The results will be published in international peer-reviewed journals. Real Time Bayesian Estimation of the Epidemic Potential of Emerging Infectious Diseases BACKGROUND: Fast changes in human demographics worldwide, coupled with increased mobility, and modified land uses make the threat of emerging infectious diseases increasingly important. Currently there is worldwide alert for H5N1 avian influenza becoming as transmissible in humans as seasonal influenza, and potentially causing a pandemic of unprecedented proportions. Here we show how epidemiological surveillance data for emerging infectious diseases can be interpreted in real time to assess changes in transmissibility with quantified uncertainty, and to perform running time predictions of new cases and guide logistics allocations. METHODOLOGY/PRINCIPAL FINDINGS: We develop an extension of standard epidemiological models, appropriate for emerging infectious diseases, that describes the probabilistic progression of case numbers due to the concurrent effects of (incipient) human transmission and multiple introductions from a reservoir. The model is cast in terms of surveillance observables and immediately suggests a simple graphical estimation procedure for the effective reproductive number R (mean number of cases generated by an infectious individual) of standard epidemics. For emerging infectious diseases, which typically show large relative case number fluctuations over time, we develop a Bayesian scheme for real time estimation of the probability distribution of the effective reproduction number and show how to use such inferences to formulate significance tests on future epidemiological observations. CONCLUSIONS/SIGNIFICANCE: Violations of these significance tests define statistical anomalies that may signal changes in the epidemiology of emerging diseases and should trigger further field investigation. We apply the methodology to case data from World Health Organization reports to place bounds on the current transmissibility of H5N1 influenza in humans and establish a statistical basis for monitoring its evolution in real time. A pandemic of H5N1 influenza in birds is presently unfolding, with over 50 countries around the world affected, resulting in hundreds of millions of dead animals through infection or culling [1] [2] [3] . This emergency and the associated risk of a devastating new human pandemic [4] [5] [6] stress the need for new approaches targeted specifically at detecting and monitoring the evolution of emerging infectious diseases [7] [8] [9] . Assessing the risk of emergence of a human epidemic at the genetic level requires accounting for rare stochastic events, associated with genetic mutation and recombination, over vast pathogen and host populations [4, 8, 10] . This makes prediction of pathogenic evolution at the molecular level typically still very difficult. Consequently, the first indications of disease emergence are usually observed as infected cases in human and animal populations. Thus, for early assessments of the epidemic potential of a new outbreak, it is essential to assign quantitative meaning to existing epidemiological surveillance data in real time, with quantified uncertainty, and to use this knowledge to enable primary prevention strategies targeted at reducing chances of pathogenic evolution.