Spatial Distribution and Risk Factors of Highly Pathogenic Avian Influenza (HPAI) H5N1 in China

Highly pathogenic avian influenza (HPAI) H5N1 was first encountered in 1996 in Guangdong province (China) and started spreading throughout Asia and the western Palearctic in 2004–2006. Compared to several other countries where the HPAI H5N1 distribution has been studied in some detail, little is known about the environmental correlates of the HPAI H5N1 distribution in China. HPAI H5N1 clinical disease outbreaks, and HPAI virus (HPAIV) H5N1 isolated from active risk-based surveillance sampling of domestic poultry (referred to as HPAIV H5N1 surveillance positives in this manuscript) were modeled separately using seven risk variables: chicken, domestic waterfowl population density, proportion of land covered by rice or surface water, cropping intensity, elevation, and human population density. We used bootstrapped logistic regression and boosted regression trees (BRT) with cross-validation to identify the weight of each variable, to assess the predictive power of the models, and to map the distribution of HPAI H5N1 risk. HPAI H5N1 clinical disease outbreak occurrence in domestic poultry was mainly associated with chicken density, human population density, and elevation. In contrast, HPAIV H5N1 infection identified by risk-based surveillance was associated with domestic waterfowl density, human population density, and the proportion of land covered by surface water. Both models had a high explanatory power (mean AUC ranging from 0.864 to 0.967). The map of HPAIV H5N1 risk distribution based on active surveillance data emphasized areas south of the Yangtze River, while the distribution of reported outbreak risk extended further North, where the density of poultry and humans is higher. We quantified the statistical association between HPAI H5N1 outbreak, HPAIV distribution and post-vaccination levels of seropositivity (percentage of effective post-vaccination seroconversion in vaccinated birds) and found that provinces with either outbreaks or HPAIV H5N1 surveillance positives in 2007–2009 appeared to have had lower antibody response to vaccination. The distribution of HPAI H5N1 risk in China appears more limited geographically than previously assessed, offering prospects for better targeted surveillance and control interventions

Influenza A H5N1 Immigration Is Filtered Out at Some International Borders

Geographic spread of highly pathogenic influenza A H5N1, the bird flu strain, appears a necessary condition for accelerating the evolution of a related human-to-human infection. As H5N1 spreads the virus diversifies in response to the variety of socioecological environments encountered, increasing the chance a human infection emerges. Genetic phylogenies have for the most part provided only qualitative evidence that localities differ in H5N1 diversity. For the first time H5N1 variation is quantified across geographic space.We constructed a statistical phylogeography of 481 H5N1 hemagglutinin genetic sequences from samples collected across 28 Eurasian and African localities through 2006. The MigraPhyla protocol showed southern China was a source of multiple H5N1 strains. Nested clade analysis indicated H5N1 was widely dispersed across southern China by both limited dispersal and long distance colonization. The UniFrac metric, a measure of shared phylogenetic history, grouped H5N1 from Indonesia, Japan, Thailand and Vietnam with those from southeastern Chinese provinces engaged in intensive international trade. Finally, H5N1's accumulative phylogenetic diversity was greatest in southern China and declined beyond. The gradient was interrupted by areas of greater and lesser phylogenetic dispersion, indicating H5N1 migration was restricted at some geopolitical borders. Thailand and Vietnam, just south of China, showed significant phylogenetic clustering, suggesting newly invasive H5N1 strains have been repeatedly filtered out at their northern borders even as both countries suffered recurring outbreaks of endemic strains. In contrast, Japan, while successful in controlling outbreaks, has been subjected to multiple introductions of the virus.The analysis demonstrates phylogenies can provide local health officials with more than hypotheses about relatedness. Pathogen dispersal, the functional relationships among disease ecologies across localities, and the efficacy of control efforts can also be inferred, all from viral genetic sequences alone

A fast algorithm to find Best Matching Units in Self-Organizing Maps

International audienceSelf-Organizing Maps (SOM) are well-known unsupervised neural networks able to perform vector quantization while mapping an underlying regular neighbourhood structure onto the codebook. They are used in a wide range of applications. As with most properly trained neural networks models, increasing the number of neurons in a SOM leads to better results or new emerging properties. Therefore highly efficient algorithms for learning and evaluation are key to improve the performance of such models. In this paper, we propose a faster alternative to compute the Winner Takes All component of SOM that scales better with a large number of neurons. We present our algorithm to find the so-called best matching unit (BMU) in a SOM, and we theoretically analyze its computational complexity. Statistical results on various synthetic and real-world datasets confirm this analysis and show an even more significant improvement in computing time with a minimal degradation of performance. With our method, we explore a new approach for optimizing SOM that can be combined with other optimization methods commonly used in these models for an even faster computation in both learning and recall phases

Spatiotemporal patterns of childhood asthma hospitalization and utilization in Memphis Metropolitan Area from 2005 to 2015

© 2017 Taylor & Francis Group, LLC. Objective: To identify the key risk factors and explain the spatiotemporal patterns of childhood asthma in the Memphis metropolitan area (MMA) over an 11-year period (2005–2015). We hypothesize that in the MMA region this burden is more prevalent among urban children living south, downtown, and north of Memphis than in other areas. Methods: We used a large-scale longitudinal electronic health record database from an integrated healthcare system, Geographic information systems (GIS), and statistical and space-time models to study the spatiotemporal distributions of childhood asthma at census tract level. Results: We found statistically significant spatiotemporal clusters of childhood asthma in the south, west, and north of Memphis city after adjusting for key covariates. The results further show a significant increase in temporal gradient in frequency of emergency department (ED) visits and inpatient hospitalizations from 2009 to 2013, and an upward trajectory from 4 per 1,000 children in 2005 to 16 per 1,000 children in 2015. The multivariate logistic regression identified age, race, insurance, admit source, encounter type, and frequency of visits as significant risk factors for childhood asthma (p \u3c 0.05). We observed a greater asthma burden and healthcare utilization for African American (AA) patients living in a high-risk area than those living in a low-risk area in comparison to the white patients: AA vs. white [odds ratio (OR) = 3.03, 95% confidence interval (CI): 2.75–3.34]; and Hispanic vs. white (OR = 1.62, 95% CI: 1.21–2.17). Conclusions: These findings provide a strong basis for developing geographically tailored population health strategies at the neighborhood level for young children with chronic respiratory conditions