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Chimeric forecasting: combining probabilistic predictions from computational models and human judgment.

ABSTRACT: Forecasts of the trajectory of an infectious agent can help guide public health decision making. A traditional approach to forecasting fits a computational model to structured data and generates a predictive distribution. However, human judgment has access to the same data as computational models plus experience, intuition, and subjective data. We propose a chimeric ensemble-a combination of computational and human judgment forecasts-as a novel approach to predicting the trajectory of an infectious agent. Each month from January, 2021 to June, 2021 we asked two generalist crowds, using the same criteria as the COVID-19 Forecast Hub, to submit a predictive distribution over incident cases and deaths at the US national level either two or three weeks into the future and combined these human judgment forecasts with forecasts from computational models submitted to the COVID-19 Forecasthub into a chimeric ensemble. We find a chimeric ensemble compared to an ensemble including only computational models improves predictions of incident cases and shows similar performance for predictions of incident deaths. A chimeric ensemble is a flexible, supportive public health tool and shows promising results for predictions of the spread of an infectious agent.

SUBMITTER: McAndrew T

PROVIDER: S-EPMC9648897 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Chimeric forecasting: combining probabilistic predictions from computational models and human judgment.

McAndrew Thomas T Codi Allison A Cambeiro Juan J Besiroglu Tamay T Braun David D Chen Eva E De Cèsaris Luis Enrique Urtubey LEU Luk Damon D

BMC infectious diseases 20221110 1

Forecasts of the trajectory of an infectious agent can help guide public health decision making. A traditional approach to forecasting fits a computational model to structured data and generates a predictive distribution. However, human judgment has access to the same data as computational models plus experience, intuition, and subjective data. We propose a chimeric ensemble-a combination of computational and human judgment forecasts-as a novel approach to predicting the trajectory of an infecti ...[more]

PMID: 36357829

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Project description:Capacity planning of ICUs is essential for effective management of health safety, quality of patient care, and the allocation of ICU resources. Whereas ICU length of stay (LOS) may be estimated using patient information such as severity of illness scoring systems, ICU census is impacted by both patient LOS and arrival patterns. We set out to develop and evaluate an ICU census forecasting algorithm using the Multiple Organ Dysfunction Score (MODS) and the Nine Equivalents of Nursing Manpower Use Score (NEMS) for capacity planning purposes.DesignRetrospective observational study.SettingWe developed the algorithm using data from the Medical-Surgical ICU (MSICU) at University Hospital, London, Canada and validated using data from the Critical Care Trauma Centre (CCTC) at Victoria Hospital, London, Canada.PatientsAdult patient admissions (7,434) to the MSICU and (9,075) to the CCTC from 2015 to 2021.InterventionsNone.Measurements and main resultsWe developed an Autoregressive integrated moving average time series model that forecasts patients arriving in the ICU and a survival model using MODS, NEMS, and other factors to estimate patient LOS. The models were combined to create an algorithm that forecasts ICU census for planning horizons ranging from 1 to 7 days. We evaluated the algorithm quality using several fit metrics. The root mean squared error ranged from 2.055 to 2.890 beds/d and the mean absolute percentage error from 9.4% to 13.2%. We show that this forecasting algorithm provides a better fit when compared with a moving average or a time series model that directly forecasts ICU census. Additionally, we evaluated the performance of the algorithm using data during the global COVID-19 pandemic and found that the error of the forecasts increased proportionally with the number of COVID-19 patients in the ICU.ConclusionsIt is possible to develop accurate tools to forecast ICU census. This type of algorithm may be important to clinicians and managers when planning ICU capacity as well as staffing and surgical demand planning over a short time horizon.

Dataset Information

Chimeric forecasting: combining probabilistic predictions from computational models and human judgment.

Publications

Chimeric forecasting: combining probabilistic predictions from computational models and human judgment.

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