COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach.
Appl Soft Comput
; 125: 109181, 2022 Aug.
Article
em En
| MEDLINE
| ID: mdl-35755299
Recent literature has revealed a growing interest in methods for anticipating the demand for medical items and personnel at hospital, especially during turbulent scenarios such as the COVID-19 pandemic. In times like those, new variables appear and affect the once known demand behavior. This paper investigates the hypothesis that the combined Prophet-LSTM method results in more accurate forecastings for COVID-19 hospital Intensive Care Units (ICUs) demand than both standalone models, Prophet and LSTM (Long Short-Term Memory Neural Network). We also compare the model to well-established demand forecasting benchmarks. The model is tested to a representative Brazilian municipality that serves as a medical reference to other cities within its region. In addition to traditional time series components, such as trend and seasonality, other variables such as the current number of daily COVID-19 cases, vaccination rates, non-pharmaceutical interventions, social isolation index, and regional hospital beds occupation are also used to explain the variations in COVID-19 hospital ICU demand. Results indicate that the proposed method produced Mean Average Errors (MAE) from 13% to 45% lower than well established statistical and machine learning forecasting models, including the standalone models.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Appl Soft Comput
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Estados Unidos