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.