ArticleBMC health services research2005
Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore.
Article in BMC health services research, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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51 citing papers in PubMed.
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- Analysis of cutaneous leishmaniasis among military personnel in the Islamic Republic of Iran: a spatiotemporal study between 2018 and 2022, trend forecasting based on ARIMA model.BMC infectious diseases · 2024Article
- Impact of the COVID-19 pandemic on hospital episodes for falls and fractures associated with new-onset disability and frailty in England: a national cohort study.Age and ageing · 2024Article
- Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models.BMC public health · 2023Article
- Automatic Forecast of Intensive Care Unit Admissions: The Experience During the COVID-19 Pandemic in Italy.Journal of medical systems · 2023Article
- Data analytics and knowledge management approach for COVID-19 prediction and control.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2023Article
- Forecasting waved daily COVID-19 death count series with a novel combination of segmented Poisson model and ARIMA models.Journal of applied statistics · 2023Article
- Modelling the persistence of Covid-19 positivity rate in Italy.Socio-economic planning sciences · 2022Article
- A comparative study for predictive monitoring of COVID-19 pandemic.Applied soft computing · 2022Article
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- Discrepancy between the Demand and Supply of Intensive Care Unit Beds in South Korea from 2011 to 2019: A Cross-Sectional Analysis.Yonsei medical journal · 2021Article
- Forecasting fully vaccinated people against COVID-19 and examining future vaccination rate for herd immunity in the US, Asia, Europe, Africa, South America, and the World.Applied soft computing · 2021Article
- Does Climate Play Any Role in COVID-19 Spreading?-An Australian Perspective.International journal of environmental research and public health · 2021Article
- Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence.JMIR public health and surveillance · 2021Article
- How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis.JMIR public health and surveillance · 2021Article
- Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia.Process safety and environmental protection : transactions of the Institution of Chemical Engineers, Part B · 2021Article
- A time series-based statistical approach for outbreak spread forecasting: Application of COVID-19 in Greece.Expert systems with applications · 2021Article
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4 authors.
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Abstract
backgroundThe main objective of this study is to apply autoregressive integrated moving average (ARIMA) models to make real-time predictions on the number of beds occupied in Tan Tock Seng Hospital, during the recent SARS outbreak.
methodsThis is a retrospective study design. Hospital admission and occupancy data for isolation beds was collected from Tan Tock Seng hospital for the period 14th March 2003 to 31st May 2003. The main outcome measure was daily number of isolation beds occupied by SARS patients. Among the covariates considered were daily number of people screened, daily number of people admitted (including observation, suspect and probable cases) and days from the most recent significant event discovery. We utilized the following strategy for the analysis. Firstly, we split the outbreak data into two. Data from 14th March to 21st April 2003 was used for model development. We used structural ARIMA models in an attempt to model the number of beds occupied. Estimation is via the maximum likelihood method using the Kalman filter. For the ARIMA model parameters, we considered the simplest parsimonious lowest order model.
resultsWe found that the ARIMA (1,0,3) model was able to describe and predict the number of beds occupied during the SARS outbreak well. The mean absolute percentage error (MAPE) for the training set and validation set were 5.7% and 8.6% respectively, which we found was reasonable for use in the hospital setting. Furthermore, the model also provided three-day forecasts of the number of beds required. Total number of admissions and probable cases admitted on the previous day were also found to be independent prognostic factors of bed occupancy.
conclusionARIMA models provide useful tools for administrators and clinicians in planning for real-time bed capacity during an outbreak of an infectious disease such as SARS. The model could well be used in planning for bed-capacity during outbreaks of other infectious diseases as well.
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