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ESTIMATING THE NUMBER OF ICU PATIENTS OF COVID-19 BY USING A SIMPLE MATHEMATICAL MODEL

  • Hyojung Lee (Department of Statistics, Kyungpook National University) ;
  • Giphil Cho (Department of Artificial Intelligence and Software, Kangwon National University)
  • Received : 2024.01.15
  • Accepted : 2024.01.29
  • Published : 2024.01.31

Abstract

Predicting the number of ICU patients holds significant importance, serving as a critical aspect in efficiently allocating resources, ensuring high-quality care for critically ill individuals, and implementing effective public health strategies to mitigate the impact of diseases. This research focuses on estimating ICU patient numbers through the development of a simple mathematical model. Utilizing data on confirmed COVID-19 cases and deaths, this model becomes a valuable tool for predicting and managing ICU resource requirements during the ongoing pandemic. By incorporating historical data on infected individuals and fatalities from previous weeks, we establish a straightforward equation. We found the substantial impact of the delay in infected individuals, particularly those occurring more than five weeks earlier, on the accuracy of ICU predictions. Proactively preparing for potential surges in severe cases becomes feasible by forecasting the demand for intensive care beds, ultimately improving patient outcomes and preventing excessive strain on medical facilities.

Keywords

Acknowledgement

The COVID-19 data was provided by the Korea Disease Control and Prevention Agency.

References

  1. Park, H. N., Lee, J. J., Choi, J. H., Lee, H. Y., Yu, M., Song, Y. J., et al Incidence and fatality rates of SARS-CoV-2 Omicron variant compared with Delta variant in long term care facilities, Public Health Weekly Report 1 (2022), 15(21), 1426-1434.
  2. Iuliano, A. D., Brunkard, J. M., Boehmer, T. K., Peterson, E., Adjei, S., Binder, A. M., et al. Trends in disease severity and health care utilization during the early Omicron variant period compared with previous SARS-CoV-2 high transmission periods-United States, December 2020-January 2022, Morbidity and Mortality Weekly Report 1 (2022), 71(4), 146-152. https://doi.org/10.15585/mmwr.mm7104e4
  3. Garrido, J. M., Martinez-Rodriguez, D., Rodriguez-Serrano, F., Perez-Villares, J. M., Ferreiro-Marzal, A., Jimenez-Quintana, M. D. M., et al. Mathematical model optimized for prediction and health care planning for COVID-19, Medicina Intensiva (English Edition) 1 (2022), 46(5), 248-258. https://doi.org/10.1016/j.medine.2022.02.020
  4. Tuite, A. R., Fisman, D. N., Greer, A. L. Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada. Cmaj 1 (2020), 192(19), E497-E505. https://doi.org/10.1503/cmaj.200476
  5. Stasinos, N., Kousis, A., Sarlis, V., Mystakidis, A., Rousidis, D., Koukaras, P., et al. A Tri-Model Prediction Approach for COVID-19 ICU Bed Occupancy: A Case Study, Algorithms 1 (2023), 16(3), 140.
  6. Coronavirus (COVID-19) Republic of Korea. Central Disaster Management Headquarters, Available from: https://ncov.kdca.go.kr/. (accessed 2024-01-13).
  7. Montcho, Y., Klingler, P., Lokonon, B. E., Tovissode, C. F., Glele Kakai, R., Wolkewitz, M. Intensity and lag-time of non-pharmaceutical interventions on COVID-19 dynamics in German hospitals, Frontiers in Public Health 1 (2023), 11, 1087580.
  8. Bekker, R., uit het Broek, M., Koole, G. Modeling COVID-19 hospital admissions and occupancy in the Netherlands, European journal of operational research 1 (2023), 304(1), 207-218.
  9. Alban, A., Chick, S. E., Dongelmans, D. A., Vlaar, A. P., Sent, D. ICU capacity management during the COVID-19 pandemic using a process simulation, European journal of operational research 1 (2020), 46(8), 1624-1626. https://doi.org/10.1007/s00134-020-06066-7
  10. Fox, S. J., Lachmann, M., Tec, M., Pasco, R., Woody, S., et al. Real-time pandemic surveillance using hospital admissions and mobility data, Proceedings of the National Academy of Sciences 1 (2020), 119(7), e2111870119.