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Pandemic Preparedness for Elective Care : Managing Spare Resources by Considering Staff Absence Dynamics
Baumgartner, Nina (2025) Pandemic Preparedness for Elective Care : Managing Spare Resources by Considering Staff Absence Dynamics.
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| Abstract: | The COVID-19 pandemic highlighted the challenge of maintaining elective care during periods of health crisis. While research has focused heavily on forecasting pandemic-related demand, less attention has been given to how hospitals can continue providing elective care when resources are limited and staff availability is uncertain. This thesis addresses that gap by analyzing the interplay between healthcare capacity, waitlist dynamics, and the postponement of elective procedures in a pandemic context. We focus on spare nursing capacity in general wards and develop a forecasting model that accounts for both pandemic patient load and staff absence. Staff availability is modeled as a Markov chain, assuming independent infection and recovery behavior. Elective care waitlists are represented through a discrete queueing model to derive backlog distributions under varying levels of resource fluctuation. This provides a clearer understanding of how capacity constraints translate into delays in elective care. We compare multiple elective scheduling strategies using Monte Carlo simulations based on both synthetic data and real data from the first COVID-19 wave. To cope with fluctuating staff availability, overbooking predicted capacity can improve resource utilization. However, this comes at the cost of a higher cancellation risk, which can be mitigated by shorter scheduling horizons. Our insights offer decision-makers a basis for planning elective care in future pandemic scenarios, aiming to mitigate long-term population health consequences. The forecasting model is designed to be simple and practical. It requires only pandemic demand forecasts, staff infection rates, and observed staff availability. Using these, hospitals can estimate daily capacity for elective care and evaluate scheduling policies tailored to their specific setting. Keywords: capacity modeling, discrete queueing theory, elective care, fluctuating resources, operations research in healthcare, pandemic preparedness, staff absence |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Programme: | Applied Mathematics MSc (60348) |
| Link to this item: | https://purl.utwente.nl/essays/107258 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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