Risk factors and characteristics of H5N1 Highly Pathogenic Avian Influenza (HPAI) post-vaccination outbreaks

The role of vaccination and environmental factors on outbreaks of high pathogenicity avian influenza H5N1 in Bangladesh

High Pathogenicity Avian Influenza (HPAI) H5N1 outbreaks continue to wreak havoc on the global poultry industry and threaten the health of wild bird populations, with sporadic spillover in humans and other mammals, resulting in widespread calls to vaccinate poultry. Bangladesh has been vaccinating poultry since 2012, presenting a prime opportunity to study the effects of vaccination on HPAI H5N1circulation in both poultry and wild birds. We investigated the efficacy of vaccinating commercial poultry against HPAI H5N1 along with climatic and socio-economic factors considered potential drivers of HPAI H5N1 outbreak risk in Bangladesh. Using a multivariate modeling approach, we estimated that the rate of outbreaks was 18 times higher before compared to after vaccination, with winter months having a three times higher chance of outbreaks than summer months. Variables resulting in small but significant increases in outbreak rate were relatively low ambient temperatures for the time of year, literacy rate, chicken and duck density, crop density, and presence of highways; this may be attributable to low temperatures supporting viral survival outside the host, higher literacy driving reporting rate, density of the host reservoir, and spread of the virus through increased connectivity. Despite the substantial impact of vaccination on outbreaks, we note that HPAI H5N1 is still enzootic in Bangladesh; vaccinated poultry flocks have high rates of H5N1 prevalence, and spillover to wild birds has increased. Vaccination in Bangladesh thus bears the risk of supporting "silent spread," where the vaccine only provides protection against disease and not also infection. Our findings underscore that poultry vaccination can be part of holistic HPAI mitigation strategies when accompanied by monitoring to avoid silent spread.

Machine learning-driven dynamic risk prediction for highly pathogenic avian influenza at poultry farms in Republic of Korea: Daily risk estimation for individual premises

Highly pathogenic avian influenza (HPAI) is a fatal zoonotic disease that damages the poultry industry and endangers human lives via exposure to the pathogen. A risk assessment model that precisely predicts high-risk groups and occurrence of HPAI infection is essential for effective biosecurity measures that minimize the socio-economic losses of massive outbreaks. However, the conventional risk prediction approaches have difficulty incorporating the broad range of factors associated with HPAI infections at poultry holdings. Therefore, it is difficult to accommodate the complexity of the dynamic transmission mechanisms and generate risk estimation on a real-time basis. We proposed a continuous risk prediction framework for HPAI occurrences that used machine learning algorithms (MLAs). This integrated environmental, on-farm biosecurity, meteorological, vehicle movement tracking, and HPAI wild bird surveillance data to improve accuracy and timeliness. This framework consisted of (i) the generation of 1788 predictors from six types of data and reconstructed them with an outcome variable into a data mart based on a temporal assumption (i.e. infected period and day-ahead forecasting); (ii) training of the predictors with the temporally rearranged outcome variable that corresponded to HPAI H5N6 infected state at each individual farm on daily basis during the 2016-2017 HPAI epidemic using three different MLAs [Random Forest, Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting]; (iii) predicting the daily risk of HPAI infection during the 2017-2018 HPAI epidemic using the pre-trained MLA models for each farm across the country. The models predicted the high risk to 8-10 out of 19 infected premises during the infected period in advance. The GBM MLAs outperformed the 7-day forecasting of HPAI prediction at individual poultry holdings, with an area under the curve (AUC) of receiver operating characteristic of 0.88. Therefore, this approach enhances the flexibility and timing of interventions against HPAI outbreaks at poultry farms.

Preventive effect of on-farm biosecurity practices against highly pathogenic avian influenza (HPAI) H5N6 infection on commercial layer farms in the Republic of Korea during the 2016-17 epidemic: A case-control study

Highly pathogenic avian influenza virus (HPAIv) H5N6 has destructive consequences on the global poultry production system. Recently, a growing number of layer farms have been heavily damaged from the HPAIv epidemic due to the increased virulence of the virus and the intensification of the production system. Therefore, stakeholders should implement effective preventive practices at the farm level that are aligned with contingency measures at the national level to minimize poultry losses. However, numerous biosecurity protocols for layer farm workers to follow have been developed, impeding efficient prevention and control. Furthermore, the effectiveness of biosecurity practices varies with the geographical condition and inter-farm contact structures. Hence, the objective of our study was to examine the preventive effect of five biosecurity actions commonly practiced at layer farms in the Republic of Korea against HPAIv H5N6: (i) fence installation around a farm, ii) rodent control inside a farm; iii) disinfection booth for visitors for disinfection protocols, iv) an anterior room in the sheds before entering the bird area and v) boots changes when moving between sheds in the same farm. We conducted a case-control study on 114 layer case farms and 129 layer control farms during the 2016-17 HPAI epidemic. The odds ratios for five on-farm biosecurity practices implemented in those study groups were estimated as a preventive effect on the HPAI infection with covariates, including seven geographical conditions and three network metrics using Bayesian hierarchical logistic regression and geographical location weighted logistic regression. The results showed that the use of a disinfection booth for personnel reduced the odds of HPAIv H5N6 infection (adjusted odds ratio [AOR] = 0.002, 95 % credible interval [CrI] = 0.00007 - 0.025) with relatively small spatial variation (minimum AOR - maximum AOR: 0.084-0.263). Changing boots between sheds on the same farm reduced the odds of HPAIv H5N6 infection (AOR = 0.160, 95 % CrI = 0.024-0.852) with relatively wide spatial variation (minimum AOR - maximum AOR = 0.270-0.688). Therefore, enhanced personnel biosecurity protocols at the farm of entry for layer farms is recommended to effectively prevent and respond to HPAIv H5N6 infection under different local condition. Our study provides an important message for layer farmers to effectively implement on-farm biosecurity actions against HPAIv H5N6 infection at their farms by setting priorities based on their spatial condition and network position.

A framework for the risk prediction of avian influenza occurrence: An Indonesian case study

Avian influenza viruses can cause economically devastating diseases in poultry and have the potential for zoonotic transmission. To mitigate the consequences of avian influenza, disease prediction systems have become increasingly important. In this study, we have proposed a framework for the prediction of the occurrence and spread of avian influenza events in a geographical area. The application of the proposed framework was examined in an Indonesian case study. An extensive list of historical data sources containing disease predictors and target variables was used to build spatiotemporal and transactional datasets. To combine disparate sources, data rows were scaled to a temporal scale of 1-week and a spatial scale of 1-degree × 1-degree cells. Given the constructed datasets, underlying patterns in the form of rules explaining the risk of occurrence and spread of avian influenza were discovered. The created rules were combined and ordered based on their importance and then stored in a knowledge base. The results suggested that the proposed framework could act as a tool to gain a broad understanding of the drivers of avian influenza epidemics and may facilitate the prediction of future disease events.

Transmission of highly pathogenic avian influenza in the nomadic free-grazing duck production system in Viet Nam

The presence of free-grazing ducks (FGD) has consistently been shown to be associated with highly pathogenic avian influenza virus (HPAIV) H5N1 outbreaks in South-East Asia. However, the lack of knowledge about the transmission pathways limits the effectiveness of control efforts. To address this gap, we developed a probabilistic transmission model of HPAIV H5N1 in the nomadic FGD production system in Viet Nam, assuming different scenarios to address parameter uncertainty. Results suggested that HPAIV H5N1 could spread within the nomadic FGD production system, with an estimated flock-level effective reproduction number (r_e) ranging from 2.16 (95% confidence interval (CI): 1.39-3.49) to 6.10 (95%CI: 3.93-9.85) depending on the scenario. Indirect transmission via boats and trucks was shown to be the main transmission route in all scenarios. Results suggest that r_e could be reduced below one with 95% confidence if 86% of FGD flocks were vaccinated in the best-case scenario or 95% in the worst-case scenario. If vaccination was combined with cleaning and disinfection of transport vehicles twice a week, vaccination coverage could be lowered to 60% in the best-case scenario. These findings are of particular relevance for prioritising interventions for effective control of HPAIV in nomadic free-grazing duck production systems.

Poultry population dynamics and mortality risks in smallholder farms of the Mekong river delta region

BACKGROUND

Poultry farming is widely practiced by rural households in Vietnam and the vast majority of domestic birds are kept on small household farms. However, smallholder poultry production is constrained by several issues such as infectious diseases, including avian influenza viruses whose circulation remains a threat to public health. This observational study describes the demographic structure and dynamics of small-scale poultry farms of the Mekong river delta region.

METHOD

Fifty three farms were monitored over a 20-month period, with farm sizes, species, age, arrival/departure of poultry, and farm management practices recorded monthly.

RESULTS

Median flock population sizes were 16 for chickens (IQR: 10-40), 32 for ducks (IQR: 18-101) and 11 for Muscovy ducks (IQR: 7-18); farm size distributions for the three species were heavily right-skewed. Muscovy ducks were kept for long periods and outdoors, while chickens and ducks were farmed indoors or in pens. Ducks had a markedly higher removal rate (broilers: 0.14/week; layer/breeders: 0.05/week) than chickens and Muscovy ducks (broilers: 0.07/week; layer/breeders: 0.01-0.02/week) and a higher degree of specialization resulting in a substantially shorter life span. The rate of mortality due to disease did not differ much among species, with birds being less likely to die from disease at older ages, but frequency of disease symptoms differed by species. Time series of disease-associated mortality were correlated with population size for Muscovy ducks (Kendall's coefficient τ = 0.49, p-value < 0.01) and with frequency of outdoor grazing for ducks (τ = 0.33, p-value = 0.05).

CONCLUSION

The study highlights some challenges to disease control in small-scale multispecies poultry farms. The rate of interspecific contact and overlap between flocks of different ages is high, making small-scale farms a suitable environment for pathogens circulation. Muscovy ducks are farmed outdoors with little investment in biosecurity and few inter-farm movements. Ducks and chickens are more at-risk of introduction of pathogens through movements of birds from one farm to another. Ducks are farmed in large flocks with high turnover and, as a result, are more vulnerable to disease spread and require a higher vaccination coverage to maintain herd immunity.

Comparative Epidemiology of Highly Pathogenic Avian Influenza Virus H5N1 and H5N6 in Vietnamese Live Bird Markets: Spatiotemporal Patterns of Distribution and Risk Factors

Highly pathogenic avian influenza (HPAI) H5N1 virus has been circulating in Vietnam since 2003, whilst outbreaks of HPAI H5N6 virus are more recent, having only been reported since 2014. Although the spatial distribution of H5N1 outbreaks and risk factors for virus occurrence has been extensively studied, there have been no comparative studies for H5N6. Data collected through active surveillance of Vietnamese live bird markets (LBMs) between 2011 and 2015 were used to explore and compare the spatiotemporal distributions of H5N1- and H5N6-positive LBMs. Conditional autoregressive models were developed to quantify spatiotemporal associations between agroecological factors and the two HPAI strains using the same set of predictor variables. Unlike H5N1, which exhibited a strong north–south divide, with repeated occurrence in the extreme south of a cluster of high-risk provinces, H5N6 was homogeneously distributed throughout Vietnam. Similarly, different agroecological factors were associated with each strain. Sample collection in the months of January and February and higher average maximum temperature were associated with higher likelihood of H5N1-positive market-day status. The likelihood of market days being positive for H5N6 increased with decreased river density, and with successive Rounds of data collection. This study highlights marked differences in spatial patterns and risk factors for H5N1 and H5N6 in Vietnam, suggesting the need for tailored surveillance and control approaches.

Global spatial risk pattern of highly pathogenic avian influenza H5N1 virus in wild birds: A knowledge-fusion based approach

Highly pathogenic avian influenza (HPAI) H5N1 viruses have continuously circulated throughout much of the world since 2003, resulting in huge economic losses and major public health problems. Wild birds have played an important role in the spread of H5N1 HPAI. To understand its spatial distribution, H5N1 HPAI have been studied by many disciplines from different perspectives, but only one kind of disciplinary knowledge was involved, which has provided limited progress in understanding. Combining risk information from different disciplines based on knowledge fusion can provide more accurate and detailed information. In this study, local k function, phylogenetic tree analysis, and logistic spatial autoregressive models were used to explore the global spatial pattern of H5N1 HPAI based on outbreak data in wild birds, genetic sequences, and risk factors, respectively. On this basis, Dempster-Shafer (D-S) evidence theory was further applied to study the spatial distribution of H5N1 HPAI. We found D-S evidence theory was more robust and reliable than the other three methods, providing technical and methodological support for application to the research of other diseases. The shortest distance to wild bird migration routes, roads and railways, elevation, the normalized difference vegetation index (NDVI), land use and land cover (LULC) and infant mortality rates (IMR) were significantly associated with the occurrence of H5N1 HPAI. The high-risk areas were mainly located in Northern and Central Europe, the eastern Mediterranean, and East and Southeast Asia. High-risk clusters were closely related to the social, economic and ecological environment of the region. Locations where the potential transmission risk remains high should be prioritized for control efforts.

Quantitative assessment of a spatial multicriteria model for highly pathogenic avian influenza H5N1 in Thailand, and application in Cambodia

The Highly Pathogenic Avian Influenza H5N1 (HPAI) virus is now considered endemic in several Asian countries. In Cambodia, the virus has been circulating in the poultry population since 2004, with a dramatic effect on farmers' livelihoods and public health. In Thailand, surveillance and control are still important to prevent any new H5N1 incursion. Risk mapping can contribute effectively to disease surveillance and control systems, but is a very challenging task in the absence of reliable disease data. In this work, we used spatial multicriteria decision analysis (MCDA) to produce risk maps for HPAI H5N1 in poultry. We aimed to i) evaluate the performance of the MCDA approach to predict areas suitable for H5N1 based on a dataset from Thailand, comparing the predictive capacities of two sources of a priori knowledge (literature and experts), and ii) apply the best method to produce a risk map for H5N1 in poultry in Cambodia. Our results showed that the expert-based model had a very high predictive capacity in Thailand (AUC = 0.97). Applied in Cambodia, MCDA mapping made it possible to identify hotspots suitable for HPAI H5N1 in the Tonlé Sap watershed, around the cities of Battambang and Kampong Cham, and along the Vietnamese border.

Introducing One Health to the Ethical Debate About Zoonotic Diseases in Southeast Asia

Pandemic plans recommend phases of response to an emergent infectious disease (EID) outbreak, and are primarily aimed at preventing and mitigating human-to-human transmission. These plans carry presumptive weight and are increasingly being operationalized at the national, regional and international level with the support of the World Health Organization (WHO). The conventional focus of pandemic preparedness for EIDs of zoonotic origin has been on public health and human welfare. However, this focus on human populations has resulted in strategically important disciplinary silos. As the risks of zoonotic diseases have implications that reach across many domains outside traditional public health, including anthropological, environmental, and veterinary fora, a more inclusive ecological perspective is paramount for an effective response to future outbreaks.

Evidence for the Convergence Model: The Emergence of Highly Pathogenic Avian Influenza (H5N1) in Viet Nam

Building on a series of ground breaking reviews that first defined and drew attention to emerging infectious diseases (EID), the 'convergence model' was proposed to explain the multifactorial causality of disease emergence. The model broadly hypothesizes disease emergence is driven by the co-incidence of genetic, physical environmental, ecological, and social factors. We developed and tested a model of the emergence of highly pathogenic avian influenza (HPAI) H5N1 based on suspected convergence factors that are mainly associated with land-use change. Building on previous geospatial statistical studies that identified natural and human risk factors associated with urbanization, we added new factors to test whether causal mechanisms and pathogenic landscapes could be more specifically identified. Our findings suggest that urbanization spatially combines risk factors to produce particular types of peri-urban landscapes with significantly higher HPAI H5N1 emergence risk. The work highlights that peri-urban areas of Viet Nam have higher levels of chicken densities, duck and geese flock size diversities, and fraction of land under rice or aquaculture than rural and urban areas. We also found that land-use diversity, a surrogate measure for potential mixing of host populations and other factors that likely influence viral transmission, significantly improves the model's predictability. Similarly, landscapes where intensive and extensive forms of poultry production overlap were found at greater risk. These results support the convergence hypothesis in general and demonstrate the potential to improve EID prevention and control by combing geospatial monitoring of these factors along with pathogen surveillance programs.

Agro-environmental determinants of avian influenza circulation: a multisite study in Thailand, Vietnam and Madagascar

Outbreaks of highly pathogenic avian influenza have occurred and have been studied in a variety of ecological systems. However, differences in the spatial resolution, geographical extent, units of analysis and risk factors examined in these studies prevent their quantitative comparison. This study aimed to develop a high-resolution, comparative study of a common set of agro-environmental determinants of avian influenza viruses (AIV) in domestic poultry in four different environments: (1) lower-Northern Thailand, where H5N1 circulated in 2004-2005, (2) the Red River Delta in Vietnam, where H5N1 is circulating widely, (3) the Vietnam highlands, where sporadic H5N1 outbreaks have occurred, and (4) the Lake Alaotra region in Madagascar, which features remarkable similarities with Asian agro-ecosystems and where low pathogenic avian influenza viruses have been found. We analyzed H5N1 outbreak data in Thailand in parallel with serological data collected on the H5 subtype in Vietnam and on low pathogenic AIV in Madagascar. Several agro-environmental covariates were examined: poultry densities, landscape dominated by rice cultivation, proximity to a water body or major road, and human population density. Relationships between covariates and AIV circulation were explored using spatial generalized linear models. We found that AIV prevalence was negatively associated with distance to the closest water body in the Red River Delta, Vietnam highlands and Madagascar. We also found a positive association between AIV and duck density in the Vietnam highlands and Thailand, and with rice landscapes in Thailand and Madagascar. Our findings confirm the important role of wetlands-rice-ducks ecosystems in the epidemiology of AI in diverse settings. Variables influencing circulation of the H5 subtype in Southeast Asia played a similar role for low pathogenic AIV in Madagascar, indicating that this area may be at risk if a highly virulent strain is introduced.

Spatio-temporal epidemiology of highly pathogenic avian influenza (subtype H5N1) in poultry in eastern India

In India, majority outbreaks of highly pathogenic avian influenza (HPAI) H5N1 have occurred in eastern states of West Bengal, Assam and Tripura. This study aimed to identify disease clusters and risk factors of HPAI H5N1 in these states, for targeted surveillance and disease control. A spatial scan statistic identified two significant disease clusters in West Bengal and Assam, occurring during January and November-December 2008, respectively. Key risk factors were identified at sub-district level using bootstrapped logistic regression and boosted regression trees model. With both methods, HPAI H5N1 outbreaks in backyard poultry were associated with accessibility in terms of time taken to access a city with >50,000 persons, human population density and duck density (P<0.005). In addition, areas at lower elevation were also identified as high risk by BRT model. It is recommended that risk-based surveillance should be implemented in high duck density areas and all live-bird markets in high-throughput locations.

Zero-inflated models for identifying disease risk factors when case detection is imperfect: application to highly pathogenic avian influenza H5N1 in Thailand

Logistic regression models integrating disease presence/absence data are widely used to identify risk factors for a given disease. However, when data arise from imperfect surveillance systems, the interpretation of results is confusing since explanatory variables can be related either to the occurrence of the disease or to the efficiency of the surveillance system. As an alternative, we present spatial and non-spatial zero-inflated Poisson (ZIP) regressions for modelling the number of highly pathogenic avian influenza (HPAI) H5N1 outbreaks that were reported at subdistrict level in Thailand during the second epidemic wave (July 3rd 2004 to May 5th 2005). The spatial ZIP model fitted the data more effectively than its non-spatial version. This model clarified the role of the different variables: for example, results suggested that human population density was not associated with the disease occurrence but was rather associated with the number of reported outbreaks given disease occurrence. In addition, these models allowed estimating that 902 (95% CI 881-922) subdistricts suffered at least one HPAI H5N1 outbreak in Thailand although only 779 were reported to veterinary authorities, leading to a general surveillance sensitivity of 86.4% (95% CI 84.5-88.4). Finally, the outputs of the spatial ZIP model revealed the spatial distribution of the probability that a subdistrict could have been a false negative. The methodology presented here can easily be adapted to other animal health contexts.

A Conceptual Framework for Analyzing Social-Ecological Models of Emerging Infectious Diseases

Unraveling mechanisms underlying new and reemerging infectious diseases (EID) requires exploring complex interactions within and among coupled natural and human (CNH) systems. To address this difficult scientific problem, we need to understand how transformations in social-ecological systems caused by multifaceted interactions with anthropogenic environmental changes such as urbanization, agricultural transformations, and natural habitat alterations, produce feedbacks that affect natural communities and ultimately their pathogens, animal host, and human populations. Focusing on the complex interactions among natural and human systems at diverse spatial, temporal, and organizational scales, we describe the development of a framework for analyzing social-ecological models, to understand how these systems function and the processes through which these systems interact with each other to influence disease outbreaks. To address multi-scale issues within the framework, we draw upon multiple social science theories and methods (e.g., environmental economics, geography, decision and risk science, urban and regional development, and spatial information science). We posit that the framework helps to identify potential vulnerabilities of CNH systems to disturbances, describing important elements as a starting point for the development and testing of more general CNH systems. We also posit that transformations in the elements and how they relate to each other are key in determining the robustness of CNH systems. Given the importance and difficulty of research on social-ecological systems, we recommend a carefully considered theoretical rationale and a model-guided methodological approach. We conclude that no single theory or method is sufficient to explain complex phenomena such as EID and the relationships between factors influencing disease outbreaks. Integrated approaches are arguably the best way to provide an in-depth description and analysis of a complex problem.

The effectiveness of preventative mass vaccination regimes against the incidence of highly pathogenic avian influenza on Java Island, Indonesia

We conducted an operational research study involving backyard and semicommercial farms on Java Island, Indonesia, between April 2008 and September 2009 to evaluate the effectiveness of two preventive mass vaccination strategies against highly pathogenic avian influenza (HPAI). One regimen used Legok 2003 H5N1 vaccine, while the other used both Legok 2003 H5N1 and HB1 Newcastle disease (ND) vaccine. A total of 16 districts were involved in the study. The sample size was estimated using a formal power calculation technique that assumed a detectable effect of treatment as a 50% reduction in the baseline number of HPAI-compatible outbreaks. Within each district, candidate treatment blocks with village poultry populations ranging from 80 000 to 120 000 were created along subdistrict boundary lines. Subsequently, four of these blocks were randomly selected and assigned one treatment from a list that comprised control, vaccination against HPAI, vaccination against HPAI + ND. Four rounds of vaccination were administered at quarterly intervals beginning in July 2008. A vaccination campaign involved vaccinating 100 000 birds in a treatment block, followed by another 100 000 vaccinations 3 weeks later as a booster dose. Data on disease incidence and vaccination coverage were also collected at quarterly intervals using participatory epidemiological techniques. Compared with the unvaccinated (control) group, the incidence of HPAI-compatible events declined by 32% (P = 0.24) in the HPAI-vaccinated group and by 73% (P = 0.00) in the HPAI- and ND-vaccinated group. The effect of treatment did not vary with time or district. Similarly, an analysis of secondary data from the participatory disease and response (PDSR) database revealed that the incidence of HPAI declined by 12% in the HPAI-vaccinated group and by 24% in the HPAI + ND-vaccinated group. The results suggest that the HPAI + ND vaccination significantly reduced the incidence of HPAI-compatible events in mixed populations of semicommercial and backyard poultry.

Improving risk models for avian influenza: the role of intensive poultry farming and flooded land during the 2004 Thailand epidemic

Since 1996 when Highly Pathogenic Avian Influenza type H5N1 first emerged in southern China, numerous studies sought risk factors and produced risk maps based on environmental and anthropogenic predictors. However little attention has been paid to the link between the level of intensification of poultry production and the risk of outbreak. This study revised H5N1 risk mapping in Central and Western Thailand during the second wave of the 2004 epidemic. Production structure was quantified using a disaggregation methodology based on the number of poultry per holding. Population densities of extensively- and intensively-raised ducks and chickens were derived both at the sub-district and at the village levels. LandSat images were used to derive another previously neglected potential predictor of HPAI H5N1 risk: the proportion of water in the landscape resulting from floods. We used Monte Carlo simulation of Boosted Regression Trees models of predictor variables to characterize the risk of HPAI H5N1. Maps of mean risk and uncertainty were derived both at the sub-district and the village levels. The overall accuracy of Boosted Regression Trees models was comparable to that of logistic regression approaches. The proportion of area flooded made the highest contribution to predicting the risk of outbreak, followed by the densities of intensively-raised ducks, extensively-raised ducks and human population. Our results showed that as little as 15% of flooded land in villages is sufficient to reach the maximum level of risk associated with this variable. The spatial pattern of predicted risk is similar to previous work: areas at risk are mainly located along the flood plain of the Chao Phraya river and to the south-east of Bangkok. Using high-resolution village-level poultry census data, rather than sub-district data, the spatial accuracy of predictions was enhanced to highlight local variations in risk. Such maps provide useful information to guide intervention.

Risk factor modelling of the spatio-temporal patterns of highly pathogenic avian influenza (HPAIV) H5N1: a review

Highly pathogenic avian influenza virus (HPAIV) H5N1 continues to impact on smallholder livelihoods, to constrain development of the poultry production sector, and to cause occasional human fatalities. HPAI H5N1 outbreaks have occurred in a variety of ecological systems with economic, agricultural and environmental differences. This review aimed to identify common risk factors amongst spatial modelling studies conducted in these different agro-ecological systems, and to identify gaps in our understanding of the disease's spatial epidemiology. Three types of variables with similar statistical association with HPAI H5N1 presence across studies and regions were identified: domestic waterfowl, several anthropogenic variables (human population density, distance to roads) and indicators of water presence. Variables on socio-economic conditions, poultry trade, wild bird distribution and movements were comparatively rarely considered. Few studies have analysed the HPAI H5N1 distribution in countries such as Egypt and Indonesia, where HPAIV H5N1 continues to circulate extensively.

Transmission rate and reproductive number of the H5N1 highly pathogenic avian influenza virus during the December 2005-July 2008 epidemic in Nigeria

We quantified the between-village transmission rate, β (the rate of transmission of H5N1 HPAI virus per effective contact), and the reproductive number, Re (the average number of outbreaks caused by one infectious village during its entire infectious period), of H5N1 highly pathogenic avian influenza (HPAI) virus in Nigeria using outbreak data collected between December 2005 and July 2008. We classified the outbreaks into two phases to assess the effectiveness of the control measures implemented. Phase 1 (December 2005-October 2006) represents the period when the Federal Government of Nigeria managed the HPAI surveillance and response measures, while Phase 2 (November 2006-July 2008) represents the time during which the Nigeria Avian Influenza Control and Human Pandemic Preparedness project (NAICP), funded by a World Bank credit of US$ 50 million, had taken over the management of most of the interventions. We used a total of 204 outbreaks from 176 villages that occurred in 78 local government areas of 25 states. The compartmental susceptible-infectious model was used as the analytical tool. Means and 95% percentile confidence intervals were obtained using bootstrapping techniques. The overall mean β (assuming a duration of infectiousness, T, of 12 days) was 0.07/day (95% percentile confidence interval: 0.06-0.09). The first and second phases of the epidemic had comparable β estimates of 0.06/day (0.04-0.09) and 0.08/day (0.06-0.10), respectively. The Re of the virus associated with these β and T estimates was 0.9 (0.7-1.1); the first and second phases of the epidemic had Re of 0.84 (0.5-1.2) and 0.9 (0.6-1.2), respectively. We conclude that the intervention measures implemented in the second phase of the epidemic had comparable effects to those implemented during the first phase and that the Re of the epidemic was low, indicating that the Nigeria H5N1 HPAI epidemic was unstable.

Incidence and risk factors for H5 highly pathogenic avian influenza infection in flocks of apparently clinically healthy ducks

A prospective longitudinal study was conducted on 96 smallholder duck farms in Indonesia over a period of 14 months in 2007 and 2008 to monitor bird- and flock-level incidence rates of H5 highly pathogenic avian influenza (HPAI) infection in duck flocks, and to identify risk factors associated with these flocks becoming H5 seropositive. Flocks that scavenged around neighbouring houses within the village were at increased risk of developing H5 antibodies, as were flocks from which carcases of birds that died during the 2 months between visits were consumed by the family. Duck flock confinement overnight on the farm and sudden deaths of birds between visits were associated with lower risk of the flock developing H5 antibodies. Scavenging around neighbouring houses and non-confinement overnight are likely to be causal risk factors for infection. With this study we have provided insights into farm-level risk factors of HPAI virus introduction into duck flocks. Preventive messages based on these risk factors should be included in HPAI awareness programmes.

Large-scale spatial population databases in infectious disease research

Modelling studies on the spatial distribution and spread of infectious diseases are becoming increasingly detailed and sophisticated, with global risk mapping and epidemic modelling studies now popular. Yet, in deriving populations at risk of disease estimates, these spatial models must rely on existing global and regional datasets on population distribution, which are often based on outdated and coarse resolution data. Moreover, a variety of different methods have been used to model population distribution at large spatial scales. In this review we describe the main global gridded population datasets that are freely available for health researchers and compare their construction methods, and highlight the uncertainties inherent in these population datasets. We review their application in past studies on disease risk and dynamics, and discuss how the choice of dataset can affect results. Moreover, we highlight how the lack of contemporary, detailed and reliable data on human population distribution in low income countries is proving a barrier to obtaining accurate large-scale estimates of population at risk and constructing reliable models of disease spread, and suggest research directions required to further reduce these barriers.

A basic strategy to manage global health with reference to livestock production in Asia

Newly emerging infectious diseases (nEIDs) have increased rapidly presenting alarming challenges to global health. We argue that for effective management of global health a basic strategy should include at least three essential tactical forms: actions of a directly focused nature, institutional coordination, and disciplinary integration in approaches to health management. Each level of action is illustrated with examples from the livestock sector in Asia. No clear example of all three tactical forms in place can be found from developing countries where food security is a significant threat although Vietnam is developing a comprehensive strategy. Finally, an ecosystem health approach to global health management is advocated; such an approach moves away from the traditional single disciplinary approach. Stronger guidance is needed to direct ecohealth research and application in the management of global health.

Epidemiology of Highly Pathogenic Avian Influenza Virus Strain Type H5N1

Highly pathogenic avian influenza (HPAI) is a severe disease of poultry. It is highly transmissible with a flock mortality rate approaching 100% in vulnerable species (Capua et al. 2007a). Due to the potentially disastrous impact the disease can have on affected poultry sectors, HPAI has received huge attention and is classified as a notifiable disease by the World Organisation for Animal Health (OIE).

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.

How backyard poultry flocks influence the effort required to curtail avian influenza epidemics in commercial poultry flocks

This paper summarizes the evidence that the contribution of backyard poultry flocks to the on-going transmission dynamics of an avian influenza epidemic in commercial flocks is modest at best. Nevertheless, while disease control strategies need not involve the backyard flocks, an analysis of the contribution of each element of the next generation matrix to the basic reproduction number indicates that models which ignores the contribution of backyard flocks in estimating the effort required of strategies focused one host type (e.g. commercial flocks only) necessarily underestimate the level of effort to an extent that may matter to policy makers.

The effects of spatial population dataset choice on estimates of population at risk of disease

Background

The spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.

Methods

The recent construction of a global map of P. falciparum malaria endemicity enabled the testing of different gridded population datasets for providing estimates of PAR by endemicity class. The estimated population numbers within each class were calculated for each country using four different global gridded human population datasets: GRUMP (~1 km spatial resolution), LandScan (~1 km), UNEP Global Population Databases (~5 km), and GPW3 (~5 km). More detailed assessments of PAR variation and accuracy were conducted for three African countries where census data were available at a higher administrative-unit level than used by any of the four gridded population datasets.

Results

The estimates of PAR based on the datasets varied by more than 10 million people for some countries, even accounting for the fact that estimates of population totals made by different agencies are used to correct national totals in these datasets and can vary by more than 5% for many low-income countries. In many cases, these variations in PAR estimates comprised more than 10% of the total national population. The detailed country-level assessments suggested that none of the datasets was consistently more accurate than the others in estimating PAR. The sizes of such differences among modeled human populations were related to variations in the methods, input resolution, and date of the census data underlying each dataset. Data quality varied from country to country within the spatial population datasets.

Conclusions

Detailed, highly spatially resolved human population data are an essential resource for planning health service delivery for disease control, for the spatial modeling of epidemics, and for decision-making processes related to public health. However, our results highlight that for the low-income regions of the world where disease burden is greatest, existing datasets display substantial variations in estimated population distributions, resulting in uncertainty in disease assessments that utilize them. Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling. In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions.

Spatio-temporal data comparisons for global highly pathogenic avian influenza (HPAI) H5N1 outbreaks

Highly pathogenic avian influenza subtype H5N1 is a zoonotic disease and control of the disease is one of the highest priority in global health. Disease surveillance systems are valuable data sources for various researches and management projects, but the data quality has not been paid much attention in previous studies. Based on data from two commonly used databases (Office International des Epizooties (OIE) and Food and Agriculture Organization of the United Nations (FAO)) of global HPAI H5N1 outbreaks during the period of 2003–2009, we examined and compared their patterns of temporal, spatial and spatio-temporal distributions for the first time. OIE and FAO data showed similar trends in temporal and spatial distributions if they were considered separately. However, more advanced approaches detected a significant difference in joint spatio-temporal distribution. Because of incompleteness for both OIE and FAO data, an integrated dataset would provide a more complete picture of global HPAI H5N1 outbreaks. We also displayed a mismatching profile of global HPAI H5N1 outbreaks and found that the degree of mismatching was related to the epidemic severity. The ideas and approaches used here to assess spatio-temporal data on the same disease from different sources are useful for other similar studies.

Avian influenza H5N1 virus infections in vaccinated commercial and backyard poultry in Egypt

In this paper, we describe results from a high-pathogenic H5N1 avian influenza virus (AIV) surveillance program in previously H5-vaccinated commercial and family-backyard poultry flocks that was conducted from 2007 to 2008 by the Egyptian National Laboratory for Veterinary Quality Control on Poultry Production. The real-time reverse transcription PCR assay was used to detect the influenza A virus matrix gene and detection of the H5 and N1 subtypes was accomplished using a commercially available kit real-time reverse transcription PCR assay. The virus was detected in 35/3,610 (0.97%) and 27/8,682 (0.31%) of examined commercial poultry farms and 246/816 (30%) and 89/1,723 (5.2%) of backyard flocks in 2007 and 2008, respectively. Positive flocks were identified throughout the year, with the highest frequencies occurring during the winter months. Anti-H5 serum antibody titers in selected commercial poultry ranged from <2 (negative) to 9.6 log(2) when determined in the hemagglutination inhibition test using a H5 AIV antigen. In conclusion, despite the nationwide vaccination strategy of poultry in Egypt to combat H5N1 AIV, continuous circulation of the virus in vaccinated commercial and backyard poultry was reported and the efficacy of the vaccination using a challenge model with the current circulating field virus should be revised.

Isolation of highly pathogenic avian influenza H5N1 from table eggs after vaccinal break in commercial layer flock

In May 2009, during routine monitoring of a commercial layer flock of about 87,000 birds kept in cages in 4 different houses that had been vaccinated 3 times with an inactivated H5N1 vaccine at weeks 1, 7, and 16, highly pathogenic avian influenza (HPAI) virus of subtype H5N1 was isolated and detected by real-time reverse transcriptase-polymerase chain reaction (RT-PCR) in tracheal and cloacal swabs collected from houses 3 and 4; 7 days after onset of clinical signs, there was an increase in mortality accompanied by a decrease in egg production and egg quality. In addition, using RT-PCR, the viral RNA could be detected from albumin and eggshell as well. Seven days after the onset of the clinical signs, the hemagglutination inhibition (HI) titers in the affected houses were 3.2 and 1.9 log2. In the other two houses, there were no clinical signs, and all tested samples were negative using virus isolation and real-time RT-PCR. The HI titers were 6.6 and 7.0 log2 in nonaffected houses. The isolated virus from egg albumin showed high nucleotides and amino-acid identities and clustered with viruses from recently H5N1-confirmed human infections and poultry from different places in Egypt. Moreover, several amino-acid substitutions of viral H5 protein were observed. The vaccinal break seems to be associated with immune escape mutants and/or improper vaccination. The role of contaminated eggs as a source of infection and as a vehicle for spread of the virus should be considered in area with avian influenza outbreaks.

Risk factors and clusters of Highly Pathogenic Avian Influenza H5N1 outbreaks in Bangladesh

Between March 2007 and July 2009, 325 Highly Pathogenic Avian Influenza (HPAI, subtype H5N1) outbreaks in poultry were reported in 154 out of a total of 486 sub-districts in Bangladesh. This study analyzed the temporal and spatial patterns of HPAI H5N1 outbreaks and quantified the relationship between several spatial risk factors and HPAI outbreaks in sub-districts in Bangladesh. We assessed spatial autocorrelation and spatial dependence, and identified clustering sub-districts with disease statistically similar to or dissimilar from their neighbors. Three significant risk factors associated to HPAI H5N1 virus outbreaks were identified; the quadratic log-transformation of human population density [humans per square kilometer, P=0.01, OR 1.15 (95% CI: 1.03-1.28)], the log-transformation of the total commercial poultry population [number of commercial poultry per sub-district, P<0.002, OR 1.40 (95% CI: 1.12-1.74)], and the number of roads per sub-district [P=0.02, OR 1.07 (95% CI: 1.01-1.14)]. The distinct clusters of HPAI outbreaks and risk factors identified could assist the Government of Bangladesh to target surveillance and to concentrate response efforts in areas where disease is likely to occur. Concentrating response efforts may help to combat HPAI more effectively, reducing the environmental viral load and so reducing the number of disease incidents.

Associations between attributes of live poultry trade and HPAI H5N1 outbreaks: a descriptive and network analysis study in northern Vietnam

Background

The structure of contact between individuals plays an important role in the incursion and spread of contagious diseases in both human and animal populations. In the case of avian influenza, the movement of live birds is a well known risk factor for the geographic dissemination of the virus among poultry flocks. Live bird markets (LBM's) contribute to the epidemiology of avian influenza due to their demographic characteristics and the presence of HPAI H5N1 virus lineages. The relationship between poultry producers and live poultry traders (LPT's) that operate in LBM's has not been adequately documented in HPAI H5N1-affected SE Asian countries. The aims of this study were to document and study the flow of live poultry in a poultry trade network in northern Vietnam, and explore its potential role in the risk for HPAI H5N1 during 2003 to 2006.

Results

Our results indicate that LPT's trading for less than a year and operating at retail markets are more likely to source poultry from flocks located in communes with a past history of HPAI H5N1 outbreaks during 2003 to 2006 than LPT's trading longer than a year and operating at wholesale markets. The results of the network analysis indicate that LPT's tend to link communes of similar infection status.

Conclusions

Our study provides evidence which can be used for informing policies aimed at encouraging more biosecure practices of LPT's operating at authorised LBM's. The results suggest that LPT's play a role in HPAI H5N1 transmission and may contribute to perpetuating HPAI H5N1 virus circulation amongst certain groups of communes. The impact of current disease prevention and control interventions could be enhanced by disseminating information about outbreak risk and the implementation of a formal data recording scheme at LBM's for all incoming and outgoing LPT's.

Anthropogenic factors and the risk of highly pathogenic avian influenza H5N1: prospects from a spatial-based model

Beginning in 2003, highly pathogenic avian influenza (HPAI) H5N1 virus spread across Southeast Asia, causing unprecedented epidemics. Thailand was massively infected in 2004 and 2005 and continues today to experience sporadic outbreaks. While research findings suggest that the spread of HPAI H5N1 is influenced primarily by trade patterns, identifying the anthropogenic risk factors involved remains a challenge. In this study, we investigated which anthropogenic factors played a role in the risk of HPAI in Thailand using outbreak data from the “second wave” of the epidemic (3 July 2004 to 5 May 2005) in the country. We first performed a spatial analysis of the relative risk of HPAI H5N1 at the subdistrict level based on a hierarchical Bayesian model. We observed a strong spatial heterogeneity of the relative risk. We then tested a set of potential risk factors in a multivariable linear model. The results confirmed the role of free-grazing ducks and rice-cropping intensity but showed a weak association with fighting cock density. The results also revealed a set of anthropogenic factors significantly linked with the risk of HPAI. High risk was associated strongly with densely populated areas, short distances to a highway junction, and short distances to large cities. These findings highlight a new explanatory pattern for the risk of HPAI and indicate that, in addition to agro-environmental factors, anthropogenic factors play an important role in the spread of H5N1. To limit the spread of future outbreaks, efforts to control the movement of poultry products must be sustained.