scholarly journals Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence

10.2196/28195 ◽  
2021 ◽  
Vol 7 (8) ◽  
pp. e28195
Author(s):  
Hieu M Nguyen ◽  
Philip J Turk ◽  
Andrew D McWilliams
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Health Care Systems ◽  
Multivariate Time Series ◽  
Arima Model ◽  
Forecast Performance ◽  
Infection Incidence ◽  
Long Run ◽  
Care Systems ◽  
Cross Correlations

Background COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. Objective The goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census. Methods The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts. Results The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave. Conclusions When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.

scholarly journals Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence (Preprint)

2021 ◽  
Author(s):  
Hieu M Nguyen ◽  
Philip J Turk ◽  
Andrew D McWilliams
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Health Care Systems ◽  
Multivariate Time Series ◽  
Arima Model ◽  
Forecast Performance ◽  
Infection Incidence ◽  
Long Run ◽  
Care Systems ◽  
Cross Correlations

BACKGROUND COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. OBJECTIVE The goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census. METHODS The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts. RESULTS The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave. CONCLUSIONS When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.

scholarly journals Impact of Crude Prices on Retail Prices – A Time Series Analysis

2019 ◽  
Vol 8 (2S7) ◽  
pp. 606-612
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Crude Oil ◽  
Energy Demand ◽  
Multivariate Time Series ◽  
Secondary Data ◽  
Series Analysis ◽  
Long Run ◽  
The Uk ◽  
The Impact

The UK has emerged as one of the largest producers of petroleum in the world. A significant amount of petroleum is used for fulfilling the energy demand within the country. However, the country witnessed a different trend from 2015. This is mainly due to the increase in imports of petroleum in order to meet domestic needs. To this, there is a need to identify the impact of changes exist in petrol and crude oil prices in the UK. In this context, the researcher has undertaken primary research to derive conclusions which are case specific and can comply with the research aim. The study used secondary data for the year 2015-2018 and conducted multivariate time series analysis. A series of tests including unit root, ARIMA, and co-integration tests were used to derive the results. The study found that there was an asymmetric relationship between the movements of prices of crude oil with respect to retail fuel prices in the long run. However, the study is not without limitations which are represented at the end of the study following with its future scope

scholarly journals Time-Series Forecasting Models for Gasoline Prices in China

2018 ◽  
Vol 10 (12) ◽  
pp. 43
Author(s):  
Feng Xu ◽  
Mohamad Sepehri ◽  
Jian Hua ◽  
Sergey Ivanov ◽  
Julius N. Anyu
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Gasoline Prices ◽  
Support Vector ◽  
Grey System ◽  
Gasoline Price ◽  
Forecasting Models ◽  
Long Run ◽  
Environmental Protection Policies

Accurate prediction of gasoline price is important for the automobile makers to adjust designs and productions as well as marketing plans of their products. It is also necessary for government agencies to set effective inflation monitoring and environmental protection policies. To predict future levels of the gasoline price, due to difficulties of obtaining accurate estimates of influential external factors, data driven time-series forecasting models thus become more suitable given the convenience and practicability they are providing. In this paper, five popular time-series forecasting models, i.e., ARIMA-GARCH, exponential smoothing, grey system, neural network, and support vector machines models, are applied to predict gasoline prices in China. Comparing the performances of these models, it is noted that for this specific time series, a parsimonious ARIMA model performs the best in predicting the gasoline prices for a short time horizon, while for the medium length and long run the SVR and FNN models outperforms others respectively.  

scholarly journals Transmisión de precios en los mercados regionales de ovino en España

2011 ◽  
Vol 3 (1) ◽  
pp. 71 ◽  
Author(s):  
L. Boshnjaku ◽  
B. Ben-Kaabia ◽  
José M. Gil
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Commodity Markets ◽  
Time Series Models ◽  
The North ◽  
Long Run ◽  
Stochastic Properties ◽  
Price Relationships

The analysis of price relationships in commodity markets provides an approximate idea on markets performance as well as allows the researcher to analyze price responses to unanticipated shocks. The objective of this paper is to explore price relationships in geographical separated markets in the Spanish lamb sector. The methodology used is based on the specification of multivariate time series models which are flexible enough to take into account the stochastic properties of data, the multivariate nature of price relationships and to distinguish between short- and long-run horizons. Results indicate that lamb markets in Spain are strongly related being Zafra the leading market. The influence of Zafra is substantial in the southern markets while in the North, the Lonja del Ebro could be considered as the most representative market.

scholarly journals A Multivariate Forecasting Model for the COVID-19 Hospital Census Based on Local Infection Incidence

2021 ◽  
Author(s):  
Hieu M. Nguyen ◽  
Philip Turk ◽  
Andrew McWilliams
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Healthcare Systems ◽  
Leading Indicators ◽  
Short Term ◽  
Correction Model ◽  
Vector Error Correction ◽  
Infection Incidence ◽  
Vector Error

AbstractCOVID-19 has been one of the most serious global health crises in world history. During the pandemic, healthcare systems require accurate forecasts for key resources to guide preparation for patient surges. Fore-casting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. In the literature, only a few papers have approached this problem from a multivariate time-series approach incorporating leading indicators for the hospital census. In this paper, we propose to use a leading indicator, the local COVID-19 infection incidence, together with the COVID-19 hospital census in a multivariate framework using a Vector Error Correction model (VECM) and aim to forecast the COVID-19 hospital census for the next 7 days. The model is also applied to produce scenario-based 60-day forecasts based on different trajectories of the pandemic. With several hypothesis tests and model diagnostics, we confirm that the two time-series have a cointegration relationship, which serves as an important predictor. Other diagnostics demonstrate the goodness-of-fit of the model. Using time-series cross-validation, we can estimate the out-of-sample Mean Absolute Percentage Error (MAPE). The model has a median MAPE of 5.9%, which is lower than the 6.6% median MAPE from a univariate Autoregressive Integrated Moving Average model. In the application of scenario-based long-term forecasting, future census exhibits concave trajectories with peaks lagging 2-3 weeks later than the peak infection incidence. Our findings show that the local COVID-19 infection incidence can be successfully in-corporated into a VECM with the COVID-19 hospital census to improve upon existing forecast models, and to deliver accurate short-term forecasts and realistic scenario-based long-term trajectories to help healthcare systems leaders in their decision making.Author summaryDuring the COVID-19 pandemic, healthcare systems need to have adequate resources to accommodate demand from COVID-19 cases. One of the most important metrics for planning is the COVID-19 hospital census. Only a few papers make use of leading indicators within multivariate time-series models for this problem. We incorporated a leading indicator, the local COVID-19 infection incidence, together with the COVID-19 hospital census in a multivariate framework called the Vector Error Correction model to make 7-day-ahead forecasts. This model is also applied to produce 60-day scenario forecasts based on different trajectories of the pandemic. We find that the two time-series have a stable long-run relationship. The model has a good fit to the data and good forecast performance in comparison with a more traditional model using the census data alone. When applied to different 60-day scenarios of the pandemic, the census forecasts show concave trajectories that peak 2-3 weeks later than the infection incidence. Our paper presents this new model for accurate short-term forecasts and realistic scenario-based long-term forecasts of the COVID-19 hospital census to help healthcare systems in their decision making. Our findings suggest using the local COVID-19 infection incidence data can improve and extend more traditional forecasting models.

scholarly journals Hospitalization for ischemic stroke was affected more in independent cases than in dependent cases during the COVID-19 pandemic: An interrupted time series analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261587
Author(s):  
Hiroyuki Nagano ◽  
Jung-ho Shin ◽  
Tetsuji Morishita ◽  
Daisuke Takada ◽  
Susumu Kunisawa ◽  
...  
Keyword(s):  
Time Series ◽  
Ischemic Stroke ◽  
Hospital Admissions ◽  
Health Care Systems ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
State Of Emergency ◽  
Its Analysis ◽  
Care Systems ◽  
The Impact

Background The pandemic of the coronavirus disease 2019 (COVID-19) has affected health care systems globally. The aim of our study is to assess the impact of the COVID-19 pandemic on the number of hospital admissions for ischemic stroke by severity in Japan. Methods We analysed administrative (Diagnosis Procedure Combination—DPC) data for cases of inpatients aged 18 years and older who were diagnosed with ischemic stroke and admitted during the period April 1 2018 to June 27 2020. Levels of change of the weekly number of inpatient cases with ischemic stroke diagnosis after the declaration of state of emergency were assessed using interrupted time-series (ITS) analysis. The numbers of patients with various characteristics and treatment approaches were compared. We also performed an ITS analysis for each group (“independent” or “dependent”) divided based on components of activities of daily living (ADL) and level of consciousness at hospital admission. Results A total of 170,294 cases in 567 hospitals were included. The ITS analysis showed a significant decrease in the weekly number of ischemic stroke cases hospitalized (estimated decrease: −156 cases; 95% confidence interval (CI): −209 to −104), which corresponds to −10.4% (95% CI: −13.6 to −7.1). The proportion of decline in the independent group (−21.3%; 95% CI: −26.0 to −16.2) was larger than that in the dependent group (−8.6%; 95% CI: −11.7 to −5.4). Conclusions Our results show a marked reduction in hospital admissions due to ischemic stroke after the declaration of the state of emergency for the COVID-19 pandemic. The independent cases were affected more in proportion than dependent cases.

scholarly journals Forecasting the COVID-19 Outbreak: An Application of Arima and Fuzzy Time Series Models

2020 ◽  
Author(s):  
Prashant Verma ◽  
Mukti Khetan ◽  
Shikha Dwivedi ◽  
Shweta Dixit
Keyword(s):  
Time Series ◽  
Health Care Systems ◽  
Medical Personnel ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Data Set ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Time Series Methodology ◽  
Near Future

Abstract Purpose: The whole world is surfaced with an inordinate challenge of mankind due to COVID-19, caused by 2019 novel coronavirus (SARS-CoV-2). After taking hundreds of thousands of lives, millions of people are still in the substantial grasp of this virus. This virus is highly contagious with reproduction number R0, as high as 6.5 worldwide and between 1.5 to 2.6 in India. So, the number of total infections and the number of deaths will get a day-to-day hike until the curve flattens. Under the current circumstances, it becomes inevitable to develop a model, which can anticipate future morbidities, recoveries, and deaths. Methods: We have developed some models based on ARIMA and FUZZY time series methodology for the forecasting of COVID-19 infections, mortalities and recoveries in India and Maharashtra explicitly, which is the most affected state in India, following the COVID-19 statistics till “Lockdown 3.0” (17th May 2020). Results: Both models suggest that there will be an exponential uplift in COVID-19 cases in the near future. We have forecasted the COVID-19 data set for next seven days. The forecasted values are in good agreement with real ones for all six COVID-19 scenarios for Maharashtra and India as a whole as well.Conclusion: The forecasts for the ARIMA and FUZZY time series models will be useful for the policymakers of the health care systems so that the system and the medical personnel can be prepared to combat the pandemic.

scholarly journals Nine Months of COVID-19 Pandemic in Europe: A Comparative Time Series Analysis of Cases and Fatalities in 35 Countries

2021 ◽  
Vol 18 (12) ◽  
pp. 6680
Author(s):  
David Meintrup ◽  
Martina Nowak-Machen ◽  
Stefan Borgmann
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Health Care Systems ◽  
Case Fatality ◽  
Multiple Regression Model ◽  
European Countries ◽  
Three Phase ◽  
Strongly Connected ◽  
Care Systems ◽  
Second Wave

(1) Background: to describe the dynamic of the pandemic across 35 European countries over a period of 9 months. (2) Methods: a three-phase time series model was fitted for 35 European countries, predicting deaths based on SARS-CoV-2 incidences. Hierarchical clustering resulted in three clusters of countries. A multiple regression model was developed predicting thresholds for COVID-19 incidences, coupled to death numbers. (3) Results: The model showed strongly connected deaths and incidences during the waves in spring and fall. The corrected case-fatality rates ranged from 2% to 20.7% in the first wave, and from 0.5% to 4.2% in the second wave. If the incidences stay below a threshold, predicted by the regression model (R2=85.0%), COVID-19 related deaths and incidences were not necessarily coupled. The clusters represented different regions in Europe, and the corrected case-fatality rates in each cluster flipped from high to low or vice versa. Severely and less severely affected countries flipped between the first and second wave. (4) Conclusions: COVID-19 incidences and related deaths were uncoupled during the summer but coupled during two waves. Once a country-specific threshold of infections is reached, death numbers will start to rise, allowing health care systems and countries to prepare.

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