@article{661cd50503d0438aaf0f71f20f5edeff,
title = "Cluster-based approach to coronavirus disease 2019 (COVID-19) response in Japan, from February to April 2020",
author = "{The Expert Members of The National COVID-19 Cluster Taskforce at The Ministry of Health, Labour, Welfare, Japan} and Hitoshi Oshitani",
note = "Funding Information: All experts listed in the appendix developed the concept of the report. Kazuaki Jindai and Natalie Linton wrote the first draft, with equal contributions. Tomoya Saito, Yuki Furuse, and Hitoshi Oshitani provided additional feedback on the first draft. All authors edited and finalized the manuscript. Secretariat members of the taskforce also contributed to this manuscript. The authors would like to acknowledge the following institutions for their contribution to support the cluster-based approach: public health centers, prefectural governments, public health institutes, and medical facilities. The authors would also like to thank volunteer members (Kozue Amemiya, Kayako Chishima, Aya Fujiwara, Yoko Hamasaki, Naomi Ikeda, Keiya Inoue, Sachi Ishida, Tomoe Mashiko, Kota Ninomiya, Akiko Sayama, Ayaka Takeuchi, Hiroto Tanaka, Fumie Tokuda, Shogo Yaegashi, Lisa Yamasaki, Fumi Yoshimatsu, and Mariko Kanamori), external experts (Kyoko Fujisawa and Megumi Kawashima), and the Bureau of International Health Cooperation at National Center for Global Health and Medicine for information gatherings and data cleaning. Natalie Linton and Sung-mok Jung received a graduate study scholarship from the Ministry of Education, Culture, Sports, Science and Technology, Japan. Akira Endo is financially supported by The Nakajima Foundation and the Alan Turing Institute and received a research grant from Taisho Pharmaceutical Co., Ltd. The opinions expressed here are that of the authors and do not reflect the official policy of the MHLW.",
year = "2020",
doi = "10.7883/yoken.JJID.2020.363",
language = "English",
volume = "73",
pages = "491--493",
journal = "Japanese medical journal",
issn = "1344-6304",
publisher = "National Institute of Health",
number = "6",
}
