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BackgroundSurveillance plays a vital role in disease detection, but traditional methods of collecting patient data, reporting to health officials, and compiling reports are costly and time consuming. In recent years, syndromic surveillance tools have expanded and researchers are able to exploit the vast amount of data available in real time on the Internet at minimal cost. Many data sources for infoveillance exist, but this study focuses on status updates (tweets) from the Twitter microblogging website.ObjectiveThe aim of this study was to explore the interaction between cyberspace message activity, measured by keyword-specific tweets, and real world occurrences of influenza and pertussis. Tweets were aggregated by week and compared to weekly influenza-like illness (ILI) and weekly pertussis incidence. The potential effect of tweet type was analyzed by categorizing tweets into 4 categories: nonretweets, retweets, tweets with a URL Web address, and tweets without a URL Web address.MethodsTweets were collected within a 17-mile radius of 11 US cities chosen on the basis of population size and the availability of disease data. Influenza analysis involved all 11 cities. Pertussis analysis was based on the 2 cities nearest to the Washington State pertussis outbreak (Seattle, WA and Portland, OR). Tweet collection resulted in 161,821 flu, 6174 influenza, 160 pertussis, and 1167 whooping cough tweets. The correlation coefficients between tweets or subgroups of tweets and disease occurrence were calculated and trends were presented graphically.ResultsCorrelations between weekly aggregated tweets and disease occurrence varied greatly, but were relatively strong in some areas. In general, correlation coefficients were stronger in the flu analysis compared to the pertussis analysis. Within each analysis, flu tweets were more strongly correlated with ILI rates than influenza tweets, and whooping cough tweets correlated more strongly with pertussis incidence than pertussis tweets. Nonretweets correlated more with disease occurrence than retweets, and tweets without a URL Web address correlated better with actual incidence than those with a URL Web address primarily for the flu tweets.ConclusionsThis study demonstrates that not only does keyword choice play an important role in how well tweets correlate with disease occurrence, but that the subgroup of tweets used for analysis is also important. This exploratory work shows potential in the use of tweets for infoveillance, but continued efforts are needed to further refine research methods in this field.

The Connor-Davidson Resilience Scale (CD-RISC) is a brief self-rating questionnaire for measuring resilience. The aims of the present study were to describe the development of a Korean version of the CD-RISC (K-CD-RISC) and to more firmly establish its psychometric properties in terms of reliability and validity. The participants consisted of a general population sample (n=194) and psychiatric outpatients (n=127) with non-psychotic mood or anxiety disorders. The K-CD-RISC score means (standard deviation) were 65.9 (13.6) in the general population and 50.4 (20.5) in the psychiatric outpatients. The mean score of the general population was significantly higher than that of the psychiatric outpatients. Exploratory factor analysis revealed five factors, and the obtained factor structure was verified through confirmatory factor analysis. In the general population, the Cronbach's α coefficient of the K-CD-RISC was found to be 0.92. Greater resilience was found to be associated with less perceived stress, anxiety and depression and with higher levels of positive affect and purpose in life. Taken together, our findings suggest that the K-CD-RISC has good psychometric properties and is a valid and reliable tool for assessing resilience.

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