Spatial and Temporal Analysis of Avian Influenza H5N1.
详细信息
文摘
Avian influenza H5N1 is one kind of important bird flu. Unfortunately, this virus has swiftly evolved and become highly pathogenic to humans and poultry, resulting in 100% of death in infected poultry and over 60% of mortality among infected human population. Moreover, the virus tends to reassort with other influenza viruses, such as the current swine flu H1N1, to establish themselves in environments and further this epidemic all over the world. The World Health Organization WHO) has in fact warned that highly pathogenic avian influenza H5N1 poses a graver risk of a global human pandemic than at any time since the Hong Kong outbreak H3N2) in the 1960s. One key to preventing such a calamity is to obtain a thorough understanding of the mechanisms of avian influenza transmission and its spatio-temporal patterns of dispersal. The issues at stake are outbreaks spatial and temporal patterns, the interrelationship of these with the evolution of influenza viruses in such a way that geography is understood as a dimension of the diseases virology, and the human and avian behaviors and socio-ecological environments associated with H5Nl spread. This thesis sets out to study these problems in detail and propose solutions. First, we apply multifractal detrended fluctuation analysis to determine the temporal scaling behavior of outbreaks in Asia, Europe, Africa, and the whole of the world between December 2003 to March 2009. Long-range correlation and multifractality, two important properties characterizing the scaling behavior of complex dynamics, are first detected in the outbreak time series. In addition, this study identifies different temporal scaling behaviors of outbreaks of these continents 8,nd specific seasonal patterns in Asia. These findings confirm our perspective that avian-influenza outbreak behaviors are self-similar over time and are spatially heterogeneous. Second, we conduct a spatial analysis for global trends and local clusters of H5N1 outbreaks at multiple geographical scales. Currently, the local K function used in a point pattern analysis searches outbreak clusters, assuming the disease is spatially homogeneous. The thesis proposes a much more efficient method to measure the degree of clusters accurately. The modified function works by weighting outbreaks through distances, counting the number of the weighted outbreaks for each lattice point no matter whether the disease emerges in a grid. This weighted local K function extends cluster analysis from a point pattern to lattice data. Spatial representation in these terms then seeks to explore local patterns of H5N1 over a continuous space. Third, we study a set of socio-environmental factors, which are plausibly associated with the occurrence of H5N1. Spatial epidemiological models are built for predicting the disease at both continental and national levels, covering Indonesia, China, and the whole of East-Southeast Asia. We evaluate the statistical models using 1,000 bootstrap replicates, showing a consistently high rate of prediction, assessed by statistics: AUC, Kappa Index, and pseudo R square. Finally, avian influenza is an inter-disciplinary issue across virology, medical geography, and spatial epidemiology. How to quantify and integrate knowledge from different disciplines remains a challenge in fully understanding the disease. We propose a method to formally integrate genetic analysis that identifies the evolution of the H5N1 virus in space and time, epidemiological analysis that determines socio-environmental factors associated with H5N1 occurrence and statistical analysis that identifies outbreak dusters. Our integrated results show a significant advance in findings over reports in, for instance, Gilbert et al. 2008) and we believe our findings are more precise and informative in representing the occurrence and the space-time dynamics of H5N1 spread. Overall, unlike traditional influenza studies, our work sets up a solid foundation for the inter-disciplinary study of this and other spatial infectious diseases.