After data sourcing, translation takes place from the twelve non-English languages used in this study using Google's online translation system. As a quality reference point we refer to a recent large-scale evaluation of machine translation for European language pairs [13] . In this study on news texts it was found that across a wide variety of metrics Google's online system consistently performed among the highest quality systems for Spanish-English, French-English and German-English language pairs. Following machine translation, text classification using Naive Bayes (F1 0.93) removes non-disease outbreak news before text mining is applied. Rules are based on a regular expression matching toolkit called the Simple Rule Language [14] and divided between 18 entity types and template rules. The final structured event frames in XML includes slot values normalized to BCO root terms for disease, pathogen (virus or bacterium), time period, country and province. Additionally we also identify 15 aspects of public health events critical to risk assessment. For the purpose of this study we only made use of disease and country slots. Events in the 13 languages are treated in this study as being part of a univariate model for comparison purposes against English events.