# A Tri-Model Prediction Approach for COVID-19 ICU Bed Occupancy: A Case Study
## 5 Conclusions  
The healthcare domain attracts a great amount of research interest, often requiring interdisciplinary approaches. It involves predictive analytics for prompt forecasting and prevention methods incorporating a mix of concepts from statistics, medicine, computer science etc. [64].  
Many countries, including Greece, early on during the COVID-19 epidemic attempted to avoid ICU bed shortage, which could put a strain on intensive care patient management. Accurate forecasting assisted Health authorities to deploy resources and prioritise patient care. ICU bed management during the COVID-19 pandemic emphasises the necessity of accurate forecasting in public health decision making. Health officials were able to respond to patient needs and prevent the healthcare system from becoming overloaded by employing data-driven approaches, saving lives.  
This work reports on findings regarding forecasting COVID-19 ICU bed needs during the pandemic. According to the literature, most of the forecasting attempts utilize a mix of SIS and SIR models or incorporate them in their approach [17]. Also, they base their forecasting accuracy in a limited number of observations [11; 12; 41; 43] or report forecasting results for a short timeframe (2 months) [44]. The evaluation of results involves a variety of metrics, such as sensitivity, specificity, accuracy ROC, AUC [11; 21; 41; 43].  
In contrast, our approach does not depend on epidemiological models like SIS and SIR; it rather uses a dataset comprising features for a whole country (Greece), it expands the forecasting timeframe to almost 5 months (141 days) and reports findings utilizing four metrics (MAPE, RMSE, \(R^{2}\) and MAE). We argue that these characteristics constitute it an efficient, comprehensive and intelligible novel approach on health related time series forecasting.  
We employed various state-of-the-art ML algorithms to address this challenge. The results show that the adopted algorithms performed very well when reporting on their 3-day MA metric values.  
For one week ahead predictions using the MAPE metric (Table 2), the best average model was 10.69% for Thessaloniki, 4.79% for Attica, 9.12% for Northern Greece and 4.73% for Greece. For the two weeks ahead predictions the results were expectedly less accurate, the best average model MAPE was 19.53% for Thessaloniki, 11.07% for Attica, 17.98% for Northern Greece and 10.24% for Greece. Even for three weeks ahead forecasting, the results may be useful for healthcare recourse management as the algorithms performed with significantly lower average MAPE, at 27.95% for Thessaloniki, 17.81% for Attica, 30.94% for Northern Greece and 17.86% for Greece. Similar reports apply for the other metrics as shown in Table 3 for RMSE, Table 4 for \(R^{2}\) and Table 5 for MAE.  
It should be noted that population-wise Thessaloniki as well as Northern Greece is much smaller than Attica, which hosts nearly half the Greek population, which may partly explain why predictions for Thessaloniki and Northern Greece were substantially less accurate than for Greece or Attica.  
Considering the results presented in Tables 2-5, we conclude that for the short term 1-week ahead prediction, ARIMA and SARIMAX is more accurate for the majority of the investigated regions. For the 2-weeks ahead prediction, Multivariate Regression outperforms the other two models. Finally, for the medium term 3-weeks ahead prediction the Multivariate Regression and ARIMA with SARIMAX show the best results.