  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.
