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 analyzed the temporal distribution of EMS calls 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 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 May 13th, by which the daily number of EMS calls climbed back to 75% of the number in pre-Covid-19 time. New daily count of the hospitalization 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, 1 person is admitted. Conclusion: The mean daily number of non-pandemic EMS demand 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. 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.
翻译:我们的回顾性研究的目的是量化Covid-19在得克萨斯州特拉维斯州特拉维斯州紧急医疗服务需求(EMS)临时分布的影响,并提出一个强有力的模型来预测Covid-19紧急医疗服务事故。方法:我们分析了奥斯汀-特拉维斯州地区紧急医疗服务电话在2019年1月1日到2020年12月31日之间的临时分布。进行了改变点检测,以确定紧急医疗服务电话分配和时间序列回归的关键日期,以预测Covid-19紧急医疗服务事件。结果:两个关键日期标志着Covid-19对紧急医疗服务需求分布的影响:3月17日,当时非大规模医疗服务事件的每日数量大幅下降,5月13日,根据这种方法,紧急医疗服务电话在奥斯汀-特拉维斯州地区的时间在奥斯汀-特拉维斯州之前的每日数量将回升至75%。仅Covid-19病人住院的新每日数量就证明了与流行病相关需求的强烈预测值,r2值相当于0.85。特别是,每2.5个病例中,EMS-19患者的住院治疗能力都比Eovid-19医院的日常需求低。