Human Dose-Response Data for Francisella tularensis and a Dose- and Time-Dependent Mathematical Model of Early-Phase Fever Associated with Tularemia After Inhalation Exposure.

Military health risk assessors, medical planners, operational planners, and defense system developers require knowledge of human responses to doses of biothreat agents to support force health protection and chemical, biological, radiological, nuclear (CBRN) defense missions. This article reviews extensive data from 118 human volunteers administered aerosols of the bacterial agent Francisella tularensis, strain Schu S4, which causes tularemia. The data set includes incidence of early-phase febrile illness following administration of well-characterized inhaled doses of F. tularensis. Supplemental data on human body temperature profiles over time available from de-identified case reports is also presented. A unified, logically consistent model of early-phase febrile illness is described as a lognormal dose-response function for febrile illness linked with a stochastic time profile of fever. Three parameters are estimated from the human data to describe the time profile: incubation period or onset time for fever; rise time of fever; and near-maximum body temperature. Inhaled dose-dependence and variability are characterized for each of the three parameters. These parameters enable a stochastic model for the response of an exposed population through incorporation of individual-by-individual variability by drawing random samples from the statistical distributions of these three parameters for each individual. This model provides risk assessors and medical decisionmakers reliable representations of the predicted health impacts of early-phase febrile illness for as long as one week after aerosol exposures of human populations to F. tularensis.

Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels.

OBJECTIVE: We discuss the use of structural models for the analysis of biosurveillance related data. METHODS AND RESULTS: Using a combination of real and simulated data, we have constructed a data set that represents a plausible time series resulting from surveillance of a large scale bioterrorist anthrax attack in Miami. We discuss the performance of anomaly detection with structural models for these data using receiver operating characteristic (ROC) and activity monitoring operating characteristic (AMOC) analysis. In addition, we show that these techniques provide a method for predicting the level of the outbreak valid for approximately 2 weeks, post-alarm. CONCLUSIONS: Structural models provide an effective tool for the analysis of biosurveillance data, in particular for time series with noisy, non-stationary background and missing data.