Prediction of COVID-19 Hospital Beds by On-Demand Cumulative-Control Analysis

Abstract

AbstractThe COVID-19 virus has been rapidly spreading around the world, and the situation in clinical treatment sites is dynamically changing. In such a situation, the prediction and management of the required number of hospital beds have become issues in order to prevent the collapse of medical care due to the number of patients exceeding the capacity. On the other hand, cumulative flow analysis and on-demand flow management method, which is an extended management system, have been developed and used as a tool for visually managing the state of inventory in factories and warehouses. Onodera et al. (2020) adopted the on-demand cumulative-control method to the estimation of the COVID-19 patient population in a case study. However, the base number of vacant hospital beds is not predicted. This study models the number of COVID-19 hospital beds by using the on-demand cumulative-control method, and estimates required number of vacant beds from the viewpoint of inventory management. Firstly, the cumulative-control analysis is modeled by the input number of hospital beds as the supply, the output number of patients testing positive as the demand, and the number of vacant hospital beds as the number of flow which means the difference between cumulative input and output number. Next, using the on-demand cumulative-control method, the base number of vacant hospital beds is estimated to prevent the overwhelmed healthcare. Finally, a numerical experiment is conducted, and potentials for the appropriate and current management/policy are discussed based on the comparison between the required number of input beds estimated by the on-demand cumulative control method and the actual number of beds.KeywordsNovel coronavirusDynamic inventory controlBed managementCumulative flow curveCase study