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Predicting Covid-19 emergency medical service incidents from daily hospitalisation trends.
Int J Clin Pract. 2021 Dec; 75(12):e14920.IJ

Abstract

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.

Authors+Show Affiliations

Department of Computer Science, University of Texas at Austin, Austin, Texas, USA.Department of Emergency Medical Services, City of Austin, Austin, Texas, USA.Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, Texas, USA.Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, Texas, USA.Department of Mathematics, University of Texas at Austin, Austin, Texas, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

34569674

Citation

Xie, Yangxinyu, et al. "Predicting Covid-19 Emergency Medical Service Incidents From Daily Hospitalisation Trends." International Journal of Clinical Practice, vol. 75, no. 12, 2021, pp. e14920.
Xie Y, Kulpanowski D, Ong J, et al. Predicting Covid-19 emergency medical service incidents from daily hospitalisation trends. Int J Clin Pract. 2021;75(12):e14920.
Xie, Y., Kulpanowski, D., Ong, J., Nikolova, E., & Tran, N. M. (2021). Predicting Covid-19 emergency medical service incidents from daily hospitalisation trends. International Journal of Clinical Practice, 75(12), e14920. https://doi.org/10.1111/ijcp.14920
Xie Y, et al. Predicting Covid-19 Emergency Medical Service Incidents From Daily Hospitalisation Trends. Int J Clin Pract. 2021;75(12):e14920. PubMed PMID: 34569674.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Predicting Covid-19 emergency medical service incidents from daily hospitalisation trends. AU - Xie,Yangxinyu, AU - Kulpanowski,David, AU - Ong,Joshua, AU - Nikolova,Evdokia, AU - Tran,Ngoc M, Y1 - 2021/10/03/ PY - 2021/05/27/received PY - 2021/09/23/accepted PY - 2021/9/28/pubmed PY - 2022/1/1/medline PY - 2021/9/27/entrez SP - e14920 EP - e14920 JF - International journal of clinical practice JO - Int J Clin Pract VL - 75 IS - 12 N2 - 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. SN - 1742-1241 UR - https://www.unboundmedicine.com/medline/citation/34569674/Predicting_Covid_19_emergency_medical_service_incidents_from_daily_hospitalisation_trends_ L2 - https://doi.org/10.1111/ijcp.14920 DB - PRIME DP - Unbound Medicine ER -