Probabilistic Daily ILI Syndromic Surveillance with a Spatio-Temporal Bayesian Hierarchical Model

BACKGROUND: For daily syndromic surveillance to be effective, an efficient and sensible algorithm would be expected to detect aberrations in influenza illness, and alert public health workers prior to any impending epidemic. This detection or alert surely contains uncertainty, and thus should be evaluated with a proper probabilistic measure. However, traditional monitoring mechanisms simply provide a binary alert, failing to adequately address this uncertainty. METHODS AND FINDINGS: Based on the Bayesian posterior probability of influenza-like illness (ILI) visits, the intensity of outbreak can be directly assessed. The numbers of daily emergency room ILI visits at five community hospitals in Taipei City during 2006-2007 were collected and fitted with a Bayesian hierarchical model containing meteorological factors such as temperature and vapor pressure, spatial interaction with conditional autoregressive structure, weekend and holiday effects, seasonality factors, and previous ILI visits. The proposed algorithm recommends an alert for action if the posterior probability is larger than 70%. External data from January to February of 2008 were retained for validation. The decision rule detects successfully the peak in the validation period. When comparing the posterior probability evaluation with the modified Cusum method, results show that the proposed method is able to detect the signals 1-2 days prior to the rise of ILI visits. CONCLUSIONS: This Bayesian hierarchical model not only constitutes a dynamic surveillance system but also constructs a stochastic evaluation of the need to call for alert. The monitoring mechanism provides earlier detection as well as a complementary tool for current surveillance programs

Genomic Characterization of Haemophilus parasuis SH0165, a Highly Virulent Strain of Serovar 5 Prevalent in China

Haemophilus parasuis can be either a commensal bacterium of the porcine respiratory tract or an opportunistic pathogen causing Glässer's disease, a severe systemic disease that has led to significant economical losses in the pig industry worldwide. We determined the complete genomic sequence of H. parasuis SH0165, a highly virulent strain of serovar 5, which was isolated from a hog pen in North China. The single circular chromosome was 2,269,156 base pairs in length and contained 2,031 protein-coding genes. Together with the full spectrum of genes detected by the analysis of metabolic pathways, we confirmed that H. parasuis generates ATP via both fermentation and respiration, and possesses an intact TCA cycle for anabolism. In addition to possessing the complete pathway essential for the biosynthesis of heme, this pathogen was also found to be well-equipped with different iron acquisition systems, such as the TonB system and ABC-type transport complexes, to overcome iron limitation during infection and persistence. We identified a number of genes encoding potential virulence factors, such as type IV fimbriae and surface polysaccharides. Analysis of the genome confirmed that H. parasuis is naturally competent, as genes related to DNA uptake are present. A nine-mer DNA uptake signal sequence (ACAAGCGGT), identical to that found in Actinobacillus pleuropneumoniae and Mannheimia haemolytica, followed by similar downstream motifs, was identified in the SH0165 genome. Genomic and phylogenetic comparisons with other Pasteurellaceae species further indicated that H. parasuis was closely related to another swine pathogenic bacteria A. pleuropneumoniae. The comprehensive genetic analysis presented here provides a foundation for future research on the metabolism, natural competence and virulence of H. parasuis

Age as a determinant for dissemination of seasonal and pandemic influenza : An open cohort study of influenza outbreaks in Östergötland County, Sweden

An understanding of the occurrence and comparative timing of influenza infections in different age groups is important for developing community response and disease control measures. This study uses data from a Scandinavian county (population 427,000) to investigate whether age was a determinant for being diagnosed with influenza 2005–2010 and to examine if age was associated with case timing during outbreaks. Aggregated demographic data were collected from Statistics Sweden, while influenza case data were collected from a county-wide electronic health record system. A logistic regression analysis was used to explore whether case risk was associated with age and outbreak. An analysis of variance was used to explore whether day for diagnosis was also associated to age and outbreak. The clinical case data were validated against case data from microbiological laboratories during one control year. The proportion of cases from the age groups 10–19 (p<0.001) and 20–29 years old (p<0.01) were found to be larger during the A pH1N1 outbreak in 2009 than during the seasonal outbreaks. An interaction between age and outbreak was observed (p<0.001) indicating a difference in age effects between circulating virus types; this interaction persisted for seasonal outbreaks only (p<0.001). The outbreaks also differed regarding when the age groups received their diagnosis (p<0.001). A post-hoc analysis showed a tendency for the young age groups, in particular the group 10–19 year olds, led outbreaks with influenza type A H1 circulating, while A H3N2 outbreaks displayed little variations in timing. The validation analysis showed a strong correlation (r = 0.625; p<0.001) between the recorded numbers of clinically and microbiologically defined influenza cases. Our findings demonstrate the complexity of age effects underlying the emergence of local influenza outbreaks. Disentangling these effects on the causal pathways will require an integrated information infrastructure for data collection and repeated studies of well-defined communities