When a natural or man-made disaster occurs, emergency medical teams (EMTs) are dispatched to provide medical surge capacity for injured and sick individuals. Accurate predictions of EMTs consultations during disasters can improve dispatch and withdrawal decisions. However, no published studies have yet demonstrated a method for predicting the number of consultations or patients based on EMT activity data.

This research explores an innovative yet simple and reliable method to predict the number of consultations needed by EMTs during disasters, aiming to enhance the effectiveness and efficiency of medical response.

Data were collected using Japan-Surveillance in Post-Extreme Emergencies and Disasters (J-SPEED) and Minimum Data Set (MDS) for five disasters in Japan and one in Mozambique. For each disaster, the number of consultations was predicted from the K value and constant attenuation model, originally developed for predicting COVID-19 patient numbers.

The total number of EMT consultations per disaster ranged from 684 to 18,468. The predicted curve and actual K data were similar for each of the disasters (R2 from 0.953 to 0.997), but offset adjustments were needed for the Kumamoto earthquake and the Mozambique cyclone because their R2 values were below 0.985. For the six disasters, the difference between the number of consultations predicted from K values and the measured cumulative number of consultations ranged from ±1.0% to ±4.1%.

The K value and constant attenuation model reliably predicted EMT consultations during six different disasters. This simple model may be useful for the coordination of future responses of EMTs during disasters.