We thank Miles Davenport, Mac Hyman, Alan Perelson, Timothy Reluga, our PLoS One Editor and anonymous referees for comments that substantially improved the manuscript. Indonesia, under the pessimistic assumption that 29% of reported cases are due to human-to-human transmission (see Table 1 ); and (c) for seasonal H3N2 human influenza isolates in the USA during the 2004-2005 season. (Note that isolates represent only a small fraction of total cases, and may contain reporting biases.) The estimate of the effective reproduction number for an epidemic outbreak asymptotes to unity at late times because initial growth and long-term decay in new case numbers (due to depletion of susceptibles) average out over the history of the outbreak. doi:10.1371/journal.pone.0002185.g004 Between weeks 74 and 75, the reproduction number is shifted R 0 = 0.8R1.3 to create an epidemic. Although we continued to iterate the R distributions via the Bayesian procedure described in the text, note that the shift in R upwards leads to many statistical anomalies (indicated by black arrows). The anomaly is detected immediately, on weeks 75 and 76. Anomalies here are defined as observed numbers of new cases that fall outside the expected 95% credible interval. These anomalies indicate a violation of the hypothesis that R is unchanged, and could be used to trigger alerts in surveillance. doi:10.1371/journal.pone.0002185.g005 DetectiV: visualization, normalization and significance testing for pathogen-detection microarray data DNA microarrays offer the possibility of testing for the presence of thousands of micro-organisms in a single experiment. However, there is a lack of reliable bioinformatics tools for the analysis of such data. We have developed DetectiV, a package for the statistical software R. DetectiV offers powerful yet simple visualization, normalization and significance testing tools. We show that DetectiV performs better than previously published software on a large, publicly available dataset. One of the key applications of metagenomics is the identification and quantification of species within a clinical or environmental sample. Microarrays are particularly attractive for the recognition of pathogens in clinical material since current diagnostic assays are typically restricted to the detection of single targets by real-time PCR or immunological assays. Furthermore, molecular characterization and phylogenetic analysis of these signatures can require downstream sequencing of genomic regions. Many microarrays have already been produced with the aim of characterizing the spectrum of microorganisms present in a sample, including detection of known viruses [1] [2] [3] [4] [5] , assessment of bioterrorism [6, 7] and monitoring food quality [8] .