Recommendations for modeling disaster responses in public health and medicine: a position paper of the society for medical decision making.
OBJECTIVE
Mathematical and simulation models are increasingly used to plan for and evaluate health sector responses to disasters, yet no clear consensus exists regarding best practices for the design, conduct, and reporting of such models. The authors examined a large selection of published health sector disaster response models to generate a set of best practice guidelines for such models.


METHODS
. The authors reviewed a spectrum of published disaster response models addressing public health or health care delivery, focusing in particular on the type of disaster and response decisions considered, decision makers targeted, choice of outcomes evaluated, modeling methodology, and reporting format. They developed initial recommendations for best practices for creating and reporting such models and refined these guidelines after soliciting feedback from response modeling experts and from members of the Society for Medical Decision Making.


RESULTS
. The authors propose 6 recommendations for model construction and reporting, inspired by the most exemplary models: health sector disaster response models should address real-world problems, be designed for maximum usability by response planners, strike the appropriate balance between simplicity and complexity, include appropriate outcomes that extend beyond those considered in traditional cost-effectiveness analyses, and be designed to evaluate the many uncertainties inherent in disaster response. Finally, good model reporting is particularly critical for disaster response models.


CONCLUSIONS
. Quantitative models are critical tools for planning effective health sector responses to disasters. The proposed recommendations can increase the applicability and interpretability of future models, thereby improving strategic, tactical, and operational aspects of preparedness planning and response.
Brandeau
Margaret L
ML
Department of Management Science and Engineering, Stanford University, Stanford, CA, USA.
McCoy
Jessica H
JH
Hupert
Nathaniel
N
Holty
Jon-Erik
JE
Bravata
Dena M
DM
D002487:Centers for Disease Control and Prevention, U.S.; D003198:Computer Simulation; D003362:Cost-Benefit Analysis; D003657:Decision Making; D054597:Disaster Medicine / Q000191:economics / Q000458:organization & administration* / Q000592:standards*; D004189:Disaster Planning / Q000191:economics / Q000458:organization & administration* / Q000592:standards*; D006801:Humans; D054527:Mass Casualty Incidents; D008962:Models, Theoretical; D011634:Public Health; D011635:Public Health Administration / Q000592:standards; D015980:Public Health Practice; D014481:United States