Introduction
The COVID-19 pandemic severely disrupted hospital nurse labor supply and wage dynamics through deteriorating working conditions, heightened occupational stress, and epidemiological pressures, exacerbating pre-existing nursing shortages. Federal relief funds were allocated to mitigate staffing shortfalls, but their effects on healthcare labor markets remain poorly characterized.
Methods
We developed a behavioral economic model using a system of coupled ordinary differential equations (ODEs) to simulate hospital nurses’ supply and wage dynamics at the national aggregate level during the COVID-19 pandemic. Epidemiological time-series (COVID-19 cases, hospitalizations, deaths) were provided as exogenous inputs. A perceived pandemic work burden variable—modeled as an exponential weighted moving average (EWMA) of fatigue and mental anguish proxies—drives nurse supply through a sigmoid-shaped supply surface. Travel nurse substitution dynamics and hospital relief fund eligibility constraints are incorporated. The model was calibrated using 15,000 Latin Hypercube Sampling prior runs and an Incremental Mixture Approximate Bayesian Computation (IMABC) procedure, converging to 500 posterior accepted runs.
Results
The calibrated model reproduces observed dynamics for staff and travel nurse workforce participation and wages during the pandemic. Scenario analyses contrasting constrained and unconstrained hospital spending on travel nurses (governed by the parameter λC) reveal a fundamental tension in relief fund design: unrestricted reimbursement reduces immediate staffing gaps but incentivizes excessive reliance on contract labor, while overly stringent requirements deter legitimate fund applications, particularly from rural and safety-net hospitals.
Discussion
This conceptual model demonstrates that it is feasible to jointly simulate epidemiological dynamics and nurse labor market responses in a unified ODE framework. The model provides a foundation for investigating the effectiveness of pandemic relief programs and informing staffing policy for future public health emergencies, and is well-suited for embedding within robust decision-making frameworks.
