Description: The influenza pandemic of 1918-20 is recognized as having generally taken place in three waves, starting in the northern spring and summer of 1918. This pattern of three waves, however, was not universal: in some locations influenza seems to have persisted into or returned in 1920. The recorded statistics of influenza morbidity and mortality are likely to be a significant understatement. Limitations of these data can include nonregistration, missing records, misdiagnosis, and nonmedical certification, and may also vary greatly between locations. Further research has seen the consistent upward revision of the estimated global mortality of the pandemic, which a 1920s calculation put in the vicinity of 21.5 million. A 1991 paper revised the mortality as being in the range 24.7-39.3 million. This paper suggests that it was of the order of 50 million. However, it must be acknowledged that even this vast figure may be substantially lower than the real toll, perhaps as much as 100 percent understated.

Description: Data requirements for the Society of Actuaries cancer experience study, for those submitting exposure and claim information.

Description: Analysis of the impact of disease progression and treatment on life expectancy provides perspective on the long term costs to the health care system of chronic medical conditions.

Description: Looks at free and inexpensive resources for the novice in predictive analytics, covering the data analysis theory as well as the most commonly used languages/software used in the field.

Description: We highlight a new approach leveraging predictive analytics to identify high risk and rising risk opioid users for more effective screening and follow-up care.

Description: The newer predictive modeling tools are powerful but they work best when supported by a lot of computing power. Parallel processing is very powerful but often very difficult to implement. Dihui and Richard take us through the maze of options with H2O, SparkNet/MLib and Mahout. Which do you use for a generalized linear model (GLM), and which is better for high-volume data manipulation?

Description: This article describes a method that can be useful when re-fitting a risk score model to a population of only moderate size. Learn how to use concepts like ridge regression to train a model with partial credibility.