This thesis, entitled “Modelling the distribution of pig production and diseases in Thailand”, presents many aspects of pig production in Thailand including the characteristics of pig farming system, distribution of pig population and pig farms, spatio-temporal distribution and risk of most important diseases in pig at present, and the suitability area for pig farming. Spatial distribution and characteristics of pig farming in Thailand were studied using time-series pig population data to describe the trend of pig productions in relation to pig numbers, holder numbers, and number of pigs per holder. In more detailed analyses, pig census data in 2010 were used to describe farming systems including type of pig (native, breeding, and fattening pigs), farm scales (extensive and intensive farming systems), type of farming systems (farrow-to-finish, nursery, and finishing systems) and to quantify the association between the geographical distribution of those and several predictor variables by using Random Forest models. The results show that over the last decades, the pig population has gradually increased over time, with a marked cyclical pattern corresponding to what has been termed the “pork cycle”. The spatial distribution of large-scale pig farms corresponds with that of commercial pig breeds, which are concentrated in lowland urban or peri-urban areas, and are close to means of transportation, facilitating supply to major markets such as provincial capitals and the Bangkok Metropolitan region. Conversely the smallholders are distributed throughout the country, with higher densities located in highland, remote, and rural areas, whence they supply local, rural markets. It is proposed that intensive pig production should be integrated with crop farming within a specific “pig zone”, designated for establishment of intensive farming, which includes the necessary input and output facilities and enhanced bio-security. For smallholder pig farmers, integration of pig-farming with crop production should be promoted and combined with capacity development for farm management, including enhancing the bio-security. Spatial epidemiology of porcine reproductive and respiratory syndrome (PRRS) in Thailand was studied by providing a first description of the spatio-temporal pattern of PRRS in Thailand and to quantify the statistical relationship between the presence of PRRS at the sub-district level and a set of risk factors using two modelling approaches: autologistic multiple regression and boosted regression trees. An atypical and more virulent PRRS (HP-PRRS) emerged in China and spread to many countries, including Thailand, causing a lot of damage to pig production. The results indicated that farms with breeding sows may be an important group for targeted surveillance and control. However, these findings obtained at the sub-district level should be complemented by farm-level epidemiological investigations in order to obtain a more comprehensive view of the factors affecting PRRS presence. In this study, the outbreaks of PRRS could not be differentiated from the potential novel HP-PPRS form, which was recently discovered in the country.Spatial characterization of colonies of the flying fox bat, a carrier of Nipah Virus in Thailand was studied. We conducted field observation, remote sensing, and ecological niche modelling to characterize flying fox colonies and their ecological neighbourhoods. A Potential Surface Analysis was applied to map contact zones among local epizootic actors. Results showed that flying fox colonies were found mainly on Thailand’s Central Plain, particularly in locations surrounded by bodies of water, vegetation, and safe havens such as Buddhist temples. High-risk areas for Nipah zoonosis in pigs include the agricultural ring around the Bangkok metropolitan region where the density of pig farms is high. It is suggested that passive and active surveillance programs should be prioritized around Bangkok, particularly on farms with low biosecurity, close to water, and/or on which orchards are concomitantly grown. Integration of human and animal health surveillance should be pursued in these same areas. Such proactive planning would help conserve flying fox colonies and should help prevent zoonotic transmission of Nipah and other pathogens.Using Spatial Multi-Criteria Decision Analysis to identify suitability of pig farming in Thailand was studied. The suitability maps were generated according to the three objectives obtained from decision making process including; i) pig producers’ profit is maximized, ii) public and environmental health are protected, and iii) pig health is protected and rural areas are developed. The maps showed that the areas surrounding the major consuming centres (Bangkok) were highly suitable for objective 1, the large areas in the Northeast were highly suitable for objective 2, and the areas with rather isolation including in the East and the South were highly suitable for objective 3. The final suitability maps were presented in 6 scenarios based on the level of trade-off and risk, which these can be applied for the appropriate situations. It is suggested that establishment of pig zoning, policy makers should take all aspects into consideration in order for sustainable development in all farm levels. The cost effectiveness should be further analyzed to evaluate the zoning plan before they are developed. Additionally, the plans to scale-up support to sustainable smallholder farmers and environmental management such as manure action plan should be developed.Bringing all these results together, this thesis are discussed into three parts, namely, 1) situation and distribution of pig farming, 2) epidemiology of major pig diseases, and 3) sustainable development of pig farming. / Doctorat en Sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished

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During 2007 the UK experienced outbreaks of three notifiable exotic livestock diseases; Foot and Mouth Disease (FMD), Highly Pathogenic Avian Influenza (HPAI) and bluetongue. Large epidemics of any of these diseases would have a serious impact on animal welfare, farming, food production and the economy. In light of this, understanding holdings which are most likely to acquire and spread infection and being able to identify areas at higher risk of an epidemic is valuable when preparing for and managing an epidemic. This thesis uses a spatial epidemiological framework and the detailed disease and demographic data from the 2001 Great Britain (GB) FMD epidemic to develop static models of the risk of FMD susceptibility and transmission. These models are used to develop maps of FMD risk. These methods are then applied to the outbreak of FMD in 2007. The inputs for this analysis comprised a set of data relating to the farms diagnosed with FMD and farms culled as part of the disease control measures. The cleaning of these data is described and data which were estimated relating to dates of infection and putative sources of infection are evaluated. The distribution of farm holdings and animals is taken from the June 2000 GB agricultural census, off-fields of farms in the agricultural census are recorded in other datasets and these have been identified and linked to census holdings. A model of holding level susceptibility is developed using both farm level variables and measures of animal numbers in the locality of the holding as well as the distance to the nearest farm infected before the ban on animal movements (seeds). The overall fit of the model was very good with an area under the Receiver Operator Characteristic (ROC) curve of 0.91. A further model was developed to describe the risk of FMD transmission. However, due to incompleteness of transmission data, this was a model of the risk of finding a subsequent Infected Premises (IP) within 3km of an IP. Risk factors were a combination of holding level variables and locality measures as well as data relevant to the infection, such as infectious period and the species initially infected. The area under the ROC curve for this model was 0.71, which is regarded as an acceptable fit. Geographical barriers to FMD transmission were investigated using a case-control methodology, linear barriers comprising rivers and railways had a significant protective effect with respect to disease transmission (odds ratio = 0.54, 95% CIs = 0.30,0.96, p=0.038). Modelled values for the transmission and susceptibility models were transformed to a raster surface in ESRI ArcMap for both the disease as it was seeded in the 2001 epidemic and a non-specific background risk surface independent of the distribution of seeds. A risk map generated for the outbreak of FMD in Surrey in August 2007 suggested that there was little risk of a large outbreak in Surrey. Potential disease introductions through livestock movements from Surrey into Scotland were identified and these suggested that if the disease were introduced into Scotland there was great danger of substantial local spread. These methods described in this thesis have been used to map risk of FMD and subsequently applied to inform the risk presented by a different outbreak of FMD. The study underlines the value of detailed data both disease and demographic, for epidemic management. Similar methods could and should be applied to other infectious diseases threats of livestock such as HPAI and bluetongue.

O presente trabalho é resultado de uma parceria entre o Ministério da Agricultura, Pecuária e Abastecimento, a Agência Estadual de Defesa Agropecuária do Maranhão, a Universidade de São Paulo e a Universidade de Brasília, fazendo parte das atividades do Programa Nacional de Controle e Erradicação da Brucelose e Tuberculose Animal. Realizou-se um estudo do tipo transversal para estimar a prevalência da brucelose bovina em animais e propriedades no Maranhão e em quatro circuitos produtores de bovinos, oriundos da divisão do estado. Em cada propriedade selecionada para o estudo, foram aleatoriamente coletadas amostras de soro de fêmeas bovinas com idade igual ou superior a 24 meses, assim como, foi aplicado um questionário epidemiológico objetivando caracterizar os tipos de criação de bovinos e determinar os fatores de risco associados à ocorrência da doença no Maranhão. No total foram amostradas 749 propriedades e 6.779 animais. As coordenadas geográficas de cada propriedade foram coletadas para identificação da distribuição espacial das propriedades positivas e de áreas geográficas onde medidas de controle da doença devem ser preferencialmente implementadas. A prevalência de propriedades positivas com, ao menos um animal reagente, foi estimada em 11,42% (9,23 - 14,06%) e a prevalência de animais soropositivos foi de 2,52% (1,73 - 3,65%). Diferenças significativas de prevalência foram observadas entre o circuito produtor 2 e os demais circuitos. As variáveis: rebanho bovino com mais de 54 fêmeas com idade ≥ 24 meses, aluguel de pastos de/para terceiros e presença de áreas alagadiças na propriedade, foram identificadas como fatores de risco para a ocorrência de brucelose no estado. Exploração de corte foi identificada como um fator protetor da doença. A performance do modelo final de fatores de risco, analisada através da área sob a curva ROC, apresentou um valor igual a 0,73, o que significa dizer que a ocorrência de focos da doença é também influenciada por outros elementos não incluídos na análise de fatores de risco. A distribuição de brucelose bovina foi estudada através de análises espaciais de prevalência e efeitos de primeira e segunda ordem. Observou-se uma distribuição heterogênea de focos da doença no estado, com destaque para áreas no centro e nas fronteiras leste e noroeste. Pequenas evidências de efeitos de segunda ordem foram observados na escala de 0 a 10 km. A análise de efeitos de primeira ordem, através dos resíduos do modelo de regressão logística, identificou áreas de maior risco da doença, principalmente no centro do estado, onde a presença de brucelose bovina não foi totalmente explicada pelo modelo de fatores de risco. Maior atenção deva ser preferencialmente prestada a esta área, onde investigações epidemiológicas e intervenções de controle da doença devem ser implementadas. / The present work is the result of a partnership among the Ministry of Agriculture, Livestock and Food Supply, the Animal Health State Agency of Maranhão, the University of São Paulo and the University of Brasília, becoming part of the activities of the National Program to Control and Eradicate Animal Brucellosis and Tuberculosis. A cross-sectional study was conducted to estimate the prevalence of bovine brucellosis in animals and farms in Maranhão State and four cattle production circuits, from the arbitrary division of the State. In each farm selected for the study, serum samples were randomly collected from cows aged 24 months or more, as well as an epidemiological questionnaire was applied aiming to characterize the types of cattle production systems and to determine the risk factors associated with occurrence of the disease in Maranhão. In total 749 farms and 6,779 animals were surveyed. Geographic coordinates of each farm were collected for identification of the spatial distribution of positive farms and identification of geographic areas where disease control measures should preferably be implemented. The prevalence of positive farms with at least one reagent animal, was estimated at 11.42% (9.23 - 14.06%) and the prevalence of seropositive animals was 2.52% (1.73 - 3.65% ). Significant differences in prevalence were observed among the circuit number 2 and the others. The variables: cattle herd over 54 cows aged 24 months or more, rent pasture from/to third parties and presence of wetlands on the farm were identified as risk factors for the occurrence of brucellosis in the State. Beef herd was identified as a protective factor of the disease. The performance of the final model of risk factors, analyzed by the area under the ROC curve, was 0.73, which means that the occurrence of disease outbreaks is also influenced by other factors not included in the risk factors analysis. The distribution of bovine brucellosis was studied beyond spatial analysis of prevalence and first and second order effects. An heterogeneous distribution of disease outbreaks in the State was observed, especially some areas in central and eastern and northwestern borders. Little evidence of second order effects were observed on a scale of 0 to 10 km. The analysis of first-order effect, through the residues of the logistic regression model, identified areas of greatest risk of disease, especially in the center of the State, where the presence of bovine brucellosis has not been fully explained by the risk factors model. Greater attention should preferably be paid to this area, where epidemiological investigations and disease control interventions should be implemented.

Lung cancer is the leading cause of cancer death in the United States, but a large fraction of cases is preventable. We use a spatial smoothing algorithm to identify a geographic pattern of high lung cancer mortality, primarily in the Southeast, which we call a lung cancer belt. Disease belts are an effective mode for conveying patterns of high incidence or mortality; formally defining this lung cancer belt may encourage increased public dialogue and more focused research. Public health officials could complement existing population lung cancer data with this information to help inform resource allocation decisions.

Environmental epidemiologists are increasingly interested in establishing causality between exposures and health outcomes. A popular model for causal inference is the Rubin Causal Model (RCM), which typically seeks to estimate the average difference in study units' potential outcomes. If the exposure Z is binary, then we may express this as E[Y(Z=1)-Y(Z=0)]. An important assumption under RCM is no interference; that is, the potential outcomes of one unit are not affected by the exposure status of other units. The no interference assumption is violated if we expect spillover or diffusion of exposure effects based on units' proximity to other units and several other causal estimands arise. For example, if we consider the effect of other study units on a unit in an adjacency matrix A, then we may estimate a direct effect, E[Y(Z=1,A)-Y(Z=0,A)], and a spillover effect, E[Y(Z,A)=Y(Z,A`)]. This thesis presents novel methods for estimating causal effects under interference. We begin by outlining the potential outcomes framework and introducing the assumptions necessary for causal inference with no interference. We present an association study that assesses the relationship of animal feeding operations (AFOs) on groundwater nitrate in private wells in Iowa, USA. We then place the relationship in a causal framework where we estimate the causal effects of AFO placement on groundwater nitrate using propensity score-based methods. We proceed to causal inference with interference, which we motivate with examples from air pollution epidemiology where upwind events may affect downwind locations. We adapt assumptions for causal inference in social networks to causal inference with spatially structured interference. We then use propensity score-based methods to estimate both direct and spillover causal effects. We apply these methods to estimate the causal effects of the Environmental Protection Agency’s nonattainment regulation for particulate matter on lung cancer incidence in California, Georgia, and Kentucky using data from the Surveillance, Epidemiology, and End Results Program. As an alternative causal method, we motivate use of wind speed as an instrumental variable to define principal strata based on which study units are experiencing interference. We apply these methods to estimate the causal effects of air pollution on asthma incidence in the San Diego, California, USA region using data from the 500 Cities Project. All our methods are proposed in a Bayesian setting. We conclude by discussing the contributions of this thesis and the future of causal analysis in environmental epidemiology.

ABSTRACT Eastern Equine Encephalitis virus (EEEV) is an alphavirus with high pathogenicity in both humans and horses. Florida continues to have the highest occurrence of human cases in the USA, with four fatalities recorded in 2010. Unlike other states, Florida supports year-round EEEV transmission. This research uses Geographic Information Science (GIS) to examine spatial patterns of documented sentinel seroconversions and horse cases in order to understand the relationships between habitat and transmission intensity of EEEV in Florida. Sentinel sites were categorized as enzootic, periodically enzootic, and negative based on the amount of chicken seroconversions to EEEV. Sentinel sites were analyzed based on land classification data d using the Kruskal-Wallis test to determine which habitats were associated with disease transmission. Cluster analyses were performed for the horse cases using density-based spatial clustering of applications with noise (DBSCAN). Ecological associations of EEEV were examined using compositional analysis and Euclidean distance analysis to determine if the proportion or proximity of certain habitats played a role in transmission. The research in these studies provides evidence of ecological associations for EEEV transmission in Florida that hasn't been previously analyzed. Furthermore, these studies provide the groundwork for better understanding of why there is a disproportionate number of horse and human cases of EEEV in Florida than in any other state.

Spatial epidemiological studies which assume perfect health status information can be biased if imperfect diagnostic tests have been used to obtain the health status of individuals in a population. This study investigates the effect of diagnostic misclassification on the spatial statistical methods commonly used to analyze regional health status data in spatial epidemiology. The methods considered here are: Moran's I to assess clustering in the data, a Gaussian random field model to estimate prevalence and the range and sill parameters of the semivariogram, and Kulldorff's spatial scan test to identify clusters. Various scenarios of diagnostic misclassification were simulated from a West Nile virus dead-bird surveillance program, and the results were evaluated. It was found that non-differential misclassification added random noise to the spatial pattern in observed data which created bias in the statistical results. However, when regional sample sizes were doubled, the effect from misclassification bias on the spatial statistics decreased.

Haemoglobinopathies, which include sickle-cell anaemia (SCA) and α- and β-thalassaemia, represent some of our few unequivocal examples of human evolution. The underlying genetic mutations reflect a recurring adaptation against one of the biggest infectious disease killers of humans, Plasmodium falciparum malaria. Inheritance of one copy of a sickle-cell or thalassaemic allele leads to protection against death from malaria, while two copies can result in a severe blood disorder. As a result, haemoglobinopathies have risen in frequency through balancing selection and pose a significant public health problem in parts of the world with a history of malaria transmission. Their study therefore lies at the interface between evolutionary biology and public health. In this thesis, I explore different aspects of the epidemiology and population genetics of haemoglobinopathies around the world. Using pre-existing epidemiological data, statistical and geostatistical methods and Geographic Information System tools, I develop detailed evidence-based maps of the α-thalassaemia allele frequency distribution and genetic diversity in Southeast Asia and sickle-cell allele frequency in India. Pairing these with birth data, I generate sub-national estimates of the number of newborns born with severe forms of α-thalassaemia and SCA in Thailand and India, respectively, together with uncertainty estimates. In addition, I use a flexible population genetic simulation model to explore evolutionary explanations for the contrasting spatial haplotype patterns observed for SCA and the severe form of β-thalassaemia (β0-thalassaemia) in sub-Saharan Africa and the Middle East, and resurrect a 20-year old question surrounding the genetic origin of sickle-cell. Understanding the fine-scale geographical heterogeneities in the distributions of malaria-protective haemoglobinopathies is critical for addressing basic science questions and applied public health queries. Working at the interface between evolutionary biology and public health has provided me with the opportunity to build a more complete overview of the neglected increasing public health burden that this group of human disorders represents.

Orientador: Carlos Magno Castelo Branco Fortaleza / Resumo: Microrganismos multidroga-resistentes (MDR) tem se disseminado em hospitais, e apresentam potencial de transmissão em serviços não hospitares e saúde e mesmo na comunidade. Partindo deste princípio, desenvolvemos um estudo de metodologia mista de base individual e ecológica. O estudo foi conduzido no Hospital Estadual Bauru e distribuiu-se, resumidamente, em três etapas: (i) identificação de preditores para infecções por MDR com origem na comunidade (Community-onset [CO]-MDR); (ii) georreferenciamento e estudo da proximidade de domicílios de sujeitos com CO-MDR e outros que receberam alta hospitalar enquanto carreados de agente MDR (Retornando à Comunidade, RC) a serviços de saúde de complexidades diversas. No primeiro estudo, de delineamento caso-controle, identificamos internação prévia (com mais de seis meses de antecedência do diagnóstico) e doença pulmonar como fatores de risco para CO-MDR. Esses fatores apresentaram diferenças entre Gram-positivos e Gram-negativos. Já no segundo estudo, foi possível constatar a proximidade de domicílios de sujeitos dos grupos CO e RC com serviços de saúde, fossem eles Unidades Básicas (UBS), hospitais ou ambulatórios de especialidade. Em geral, quando comparados ao Methicillin-resistant Staphylococcus aureus (MRSA), outros agentes apresentavam maior dispersão em torno dos serviços de saúde. Os dados sugerem, ainda que indiretamente, a possibilidade de transmissão de microrganismos multidrogra-resistentes em espaços comunitários ou de as... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Multidrug-resistant microorganisms (MDR) have spread in hospitals, and have a potential for transmission in non-hospital healthcare settings and even in the community. This study was conducted in Bauru State Hospital and was distributed in three stages: (i) identification of predictors for community-onset MDR infections (CO-MDR); (ii) georeferencing and study of the proximity of CO-MDR and others who are discharged while harboring MDR (Returning to the Community, RC) to health services of diverse complexities.In the first case-control study, we identified previous hospitalization (with more than six months of the diagnosis) and pulmonary disease as risk factors for CO-MDR. These factors showed differences between Gram-positive and Gram-negative pathogens. In the second study, it was possible to verify the proximity of households of CO and RC subjects to health services, whether they were Basic Units (BHU), hospitals or specialty outpatient clinics. In general, when compared to Methicillin-resistant Staphylococcus aureus (MRSA), other agents were more dispersed around health services. The data suggest, albeit indirectly, the possibility of transmission of multidrug-resistant microorganisms in community or non-hospital care spaces. / Mestre