The fi eld of electronic biosurveillance is in its infancy and is rapidly changing. Early work focused on attempts to detect outbreaks (early event detection) by using broadly defi ned syndromes (e.g., respiratory syndrome) based on chief complaints and diagnoses. Emphasis has recently shifted to monitoring for ongoing outbreaks (situational awareness) and for specifi c disease indicators (e.g., cough, dyspnea) called subsyndromes. The fi eld is now beginning to develop methods for case-based surveillance (i.e., automated application of a formal case defi nition using computerized data) (18) . Each data type and disease indicator may have unique characteristics that require modifi cations of standard data analysis methods. However, because the adaptation of time-series methods to recognize outbreaks will be an ongoing need, the enhanced methods identifi ed by this study are likely to have lasting usefulness. Figure 3 . Comparison of initial versus best algorithms for analysis of fever syndrome data at an example emergency department, October-November 2006. A) SD comparison. Count, fever syndrome counts; SD (initial), SD by using initial algorithm (minimum SD = 0.2, 7-day baseline, count method, unstratifi ed baseline); SD (best), SD by using best algorithm (minimum SD = 1.0, 28-day baseline, rate method, unstratifi ed baseline). B) Count threshold comparison. Count, fever syndrome counts; threshold 1, minimum count needed to trigger an alert by using initial method; threshold 2, minimum count needed to trigger an alert by using best method (for the best algorithm, which accounts for rate, 8 counts were added to total visits for calculating the threshold). C) Detection of 8 additional counts. Count, daily fever syndrome counts; count + 8, daily count plus 8 counts; both methods, 30 days with the additional counts detected by both the initial and best methods; initial only, 2 days with the additional counts detected by using initial method only; and best only, 19 days with additional counts detected by using best method only. Molecular Mechanisms of Recombination Restriction in the Envelope Gene of the Human Immunodeficiency Virus The ability of pathogens to escape the host's immune response is crucial for the establishment of persistent infections and can influence virulence. Recombination has been observed to contribute to this process by generating novel genetic variants. Although distinctive recombination patterns have been described in many viral pathogens, little is known about the influence of biases in the recombination process itself relative to selective forces acting on newly formed recombinants. Understanding these influences is important for determining how recombination contributes to pathogen genome and proteome evolution. Most previous research on recombination-driven protein evolution has focused on relatively simple proteins, usually in the context of directed evolution experiments. Here, we study recombination in the envelope gene of HIV-1 between primary isolates belonging to subtypes that recombine naturally in the HIV/AIDS pandemic. By characterizing the early steps in the generation of recombinants, we provide novel insights into the evolutionary forces that shape recombination patterns within viral populations. Specifically, we show that the combined effects of mechanistic processes that determine the locations of recombination breakpoints across the HIV-1 envelope gene, and purifying selection acting against dysfunctional recombinants, can explain almost the entire distribution of breakpoints found within this gene in nature. These constraints account for the surprising paucity of recombination breakpoints found in infected individuals within this highly variable gene. Thus, the apparent randomness of HIV evolution via recombination may in fact be relatively more predictable than anticipated. In addition, the dominance of purifying selection in localized areas of the HIV genome defines regions where functional constraints on recombinants appear particularly strong, pointing to vulnerable aspects of HIV biology. Pathogens, and viruses in particular, are subject to strong selective pressures during infection and often have characteristically high degrees of genetic variation [1] . Recombination is an important evolutionary mechanism that contributes to this genetic diversification. By creating novel combinations of pre-existing genetic polymorphisms in a single replication cycle, recombination enables greater movements through sequence space than can be achieved by individual point mutations. As a consequence, recombination provides access to evolutionary ''shortcuts''. In addition, since recombination generally involves genes that already encode functional products, the probability of producing viable progeny is higher compared to the insertion of an equivalent number of random point mutations [2] . However, the generation of recombinant forms is not an unconstrained process. Genes and genomes generally evolve through the slow accumulation of point mutations, which often requires the progressive insertion of compensatory mutations at ''linked'' sites. This coevolution permits the preservation of epistatic interactions. By simultaneously introducing several substitutions, recombination has the potential to substantially perturb such coevolved intra-genome interaction networks [2, 3] , impairing the functionality of the genes involved. Thus, the balance between the advantages of taking evolutionary shortcuts and the risk of chimeras being dysfunctional [2] determines the role played by recombination in the evolution of a given gene or organism.