Dr. Shweta Bansal - Modeling Emerging Disease in the US Swine Herd
•Transferir como PPTX, PDF•
1 gostou•682 visualizações
Modeling Emerging Disease in the US Swine Herd - Dr. Shweta Bansal, PhD, Assistant Professor, Department of Biology at Georgetown University, from the 2015 NIAA Annual Conference titled 'Water and the Future of Animal Agriculture', March 23 - March 26, 2015, Indianapolis, IN, USA. More presentations at http://www.trufflemedia.com/agmedia/conference/2015_niaa_water_future_animal_ag
Recomendados
Recomendados
Mais conteúdo relacionado
Mais procurados
Mais procurados (20)
Destaque
Destaque (9)
Semelhante a Dr. Shweta Bansal - Modeling Emerging Disease in the US Swine Herd
Semelhante a Dr. Shweta Bansal - Modeling Emerging Disease in the US Swine Herd (20)
Mais de John Blue
Mais de John Blue (20)
Último
Último (20)
Dr. Shweta Bansal - Modeling Emerging Disease in the US Swine Herd
- 1. Modeling Emerging Disease in the U.S. Swine Herd Shweta Bansal Assistant Professor of Biology, Georgetown University Faculty Fellow, National Institutes of Health
- 2. U.S. Agricultural Disease Preparedness “New, emerging, and evolving pathogens are a challenge to animal health and production. Although improved management and better diagnostic and prophylactic tools are now available, emerging diseases and changes in animal management will always create new opportunities for old diseases.” “Successfully overcoming these challenges requires a U.S. agricultural research enterprise that harnesses the newest advances from across the physical and life sciences, builds on a broader public investment in science and technology, and then applies these discoveries to the specific challenges of agriculture.”
- 3. U.S. Livestock Diseases Mole, Nature (2013) Current outbreak: PEDV Potential threat: FMD Zoonotic impact: Influenza
- 4. U.S. Swine Industry The U.S. swine industry is highly connected and intensely aggregated U.S. Swine Movement Shields & Mathews, USDA (2002) U.S. swine production density (USDA Agricultural Census)
- 5. Linking Disease to Livestock Movement Increased livestock movement Increased prevalence of bTB Gilbert et al, Nature (2005) Shirley & Rushton, Epidemiology and Infection (2005) High animal throughputs High risk of infection
- 6. Role of Disease Modeling During Epidemic Assess means of spread Predict future spread Design control Endemic Stage Disease Management Prior to Emergence Surveillance Preparedness RISK ANALYSIS DETERMINE TRANSMISSION PATHWAYS CONTAIN SPREAD
- 8. PEDV Spread in U.S. Data: USDA/AASV; O’Dea et al (2014)
- 9. Open Questions About PEDV Is swine movement responsible for propagating the infection? What other factors increase risk of infection? What was the source of the infection in the U.S.? Important for surveillance, control and preparedness
- 10. Role of Swine Movement in PEDV Is swine movement responsible for propagating the infection? Evidence of spread by trailers found (Lowe et al, 2014) Many states have responded by limiting imports from PEDV-free premises However, mode of transmission remains open to speculation
- 11. Role of Movement in PEDV Spread O’Dea, Snelson, Bansal (2015, In Review)
- 12. What is the impact of movement direction? Evidence for trailer-based transmission
- 13. Source of Infection What was the source of the infection in the U.S.? First cases detected in OH and IN but entry unknown
- 14. Source of PEDV in US Do arrival times of PEDV in different states provide information on origin? Arrival times of PEDV Dec 2013April 2013
- 15. Estimation of Likely Disease Source Input: probability of disease spread between areas, genetic data, time of disease detection, model of detection error Output: approximate likelihood of each state as source Source: IN Source: KS Distribution of arrival times from IN OH
- 17. Movement Restrictions to Control PEDV Self-imposed restrictions Optimized restrictions
- 18. Regionalization as a Tool for Livestock Disease Control Complex network community structure analysis
- 19. Large-Scale U.S. Regionalization Density- dependent transmission Frequency- dependent transmission
- 20. Feasibility of Regionalization Optimized for feasibility and efficiency 1) Spatial cohesiveness 2) Spatial contiguity
- 21. Assessing Risk of Emergence
- 22. Quantifying Risks for Swine Disease Emergence Swine density Live swine movement + + Importation of swine + Shipment of pork + animal feed
- 23. Quantifying Risks for Swine Disease Emergence Network-based model of risk for swine disease emergence Identification of hotspots for introduction and spread
- 24. Summary Network modeling swine industry connectivity PEDV: identification of disease transmission pathways and sources Crucial for identifying biosecurity gaps and preventing future introductions Design of control strategies for containing spread Optimal strategies adapted for feasibility and business continuity Risk analysis for swine disease emergence Improved biosurveillance and preparedness Increased access to global trading partners
- 25. Acknowledgements Group Eamon O’Dea Ian Carroll Madeline Campbell Collaborators: Harry Snelson (AASV) Ryan Miller (USDA APHIS) Jason Lombard (USDA APHIS) Katie Portacci (USDA APHIS) Colleen Webb (CSU/RAPIDD) Michael Buhnerkempe (UCLA/RAPIDD) James Wood (Cambridge) John Korslund (USDA APHIS) Laura Pomeroy (OSU) Bryan Grenfell (Princeton) Support: Department of Homeland Security Foreign Animal Disease Modeling Program RAPIDD Program of the Science & Technology Directorate of the DHS and the Fogarty International Center, National Institutes of Health. Images from Wikimedia Commons. Michigan Farm Bureau, USDA, BBC News
- 26. Modeling Emerging Disease in the U.S. Swine Herd Shweta Bansal Web: http://bansallab.com Twitter: @bansallab
Notas do Editor
- First, I want to thank Harry for this opportunity to come speak to all of you today. What I’d like to do is spend about 15 minutes to give you a flavor of the type of research that we do in my group on emerging diseases in the U.S. Swine Herd. And then I’d like to spend the rest of the time answering questions and in discussion with all of you. I’ll also be around for the rest of the afternoon and would be eager to continue discussions if any of you might be interested.
- No one here of course needs to be reminded of the importance of emerging diseases of agricultural importance, but as you’ll all know, the PCAST report out at the end of 2012 on US agricultural preparedness lists pests and pathogens as one of the most pressing challenges to American agriculture, and highlights that successfully overcoming this challenge requires advances from the physical and life sciences, with a broad investment in science with specific applications to challenges in agriculture. And with these advances in the physical and life science, mathematical models can play an important role at the interface by providing a quantitative framework for characterizing disease dynamics and planning the prevention and of agricultural infectious diseases.
- And emerging livestock diseases are certainly at the forefront of our attention curretnly. On May 10, 2013, a novel case of a lethal swine coronavirus, known as porcine epidemic diarrhea (PED) virus, was confirmed in Iowa, the leading producer of U.S. pork. This virus spread rapidly throughout the country reaching the 20 states that are responsible for 95% of pork production within 8 months, and has had a significant impact on the pork industry. But PEDV of course doesn’t represent a worse-case scenario The greatest infectious disease threat to U.S. livestock populations is likely from foot and mouth disease (FMD) virus, which has spread with catastrophic consequences across many countries. The largest outbreak to date occurred in 2001 in the United Kingdom and led to a culling of 8% of all U.K. livestock farms, with losses to agriculture and the food chain of over $4 billion. Influenza… liberal use of the term swine flu in this context led to severe economic impact on the swine industry with 27 countries blocking imports from the US, domestic hog demand decreasing, and hog futures dropping significantly. And of course in the context of a zoonotic, there can be some dire consequenes on public health
- In veterinary epidemiology, we are used to think about disease risk and spread in terms of density… But I will argue today that it is the movement of animals that really highlights that grave risks that pose to the US livestock industry. And in the swine movement is a key component of a highly specialized industry, and is unlikely to change. Today, the U.S. swine industry is geographically distributed and intensely connected, 1 out of every 4 hogs born in a year cross state borders, with particular concentrations in some. And this map highlights that high aggregation along with the long distance movements. It is movement of livestock, summarized through this map, that takes local infectious disease problems to national scales at very rapid time scales. And this is what my group’s research focuses on. We take the idea of connectivity and explicitly incorporate it into developing models of emerging disease emergence, spread and contorl.
- In the U.K., both epidemiological data and molecular evidence have shown that increased livestock movement is associated with higher prevalence of bovine tuberculosis in cattle and the establishment of novel strains.. When infection is being disseminated through animal movements, there is a particularly high risk of infection reaching premises with high animal throughputs such as livestock markets, which can amplify disease spread, as was observed in the U.K. FMD outbreak in 2001. In this case, infected sheep were sold at a local market, and sold again two days later at a larger market; by the time the first case was identified a week later, 30-79 farms had been infected across the U.K.
- Infectious disease modeling provides a systematic and quantitative approach allows us to consider “what-if” scenarios in a system where we can’t ethically carry out experiments; and allows us to test hypotheses about driving factors in a system. And modeling can play many roles at different stages of a disease’s life. During an outgoing disease outbreak, we can use models to assess how a pathogen is spreading, predict future spread, and design control strategies to stop or slow down the spread. And after an epidemic is over, and a disease has become endemic or chronic in a population, models can guide disease management plans Before a disease has emerged in a population, modeling can be used to optimize surveillance and preparedness What I’d like to do today is talk about examples from my work of each of these uses.
- Applying this to the case of the emerging livestock disease outbreak we currently have in the US, which is of course PEDV.
- Emerging in the U.S. in April 2013, the PEDV outbreak reached the 20 states responsible for over 95\% of U.S. pork production in as little as eight months. The virus acutely infects the intestine and causes severe diarrhea and vomiting. Farms experiencing outbreaks have suffered 90 % and higher losses of unweaned pigs, and the time it takes for a farm to return to stable production is highly variable, leading to great expenses in infection control costs and production losses alike. The maps we see here show on the left the annual swine inventory levels in the thousands of head, and on the right, the cummulative number of laboratory-confirmed cases of PEDV in a state (up to earlier this year).
- Important for insights into surveillance control and preparedness for both the current outbreak, and more importantly future outbreaks.
- Small scale studies
- Question is: is there a role for livestock movement in the propagation of PEDV throughout the country? Our hypothesis here is that if there is flow of animals between two states, there should be a coupling between the disease dynamics in those states (and the stronger the flow, the stronger the coupling) Based on information on swine flows or movement throughout the country (which as you can see is highly focused in the Midwest with a strong link to North Carolina), as well as weekly lab-confirmed PEDV cases at the state level, we answer this question by considering the correlation between swine flows from one state to another and the similarity in the time series of infection cases in those two states. And this map here shows us the results of that cross-correlation analysis, with an average correlation of 0.36 These correlations are driven primarily by the presence of a small wave of cases early in the outbreak and a much larger wave towards the end of the time series (as can be seen for states like MN, IL, IA)). And secondly by states like Kansas and Oklahoma which share a distinctive period of high cases in the middle of the time series and fairly large flows. This provides some evidence, but not strong evidence for the relationship between swine movement and similarity in disease dynamics. But this doesn’t necessarily mean that movement is not a transmission pathway for PEDV.
- Locating a disease source is like finding a phone’s location.
- This optimized control strategy may not be practical to implement, but additional constraints in implementation can be taken into account in a model.
- I’ve talked about the importance of livestock contact structure as this is really the backbone over which any potential disease outbreaks would spread, and the use of networks as an intuitive modeling technique to represent this contact structure. On this front, we’ve been focusing our other efforts on extending the knowledge that we’re gaining of the structure of US swine movement to questions of disease control and management in the context of an emerging livestock disease like PEDV.
- Question: Do we have enough data? There are two things you’ll never catch a modeler saying no to: more funding and more data. We can always do more with more data, but modeling also provides us in some cases the techniques to answer some questions with the little data that is available (and you see evidence of that in what I’ve talked about). But what limited data does is that it constrains the space of questions. What more data would do for us is that it would let us provide better answers with more confidence to the existing questions that we’re already asking, and more importantly answer a wider range of questions. One of the things we are trying to do with our work is clearly characterize which policy and decision questions can be answered with how much and which quality of data..