Phylodynamics of H5N1 Highly Pathogenic Avian Influenza in Europe, 2005-2010: Potential for Molecular Surveillance of New Outbreaks.

Previous Bayesian phylogeographic studies of H5N1 highly pathogenic avian influenza viruses (HPAIVs) explored the origin and spread of the epidemic from China into Russia, indicating that HPAIV circulated in Russia prior to its detection there in 2005. In this study, we extend this research to explore the evolution and spread of HPAIV within Europe during the 2005-2010 epidemic, using all available sequences of the hemagglutinin (HA) and neuraminidase (NA) gene regions that were collected in Europe and Russia during the outbreak. We use discrete-trait phylodynamic models within a Bayesian statistical framework to explore the evolution of HPAIV. Our results indicate that the genetic diversity and effective population size of HPAIV peaked between mid-2005 and early 2006, followed by drastic decline in 2007, which coincides with the end of the epidemic in Europe. Our results also suggest that domestic birds were the most likely source of the spread of the virus from Russia into Europe. Additionally, estimates of viral dispersal routes indicate that Russia, Romania, and Germany were key epicenters of these outbreaks. Our study quantifies the dynamics of a major European HPAIV pandemic and substantiates the ability of phylodynamic models to improve molecular surveillance of novel AIVs

The Dynamics of Avian Influenza: Individual-Based Model with Intervention Strategies in Traditional Trade Networks in Phitsanulok Province, Thailand

Avian influenza virus subtype H5N1 is endemic to Southeast Asia. In Thailand, avian influenza viruses continue to cause large poultry stock losses. The spread of the disease has a serious impact on poultry production especially among rural households with backyard chickens. The movements and activities of chicken traders result in the spread of the disease through traditional trade networks. In this study, we investigate the dynamics of avian influenza in the traditional trade network in Phitsanulok Province, Thailand. We also propose an individual-based model with intervention strategies to control the spread of the disease. We found that the dynamics of the disease mainly depend on the transmission probability and the virus inactivation period. This study also illustrates the appropriate virus disinfection period and the target for intervention strategies on traditional trade network. The results suggest that good hygiene and cleanliness among household traders and trader of trader areas and ensuring that any equipment used is clean can lead to a decrease in transmission and final epidemic size. These results may be useful to epidemiologists, researchers, and relevant authorities in understanding the spread of avian influenza through traditional trade networks

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

Glycan receptor specificity as a useful tool for characterization and surveillance of influenza A virus

Influenza A viruses are rapidly evolving pathogens with the potential for novel strains to emerge and result in pandemic outbreaks in humans. Some avian-adapted subtypes have acquired the ability to bind to human glycan receptors and cause severe infections in humans but have yet to adapt to and transmit between humans. The emergence of new avian strains and their ability to infect humans has confounded their distinction from circulating human virus strains through linking receptor specificity to human adaptation. Herein we review the various structural and biochemical analyses of influenza hemagglutinin–glycan receptor interactions. We provide our perspectives on how receptor specificity can be used to monitor evolution of the virus to adapt to human hosts so as to facilitate improved surveillance and pandemic preparedness.National Institutes of Health (U.S.) (Merit Award R37 GM057073-13)Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology)Skolkovo Institute of Science and Technolog

Predictive Modeling of Avian Influenza in Wild Birds

Thesis (Ph.D.) University of Alaska Fairbanks, 2013Over the past 20 years, highly pathogenic avian influenza (HPAI), specifically Eurasian H5N1 subtypes, caused economic losses to the poultry industry and sparked fears of a human influenza pandemic. Avian influenza virus (AIV) is widespread in wild bird populations in the low-pathogenicity form (LPAI), and wild birds are thought to be the reservoir for AIV. To date, however, nearly all predictive models of AIV focus on domestic poultry and HPAI H5N1 at a small country or regional scale. Clearly, there is a need and an opportunity to explore AIV in wild birds using data-mining and machinelearning techniques. I developed predictive models using the Random Forests algorithm to describe the ecological niche of avian influenza in wild birds. In “Chapter 2 - Predictive risk modeling of avian influenza around the Pacific Rim”, I demonstrated that it was possible to separate an AIV-positivity signal from general surveillance effort. Cold winters, high temperature seasonality, and a long distance from coast were important predictors. In “Chapter 3 - A global model of avian influenza prediction in wild birds: the importance of northern regions”, northern regions remained areas of high predicted occurrence even when using a global dataset of AIV. In surveillance data, the percentage of AIV-positive samples is typically very low, which can hamper machine-learning. For “Chapter 4 - Modeling avian influenza with Random Forests: under-sampling and model selection for unbalanced prevalence in surveillance data” I wrote custom code in R statistical programming language to evaluate a balancing algorithm, a model selection algorithm, and an under-sampling method for their effects on model accuracy. Repeated random iv sub-sampling was found to be the most reliable way to improved unbalanced datasets. In these models cold regions consistently bore the highest relative predicted occurrence scores for AIV-positivity and describe a niche for LPAI that is distinct from the niche for HPAI in domestic poultry. These studies represent a novel, initial attempt at constructing models for LPAI in wild birds and demonstrated high predictive power.TABLE OF CONTENTS Page SIGNATURE PAGE ... i TITLE PAGE ... ii ABSTRACT ... iii TABLE OF CONTENTS ... v LIST OF FIGURES ... viii LIST OF TABLES ... x LIST OF ADDITIONAL MATERIALS ... x LIST OF APPENDICES ... xi DEDICATION ... xiii ACKNOWLEDGEMENTS ... xiv CHAPTER 1: General Introduction ... 1 Avian influenza virus, transmission, and pandemic potential ... 1 Modeling AIV ... 7 Specific aims ... 10 FIGURES ... 14 LITERATURE CITED .. 17 CHAPTER 2: Predictive risk modeling of avian influenza around the Pacific Rim ... 26 ABSTRACT ... 26 INTRODUCTION ... 28 MATERIALS AND METHODS ... 31 Data layers ... 31 Modeling methods ... 33 Model evaluation ... 35 RESULTS ... 35 DISCUSSION ... 37 ACKNOWLEDGEMENTS ... 40 TABLES ... 42 vi FIGURES ... 45 LITERATURE CITED .. 49 CHAPTER 3: A global model of avian influenza prediction in wild birds: the importance of northern regions ... 54 ABSTRACT ... 54 INTRODUCTION ... 55 MATERIALS AND METHODS ... 57 Wild bird data ... 57 Environmental variable layers ... 57 Defining the outbreak niche ... 59 Predictive map ... 60 RESULTS ... 61 Important predictor variables ... 61 Ecological niche model ... 63 DISCUSSION ... 63 ACKNOWLEDGEMENTS ... 68 TABLES ... 69 FIGURES ... 72 LITERATURE CITED .. 76 CHAPTER 4: Modeling avian influenza with Random Forests: under-sampling and model selection for unbalanced prevalence in surveillance data ... 80 ABSTRACT ... 80 1. INTRODUCTION . 81 2. MATERIALS AND METHODS ... 86 2.1 Predictor variables ... 86 2.2 Wild bird data ... 87 2.3 Random Forests, balancing, and model selection ... 89 2.4 Predictive map .... 92 2.5 Statistical analyses ... 92 2.6 Variable importance ... 93 vii 2.7 Cross-model comparisons ... 94 2.8 Research design .. 94 3. RESULTS ... 95 3.1. Model Performance ... 95 3.2. Cross-model comparison ... 97 3.3. Variable importance ... 98 3.4 Predictive map ... 100 4. DISCUSSION ... 101 4.1. Random sub-sampling and model selection ... 101 4.2. Database comparisons ... 102 4.3. Predictive map ... 103 4.4. Important variables ... 104 4.5 Conclusions ....... 105 ACKNOWLEDGEMENTS ... 106 TABLES ... 107 FIGURES ... 113 LITERATURE CITED ... 123 CHAPTER 5: General Discussion ... 130 Overview ... 131 The LPAI niche vs. the HPAI niche ... 135 Technical aspects and software ... 138 Future work ... 140 Surveillance and Adaptive Management principles ... 144 FIGURES ... 146 LITERATURE CITED ... 147 APPENDICES ... 150 viii LIST OF FIGURES Page INTRODUCTION FIGURES Figure 1.1. Pacific Rim study area and wild bird surveillance locations ... 14 Figure 1.2. Global study area and wild bird surveillance locations ... 15 Figure 1.3. Pacific Rim study area and wild bird surveillance locations ... 16 CHAPTER 2 FIGURES Figure 2.1. Map of predicted relative occurrence index of avian influenza virus (AIV) in wild birds around the Pacific Rim study area and surveillance locations .. 45 Figure 2.2. Notched box plots for important variables. ... 46 Figure 2.3. Histogram density plots for important variables ... 47 Figure 2.4. Partial dependence plots for important variables ... 48 CHAPTER 3 FIGURES Figure 3.1. Histogram density plots for important variables ... 72 Figure 3.2. Partial dependence plots for important variables ... 73 Figure 3.3. Map of predicted relative occurence index of avian influenza virus (AIV) in wild birds and surveillance locations ... 75 CHAPTER 4 FIGURES Figure 4.1. Research design ... 113 Figure 4.2. Receiver Operating Characteristic (ROC) curves for experimental methods ... 114 Figure 4.3. Mean area under the receiver operating characteristic curves (AUC) of the four different experimental methods that generated them ... 115 Figure 4.4. Cross-model comparison results ... 116 Figure 4.5. Density plots for the mean temperature in April ... 117 ix Figure 4.6. Density plots for important variables ... 118 Figure 4.7. Partial dependence plots for important predictor variables ... 119 Figure 4.8. Map of predicted relative occurence index of avian influenza virus (AIV) in wild birds and surveillance locations around the Pacific Rim study area ... 121 Figure 4.9. A conceptual diagram illustrating differences between traditional and collaborative surveillance methods and their interaction with laboratory and machine-learning work. ... 122 GENERAL DISCUSSION FIGURES Figure 5.1. Density plot of latitude. ... 146 x LIST OF TABLES Page CHAPTER 2 TABLES Table 2.1. Predictor variables used to construct model of avian influenza in wild birds ...42 Table 2.2. Normalized importance scores for top predictor variables ...44 CHAPTER 3 TABLES Table 3.1. The predictor variables used by the Random Forests algorithm to create a global prediction map for avian influenza virus in wild birds ...69 CHAPTER 4 TABLES Table 4.1. Selected examples of the prevalence of birds testing positive for avian influenza virus (AIV) from wild bird surveillance projects ...107 Table 4.2. Predictor variables used by the Random Forests to create a prediction map for AIV in wild birds .108 Table 4.3. Descriptive summary table for databases. ...110 Table 4.4. Summary table for experimental methods. ...111 Table 4.5. Descriptive statistics for databases and models. ...112 LIST OF ADDITIONAL MATERIALS Additional Materials ... CD xi LIST OF APPENDICES Page Appendix A. List of bird species in the Alaska Asia Avian Influenza Research 2005-2007 database... 150 Appendix B. List of bird species from the NIH Influenza Research Database (IRD). ... 157 Appendix C. List of bird species in the Alaska Asia Avian Influenza Research 2005-2020 database ... 157 Appendix D. List of bird species in the Canada’s Inter-agency Wild Bird Influenza survey (CIWBI) database . 169 Appendix E. Global Layers.xml: Metadata for bioclimatic, anthropogenic, and geographic data layers ...CD Appendix F. Georeferenced Bird Data.xml: Metadata for Pacific Rim model (Chapter 1), global model (Chapter 2), and the four datasets used in Chapter 3 ...CD Appendix G. Global Layers (folder): bioclimatic, anthropogenic, and geographic data layers used in the PhD thesis “Mapping Avian Influenza in Wild Birds” Datasets (subfolder) Chapter 1 flupacV5.shp ...CD Chapter 2 globfluV6.shp ...CD Chapter 3 A3IRB.shp ...CD Chapter 3 ALL.shp ...CD Chapter 3 CIWBI.shp ...CD Chapter 3 UNIQUE.shp ...CD GEM landcover 2000 (subfolder) glc2000_v1_1_Grid: landcover ...CD GEM-Metadata.pdf...CD GLC2000_legend_summary.doc ...CD Last of the Wild (subfolder) hfp_global_geo_grid: Human Footprint ...CD hii_global_geo_grid: Human Influence Index ...CD ltw_global_geo: Last of the Wild ...CD xii livestock (subfolder) glbpgtotcor (subfolder): estimated pig density ...CD glbpototcor (subfolder): estimated poultry density ...CD sedac human world popn (subfolder) glfedens10: human population density ...CD WorldClim (subfolder) alt_30s_esri: elevation ...CD bio_30s_esri: bioclimatic variables ...CD prec_30s_esri: monthly precipitation means ...CD tmean_30s_esri: monthly temperature means ...CD WWF GLWD (subfolder) euc_hydro_1k: distance to hydrologic feature ...CD GLWD_Data_Documentation.pdf ...CD Appendix H. Example Code (folder) random subsetting 07112012.R ...CD rocr_code_071012.R ....CD Partial_plots 71712.R ...C

Transmission potential of influenza A/H7N9, February to May 2013, China

abstract: Background On 31 March 2013, the first human infections with the novel influenza A/H7N9 virus were reported in Eastern China. The outbreak expanded rapidly in geographic scope and size, with a total of 132 laboratory-confirmed cases reported by 3 June 2013, in 10 Chinese provinces and Taiwan. The incidence of A/H7N9 cases has stalled in recent weeks, presumably as a consequence of live bird market closures in the most heavily affected areas. Here we compare the transmission potential of influenza A/H7N9 with that of other emerging pathogens and evaluate the impact of intervention measures in an effort to guide pandemic preparedness. Methods We used a Bayesian approach combined with a SEIR (Susceptible-Exposed-Infectious-Removed) transmission model fitted to daily case data to assess the reproduction number (R) of A/H7N9 by province and to evaluate the impact of live bird market closures in April and May 2013. Simulation studies helped quantify the performance of our approach in the context of an emerging pathogen, where human-to-human transmission is limited and most cases arise from spillover events. We also used alternative approaches to estimate R based on individual-level information on prior exposure and compared the transmission potential of influenza A/H7N9 with that of other recent zoonoses. Results Estimates of R for the A/H7N9 outbreak were below the epidemic threshold required for sustained human-to-human transmission and remained near 0.1 throughout the study period, with broad 95% credible intervals by the Bayesian method (0.01 to 0.49). The Bayesian estimation approach was dominated by the prior distribution, however, due to relatively little information contained in the case data. We observe a statistically significant deceleration in growth rate after 6 April 2013, which is consistent with a reduction in A/H7N9 transmission associated with the preemptive closure of live bird markets. Although confidence intervals are broad, the estimated transmission potential of A/H7N9 appears lower than that of recent zoonotic threats, including avian influenza A/H5N1, swine influenza H3N2sw and Nipah virus. Conclusion Although uncertainty remains high in R estimates for H7N9 due to limited epidemiological information, all available evidence points to a low transmission potential. Continued monitoring of the transmission potential of A/H7N9 is critical in the coming months as intervention measures may be relaxed and seasonal factors could promote disease transmission in colder months.The electronic version of this article is the complete one and can be found online at: http://bmcmedicine.biomedcentral.com/articles/10.1186/1741-7015-11-21

Transmission potential of influenza A/H7N9, February to May 2013, China

Background On 31 March 2013, the first human infections with the novel influenza A/H7N9 virus were reported in Eastern China. The outbreak expanded rapidly in geographic scope and size, with a total of 132 laboratory-confirmed cases reported by 3 June 2013, in 10 Chinese provinces and Taiwan. The incidence of A/H7N9 cases has stalled in recent weeks, presumably as a consequence of live bird market closures in the most heavily affected areas. Here we compare the transmission potential of influenza A/H7N9 with that of other emerging pathogens and evaluate the impact of intervention measures in an effort to guide pandemic preparedness. Methods We used a Bayesian approach combined with a SEIR (Susceptible-Exposed-Infectious-Removed) transmission model fitted to daily case data to assess the reproduction number (R) of A/H7N9 by province and to evaluate the impact of live bird market closures in April and May 2013. Simulation studies helped quantify the performance of our approach in the context of an emerging pathogen, where human-to-human transmission is limited and most cases arise from spillover events. We also used alternative approaches to estimate R based on individual-level information on prior exposure and compared the transmission potential of influenza A/H7N9 with that of other recent zoonoses. Results Estimates of R for the A/H7N9 outbreak were below the epidemic threshold required for sustained human-to-human transmission and remained near 0.1 throughout the study period, with broad 95% credible intervals by the Bayesian method (0.01 to 0.49). The Bayesian estimation approach was dominated by the prior distribution, however, due to relatively little information contained in the case data. We observe a statistically significant deceleration in growth rate after 6 April 2013, which is consistent with a reduction in A/H7N9 transmission associated with the preemptive closure of live bird markets. Although confidence intervals are broad, the estimated transmission potential of A/H7N9 appears lower than that of recent zoonotic threats, including avian influenza A/H5N1, swine influenza H3N2sw and Nipah virus. Conclusion Although uncertainty remains high in R estimates for H7N9 due to limited epidemiological information, all available evidence points to a low transmission potential. Continued monitoring of the transmission potential of A/H7N9 is critical in the coming months as intervention measures may be relaxed and seasonal factors could promote disease transmission in colder months