Metapopulation dynamics and determinants of H5N1 highly pathogenic avian influenza outbreaks in Indonesian poultry.

In 2008, the Indonesian Government implemented a revised village-level Participatory Disease Surveillance and Response (PDSR) program to gain a better understanding of both the magnitude and spatial distribution of H5N1 highly pathogenic avian influenza (HPAI) outbreaks in backyard poultry. To date, there has been considerable collection of data, but limited publically available analysis. This study utilizes data collected by the PDSR program between April 2008 and September 2010 for Java, Bali and the Lampung Province of Sumatra. The analysis employs hierarchical Bayesian occurrence models to quantify spatial and temporal dynamics in backyard HPAI infection reports at the District level in 90 day time periods, and relates the probability of HPAI occurrence to PDSR-reported village HPAI infection status and human and poultry density. The probability of infection in a District was assumed to be dependent on the status of the District in the previous 90 day time period, and described by either a colonization probability (the probability of HPAI infection in a District given there had not been infection in the previous 90 day time period) or a persistence probability (the probability of HPAI infection being maintained in the District from the previous to current 90 day period). Results suggest that the number of surveillance activities in a district had little relationship to outbreak occurrence probabilities, but human and poultry densities were found to have non-linear relationships to outbreak occurrence probabilities. We found significant spatial dependency among neighboring districts, indicating that there are latent spatial processes that are not captured by the covariates available for this study, but which nonetheless impact outbreak dynamics. The results of this work may help improve understanding of the seasonal nature of H5N1 in poultry and the potential role of poultry density in enabling endemicity to occur, as well as to assist the Government of Indonesia target scarce resources to regions and time periods when outbreaks of HPAI in poultry are most likely to occur.

[1]  M. Conroy,et al.  Modeling demographic processes in marked populations , 2009 .

[2]  D. Normile Indonesia Taps Village Wisdom to Fight Bird Flu , 2007, Science.

[3]  Christopher K. Wikle,et al.  Hierarchical Bayesian Models for Predicting The Spread of Ecological Processes , 2003 .

[4]  V. Martin,et al.  Origin and evolution of highly pathogenic H5N1 avian influenza in Asia , 2005, Veterinary Record.

[5]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[6]  Kenneth H. Pollock,et al.  Disease dynamics in wild populations: modeling and estimation: a review , 2010, Journal of Ornithology.

[7]  D. Pfeiffer,et al.  An analysis of the spatial and temporal patterns of highly pathogenic avian influenza occurrence in Vietnam using national surveillance data. , 2007, Veterinary journal.

[8]  E. S. Siregar,et al.  Participatory Disease Surveillance and Response in Indonesia: Strengthening Veterinary Services and Empowering Communities to Prevent and Control Highly Pathogenic Avian Influenza , 2010, Avian diseases.

[9]  C. Jost,et al.  Participatory epidemiology in disease surveillance and research. , 2007, Revue scientifique et technique.

[10]  W. Kendall,et al.  Seeking a second opinion: uncertainty in disease ecology. , 2010, Ecology letters.

[11]  M. Gilbert,et al.  Free-grazing Ducks and Highly Pathogenic Avian Influenza, Thailand , 2006, Emerging infectious diseases.

[12]  J. Besag,et al.  Bayesian image restoration, with two applications in spatial statistics , 1991 .

[13]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[14]  J. Andrew Royle,et al.  Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities , 2008 .

[15]  F. Black,et al.  Measles endemicity in insular populations: critical community size and its evolutionary implication. , 1966, Journal of theoretical biology.

[16]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[17]  Marius Gilbert,et al.  Risk factors and clusters of Highly Pathogenic Avian Influenza H5N1 outbreaks in Bangladesh. , 2010, Preventive veterinary medicine.

[18]  D. MacKenzie Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence , 2005 .

[19]  M. Gilbert,et al.  Mapping H5N1 highly pathogenic avian influenza risk in Southeast Asia , 2008, Proceedings of the National Academy of Sciences.

[20]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[21]  J. Mariner,et al.  Rinderpest surveillance performance monitoring using quantifiable indicators. , 2003, Revue scientifique et technique.

[22]  Stephanie Fitchett,et al.  Comparing national and global data collection systems for reporting, outbreaks of H5N1 HPAI. , 2010, Preventive veterinary medicine.

[23]  B. T. Grenfell,et al.  Disease Extinction and Community Size: Modeling the Persistence of Measles , 1997, Science.