Forecasting Daily COVID-19 Related Calls in VA Health Care System: Predictive Model Development

TL;DR

This paper presents a predictive modeling approach to forecast daily COVID-19 related calls in VA health centers, enabling better resource planning by leveraging clustering and hyper-parameter optimization techniques.

Contribution

The study introduces a novel method combining clustering, feature selection, and hyper-parameter tuning to accurately forecast COVID-19 call volumes at individual VA medical centers.

Findings

The model effectively predicts call surges ahead of time.

Clustering improves model accuracy by enlarging training datasets.

Hyper-parameter optimization enhances predictive performance.

Abstract

Background: COVID-19 has become a challenge worldwide and properly planning of medical resources is the key to combating COVID-19. In the US Veteran Affairs Health Care System (VA), many of the enrollees are susceptible to COVID-19. Predicting the COVID-19 to allocate medical resources promptly becomes a critical issue. When the VA enrollees have COVID-19 symptoms, it is recommended that their first step should be to call the VA Call Center. For confirmed COVID-19 patients, the median time from the first symptom to hospital admission was seven days. By predicting the number of COVID-19 related calls, we could predict imminent surges in healthcare use and plan medical resources ahead. Objective: The study aims to develop a method to forecast the daily number of COVID-19 related calls for each of the 110 VA medical centers. Methods: In the proposed method, we pre-trained a model using a cluster of medical centers and fine-tuned it for individual medical centers. At the cluster level, we performed feature selection to select significant features and automatic hyper-parameter search to select optimal hyper-parameter value combinations for the model. Conclusions: This study proposed an accurate method to forecast the daily number of COVID-19 related calls for VA medical centers. The proposed method was able to overcome modeling challenges by grouping similar medical centers into clusters to enlarge the dataset for training models, and using hyper-parameter search to automatically find optimal hyper-parameter value combinations for models. With the proposed method, surges in health care can be predicted ahead. This allows health care practitioners to better plan medical resources and combat COVID-19.