Introduction The aim of our retrospective study was to quantify the impact of Covid‐19 on the temporal distribution of emergency medical services (EMS) demand in Travis County, Austin, Texas and propose a robust model to forecast Covid‐19 EMS incidents. Methods We analysed the temporal distribution of EMS calls in the Austin‐Travis County area between 1 January 2019 and 31 December 2020. Change point detection was performed to identify the critical dates marking changes in EMS call distributions, and time series regression was applied for forecasting Covid‐19 EMS incidents. Results Two critical dates marked the impact of Covid‐19 on the distribution of EMS calls: March 17th, when the daily number of non‐pandemic EMS incidents dropped significantly, and 13 May, by which the daily number of EMS calls climbed back to 75% of the number in pre‐Covid‐19 time. The new daily count of the hospitalisation of Covid‐19 patients alone proves a powerful predictor of the number of pandemic EMS calls, with an r2 value equal to 0.85. In particular, for every 2.5 cases, where EMS takes a Covid‐19 patient to a hospital, one person is admitted. Conclusion The mean daily number of non‐pandemic EMS demand was significantly less than the period before the Covid‐19 pandemic. The number of EMS calls for Covid‐19 symptoms can be predicted from the daily new hospitalisation of Covid‐19 patients. These findings may be of interest to EMS departments as they plan for future pandemics, including the ability to predict pandemic‐related calls in an effort to adjust a targeted response.

Emergency Medical Systems (EMS) provide crucial pre-hospital care and transportation. Faster EMS response time provides quicker pre-hospital care and thus increases survival rate. We reduce response time by providing optimal ambulance stationing and routing decisions by solving two stage stochastic and robust linear programs. Although operational research on ambulance systems is decades old, there is little open-source code and consistency in simulations. We begin to bridge this gap by publishing OpenEMS, in collaboration with the Austin-Travis County EMS (ATCEMS) in Texas, an end-to-end pipeline to optimize ambulance strategic decisions. It includes data handling, optimization, and a calibrated simulation. We hope this open source framework will foster future research with and for EMS. Finally, we provide a detailed case study on the city of Austin, Texas. We find that optimal stationing would increase response time by 88.02 seconds. Further, we design optimal strategies in the case where Austin EMS must permanently add or remove one ambulance from their fleet.