In today’s society, the use of social media has increased the public’s desire to receive information quickly and to be able to interact with communicators. During a disaster, the trend to turn to social media for information has risen in popularity. Society’s reliance on social media and quick access to information has led the field of emergency management and the role of a Public Information Officer to adapt to include social media as a crisis communication channel for information dispersal. Existing frameworks for the use of social media as a channel for crisis communications provide guidance for emergency management agencies across all levels of government but fail to account for the varying access to communication resources at the local level. Due to the differing access to communication resources and unique relationships with stakeholders at the local level, there is a need for guidance on how local emergency management agencies can use social media to disperse essential information. The proposed Communication Hub Framework utilizes local emergency management professionals’ relationships with key community stakeholders to aid in the distribution of essential information to community members via social media during a disaster.

Introduction A nuclear disaster would generate an unprecedented volume of thermal burn patients from the explosion and subsequent mass fires (Figure 1). Prediction models characterizing outcomes for these patients may better equip healthcare providers and other responders to manage large scale nuclear events. Logistic regression models have traditionally been employed to develop prediction scores for mortality of all burn patients. However, other healthcare disciplines have increasingly transitioned to machine learning (ML) models, which are automatically generated and continually improved, potentially increasing predictive accuracy. Preliminary research suggests ML models can predict burn patient mortality more accurately than commonly used prediction scores. The purpose of this study is to examine the efficacy of various ML methods in assessing thermal burn patient mortality and length of stay in burn centers. Methods This retrospective study identified patients with fire/flame burn etiologies in the National Burn Repository between the years 2009 – 2018. Patients were randomly partitioned into a 67%/33% split for training and validation. A random forest model (RF) and an artificial neural network (ANN) were then constructed for each outcome, mortality and length of stay. These models were then compared to logistic regression models and previously developed prediction tools with similar outcomes using a combination of classification and regression metrics. Results During the study period, 82,404 burn patients with a thermal etiology were identified in the analysis. The ANN models will likely tend to overfit the data, which can be resolved by ending the model training early or adding additional regularization parameters. Further exploration of the advantages and limitations of these models is forthcoming as metric analyses become available. Conclusions In this proof-of-concept study, we anticipate that at least one ML model will predict the targeted outcomes of thermal burn patient mortality and length of stay as judged by the fidelity with which it matches the logistic regression analysis. These advancements can then help disaster preparedness programs consider resource limitations during catastrophic incidents resulting in burn injuries.