The motivation for the work stems from a grim statistical reality. Roughly 1.4 million Americans live in nursing homes, and within the first year of the pandemic, most facilities in the United States experienced outbreaks, with residents accounting for an estimated 30 to 50 percent of COVID-19 deaths. Previous modeling studies have largely concentrated on resident-focused measures such as isolation, personal protective equipment, testing, and visitor restrictions. Far fewer have asked how the structure of the workforce itself—staff shortages, low wages, multiple job holding, and weak benefits—shapes the epidemiology of infection inside these buildings.

That gap matters because the economics of nursing home work create well-documented transmission risks. Low compensation pushes many direct-care workers to hold jobs at two or even three facilities simultaneously, moving between buildings each week and potentially carrying the virus with them. The absence of paid time off means that a symptomatic worker who cannot afford unpaid leave may clock in anyway. And despite strong evidence that vaccination protects both staff and residents, skilled nursing facilities report some of the lowest vaccination rates among healthcare settings, reflecting persistent hesitancy and the lack of comprehensive institutional policies.

To capture these dynamics, the team, led by Abigail Sweet of North Carolina State University’s Biomathematics Graduate Program, constructed an agent-based model in which every individual—resident, healthcare personnel, or community member—is represented as a discrete agent moving through infection and hospitalization states. The synthetic populations were derived from 2010 U.S. Census data using the Framework for Reconstructing Epidemiological Dynamics, or FRED, which supplies demographic and spatial detail representative of the actual two million people living across Franklin, Jefferson, St. Charles, and St. Louis counties and the City of St. Louis. Zip-code-level data on social determinants of health were layered on to modulate mortality and hospitalization risk, while facility-specific characteristics such as bed capacity came from Centers for Medicare and Medicaid Services records and the LTCFocus dataset.

The technical architecture of the model is considerable. Ten submodels, each executed once per simulated day for 365 days, govern death, personnel replacement, infection transmission, hospital admission and discharge, recovery, retirement, nursing home admission and discharge, and vaccination. Transmission within facilities follows a stochastic formulation in which the infection probability per contact is combined with the prevalence of infection among residents, personnel, and the surrounding community, along with the number of daily contacts of each type. Because the model does not trace individual contacts, the probability that a given contact is infectious is approximated from the proportion of each category that is infected, recalculated at every time step for every facility. Infection status is then adjusted for each agent’s immunity, whether from prior infection or vaccination. Roughly 46 percent of simulated infections are asymptomatic, and transmission is only modeled after incubation periods of 5.47 days for asymptomatic and 2.63 days for symptomatic cases, reflecting the reduced per-contact transmission probability outside symptomatic illness.

Hospitals were included deliberately, because the movement of patients between acute care and skilled nursing facilities is a critical pathway. Up to 94 percent of nursing home residents arrive directly from hospital discharge, and many are rehospitalized as their complex medical needs evolve. Capturing these flows allowed the model to reproduce healthcare-associated exposure routes that would be invisible if facilities were simulated in isolation. The model was calibrated against empirical data collected by CMS through May 2023, focusing on discharge rates, average length of stay, and reported facility-level COVID-19 cases, and validated with 10-fold cross-validation. The experimental model achieved a root mean squared error of 0.063 against a naive baseline of 0.114 and explained approximately 70 percent of the variation in observed infection rates across facilities.

Each intervention scenario was simulated 250 times, and the results were striking in their pattern. In the baseline scenario, with no policies enforced, facilities recorded an average of 172.65 resident infections per year. The single most effective measure for protecting residents was compulsory paid sick leave, which removes symptomatic staff from the workforce pool. Combining sick leave with a full-time employment policy—which restricts each worker to a single facility at 40 hours per week instead of two or three jobs—cut resident infections by 12 percent, to 150.72, and slashed personnel infections by 45 percent, from 8.45 to 4.73 per facility. Full-time employment alone had the greatest impact on personnel infections, producing a reduction of nearly 35 percent, and on personnel-introduced outbreaks, which fell by more than half.

The vaccination mandate produced a more nuanced and somewhat sobering picture. Implementing the policy raised simulated personnel vaccination coverage from 48 percent to 81 percent, exceeding the roughly 80 percent threshold that earlier studies identified as necessary for meaningful effect. Yet the impact on resident infections remained minimal. The authors attribute this to two converging factors: the reduced efficacy of vaccines against later SARS-CoV-2 variants, and the sheer thinness of nursing home staffing, which meant that an 81 percent uptake translated to an average of only five additional vaccinated personnel per site. Larger facilities with more staff might see greater benefit, but the finding underscores that vaccination mandates alone, however well-intentioned, cannot substitute for broader workforce investment.

The outbreak findings add another dimension. Total outbreaks per facility, defined as any occurrence of two or more resident infections, changed by less than 2 percent across scenarios, but personnel-introduced outbreaks responded dramatically to staffing policies. Full-time employment alone reduced them by 51.73 percent, and combining it with sick leave or vaccination pushed the reduction to nearly 65 percent. This distinction matters because personnel-introduced outbreaks are precisely the events that facility administrators and public health agencies can most directly prevent through workforce policy, whereas outbreaks seeded by visitors fall largely outside institutional control, particularly now that federal visitor restrictions have long been lifted.

The study’s limitations are acknowledged candidly by its authors. The model assumes a simplified staffing structure without floating personnel or shift variability, excludes resident-to-personnel transmission to keep the computation tractable, omits privately funded facilities, and implements social determinants of health at a surface level that does not capture their co-occurrence. Even so, the central conclusion carries real policy weight: while no workforce intervention matched the protective power of isolation of symptomatic individuals, the combination of paid sick leave and full-time employment delivered meaningful reductions in infections among both residents and staff. Given the chronic nursing shortage, low wages, and burnout that continue to erode the long-term care workforce, the findings suggest that improving compensation and benefits is not merely a labor issue but an infection-control strategy—one that could make nursing homes safer for their residents while simultaneously making them more sustainable places to work. The modeling framework itself, publicly available on GitHub, is designed to extend beyond COVID-19 to other healthcare-associated infections in acute and post-acute settings.

Subject of Research: Agent-based modeling of workforce compensation and benefit policies on COVID-19 transmission in skilled nursing facilities

Article Title: Modeling the impact of healthcare personnel compensation and benefits on the spread of COVID-19 in skilled nursing facilities

Article References: Sweet, A., Rhea, S., Lenhart, S. M., Odoi, A., Lanzas, C., & Lloyd, A. (2026). Modeling the impact of healthcare personnel compensation and benefits on the spread of COVID-19 in skilled nursing facilities. PLOS Aging and Health, 1(3), e0000040. https://doi.org/10.1371/journal.page.0000040

Image Credits: AI Generated

DOI: 10.1371/journal.page.0000040

Keywords: COVID-19, skilled nursing facilities, agent-based model, healthcare personnel, paid sick leave, full-time employment, vaccination policy, nursing shortage, infection control, long-term care, outbreak modeling, workforce policy