Predicting Covid-19 EMS Incidents from Daily Hospitalization Trends

Introduction: The aim of our retrospective study was to quantify the impact of Covid-19 on the spatiotemporal distribution of Emergency Medical Services (EMS) demands in Travis County, Austin, Texas and propose a robust model to forecast Covid-19 EMS incidents in the short term to improve EMS performance. Methods: We analyzed the number of EMS calls and daily Covid-19 hospitalization in the Austin-Travis County area between January 1st, 2019 and December 31st, 2020. Change point detection was performed to identify critical dates marking changes in EMS call distributions and time series regression was applied for our prediction model. Results: Two critical dates mark the impact of Covid-19 on EMS calls: March 17th, when the daily number of Non-Pandemic EMS incidents dropped significantly, and May 13th, by which the daily number of EMS calls climbed back to 75% of pre-Covid-19 demand. New daily Covid-19-hospitalization alone proves a powerful predictor of pandemic EMS calls, with an $r^2$ value equal to 0.85. Conclusion: The mean daily number of non-pandemic EMS demands was significantly less than the period prior to Covid-19 pandemic. The number of EMS calls for Covid-19 symptoms can be predicted from the daily new hospitalization of Covid-19 patients. In particular, for every 2.5 cases where EMS takes a Covid-19 patient to a hospital, 1 person is admitted.