Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan

TL;DR

This paper presents a deep learning framework using recurrent neural networks to forecast emergency ambulance dispatches during COVID-19, aiding resource management in critical situations like pandemics.

Contribution

It introduces a novel data fusion approach combining environmental, localization, and historical data for accurate EAD estimation during emergencies.

Findings

Effective estimation of EADs during COVID-19 pandemic

Data fusion improves prediction accuracy

Framework applicable to real-world emergency management

Abstract

Accurate forecasting of medical service requirements is an important big data problem that is crucial for resource management in critical times such as natural disasters and pandemics. With the global spread of coronavirus disease 2019 (COVID-19), several concerns have been raised regarding the ability of medical systems to handle sudden changes in the daily routines of healthcare providers. One significant problem is the management of ambulance dispatch and control during a pandemic. To help address this problem, we first analyze ambulance dispatch data records from April 2014 to August 2020 for Nagoya City, Japan. Significant changes were observed in the data during the pandemic, including the state of emergency (SoE) declared across Japan. In this study, we propose a deep learning framework based on recurrent neural networks to estimate the number of emergency ambulance dispatches (EADs) during a SoE. The fusion of data includes environmental factors, the localization data of mobile phone users, and the past history of EADs, thereby providing a general framework for knowledge discovery and better resource management. The results indicate that the proposed blend of training data can be used efficiently in a real-world estimation of EAD requirements during periods of high uncertainties such as pandemics.