioid treatment facility andtreatment budget allocations for each US state. Our predictive model is adifferential equation-based epidemiological model that captures opioid epidemicdynamics. We use a process inspired by neural ODEs to fit this model to opioidepidemic data for each state and obtain estimates for unknown parameters in themodel. We then incorporate this epidemiological model into a mixed-integeroptimization problem (MIP) that aims to minimize opioid overdose deaths and thenumber of people with OUD. We develop strong relaxations based on McCormickenvelopes to efficiently compute approximate solutions to our MIPs with a meanoptimality gap of 3.99%. Our method provides socioeconomically equitablesolutions, as it incentivizes investments in areas with higher socialvulnerability (from the US Centers for Disease Control's Social VulnerabilityIndex) and opioid prescribing rates. On average, our approach decreases thenumber of people with OUD by 9.03 $\pm$ 1.772%, increases the number of peoplein treatment by 88.75 $\pm$ 26.223%, and decreases opioid-related deaths by0.58 $\pm$ 0.111% after 2 years compared to baseline epidemiological modelpredictions. Our solutions show that policy-makers should target addingtreatment facilities to counties that have less facilities than theirpopulation share and are more socially vulnerable. We demonstrate that ouroptimization approach should help inform these decisions, as it yieldspopulation health benefits in comparison to benchmarks based solely onpopulation and social vulnerability.

Equitable Data-Driven Facility Location and Resource Allocation to Fight the Opioid Epidemic