Predicting the Number of Consultations by Emergency Medical Teams During Disasters Using a New Statistical Model

Introduction: When a natural or man-made disaster occurs, emergency medical teams (EMTs) are dispatched to deliver medical surge capacity for injured and sick individuals. Accurate prediction of EMT 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 required by EMTs during disasters, aiming to enhance the effectiveness and efficiency of medical response. Methods: Data for this study were collected using the Japan-Surveillance in Post-Extreme Emergencies and Disasters (J SPEED) system and the Minimum Data Set (MDS) framework. These datasets encompassed five disasters in Japan and one in Mozambique. For each disaster, the number of consultations was predicted using the K-value and the constant attenuation model, an approach originally developed to forecast the number of COVID-19 patients. Results: The total number of Emergency Medical Team (EMT) consultations per disaster varied between 684 and 18,468. The predicted curves closely matched the actual K-value data for each disaster, with R 2 values ranging from 0.953 to 0.997. However, offset adjustments were required for the Kumamoto earthquake and the Mozambique cyclone, as their R 2 values were below 0.985. Across all six disasters, the differences between the predicted cumulative number of consultations, based on K-values, and the observed numbers ranged from ±1.0% to ±4.1%. Conclusion: 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.