Wednesday 23nd November 2016 - Conference
Track 2: Animal Health and Disease (Chair: Naomi Cogger)
Keynote: Mapping the risk of infectious diseases
Dr Nick Golding
Coordinating global public health strategy relies heavily on broad-scale assessments of the risks posed by different infectious diseases. Encoding these risk assessments as high-resolution disease risk maps enables us to answer several important policy questions, including: which diseases pose the greatest risk in a given place, and which diseases threaten the most people globally. Answers to these questions can guide global health policy makers in deciding on which diseases and locations to target their sparse resources.
In highly developed countries, data from national health systems provide detailed and reliable information on disease risk, transmission intensity and how these change over time. However for most of the rest of the world, and for most infectious diseases, data on disease occurrence is very scarce, let alone data on transmission intensity. Spatial variation in reporting of cases is often more related to differences in access to healthcare, and diagnostic facilities, than the true variation in disease risk.
Disease risk mapping therefore uses statistical models to account for sources of bias in order to make quantitative predictions about disease risk at broad spatial scales. These predictions rely on detailed spatial information on environmental, demographic and socioeconomic conditions in addition to the locations of disease reports. Modern disease risk mapping borrows methods from the fields of ecology, machine learning and geostatistics to make the most accurate possible predictions of disease risk, from limited data.
Recent applications of disease risk mapping have quantified the changing risk and burden of malaria, and identified regions where multiple diseases could be controlled using the same interventions. Disease risk maps have also highlighted areas at risk from emerging pathogens such as avian influenza, Ebola and Zika.
As in other areas of research, recent years have seen a rapid increase in availability of, and access to data. Automated online disease reporting, high-throughput genome sequencing and tracking of human movements all provide massive data sources that can help map disease risk. Developing methods that can incorporate these data streams will be crucial to improving disease maps and anticipating novel disease outbreaks.
Biography
Nick Golding is a McKenzie fellow in the Department of BioSciences at the University of Melbourne. Nick trained as an ecologist, drifted into epidemiology, and somewhere along the way became a statistician. He moved to Melbourne earlier this year and is focused on developing new modelling approaches and scientific software to map the distributions of both species and diseases.
In highly developed countries, data from national health systems provide detailed and reliable information on disease risk, transmission intensity and how these change over time. However for most of the rest of the world, and for most infectious diseases, data on disease occurrence is very scarce, let alone data on transmission intensity. Spatial variation in reporting of cases is often more related to differences in access to healthcare, and diagnostic facilities, than the true variation in disease risk.
Disease risk mapping therefore uses statistical models to account for sources of bias in order to make quantitative predictions about disease risk at broad spatial scales. These predictions rely on detailed spatial information on environmental, demographic and socioeconomic conditions in addition to the locations of disease reports. Modern disease risk mapping borrows methods from the fields of ecology, machine learning and geostatistics to make the most accurate possible predictions of disease risk, from limited data.
Recent applications of disease risk mapping have quantified the changing risk and burden of malaria, and identified regions where multiple diseases could be controlled using the same interventions. Disease risk maps have also highlighted areas at risk from emerging pathogens such as avian influenza, Ebola and Zika.
As in other areas of research, recent years have seen a rapid increase in availability of, and access to data. Automated online disease reporting, high-throughput genome sequencing and tracking of human movements all provide massive data sources that can help map disease risk. Developing methods that can incorporate these data streams will be crucial to improving disease maps and anticipating novel disease outbreaks.
Biography
Nick Golding is a McKenzie fellow in the Department of BioSciences at the University of Melbourne. Nick trained as an ecologist, drifted into epidemiology, and somewhere along the way became a statistician. He moved to Melbourne earlier this year and is focused on developing new modelling approaches and scientific software to map the distributions of both species and diseases.
Avian Influenza – Exposure and Consequence Assessments for Australian commercial chicken farms
Keywords: Avian influenza, poultry, scenario trees
Author: Dr Angela Scott
Affiliation: University of Sydney
Email address: Angela.scott@sydney.edu.au
Lay summary:
The risk of avian influenza virus to Australian commercial egg and meat chicken farms was assessed in this study. Specifically, the probability a chicken will be exposed to the virus and the probability the virus will spread between sheds on a farm and between different farms were estimated. Methods to reduce the risks were also explored.
Abstract:
The purpose of this study was to investigate pathways of introduction and spread of avian influenza (AI) virus to Australian commercial egg and meat chicken farms, to estimate the probabilities of these pathways occurring and to identify biosecurity practices that influence these probabilities.
Exposure and spread assessment models were developed using the World Organisation of Animal Health (OIE) methodology for risk analysis (OIE 2010). Input parameters for these models were estimated from a cross-sectional study on commercial chicken farms, an expert opinion workshop and scientific literature. All sectors of the commercial chicken industry present in Australia were included in the study, where a commercial farm was defined as having more than 1,000 and 50,000 chickens for layer and meat chicken farms respectively The exposure assessment considers all potential pathways by which Australian commercial chickens can be exposed to AI virus. The spread assessment considers pathways by which AI virus can spread between sheds on the same farm and to other farms. The pathways were portrayed using scenario trees (Martin, Cameron et al. 2007) and the probabilities estimated using Monte Carlo stochastic simulation modelling using @RISK 6.0 software (Palisade, USA). Each simulation consisted of 50,000 iterations sampled using the Latin hypercube method with a fixed random seed of one. A sensitivity analysis will be conducted to evaluate the influence of some input parameters on the outcome probabilities.
According to the models, the probability of AI virus exposure to Australian commercial chickens is very low. Across the five farm types (non-free range meat chicken, free range meat chicken, layer cage, layer barn and layer free range farms), an average median probability of exposure of 0.00042 (5% and 95%, 0.00025-0.0007) was obtained. The farm types with the highest and lowest probabilities of exposure were free range layer and barn layer farms respectively. The probability of low pathogenic AI (LPAI) and high pathogenic AI (HPAI) virus spread across all five farm types was similar between sheds and between farms. Average median probabilities of 0.042 (0.012-0.09) and 0.035 (0.0089-0.077) were obtained for LPAI spread between sheds and between farms respectively. Spread of HPAI virus is less likely to occur with an average median probability of 0.00016 (6.84E-06-0.0014) for both between sheds and to other farms. The small differences in probability of spread between farm types will be discussed.
This study quantifies the probabilities of exposure and spread of AI virus for Australian commercial chicken farms and compares this probability between the different farm types. Although exposure is most influenced by the housing conditions (free-range vs. conventional), spread is most influenced by the production type (meat or eggs). Improvement of on-farm biosecurity practices could minimize the risk of both exposure to AI viruses from wildlife and spread of the virus to other farms and subsequently the risk of the occurrence of an HPAI outbreak. These findings will be used to inform biosecurity guidelines for the Australian poultry industry.
The risk of avian influenza virus to Australian commercial egg and meat chicken farms was assessed in this study. Specifically, the probability a chicken will be exposed to the virus and the probability the virus will spread between sheds on a farm and between different farms were estimated. Methods to reduce the risks were also explored.
Abstract:
The purpose of this study was to investigate pathways of introduction and spread of avian influenza (AI) virus to Australian commercial egg and meat chicken farms, to estimate the probabilities of these pathways occurring and to identify biosecurity practices that influence these probabilities.
Exposure and spread assessment models were developed using the World Organisation of Animal Health (OIE) methodology for risk analysis (OIE 2010). Input parameters for these models were estimated from a cross-sectional study on commercial chicken farms, an expert opinion workshop and scientific literature. All sectors of the commercial chicken industry present in Australia were included in the study, where a commercial farm was defined as having more than 1,000 and 50,000 chickens for layer and meat chicken farms respectively The exposure assessment considers all potential pathways by which Australian commercial chickens can be exposed to AI virus. The spread assessment considers pathways by which AI virus can spread between sheds on the same farm and to other farms. The pathways were portrayed using scenario trees (Martin, Cameron et al. 2007) and the probabilities estimated using Monte Carlo stochastic simulation modelling using @RISK 6.0 software (Palisade, USA). Each simulation consisted of 50,000 iterations sampled using the Latin hypercube method with a fixed random seed of one. A sensitivity analysis will be conducted to evaluate the influence of some input parameters on the outcome probabilities.
According to the models, the probability of AI virus exposure to Australian commercial chickens is very low. Across the five farm types (non-free range meat chicken, free range meat chicken, layer cage, layer barn and layer free range farms), an average median probability of exposure of 0.00042 (5% and 95%, 0.00025-0.0007) was obtained. The farm types with the highest and lowest probabilities of exposure were free range layer and barn layer farms respectively. The probability of low pathogenic AI (LPAI) and high pathogenic AI (HPAI) virus spread across all five farm types was similar between sheds and between farms. Average median probabilities of 0.042 (0.012-0.09) and 0.035 (0.0089-0.077) were obtained for LPAI spread between sheds and between farms respectively. Spread of HPAI virus is less likely to occur with an average median probability of 0.00016 (6.84E-06-0.0014) for both between sheds and to other farms. The small differences in probability of spread between farm types will be discussed.
This study quantifies the probabilities of exposure and spread of AI virus for Australian commercial chicken farms and compares this probability between the different farm types. Although exposure is most influenced by the housing conditions (free-range vs. conventional), spread is most influenced by the production type (meat or eggs). Improvement of on-farm biosecurity practices could minimize the risk of both exposure to AI viruses from wildlife and spread of the virus to other farms and subsequently the risk of the occurrence of an HPAI outbreak. These findings will be used to inform biosecurity guidelines for the Australian poultry industry.
Psychosocial impact of a Foot-and-Mouth Disease Outbreak: A systematic mapping of the literature
Keywords: FMD, biosecurity, consequence, review
Author: Dr Naomi Cogger
Affiliation: Massey University
Email address: n.cogger@massey.ac.nz
Lay summary:
A Foot-and-Mouth disease outbreak in New Zealand would be a disaster unlike any other that the people and communities have faced. To better prepare for recovery we need to understand the full range of impacts on the economy and society. This paper focuses on better understanding the pyscho-social impacts of an outbreak to assist in the development of strategies to enhance resilience.
Abstract:
Agriculture and processing of products accounts for 8% of New Zealand’s GDP and over half of our export earnings which makes the New Zealand people and society are vulnerable to the risk associated with the biological hazards, such as foot-and-mouth (FMD). While the likelihood of an FMD incursion is extremely low the consequences to New Zealand way of life would be extreme. Recent modelling predicted that in the year following the incursion on-farm employment in meat and dairy would fall by over 20% and employment in meat and dairy processing would drop by 79% and 89%, respectively. The impact of high unemployment combined with wide-spread culling of livestock is likely to result in significant psychosocial impacts across New Zealand. The aim of this study was to conduct a systematic mapping of the literature surrounding of wide scale biosecurity events to better understand the range of psychosocial consequences of an outbreak. While systematic reviews have been used for some time in medicine this is the first time the approach has been applied to answer questions around how people experience events. The FMD specific events include the review are outbreaks in the United Kingdom, Europe, Korea and Japan. The non-FMD specific events were the Classical Swine Fever in Netherlands, the Equine Influenza outbreak in Australia. Researchers searched the peer review and grey literature (e.g. government reports) to identify potential papers. For each paper the follow was recorded: the event, the nature of the field of research (e.g. observational, qualitative), group studied and specific psychosocial impacts. This paper will present the results of the systematic mapping.
A Foot-and-Mouth disease outbreak in New Zealand would be a disaster unlike any other that the people and communities have faced. To better prepare for recovery we need to understand the full range of impacts on the economy and society. This paper focuses on better understanding the pyscho-social impacts of an outbreak to assist in the development of strategies to enhance resilience.
Abstract:
Agriculture and processing of products accounts for 8% of New Zealand’s GDP and over half of our export earnings which makes the New Zealand people and society are vulnerable to the risk associated with the biological hazards, such as foot-and-mouth (FMD). While the likelihood of an FMD incursion is extremely low the consequences to New Zealand way of life would be extreme. Recent modelling predicted that in the year following the incursion on-farm employment in meat and dairy would fall by over 20% and employment in meat and dairy processing would drop by 79% and 89%, respectively. The impact of high unemployment combined with wide-spread culling of livestock is likely to result in significant psychosocial impacts across New Zealand. The aim of this study was to conduct a systematic mapping of the literature surrounding of wide scale biosecurity events to better understand the range of psychosocial consequences of an outbreak. While systematic reviews have been used for some time in medicine this is the first time the approach has been applied to answer questions around how people experience events. The FMD specific events include the review are outbreaks in the United Kingdom, Europe, Korea and Japan. The non-FMD specific events were the Classical Swine Fever in Netherlands, the Equine Influenza outbreak in Australia. Researchers searched the peer review and grey literature (e.g. government reports) to identify potential papers. For each paper the follow was recorded: the event, the nature of the field of research (e.g. observational, qualitative), group studied and specific psychosocial impacts. This paper will present the results of the systematic mapping.
Horse Owners in Hendra Hot Spots - Attitudes, Perceptions and Practices in Response to Hendra Virus
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