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.

翻译：我们的回顾性研究的目的是量化Covid-19对得克萨斯州特拉维斯县紧急医疗服务(EMS)需求临时分布的影响,并提出一个强有力的模型来在短期内预测Covid-19紧急医疗服务(EMS)事件,以提高EMS绩效。方法:我们分析了2019年1月1日至2020年12月31日奥斯丁特拉维斯县地区紧急医疗服务电话和每日Covid-19住院数量。我们进行了改变点检测,以确定显示紧急医疗服务(EMS)呼叫分配变化和时间序列回归的关键日期,用于我们的预测模型。结果:Covid-19(Covid-19)对紧急医疗服务电话的影响有两个关键日期:3月17日,非Povid-19(EMS)事件的每日数量大幅下降,5月13日,根据这个日期,紧急医疗服务(EMS)每天的电话数量回升至19前需求的75%。 新的Covid-19(C-19美元)和C-19(C-19)住院病人的每日需求值相等于0.85(C-19),结论:EMS(C-S-S)的每日正常病例数量比C-S的频率要低。