Prediction of hand, foot, and mouth disease epidemics in Japan using a long short-term memory approach

• Published in: PloS one Vol. 17; no. 7; p. e0271820
• Main Authors: Yoshida, Kazuhiro, Fujimoto, Tsuguto, Muramatsu, Masamichi, Shimizu, Hiroyuki
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28.07.2022; Public Library of Science (PLoS)
• Subjects: Analysis; Biology and Life Sciences; Care and treatment; China - epidemiology; Communicable Diseases - epidemiology; Computer and Information Sciences; Coxsackievirus infections; Deep learning; Diagnosis; Disease Outbreaks; Encephalitis; Enteroviruses; Epidemics; Fever; Hand, Foot and Mouth Disease - epidemiology; Hand-foot-and-mouth disease; Humans; Infectious diseases; Japan - epidemiology; Long short-term memory; Maximum entropy method; Medicine and Health Sciences; Meningitis; Neural networks; Neural Networks, Computer; Pediatrics; People and Places; Physical Sciences; R&D; Recurrent neural networks; Research & development; Research and Analysis Methods; Risk factors; Signs and symptoms; Surveillance; Websites; China; Japan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0271820

Hand, foot, and mouth disease (HFMD) is a common febrile illness caused by enteroviruses in the Picornaviridae family. The major symptoms of HFMD are fever and a vesicular rash on the hand, foot, or oral mucosa. Acute meningitis and encephalitis are observed in rare cases. HFMD epidemics occur annually in Japan, usually in the summer season. Relatively large-scale outbreaks have occurred every two years since 2011. In this study, the epidemic patterns of HFMD in Japan are predicted four weeks in advance using a deep learning method. The time-series data were analyzed by a long short-term memory (LSTM) approach called a Recurrent Neural Network. The LSTM model was trained on the numbers of weekly HFMD cases in each prefecture. These data are reported in the Infectious Diseases Weekly Report, which compiles the national surveillance data from web sites at the National Institute of Infectious Diseases, Japan, under the Infectious Diseases Control Law. Consequently, our trained LSTM model distinguishes between relatively large-scale and small-scale epidemics. The trained model predicted the HFMD epidemics in 2018 and 2019, indicating that the LSTM approach can estimate the future epidemic patterns of HFMD in Japan.