In chapter 1, using a Galton-Watson branching-process analysis, we characterize differences in measles transmission by estimating the association between genotype and the reproduction number among post-elimination California measles cases. Genotype B3 is found to be a significant predictor of transmissibility.
In chapter 2, we determine whether data from FDA-cleared wired and Bluetooth smart thermometers sold by a San Francisco-based company aid influenza forecasting efforts. We compare this smart thermometer data to regional influenza and ILI surveilance data from the California Department of Public Health. We evaluated the correlation between the regional California surveillance data and smart thermometer data, tested the hypothesis that smart thermometer readings and symptom reports provide regionally specific predictions, and determined whether smart thermometer and mobile application improved disease forecasts. Our results are consistent with the hypothesis that smart thermometer readings and symptom reports reflect underlying disease transmission in California. Data from such cloud-based devices could supplement syndromic influenza surveillance data.
In chapter 3, we examine whether changes in varicella transmissibility may have occurred following the change from one- to two-dose vaccination scheduling in 2007. Following the change in ACIP recommendations for varicella vaccination in 2007, the median outbreak size decreased. However, while the number of outbreaks has continued to decrease following 2008, we do not find evidence that the distribution of sizes has changed since 2008. Using insights from branching process models assuming both subcritical and supercritical transmission and with and without depletion of susceptibles, we cannot rule out that varicella transmission is supercritical in school-based settings.