Yoshida Takahito, Hayashi Tomohito, Chimed-Ochir Odgerel, Yumiya Yui, Fukunaga Ami, Taji Akihiro, Nakano Takashi, Ikeda Yoichi, Sasaki Kenji, Cossa Matchecane, Ussene Isse, Kayano Ryoma, Salio Flavio, Akahoshi Kouki, Toyokuni Yoshiki, Chishima Kayako, Mimura Seiji, Wakai Akinori, Kondo Hisayoshi, Koido Yuichi, Kubo Tatsuhiko
Department of Public Health and Health Policy, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, Hiroshima 734-0037, Japan.
Center for Infectious Disease Education and Research, Osaka University, 2-1 Yamadaoka Suita city, Osaka 565-0871, Japan.
Arch Acad Emerg Med. 2025 Mar 10;13(1):e38. doi: 10.22037/aaemj.v13i1.2457. eCollection 2025.
Predicting the number of emergency medical team (EMT) consultations that are needed following a natural or man-made disaster can help improve decisions regarding the dispatch and withdrawal of these teams. This study aimed to predict the number of consultations by EMTs using the value and constant attenuation model.
Data were collected using the Japan-Surveillance in Post-Extreme Emergencies and Disasters (J-SPEED) and Minimum Data Set (MDS) for five disasters in Japan and one disaster in Mozambique. We compared the number of consultations, which was predicted based on value and constant attenuation model with actual data collected with J-SPEED/Minimum Data Set (MDS) tools.
The total number of EMT consultations per disaster ranged from 684 to 18,468. The predicted curve and actual data were similar for each of the disasters (R from 0.953 to 0.997), but offset adjustments were needed for the Kumamoto earthquake and the Mozambique cyclone because their R values were below 0.985. For the six disasters, the difference between the number of consultations predicted based on values and the measured cumulative number of consultations ranged from ±1.0% to ± 4.1%.
The value and constant attenuation model, although originally developed to predict the number of patients with COVID-19, provided reliable predictions of the number of EMT consultations required during six different disasters. This simple model may be useful for the coordination of future responses of EMTs during disasters.
预测自然或人为灾害后所需的紧急医疗团队（EMT）会诊次数有助于改善有关这些团队派遣和撤离的决策。本研究旨在使用值和恒定衰减模型预测EMT的会诊次数。
使用日本极端紧急情况和灾害后监测（J-SPEED）和最小数据集（MDS）收集了日本五次灾害和莫桑比克一次灾害的数据。我们将基于值和恒定衰减模型预测的会诊次数与使用J-SPEED/最小数据集（MDS）工具收集的实际数据进行了比较。
每次灾害的EMT会诊总数在684至18468次之间。每次灾害的预测曲线和实际数据相似（R值在0.953至0.997之间），但熊本地震和莫桑比克气旋需要进行偏移调整，因为它们的R值低于0.985。对于这六次灾害，基于值预测的会诊次数与实测累计会诊次数之间的差异在±1.0%至±4.1%之间。
值和恒定衰减模型虽然最初是为预测COVID-19患者数量而开发的，但对六种不同灾害期间所需的EMT会诊次数提供了可靠的预测。这个简单的模型可能有助于未来灾害期间EMT应对的协调。
