Archives of Academic Emergency Medicine

Centre for Research on the Epidemiology of Disasters, University of Louvain. EM-DAT [Internet]. Brussels: CRED; 2023 [cited 2023 Jun 13]. Available from: https://www.emdat.be/

World Meteorological Organization. Economic costs of weather-related disasters soars but early warnings save lives [Internet]. Geneva: WMO; 2023 [cited 2023 Jun 13]. Available from: https://wmo.int/media/news/economic-costs-of-weather-related-disasters-soars-early-warnings-save-lives

World Health Organization. Classification and Minimum Standards for Emergency Medical Teams [Internet]. Geneva: WMO; 2017 [cited 2023 Jun 6]. Available from: https://extranet.who.int/emt/guidelines-and-publications

Hamilton ARL, Södergård B, Liverani M. The role of emergency medical teams in disaster response: a summary of the literature. Nat Hazards. 2022;110(3):1417–26.

Fujimoto M, Nishiura H. Predicting the cumulative number of disaster deaths during the early stage of earthquakes. Ann Transl Med. 2021;9(3):241.

Yamanouchi S, Sasaki H, Tsuruwa M, Ueki Y, Kohayagawa Y, Kondo H, et al. Survey of preventable disaster death at medical institutions in areas affected by the Great East Japan Earthquake: a retrospective preliminary investigation of medical institutions in Miyagi Prefecture. Prehosp Disaster Med. 2015;30(2):145–51.

Technical Resources, Assistance Center, and Information Exchange. Mass Casualty Trauma Triage Paradigms and Pitfalls [Internet]. Washington, DC: TRACIE; 2019 [cited 2024 Apr 17]. Available from: https://files.asprtracie.hhs.gov/documents/aspr-tracie-mass-casualty-triage-final-508.pdf

Safi-Keykaleh M, Khorasani-Zavareh D, Ghomian Z, Bohm K. A model to explain the challenges of emergency medical technicians’ decision making process in emergency situations: a grounded theory. J Inj Violence Res. 2022;14(1):53–63.

Paulin J, Reunamo A, Kurola J, Moen H, Salanterä S, Riihimäki H, et al. Using machine learning to predict subsequent events after EMS non-conveyance decisions. BMC Med Inform Decis Mak. 2022 Jun 23;22(1):166.

Kubo T, Kondo H, Koido Y. The J-SPEED: A Medical Relief Activities Reporting System for Emergency Medical Teams in Japan. Prehosp Disaster Med. 2017;32(S1):S228–S228.

Nakano T, Ikeda Y. Novel Indicator to Ascertain the Status and Trend of COVID-19 Spread: Modeling Study. J Med Internet Res. 2020;22(11):e20144.

Chimed-Ochir O, Yumiya Y, Taji A, Kishita E, Kondo H, Wakai A, et al. Emergency Medical Teams’ Responses during the West Japan Heavy Rain 2018: J-SPEED Data Analysis. Prehosp Disaster Med. 2022;37(2):1–7.

Joint Committee for Disaster Medical Recording. J-SPEED information site [Internet]. J-SPEED information site. Hiroshima; 2018 [cited 2024 Dec 26]. Available from: https://www.j-speed.org/

Kubo T, Chimed-Ochir O, Cossa M, Ussene I, Toyokuni Y, Yumiya Y, et al. First Activation of the WHO Emergency Medical Team Minimum Data Set in the 2019 Response to Tropical Cyclone Idai in Mozambique. Prehosp Disaster Med. 2022;37(6):727–34.

WHO EMT MDS Working Group, JDR MDS Dissemination Supporting Unit. Emergency Medical Team Minimum Data Set Gateway [Internet]. EMT MDS Gateway. WHO EMT MDS Working Group; 2021 [cited 2024 Dec 26]. Available from: https://www.mdsgateway.net/

Ohnishi A, Namekawa Y, Fukui T. Universality in COVID-19 spread in view of the Gompertz function. Prog Theor Exp Phys. 2020;2020(12):123J01.

Tjørve KMC, Tjørve E. The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family. PLoS One. 2017;12(6):e0178691.

Huang D, Wang S, Liu Z. A systematic review of prediction methods for emergency management. Int J Disaster Risk Reduct. 2021;62:102412.

Humagain S, Sinha R, Lai E, Ranjitkar P. A systematic review of route optimisation and pre-emption methods for emergency vehicles. Transp Rev. 2019;40(1):35–53.

Abdelgawad H, Abdulhai B. Emergency evacuation planning as a network design problem: a critical review. Transp Lett. 2009;1(1):41–58.

Anaya-Arenas AM, Renaud J, Ruiz A. Relief distribution networks: a systematic review. Ann Oper Res. 2014;223(1):53–79.

Suzuki S, Hayashi H. [Development of Simple Earthquake Disaster Estimation Web Application Combining Web GIS Services]. Journal of Social Safety Science. 2005;27:215–20. [in Japanese]

Shapira S, Novack L, Bar-Dayan Y, Aharonson-Daniel L. An Integrated and Interdisciplinary Model for Predicting the Risk of Injury and Death in Future Earthquakes. PLoS One. 2016;11(3):e0151111.

Federal Emergency Management Agency. HAZUS-MH MR4 technical manual: FEMA [Internet]. 2003 [cited 2023 Jun 20]. Available from: https://www.fema.gov/flood-maps/products-tools/hazus

Ceferino L, Mitrani-Reiser J, Kiremidjian A, Deierlein G, Bambarén C. Effective plans for hospital system response to earthquake emergencies. Nat Commun. 2020;11(1):4325.

Liao Y, Wang Z, Lai C, Xu C-Y. A framework on fast mapping of urban flood based on a multi-objective random forest model. Int J Disaster Risk Sci. 2023;14(2):253–68.

Klus H, Niebuhr D, Rausch A. A component model for dynamic adaptive systems. In: International workshop on Engineering of software services for pervasive environments: in conjunction with the 6th ESEC/FSE joint meeting. New York, NY, USA: ACM; 2007. p. 21–8.