Does the development of web-based surveillance tools represent a revolution in how we conceptualize surveillance? We think not: current high-quality public health surveillance already utilizes multiple sources of information to gain a more complete picture of the incidence and distribution of disease. For example, influenza surveillance may include laboratory-based virological surveillance, sentinel syndromic surveillance (e.g., school-based absenteeism reports) and evaluation of mortality trends for pneumonia and influenza, which taken together may provide a more complete picture of disease risk and impacts. Searchtermbased surveillance and other modalities mentioned above thus provide an additional tool in the surveillance toolbox, which has advantages over traditional surveillance as well as limitations. It should be noted, however, that limitations such as those described above are not absent from traditional surveillance systems either: estimates of incidence can change markedly with changing case definitions, incidence of laboratory-confirmed disease can change markedly with augmentation or restriction of clinical testing or changes in diagnostic test methodologies, and syndromic surveillance systems can be subject to poor specificity and frequent false alarms. Thus supplementary information derived using methods such as the one developed by Chan and colleagues should be welcomed by public health professionals. The transparency of such systems may also help demonstrate the value of openness in disease reporting, which may have ''spillover effects'' on traditional surveilance systems. Figure 2 . Screenshot of search performed on the term ''influenza'' using the Google Insights for Search tool (http://www.google. com/insights/search/#). Although influenza searches would expect to display similar wintertime seasonality to pneumonia searches, depicted in Figure 1 , the public concern and interest generated by the 2009 influenza pandemic generated a large spike in searches in that year, which obscures seasonal oscillation in other years. doi:10.1371/journal.pntd.0001215.g002 www.plosntds.org Dynamic variations in the peripheral blood lymphocyte subgroups of patients with 2009 pandemic H1N1 swine-origin influenza A virus infection BACKGROUND: Novel Influenza A (H1N1) is an acute respiratory infectious disease. Animal experiments indicated that when H1N1 virus infected early hosts, it showed strong CD4(+), CD8(+), and CD4(+)CD25(+ )T cell reactions. The aim of this study was to investigate the dynamic fluctuations of the peripheral blood lymphocyte subgroups in patients infected with H1N1 swine-origin influenza A virus (S-OIV). METHODS: The frequency of T cells, B cells, natural killer (NK) cells, and regulatory T cells (Treg) in 36 severe H1N1 and 40 moderate H1N1 patients were detected at different periods by flow cytometry. In parallel, serum cytokines were detected by enzyme-linked immunosorbent assay and C-reactive protein (CRP) was analyzed through an image-type automatic biochemical analyzer. In addition, 20 healthy volunteers, who were not infected with 2009 H1N1 virus, were selected as controls. RESULTS: The frequency of NK cells were decreased in all cases and CD19(+ )B cells were increased in severe cases than those of the controls. At 1-2d from onset, the frequency of CD4(+ )and CD4(+)CD25(+ )T cells in moderate cases was higher than in the severe cases. Serum cytokines, specifically IL-2, IL-4, IL-6, IL-10, and IFN-γ exhibited no significant change both in the moderate and the severe cases during the whole monitoring process. In the early stage of the disease, serum CRP levels in the severe and moderate groups were significantly higher than that in the control group. CONCLUSIONS: Patients showed different lymphocyte subgroup distributions between mild and severe cases, which might affect the incidence and development of 2009 H1N1. The latest public data from the Chinese Ministry of Health showed 120,940 confirmed H1N1 cases and 659 deaths were reported from the Chinese mainland as of January 2, 2010. Data analysis suggests that patients with chronic diseases, obese patients, and pregnant women are more liable to develop severe H1N1 influenza. After initial infection by the virus, the host innate immune system, as a first line of defense, takes protective measures earlier than the adaptive immune system to avoid further viral invasion or replication [1] . Animal experiments indicated that when the swine-origin influenza A virus (S-OIV) infected early hosts, it elicited strong CD4 + , CD8 + , and CD4 + CD25 + T cell reactions [2] . CD4 + and CD8 + T cells are related to viral immunity and the lack of these cells would lead to a delay in viral clearance and an increase in mortality [3] [4] [5] . On the other hand, CD4 + CD25 + cells are T cell subsets with an immune inhibition function and could play an important regulatory role in a variety of infectious diseases [6] [7] [8] . These complex molecular and cellular mechanisms are helpful in controlling and eliminating the acute stage of viral infection. Although successfully eliminating the virus is * Correspondence: ljli@zju.edu.cn † Contributed equally 1 State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, PR China Full list of author information is available at the end of the article essential, the response of virus-specific T cells to the hosts could also result in tissue damage and autoimmune responses. Therefore, monitoring the variation of cellular immune functions in H1N1 patients have important clinical significance. The aim of this study is to analyze the dynamic fluctuations of T cells, B cells, natural killer (NK) cells, and regulatory T cells in patients infected with novel influenza H1N1, as well as serum cytokines and C-reactive protein (CRP).