Intensive poultry production and highly pathogenic avian influenza H5N1 in Thailand: statistical and
process-based models
Thomas P. Van Boeckel
PhD Thesis September 2013
Université Libre de Bruxelles, School of Bioengineering Advisor: Marius GILBERT
Intensive poultry production and highly pathogenic avian influenza H5N1 in Thailand: statistical and
process-based models
Thomas P. Van Boeckel
UNIVERSITE LIBRE DE BRUXELLES
Thèse présentée en vue de l'obtention du grade de Docteur en Sciences Agronomiques et Ingénierie Biologique
September 2013
Promoteur: Marius GILBERT
Remerciements / Acknowledgments
Mes premiers remerciements s'adressent à Marius, le promoteur de ce travail.
"Un bon maître a ce souci constant, enseigner à se passer de lui". A. Gide Arrivé au bout de ces quatre années, je mesure pleinement l'opportunité qui m'a été
donnée de travailler sous ta direction. Au delà de tes enseignements scientifiques et technique qui furent bien entendu exemplaires, je te suis profondément reconnaissant pour ta gentillesse et la liberté que tu m'as laissée dans mon travail. Merci, également, de m'avoir ouvert tes réseaux de collaborateurs et soutenu activement dans mes démarches pour me permettre de tenter ma chance à l'étranger. Travailler avec toi, m'a permis d’acquérir d’avantage d'indépendance dans ma vie scientifique et personnelle, Merci.
I would also like to thank my supervisors in Britain, who substantially contributed to the success of this work. Firstly, Prof. Simon Hay for inviting me to spend the first year of my PhD as part of the Malaria Atlas Project: a life changing experience in my early scientific career. Secondly, Dr. Pete Gething, for taking me under his wing during that stay and for his contagious hard working mentality. Thirdly, I wish to express my most sincere gratitude to Dr. Michael Tildesley from Warwick for his time, generosity, patience and understanding towards non mathematicians.
I am greatly thankful to all my collaborators for their contributions to my works, T.
Robinson, W. Wint, R. Houben, D. Prosser, M. Keeling and X. Xiao. I would like to address my special thanks to Weerapong Thanapongtharm from the Department of Livestock Development, Bangkok for his much valued field experience with H5N1 in Thailand and for introducing me to sustained meditation, a most useful skill when you work as a programmer !
Je tiens tout autant à remercier les autorités académiques de l’ULB, les lecteurs de ce travail ainsi que le FNRS, la Fondation Wiener-Anspach et les Bourses de Voyages de la Communauté Wallonie-Bruxelles.
A mes collègues de l'ULB: Je tiens à remercier Aïko, Catherine et Thibaud pour leur bonne humeur journalière, pour avoir partagé leur bureau avec moi et fait preuve d'un sang froid remarquable lors des (très occasionnels) moments d'énervement, lorsque mon ordinateur se trompait... Je tiens également à remercier les membres du service de Lutte Biologique et Ecologie Spatiale, en particulier son directeur Jean Claude Grégoire, pour son agréable curiosité intellectuelle et ses efforts pour maintenir une atmosphère chaleureuse dans le laboratoire
Je tiens à remercier du fond du cœur mes amis et collègues doctorants: Nathan,
agrémenté de leurs pensées scientifiques et souvent moins scientifiques nos nombreux débats de mi-journée à l'ULB. Je leur suis sincèrement reconnaissant pour le soutien qu'ils m’ont apporté durant les derniers mois de ce travail. Je vous souhaite une vie postdoctorale épanouie.
Je tiens à avoir une pensée pour mes amis grimpeurs, fidèles compagnons de mes nécessaires moments de détente durant ce travail. I would especially like to thank Brieuc and Ross McKerchar, for the long discussions we had while driving on both sides of the Channel, thanks for belaying a dangerous lefty from the peaks of the Alps to the caves of Mallorca. Thanks also to Matt Gaddes for applying your Oxonian language skills to proofread this Thesis, in compensation for the unforgettable three weeks of Norwegian rain in the Arctic.
Merci également à mes amis de longue date, Benjamin, Sefia et Lauréline pour votre soutien moral et votre intérêt pour mes conversations scientifiques durant toutes ces années. Merci à Sylvestre pour ta présence dans le bons et les moins bons moments, pour ta fraicheur d'esprit et ton bon sens de non scientifique !
Enfin, je tiens à remercier mes parents et ma sœur, d'avoir donné beaucoup de leur temps et de leur personne pour me permettre de persévérer dans la voie académique.
Summary
Highly pathogenic avian influenza (HPAI) of sub-type H5N1 first emerged in China in 1996 and has since remained an important threat to human health because of its circulation in domestic poultry and its zoonotic potential. In birds, the severity of HPAI H5N1 varies across species: some domestic anatidae are healthy and asymptomatic carriers of the virus whilst in chicken, HPAI H5N1 is highly contagious and characterized by mortality rates up to 90%. In humans, the impact of HPAI H5N1 has remained moderate to date (633 cases, 377 death, World Health Organization July, 2013) thanks to a low transmission of the virus from poultry to humans and from human to human. However, given the high case fatality rate (> 50%), any increase in the cross-species transmission capacity could lead to a much higher impact on human health.
HPAI H5N1 also had important economic consequences on the poultry production sector in several South East Asian countries. Thailand is one of the largest poultry products exporters in the world and has been severely hit by multiple epidemic waves between 2003 and 2005. These successive outbreaks have affected the livelihoods of middle and small-scale poultry producers but also impacted the growing intensive poultry production sector, mainly as a consequence of the export ban imposed to Thailand by its mains export markets.
The objective of the present work is to quantify the association between the intensive poultry production sector and the spatial distribution of HPAI H5N1 in Thailand. Two approaches have been developed in this work. Firstly, we used statistical models to identify the factors that are associated with the spatial distribution of HPAI H5N1. Secondly, we used prior knowledge regarding the transmission mechanisms of HPAI to develop a process-based epidemic model in order to simulate the spread of the disease and test the effects of changing production structure and intervention strategies.
Firstly, we showed that the spatial distribution of domestic ducks could be predicted across Asia using a non linear regression model and a set of environmental and anthropogenic predictors.
Secondly, we showed that poultry production could be disaggregated between intensive and extensive production systems using the number of birds per holder as a discriminating factor. Thirdly, we showed that Boosted Regression Trees (BRT) could be used the quantify the probability of presence of HPAI H5N1 in Thailand, and that the main factors associated with the presence of the disease were the number of ducks raised in intensive production systems, the number of crop cycles and the proportion of water in the landscape. Finally, we illustrated how process-based epidemic models could be used to assess the effect of intervention measures, test the effect of alternative intervention scenarios and identify optimal prevention and intervention strategies against future outbreaks.
Résumé
Le virus de l’influenza aviaire hautement pathogène (IAHP) de type H5N1 apparu en Chine en 1996 constitue une menace pour la santé humaine en raison de sa circulation endémique dans les volailles domestiques et de son potentiel zoonotique. La sévérité de l'infection liée à l'IAHP H5N1 est variable selon les espèces d'oiseaux: certains anatidés sont porteurs sains et asymptomatiques du virus tandis que dans les élevages de poulets, l'IAHP est fortement contagieux et caractérisé par des taux de mortalité supérieurs à 90%. Chez les humains, l'impact de l'IAHP H5N1 reste à ce jour modéré (630 cas humains dont 375 morts, World Health Organization Juin, 2013) en raison de la faible transmission du virus des volailles aux humains et d'humain à humain. Cependant, étant donné les taux de létalité élevés (>50%), un changement des modalités de transmission pourrait mener à un impact beaucoup plus élevé.
Depuis son émergence, l'IAHP H5N1 a eu un impact économique important dans de nombreux pays d’Asie du Sud-Est. La Thaïlande, pays qui fait partie des principaux exportateurs mondiaux de viande de volaille, a été sévèrement touchée par les multiples vagues épidémiques entre 2003 et 2005. Ces épisodes ont eu un impact sur les revenus des petits et moyens producteurs, mais également causé des pertes économiques importantes dans le secteur de la production intensive de volailles en raison de l'embargo imposé par les principaux marchés d'exportation.
L'objectif de ce travail est d’étudier quantitativement l'association entre la production intensive de la volaille et la distribution spatio-temporelle de l'IAHP H5N1 en Thaïlande. Deux approches ont été développées pour aborder cette étude: le développement d’une part de modèles statistiques visant à identifier les déterminants du risque d'IAHP H5N1, et d'autre part, de modèles mécanistiques visant à simuler des trajectoires épidémiques sur base de la connaissance des mécanismes de transmission de l'IAHP H5N1, de la structure du secteur de la production de volaille et des mesures d'intervention mises en place.
A l’aide de facteurs environnementaux et anthropogéniques, nous montrons que: (i) la distribution des canards domestiques en Asie peut être prédite en utilisant des modèles de régression non- linéaire, et (ii) la production de volailles peut être désagrégée entre production extensive et intensive sur base du nombre de volailles par éleveur. Enfin (iii), nous montrons en utilisant des arbres de régression boostés ("Boosted Regression Trees", BRT) que les principaux déterminants de la distribution du risque d'IAHP H5N1 sont les canards élevés en systèmes intensifs, le nombre de cycles de culture de riz et la proportion d'eau présente dans le paysage. Finalement, nous illustrons les potentialités des modèles mécanistiques pour évaluer l'efficacité des mesures d'intervention implémentées, tester des scénarios alternatifs d'intervention et identifier des stratégies optimales de prévention et d'intervention contre de futures épidémies.
Table of Content
Acknowledgments ... 1
Summary... 3
Résumé... 4
List of acronyms... 7
Introduction... 8
Zoonotic disease and Intensification of animal production... 9
Epidemiological implications of intensive livestock production... 10
Antibiotic resistance...11
Selection pressure for virulent strains... 11
Origins and Evolution of Highly Pathogenic Avian Influenza H5N1... 11
HPAI H5N1 in Thailand: A case study... 12
A spatial perspective... 15
Thesis outline... 17
Chapter I. Modelling the distribution of domestic ducks in Monsoon Asia... 19
Introduction... 20
Materials and Methods... 21
Results... 24
Discussion... 26
References... 27
Supplementary material... 28
Chapter II. Predicting the distribution of intensive poultry farming in Thailand... 33
Introduction... 34
Materials and Methods... 35
Census data... 35
Predictors... 35
Disaggregating survey data... 35
Statistical analysis... 37
Results... 38
Discussion... 40
References...42
Supplementary material... 44
Chapter III. Improving risk models for avian influenza: the role of intensive poultry farming and flooded land during the 2004 Thailand epidemics... 52
Introduction... 53
Methods... 54
Data... 54
Preprocessing... 55
Modelling... 56
Evaluation and predictions... 56
Results... 56
Discussion... 58
References...61
Supplementary material... 62
Key Results... 64
Discussion... 66
Understanding the epidemiology of HPAI H5N1 in Thailand: main lessons learned... 66
Opportunities and limitations in predicting the distribution of intensive livestock systems... 68
Appendix I Boosted Regression Trees... 79
Appendix II Process-based models and intervention strategies for avian influenza in Thailand.... 82
Appendix III Sequential Monte Carlo Approximate Bayesian Computation... 93
Appendix IV High resolution figures... 97
Appendix V Accuracy assessment of secondary data...103
List of Publications...108
References...110
List of acronyms
AIC: Akaike's Information Criterion CP: Charoen Pokphand Group
DLD: Department of Livestock Development, Bangkok HIV: Human immunodeficiency virus
BRT: Boosted Regression Trees CPU: Central Processing Unit
FAO: Food and Agriculture Organization of the United Nations FMD: Foot and Mouth Disease
GLM: Generalized Linear Model
HPAI H5N1: Highly Pathogenic Avian Influenza of subtype H5N1 ILRI: International Livestock Research Institute
IVs: Influenza viruses LBM: Live Bird Markets
MARS: Multivariate Adaptative Regression Splines ML: Machine Learning
MODIS: Moderate Resolution Imaging Spectroradiometer
OIE: Office International des Epizooties / World Organization for Animal Health OLS: Ordinary Least Square
SARLM: Simultaneous Auto Regressive Linear Model SDM: Species Distribution Modelling
WHO: World Health Organisation of the United Nations
Introduction
Epidemics have shaped the course of human history. Since antiquity, pathogens have influenced the rise and fall of civilizations: plague is believed to have stopped the re-expansion of the Byzantine Empire under the reign of Emperor Justinian (Rosenwein, 2009) and also to have precipitated the fall of the Han Dynasty in China (Jian, 1994). During the Middle Ages, the Black Death killed 25 to 50% of the European population (Gottfried, 2010) and cause the rapid depletion of the available agricultural labour force; this in turn contributed to the end of the feudal system and paved the way for the emergence of capitalism (Bowsky, 1971).
Other infectious agents facilitated the discovery of new territories: smallpox helped the Spanish Conquistador’s conquest of the Inca empire by killing more native Americans than any other weapon (Diamond, 1997). Conversely, Napoleon's Russian campaign was perhaps halted by an epidemic of typhus, marking the turning point in his rule over Europe (Burki, 2010; Knight, 2012). During more modern times, the potato blight epidemic which affected Ireland in 1845 was the origin of the 'Great Famine', responsible for the deaths of a million people and the migration of at least another million to the New World (Woodham-Smith, 1962). Currently, the ongoing malaria and AIDS epidemics still undermine the development of some of the poorest nations in the world, killing 1.8 million and 0.6 million respectively each year (WHO, 2010; Barnett, 1999; Gallup, 2000; Piot et al., 2007). In developed countries, despite the improvement of hygiene and medical knowledge, emerging pathogens fuel fears over the vulnerability of our societies to epidemics as a consequence of their increasing inter-dependence and connectedness (Normile, 2004; Peiris et al., 2004a).
A particularly dramatic type of epidemic is known as a pandemic; meaning that an infectious agent has successfully spread amongst human populations and reached a widespread geographical extension (continental or global). Pandemics have occurred in our distant (Achtman et al., 2004) and close (Smith et al., 2009) past and will, in all likelihood, occur again in the future (Barclay, 2008). The impacts of past pandemics have ranged in magnitude from moderate for diseases such as the recent H1N1 ('Swine-flu') epidemic, to catastrophic, for the 1918 'Spanish' flu pandemic or the Black Death.
Given the growing global population and its increased connectivity through the generalization of intercontinental transport (Hosseini et al., 2010), the next great pandemic could have important consequences. Primary concerns for public health in the 21st century will therefore be to understand the factors determining the (re)emergence of potentially pandemic pathogens, to understand the mechanisms of the spatio-temporal dynamics of these pathogens, and to prepare potential intervention strategies.
Amongst the potential candidates for the next great pandemic, influenza viruses (IVs) hold a prominent place. Firstly, they are associated with rapid and regular gene reassortment, which, by allowing rapid exchanges of genetic material, can be advantageous for viral fitness (Holmes et al., 2005; Nelson et al., 2008). Secondly, they are known to infect a wide range of animal hosts, which may act as disease reservoir between epidemic phases (Rambaut et al., 2008; Webster et al., 1992).
Thirdly, IVs are associated with seasonal presence in human populations, enabling potentially dangerous combination between highly pathogenic pandemic strains and low pathogenic seasonal strains. Finally, IVs have a been at the origin of several important pandemics during the 20th (Kilbourne, 2006) and 21st centuries (Li et al., 2004; Smith et al., 2009), including the 1918 'Spanish
flu' that has been estimated to have affected up to a billion people and to have killed 50-100 million (Johnson et al., 2002).
Zoonotic disease and Intensification of animal production
A large number of diseases which affect humans (influenza in particular) originated from animals (Furuse et al., 2010; Li et al., 2004, 2007; Reid et al., 1999; Sharp et al., 1999; Woolhouse, 2005) and are known as zoonotic disease. Livestock play an important role in the transmission of zoonotic diseases, owing to their position at the interface between humans and wildlife, which can act as reservoir for pathogens).
Since the introduction of agriculture humans have consistently discovered more efficient ways to farm their land, thereby sustaining a growing human population (Mazoyer et al., 2002). This has been achieved through a succession of technological innovations, the adoption of new farming practices, and an increased understanding of the biological principles underlying the cultivation of plants and the raising of livestock. A few examples of these innovations include slash and burn cropping, cyclical cropping, irrigation, the use of the plough, exploitation of hybrid vigour, and so on.
The industrial revolution accelerated this process by providing cheap manufactured tools and the first fuel-powered plough in the late 19th century (Jones et al., 1974). The period following the second world war -the green revolution- saw an unprecedented increase in the yield of farming through the mass-production of fuel-powered tractors, generalization of irrigation networks and water pumps, an increased use of chemical inputs (fertilizers, herbicides, fungicides, antibiotics) and the use of high- yield cereal seeds selected to respond well to fertilizers (Mazoyer and Roudart, 2002). In the developed world, agricultural production progressively shifted from a low input and low output systems, to of high input and high outputs systems.
In the case of livestock farming, the high levels of input can be described as: selection of high yield breeds; investment in infrastructure such as roads, for transport of animals and feed; investment in hatcheries, slaughterhouses and mechanical tools; hiring of a large labour force; and purchase of chemical and veterinary inputs (such as drugs, antiseptics, and feeds with nutritional value tuned to optimize food conversion ratios and cost/benefit). These different inputs act cooperatively in order to maximize the ratio of output (quantity of meat of dairy products) to inputs per animal per year.
This is diametrically opposed to extensive farming, which is characterized by low output/input ratios (i.e. is less efficient), but does not require much investment, time or labour.
This process of progressive intensification has released millions of people from the burden of subsistence farming, and has contributed significantly to economic development in poor countries. It has also broken the historical circle of food scarcity, and provided cheap sources of protein to populations suffering from chronic malnutrition.
However, the intensification of animal production has also created numerous side-effects, which have severe consequences and could outweigh its long-term benefits (Food and Agriculture Organization of the United Nations, 2009; Robinson et al., 2011). Intensive agricultural systems affect soil degradation as a result of a high turn-over in crop cycles; generate soil and drinking water pollution through the recurrent use of fertilizers, pesticides and antibiotics; cause scarcity of water resources to sustain high yields with irrigation; and affect species conservation by modifying
Epidemiological implications of intensive livestock production
Important side-effects of the intensification process are also of an epidemiological nature. Intensive livestock production has been associated with emerging zoonoses such as Bovine Spongiform Encephalitis, Nipah virus (Matson et al., 1997; Weiss et al., 2004) and, more recently, avian influenza (Steinfeld et al., 2006; Wallace et al., 2009; Wallace, 2009).
In the last decades, the unprecedented economic growth in South Asia and China (Bloom et al., 2009) has resulted in a fast increase in demand for livestock products and contributed to a rapid intensification of the livestock production sector in this region of the world. In particular, demand for poultry meat is expected to rise by 121% in China and 844% in India between 2000 and 2030 (Robinson et al., 2011). As a general result of this ongoing intensification process, the total biomass of livestock today greatly exceeds the total biomass of humans and wildlife on earth (Figure 1, Smil, 2002).
Figure 1. Biomass of people, livestock and wildlife, adapted and updated from Smil (2002) and FAOSTAT (2010).
From a pathogen's perspective, this situation suggests that a higher reproductive success could potentially be achieved by infecting humans or one of the 14 main domesticated livestock species (Diamond et al., 1997), which are abundant and distributed across a wide range of agro-ecosystems, rather than any of the millions of wild animal species (Mora et al., 2011).
0 200 400 600 800 1000 1200
People Livestock Wildlife
1,010
M ill io ns of T on ne s
350
40
Biomass of terrestrial vertebrates
The structural changes in production structure resulting from the progressive shift from extensive to intensive production have led to two key epidemiological challenges.
i) Antibiotic resistance:
In some parts of the world, antibiotics are used routinely for animal husbandary purposes and as growth promoters in intensive livestock farming (Witte, 1998). In the United States, 80% of the total volume of antibiotics consumed in 2012 was attributed to the agricultural sector (Laxminarayan, 2012). This practice contributes to an increased selection pressure for potentially antibiotic resistant strains of common human pathogens, including food-borne bacteria directly present in livestock products (Teuber, 1999). Resistance to antibiotics is one of the major public health concerns for the next century. A significant loss in the efficiency of antibiotics for fighting common infections could "bring an end the modern medicine as we know it" (Dr. Margaret Chan, (“WHO | Antimicrobial resistance in the European Union and the world,” 2012)
ii) Selection pressure for virulent strains
The production of livestock with low genetic diversity, raised in very high densities and fast production cycles could allow for the selection of specialist and potentially virulent strains.
Several results from evolutionary theories have suggested that intensive poultry farms meet the criteria for the selection of virulent strains (Greger, 2007). The underlying mechanism that supports this hypothesis is that highly virulent pathogens could not sustain themselves in sparsely populated environments because their survival would otherwise be compromised by a rapid depletion of the pool of susceptible hosts to infect (Suarez et al., 2003). However, the close proximity, high contact rates, and large pool of susceptible hosts in intensive farms therefore favoured the emergence of highly pathogenic strains. Additionally, in order to maximize the conversion from feeds to animal products, intensive farms re-stock their pool of livestock directly from dedicated breeders for successive production cycles. This mechanism prevents de facto a process of natural selection for livestock genotypes that could develop a resistance to emerging virulent pathogens. Meanwhile, the fast turnover in production cycles favours the evolution of a pathogen toward virulent strains: short lifespan increase the selection pressure for pathogens that can reach their transmission threshold. Provided that it is passed through successive generations of livestock (by remaining present in the farm or its environment) a pathogen can thereby evolve towards greater virulence).
Whilst both these aspects are key challenges for the future of research in epidemiology, this work will focus on the latter. Based on the description of this mechanism, previous works have claimed that, "identifying the processes underlying the transformation of livestock production and its intensification are central to an understanding of the forces affecting livestock disease emergence and transmission" (Slingenbergh et al., 2004)". However, to date little evidence has been produced by the scientific community for a quantitative data-driven assessment of this assertion.
Origins and Evolution of Highly Pathogenic Avian Influenza H5N1
In 1996, a novel strain of influenza virus (denoted sub-type H5N1 according to its type of surface
world (Li et al., 2004; Xu et al., 1999). Despite the region being a major poultry production centre at this time, this first outbreak had limited consequences in comparison with the events of 2003, and resulted in the culling of 'only', 1.5 million poultry by the Hong Kong Government (Chan, 2002).
After a quiet period during which the virus was sporadically detected in wild birds and underwent a series of genetic reasortments the highly pathogenic avian influenza H5N1 strain (HPAI H5N1) re- emerged in late 2002 in Southern China (Ellis et al., 2004; Sturm-Ramirez et al., 2004), leading to multiple human victims in early 2003 (Peiris et al., 2004b). The disease quickly spread to neighbouring countries (WHO, 2012) and subsequently unfolded into the first HPAI H5N1 pandemic in birds, becoming known to the general public in 2004 and 2005 as 'bird flu'. HPAI H5N1 had a major impact on the poultry industry in south East Asia due to its short infectious period (1-2 days, Bouma et al., 2009), and high mortality rates in chickens (>90%, Perkins, 2001). Several species of anatidae (ducks, geese, swans) also showed the ability to be healthy carriers of avian influenza viruses (IVs) (Stallknecht et al., 1988). Whilst large inter species variation was observed in the pathogenicity and shedding capacities for infectious avian IVs (Brown et al., 2008), it became clear that some species of anatidae were able to spread HPAI H5N1 both regionally, through migratory pathways of wild species (Gilbert et al., 2011; Newman et al., 2009), and locally, through the movement of domestic free grazing ducks in rice cropping and wetlands areas (Gilbert et al., 2006). In countries such as Vietnam and Thailand, the epidemic had a large impact on the poultry industry, compromising the income of thousands of subcontractors of large poultry production companies, as well as affecting the livelihood of rural farmers who rely on livestock for subsistence (Burns et al., 2008). From the human perspective, the epidemic had limited impact, due to the very low transmission rate from birds to humans (Hinjoy et al., 2008; Ungchusak et al., 2005; Vong et al., 2006); although it raised major concerns because of its unusually high mortality rates once transmitted to humans (633 cases and 377 deaths, July 2013, WHO).
The molecular basis of transmission from birds to humans is complex, and still poorly understood.
Recent controversial, but crucial, experiments (Herfst et al., 2012) have allowed important breakthroughs in the understanding of these determinants. The results of Herfst et al. 2012 suggest that only require a few mutations in currently circulating strain of HPAI H5N1 could generate strain of HPAI H5N1 capable of airborne transmission in mammals. For those reasons, and because of its high case fatality rate in humans, HPAI H5N1 remains a major public health concern. Efforts to understand its spatio-temporal dynamics and prevent its circulation in poultry need to be continued with the medium-term objective to stop its circulation in poultry. In February 2013, the Influenza H7N9 epidemic in eastern China, brought avian influenza back to prominence as an emerging pathogen (Horby, 2013). Efforts to better understand the emergence of avian IVs and their interactions with other livestock and humans (Butler, 2013) should be at the forefront of future public health initiatives, in order to mitigate the risk of future pandemic.
HPAI H5N1 in Thailand: A case study
In Thailand, the first outbreaks of HPAI H5N1 were reported on the 23rd of January 2004. The country subsequently encountered three epidemics waves (respectively the first wave from January to May 2004, followed by the second wave from July 2004 to March December 2005 and finally the third wave from July 2005 to November 2005, Tiensin et al., 2005). The majority of HPAI H5N1 outbreaks occurred in the central eastern region and North from the capital city of Bangkok. Both areas are
located along the floodplain of the Chao Phraya River, home to large duck populations raised in free grazing systems (Gilbert et al., 2006).
Figure 2. Geographical distribution of intensively raised ducks (left) and location of Highly Pathogenic Avian Influenza type H5N1 outbreaks (rigth).
Figure 3. Epidemic profile and moving average of the number of daily HPAI H5N1 outbreaks reported in Thailand between 2004 and 2005.
Despite the relative importance of the number of cases reported during each wave, the first wave is assumed to have been associated with the highest number of infected premises. This stems from the fact that during the first wave, the country faced an emergency situation regarding the potential economic and public health impact of the epidemic. A pre-emptive culling policy was introduced and culls were carried out within 5 km distance of any reported outbreak. Consequently, most veterinary resources were devoted to this effort, rather than disease detection. This eventually resulted in a substantial under reporting of outbreaks. The culling policy was associated with a costly compensation scheme at an estimated cost of $132.5 million (Tiensin et al., 2005). However, the true
economic impact of HPAI H5N1 in Thailand is likely to have been far more than this due to the export ban imposed on poultry products until 2008.
Before 2004, Thailand was a major poultry exporter (ranked 4th globally, and accounting for 7.5% of global export sales, (NaRanong, 2007) and employed 400,000 people in the poultry production industry, which represented up to 1% of the GDP (Ratananakornaet al., 2011; Tiensin et al., 2005).
The poultry industry in Thailand had developed rapidly over the previous two decades, with the number of chickens rising from 130 million in 1991, to 241 million by 2003. Notably, this shift occurred under the influence of large corporations such as Charoen Pokphand (CP), a company which has consistently been at the forefront of technological innovation to achieve economies of scale and higher efficiency in the broiler industry. The Thai poultry sector is traditionally divided in three categories of producers: i) backyard holders that represent the vast majority of owners, who raise native poultry breeds for subsistence and sell surplus at the local market, ii) industrial vertically integrated companies oriented toward standardized products (fast growth breeds) for supplying supermarkets and export, iii) sub-contractors who work closely with large companies. These buy day old chicks from the breeding facilities of large producers and after six to eight week sell them back adult birds. This system exempt mid-size producers the invest in breeding whilst it provides large poultry producers with the necessary flexibility to meet changes in market demand.
During the onset of the second wave of the epidemic, the Thai government moved away from the costly and unpopular mass culling strategy previously implemented to control HPAI H5N1. From July 2004 onward, culls were only operated in infected premises and their direct neighbours; and a policy of movement ban was introduced for poultry within 10 km of an infected premises for a period of 30 days. On the 1st of October 2004, a large scale census and surveillance campaign known as the X-Ray Survey was launched. The survey involved training a network of approximately 500,000 volunteers coordinated by the Department of Livestock Development (DLD) to perform active surveillance of HPAI H5N1 cases, and establish a census of the poultry population at the sub-district level.
Aside from the surveillance activity, the Thai government also worked in close collaboration with large poultry meat exporting companies and international organization (World Organization for Animal Health, OIE and Food and Agriculture Organization of the United Nations, FAO) to seek solutions to the problem of export bans, with the aim of restoring the confidence of their overseas markets for fresh poultry meat. From 2005, The Thai government started to support structural changes in the poultry production sector in order to maintain its development, despite the presence of HPAI H5N1 (Ratananakorn et al., 2011). This shift was supported by the introduction of the concept of compartmentalisation (Ratananakorn et al., 2011; Scott et al., 2006) defined as ‘the ability that enables a country to create compartments that contain subpopulations of disease-free animals that may be in different locations but are kept under a common biosecurity management system’. In turn a country ‘that has eradicated a disease from only part of its territory, or from a particular industry sector, may be able to resume trade under certain restrictions even though the rest of the country remains infected’. Major poultry meat exporters (such as CP) played a key role in the process of compartmentalisation and vertical integration of the production of broilers by raising biosafety standards for disease prevention, feeds management, water supply, etc.
In practice, the system consisted of a collaboration between the DLD, the poultry meat exporters, and contract farmers. All were required to engage in routine surveillance and risk assessment
activities within a buffer zone of 1 kilometre around their premises. Companies taking part in the project were issued a certificate by the DLD, provided that they engaged in these activities and did not tested positive for HPAI H5N1 for at least 3 production cycles, for a period of 6 months (Kasemsuwan et al., 2009).
As a result of these various surveillance measures, including the X-Ray surveys, Thailand generated a very detailed and extensive data set of HPAI H5N1 records in comparison to other countries affected by the disease in the region. This made the country an ideal candidate to train models to predict the geographical distribution of HPAI H5N1.
A Spatial perspective
From a geographical perspective, the spatial distribution of HPAI H5N1 has been associated with the density of domestic ducks and intensive rice cropping (Gilbert et al., 2007; Songserm et al., 2006;
Tiensin et al., 2009). An extensive review of the factors influencing the distribution of HPAI H5N1 is presented in (Gilbert et al., 2012). In recent years, advances in remote sensing technology have allowed the collection of quantitative ecological data over large geographical areas to be combined and transformed into anthropogenic (Balk et al., 2006; Nelson, 2008) and biologically (Scharlemann et al., 2008) meaningful variables. These are produced routinely and used as predictors for the distribution of diseases (Gilbert et al., 2008; Hay, 2000; Hay et al., 2013a, 2009; Rogers and Randolph, 2003).
Predicting the distribution of disease such as HPAI H5N1 can be understood as a particular case of Species Distribution Modelling (SDM). The response variable modelled is the probability of presence of the disease caused by the pathogen. The cornerstone of SDM is to establish a statistical relationship between the observed distribution of the response and its corresponding covariate values, and then to subsequently re-apply this relationship to new covariate values (remote sensed imagery or vector data) to generate an estimate of the distribution of the disease in other areas.
Historically, the models used to predict the occurrence of either species or diseases were simple ordinary least square (OLS) for abundance data, and logistic regression for presence-absence data.
These models were progressively improved to take into account multiple predictors (multivariate and stepwise regression (Dalgaard, 2008)) as well as elements of spatial autocorrelation in the response variable (Dormann, 2007; Gilbert et al., 2008) and in the predictors (Kissling et al., 2008). More recent improvements include mixed models that can take account for the dependency between observations by including complex spatial and temporal autocorrelation structures. However, these models remain an extension of the classic OLS structure. A comprehensive inventory of this family of models is presented in Zuur (2009). With the rise of computing power and the onset of machine learning algorithms, novel non-parametric SDM methods have appeared within the SDM community.
These generally consist of a stochastic combination of a large number of very simple statistical models, typically classification or regression trees, or simple OLS, into larger models. Examples of these methods include Random Forest (Breiman, 2001; Liaw and Wiener, 2002), Boosted Regression Trees (Elith et al., 2008), and MARS (Friedman et al., 2003; Friedman, 1991).
In the case of HPAI H5N1, several studies have started to address the association between the presence of the disease in the landscape and the distribution of different poultry types, (Paul et al., 2011; Walker et al., 2012), including intensively raised poultry (Graham et al., 2008; Leibler et al.,
the lack of available information on the spatial distribution of intensively raised poultry in South Asia.
During the 2004-2005 epidemic wave of HPAI H5N1 in South Asia, agricultural census and epidemic data with an unprecedented level of detail were collected, offering new opportunities to develop and validate methods to map intensively raised poultry and revise previous maps of the probability of presence of HPAI H5N1.
Thesis outline:
The general objective of this work is to quantify the association between intensive poultry production and the spatial distribution of Highly Pathogenic Avian Influenza subtype H5N1 in Thailand. The chapters of this thesis are arranged in an order that reflects the progressive building of the knowledge necessary to address the general objective. Firstly, we predict the geographical distribution of poultry (Chapter I). Secondly, we disaggregate the distribution of poultry between extensive and intensive production systems (Chapter II). Thirdly, we analyse the link between intensive poultry production and the spatial distribution of HPAI H5N1 (Chapter III).
Chapter I. Modelling the distribution of domestic ducks in Monsoon Asia. In chapter I, we present a methodology based on stepwise regression that was developed to predict the geographic distribution of domestic duck density across fourteen countries in South Asia. The motivation for this work came from the fact that no standardized maps of domestic duck distributions existed for Asia, despite their importance as a risk factor for HPAI H5N1. More critically, countries affected by several HPAI H5N1 epidemic waves such as India, Myanmar, and China lacked high resolution data. Obtaining information regarding the distribution of ducks in these regions was therefore crucial to generate reliable risk maps. The material of this chapter is covered by two linked publications in Agriculture, Ecosystems and Environment (Diann J Prosser et al., 2011; Van Boeckel et al., 2011) as well as an additional publication currently under evaluation in PLoS ONE that extends on the methods revision developed in this chapter.
Chapter II. Predicting the distribution of intensive poultry farming in Thailand. In chapter II and the following chapters, we use a large poultry census dataset collected during the X-Ray survey in Thailand. First, we show the co-existence of two types of production systems in the census data.
Secondly, we present a new method based on the number of birds per holder to disaggregate extensively from intensively raised poultry. The methodology was applied to both chickens and ducks to analyse the structure of the poultry sector in Thailand and generate maps of chicken and duck density by production type. The spatial distribution of each type was then analysed through a Simultaneous Autoregressive Linear Model (SARLM) to identify its predictors. The material in this chapter is covered by a paper in Agriculture Ecosystems and Environment (Van Boeckel et al., 2012b).
The methodology developed in this study has inspired further work to map production types at a global scale by Robinson and Gilbert, more specifically in focussing on the use of animal per holder as a discriminating factor between intensive and extensive production systems (Gilbert & Robinson, in progress).
Chapter III. Improving risk models for avian influenza: the role of intensive poultry farming and flooded land during the 2004 Thailand epidemics. In chapter III, emphasis is placed on the spatial distribution of HPAI H5N1 during the 2004-2005 epidemic. This section builds upon the maps of disaggregated livestock presented in chapter II, as well as a new map of the distribution of water obtained from LandSat imagery. This chapter also compares predictions of probability of presence for HPAI H5N1 at two spatial scales (villages and sub-district) and evaluates the added value of the BRT methodology. The material in this chapter is covered by a publication in PLoS ONE (Van Boeckel et al., 2012a)
This thesis is fundamentally methodological and multidisciplinary, the issues addressed in the
Computation (ABC). In order to facilitate the understanding of the technical aspects of this thesis, appendices have been included at the end. These appendices are not a comprehensive description of the corresponding mathematical methods used, (which are extensively described in the cited literature) but rather aim to ease the reading by a non-specialist. Furthermore, the integration of published papers in the thesis resulted in large maps being displayed as small figures. Those have also been provided as appendix in much larger displays.
Chapter I. Modelling the distribution of domestic ducks in
Monsoon Asia
Agriculture,EcosystemsandEnvironment141 (2011) 373–380
ContentslistsavailableatScienceDirect
Agriculture, Ecosystems and Environment
j ou rna l h o m e p a g e :w w w . e l s e v i e r . c o m / l oc a t e / a g e e
Modelling the distribution of domestic ducks in Monsoon Asia
ThomasP.VanBoeckela,b, DiannProsserc,d, GianlucaFranceschinie, ChandraBiradarf, WilliamWintg,TimRobinsone,MariusGilberta,b,∗
aBiologicalControlandSpatialEcology,UniversitéLibredeBruxellesCP160/12,AvFDRoosevelt50,B-1050Brussels,Belgium
bFondsNationaldelaRechercheScientifique,rued’Egmont5,B-1000Brussels,Belgium
cUSGSPatuxentWildlifeResearchCenter,BaltimoreAvenue10300,Beltsville,MD20705,USA
dUniversityofMaryland,CollegePark,USA
eFoodandAgricultureOrganizationoftheUnitedNations(FAO),VialedelleTermediCaracalla,00153Rome,Italy
fDepartmentofBotanyandMicrobiology,CenterforSpatialAnalysis,UniversityofOklahoma,Norman,OK73019,USA
gEnvironmentalResearchGroupOxford,P.O.Box346,OxfordOX13QE,UnitedKingdom
a r t i c l e i n f o
Articlehistory:
Received2December2010
Receivedinrevisedform11April2011 Accepted25April2011
Available online 24 May 2011
Keywords:
Livestockmapping Domesticducks MonsoonAsia Regressionmodels
Highlypathogenicavianinfluenza
a b s t r a c t
Domesticducksareconsideredtobeanimportantreservoirofhighlypathogenicavianinfluenza(HPAI), asshownbyanumberofgeospatialstudiesinwhichtheyhavebeenidentifiedasasignificantrisk factorassociatedwithdiseasepresence.DespitetheirimportanceinHPAIepidemiology,theirlarge-scale distributioninMonsoonAsiaispoorlyunderstood.Inthisstudy,wecreatedaspatialdatabaseofdomestic duckcensusdatainAsiaandusedittotrainstatisticaldistributionmodelsfordomesticduckdistributions ataspatialresolutionof1km.ThemethodwasbasedonamodellingframeworkusedbytheFoodand AgricultureOrganisationtoproducetheGriddedLivestockoftheWorld(GLW)database,andrelieson stratifiedregressionmodelsbetweendomesticduckdensitiesandasetofagro-ecologicalexplanatory variables.Weevaluateddifferentwaysofstratifyingtheanalysisandofcombiningthepredictionto optimizethegoodnessoffitofthepredictions.Wefoundthatdomesticduckdensitycouldbepredicted withreasonableaccuracy(meanRMSEandcorrelationcoefficientbetweenlog-transformedobservedand predicteddensitiesbeing0.58and0.80,respectively),usingastratificationbasedonlivestockproduction systems.WetestedtheuseofartificiallydegradeddataonduckdistributionsinThailandandVietnamas trainingdata,andcomparedthemodelledoutputswiththeoriginalhigh-resolutiondata.Thisshowed, forthesetwocountriesatleast,thattheseapproachescouldbeusedtoaccuratelydisaggregateprovincial level(administrativelevel1)statisticaldatatoprovidehighresolutionmodeldistributions.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Mapsoflivestockdistributionareaconcisewaytovisualizeand analyzelargecensusdatasets.Theyhaveawidevarietyofappli- cationssuchasassessingtheriskofzoonoticdisease,foodsafety management,determinationofthepotentialdailyproteinproduc- tioncapacity,monitoringoftheland-usechanges,assessmentof theenvironmentalriskassociatedwithanimalproduction(Wint andRobinson,2007).
Thehighlypathogenicavianinfluenza(HPAI)H5N1virusthat firstappearedinsouthernChinainthelate1990s(Lietal.,2004) is one of themost significantrecent epizooticswhich has had dramaticconsequencesonsmallholders’livelihoodsandpoultry
∗Correspondingauthorat:BiologicalControlandSpatialEcology,UniversitéLibre deBruxellesCP160/12,AvFDRoosevelt50,B-1050Brussels,Belgium.Tel.:+322650 3780;fax:+3226502445.
E-mailaddress:mgilbert@ulb.ac.be(M.Gilbert).
productioninmanycountries(Brown,2010).Todate,thehuman deathtolloftheseeventsremainsmoderatedespitetheveryhigh mortalityratesobservedinwildanddomesticfowl(WorldHealth Organization,October2010:507casesreported,302deathscon- firmed).
Domesticducksplayasignificantroleintheepidemiologyof HPAIH5N1virus.First,experimentalstudieshavedemonstrated thattheycanbeapparentlyhealthycarriersoftheHPAIH5N1virus andhaveevenbeenreferredtoasthe“Trojanhorseoftheavianflu”
(Kimetal.,2009).DomesticduckshavebeenshowntosurviveHPAI H5N1virusinfectionsandexcretelargequantitiesoftheviruswith- outshowingclinicalsignsofdisease(Hulse-Postetal.,2005).Asa result,domesticducksmayplayadeterminantroleinthetransmis- sionofthevirusduringthemovementofflocksbetweendifferent feedinglocations.StudieshaveshownthatthedistributionofHPAI H5N1virusinpartsofAsiaisheavilyinfluencedbythedistribution ofduckfarmingsystems(Gilbertetal.,2007).Morespecifically, thedensityofduckshasbeenfoundtobeakeyvariableforthe predictingofthepresenceofHPAIH5N1virusinThailand(Gilbert 0167-8809/$–seefrontmatter© 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.agee.2011.04.013
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