Epidemiology of African Swine Fever: A prerequisite to control
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Epidemiology of African Swine Fever: A prerequisite to control

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Presentation by Richard Bishop, Edward Okoth, Jocelyn Davies at the 'BecA-CSIRO Partnership Review meeting' held on 10-14 September 2012 at ILRI Nairobi, Kenya Campus.

Presentation by Richard Bishop, Edward Okoth, Jocelyn Davies at the 'BecA-CSIRO Partnership Review meeting' held on 10-14 September 2012 at ILRI Nairobi, Kenya Campus.

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  • Include your partner logos only on the 1st slide and last slide
  • Value chain diagram
  • Model 1 is not likely to address equity issues at the farm level and require high technology approachesThe second approach is a more pro-poor approach that can also have a global impactWorld pig keeping model is the smallholder type of production
  • This is a DNA virus that is very stable and persistent in the environment

Epidemiology of African Swine Fever: A prerequisite to control Epidemiology of African Swine Fever: A prerequisite to control Presentation Transcript

  • Epidemiology of African Swine Fever: A prerequisite to controlRichard Bishop, Edward Okoth,Jocelyn Davies10th September 2012
  • Outline• Background• Project objectives & partnerships• Progress on objectives 1. Genotyping and whole genome sequencing 2. Evaluate rapid ASF diagnosis methods 3. Understand ASF epidemiology in the field 4. Assess livelihood impact of ASF 5. Identify feasible biosecurity measures 6. Understand social networks relevant to ASF• Path to impact• Lessons
  • Global trends in pork production Half of the worlds pork is eaten in ChinaAll of Africa at 1500 U.S. Census Bureau, Statistical Abstract of the United States: 2012
  • Pork production in AfricaAfrica’s pig populationestimated at 25 Million
  • ‘African livestock revolution’ The pig population in Africa increased 284% during the 20year period 1980–1999, far more than for any other livestock species. The trend continues.Global projections of total demand for pork: PORK Consumption1 1993 2020 Developed Region 38 41 Developing Region 39 81 1 million tonnes
  • Pigs in smallholder production systems Pigs are important for both food and income to smallholder farmers in Africa Market demand can be exploited by smallholder pig keepers to increase incomes.
  • Gender On smallholder farms, pigs are almost always womens business.
  • Potential income generation Average 10 piglets x$ 3 farrowings/year @ USD 12/piglet = USD 360/year = 1 year secondary school fees
  • Value chain livelihoods e.g. Average net annual income for butchers in western Kenya - USD 887 Profit per pig - USD 3.80 (Kagira et al.2010)
  • Pork consumption in villages Pork is easy for village households to access regularly, compared to beef. One pig provides a manageable quantity of meat for a day’s trade in a village market.
  • Constraints to smallholder pig production Communication Customer Africa s Breed n Swine Constraints Fever Housing to pig Trader production s Other pig Feed health problems Roads
  • Why research African Swine Fever? • ASF causes heavy losses to farmers. Almost all pigs that catch ASF die, fast. • ASF is a constraint to incomes and food security among African smallholder producers. • ASF also poses a global food security threat.
  • African Swine Fever virus • A DNA virus that is very stable and persistent in the environment • No vaccine exists • No effective treatment or cure • Biosecurity is the main prevention strategy • Culling (stamping out) is the main control strategy
  • ASF global spread Li sCuba 1971, 1980 bDom. Rep 1978 oHaiti 1978 n 195 7, 6 0 Brasil 1978Related ASF-West Africa viruses Georgia June 2007 1957 from Angola: genotype I to Lisbon, now spreading in Europe and central & south America. 2007 from Eastern Africa: genotype II to Caucasus Region, now spreading in Ukraine.
  • ASF global risk ASF spread in eastern Russia poses a big food security risk to Europe and Asia.
  • Project objectives1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess livelihood impact of ASF5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
  • Collaborations and partnershipsCollaborations• FAO, AU-IBAR, CISA-INIA, Makerere University, University of Pretoria, Royal Veterinary College London, University of Nairobi, Swedish Veterinary Institute, University of EdinburghImplementation partners (National Institutions)• DVOs, MAAIF-Uganda, MLD-Kenya, LANAVET- Cameroon
  • Implementation partnershipsKenya MLD & Uganda MAAIF Implementation partnerships: DVS Kenya & MAAIF Uganda Jennifer Swara, farmer in Busia area Kenya with Project researchers: • Dr Jacqueline Kasiiti, Kenya Ministry of Livestock Development • Dr Noelina Nantima, Uganda Ministry of Agriculture, Animal Industries & Fisheries. Also links to CGIAR CRP 3.7 Pig value chains, Uganda
  • Multi-disciplinary multi-lingual team At ILRI Nairobi, training and team building, May 2012 Animal health, virology, veterinary epidemiology, mathematics, modeling, livestock economics, social science, systems science, geography, animal handling.
  • Capacity building• Senior scientist training: Dr Charles Masembe, Makarere University Uganda; Dr Abel Wade, LANAVET, Cameroon• Acquisition of technical skills: Ms Cynthia Onzere, Project lab mgr• 3 associated PhDs – Epidemiological modeling: Mike Barongo, Uni of Pretoria – Social & economic factors in AFS control: Dr Noelina Nantima, Makerere University – Role of social networks in AFS transmission: Dr Jacqueline Kasiiti, University of Nairobi• 2 associated Masters through analysis of pig samples – Tick borne infections: Dr Selestine Naliaka, University of Nairobi – Co-infection load: Dr Beatrice Abutto, Royal Vet College London• Smallholder awareness of ASF & biosecurity
  • CSIRO role in project• Planning & mentoring• Lead role in social science integration• Co-supervision of 2 PhDs• GIS and spatial analysis support• Database & communications supportDr Jocelyn Davies, geographerMs Tracey May, GIS and data base expertiseDr Yiheyis Maru, social-economic systems scientist & veterinarianMs Larelle McMillan, communications
  • Project objectives1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess livelihood impacts of ASF5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
  • Why do genotyping and whole genome sequencing? • There are many different genotypes of the virus based on analysis of three marker genes • The genotype can be used to track whether two or more recent outbreaks might be connected • The genotype can also be used to identify origin of outbreaks outside Africa (e.g. 2007 Caucasus outbreak was traced to South East Africa) • Whole ASFV genomes from pigs with known clinical outcomes allow genotype-phenotypeAutomated sequencer correlations • The overall level of diversity has implications for the feasibility of developing a vaccine that is effective in the field.
  • Genomics work flow and outputs BioinformaticsField sampling BecA laboratory analysis research Publicly available Genotyping Annotated virus Virus Isolation and genotypes information genome sequencing of regional isolates
  • Research progress in genomics Our analysis has shown that genotype IX viruses in East Africa from 2005-2006 outbreak are in a distinct lineage that is close to genotype X, another East African genotype. An important finding for potential vaccine development.Tree diagram showing virusrelationships
  • Senior scientist training at BecA-ILRIDr Charles Masembe, Makerere University Challenge Fund Fellow + project researcher: Whole genome shotgun 454 sequencing to characterize Ugandan ASF viruses from virus infected pig tissues. Result: p72 gene sequence genotype IX is similar to Kenyan viruses Bonus Finding! Ndumu virus: potentially human infective virus, previously known only from mosquitoes, discovered in domestic pig genome . (Masembe et al., in press, Virology Journal)
  • Kenya outbreaks: Project genomic studies We established that genotype IX virus had spread in only 2 months from Uganda border to Kenya coast. As a result of our work, Kenya coast is now recognised as an ASF risk area. Coast outbreak
  • Cameroon : Project genomic studies Dr Abel Wade from LANAVET (Cameroon) has been trained in CISA-INIA Spain to analyse samples from recent Cameroon outbreaks.
  • Project objectives1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess role of pigs in livelihoods & impact of ASF5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
  • Why evaluate rapid ASF diagnosis methods? Kenya and Uganda veterinarians at Project workshop in Kisumu, July 2011, said: • Testing labs are distant and hard to access. • It takes many weeks to get a confirmed ASF diagnosis. • The time lag hampers action to contain ASF outbreaks.
  • Progress: Evaluate rapid diagnosis methodsHere isthe LabField laboratory test run from a basic set-up (i.e. table) or back of a vehicle BSL-2 lab BSL-3 lab
  • Progress: Evaluate rapid diagnosis methods Three DNA extraction methods have been tested Dr Neil LeBlanc, Swedish Veterinary Institute
  • Progress: Evaluate rapid diagnosis methodsField lab tests have screened for ASF virus and prevalence ofother pathogens.Results replicated in ILRI conventional labs in Busia & Nairobi. “Best practice for rapid remote area testing” “Applicable to many health care needs” Dr Neil LeBlanc Swedish Veterinary Inst
  • Project objectives1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess livelihood impact of ASF5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
  • Why try to understand ASF epidemiology in the field? ASF virus can spread to healthy Swill pigs in many different ways: Direct • From wild pigsFeces conta ct • From ticks • From infected pork fed to pigs • From contact with sick pigs or their feces We don’t understand what Bus Tick h pathways are most important. s pigs Warthogs
  • Virus prevalence is variable and role of carrier pigs is poorly understood In Homa Bay, many pigs Busia carry genotype X ASF virus but there are no ASF outbreaks (Okoth 2012) Homabay Busia (100km away) has frequent ASF outbreaks caused by genotype IX. • Are there also carrier pigs in Busia? • What triggers outbreaks?
  • We don’t understand what roles people play in transmission Virus What do people do that causes ASF toSOURCES TRANSMISSION spread? Why? PATHWAYS Carcasses People What would it take for people to behaveUndercooked meat Pigs differently? Swill VehiclesFaeces Scavengers Pig immune Carrier Wildlife Reservoirs system PigSlaughter waste Nutrition Ticks (Vector) Co-infection ENVIRONMENT load Parasites Immune Vet services Pig Susceptible Infected Pig Pig Recovered Pig Dead Pig
  • Field study will inform modeling Mathematical modeling by Mike Barongo (PhD scholar) will help us to understand and predict: • the pathways of ASF virus transmission and infection • the impact of interventions . Mike’s epidemiological model will draw on the field study data and findings.
  • Cross-border study area: Uganda-Kenya Facilitates: • Understanding trans-boundary ASF risks • Comparative analysis of laws, policies and customs relevant to ASF transmission and controlAfrica agro-ecological zones
  • Field study design Data from Pigs People When? 1 Cross-sectional * * Kenya: July–Aug 12 survey (c.600 HH) Blood Structured Uganda: Sept -Nov serum survey 12 feces 2 Longitudinal Kenya: Sept 12- * * Mar 13 “sentinel pig” Blood Inc. semi- Uganda: Jan to June study (100 pigs & serum structured 13 feces interviews HH, 6 mths) 3 Extended social * * Jan -June 13 network survey Inc. semi- Tissue at structured (pig trades, slaughter interviews trust/advice slabs networks) 4 Focus groups * Mar -June 13 5 Outbreaks * *
  • : Progress: sampling strategy Stratified randomised design used to select study villages. Pig keeping households Busia identified in selected (fieldwork base) villages, with help from district vet officers & local leaders. 0 20 kmFirst round stratified randomised spatial selection
  • Project field activities: Phase 1 Cross sectional survey Cross-sectional study interviews & pig sampling completed in Kenya (>300 households; >500 pigs) Next: • Sample at recent ASF outbreak • Select 50 Kenya “sentinel pigs”; negotiate purchase and on-farm care with farmers; resample after 3 and 6 months • Cross-sectional study interviews & pig sampling in Uganda • “Sentinel pig” selection in Uganda
  • Progress: virus prevalence in study area In Homa Bay, many pigs carry genotype X ASF Virus but there are Busia no ASF outbreaks (Okoth 2012). Busia (100km away) has frequent ASF outbreaks. Homabay Project has now tested 400 pig samples from Busia-Teso Kenya study area. None were positive for ASF virus. Preliminary conclusion: In Busia Kenya, outbreaks are not due to long-term carrier pigs. Other factors must be responsible.
  • Project objectives1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess livelihood impact of ASF5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
  • Why assess livelihood impact of ASF? Helps understand: • How much ASF constrains pig production, compared to other factors • Value chain participants’ willingness & capacity to invest in preventing ASF spread • Cost:benefit of investments by governments and funders in ASF prevention and control.
  • Progress: Structured survey developed Participant information and consent forms Household questions include: • Education, income, assets • Pig keeping history, income, use of income, feeding, housing, production constraints & risks • ASF awareness • Social networks: trust, advice, memberships • Current pigs: source, mating, illness, agistment • Past pigs (since crop planting c.Aug11): source, disposal, illnessFirst pilot Feb 2012: at Jennifer Swara’s farm
  • Context for ASF livelihood impactSelected very preliminary findings: Kenya cross-sectional study • About 75% of survey participants are women • Wealth level varies a lot within and between villages • Even the poorest households usually have a phone • Average 2 pigs per pig-keeping household (range 1-5 pigs) • Pig ownership is very dynamic, driven by: – seasonal food gaps for people & pigs – cash needs
  • Progress: ASF livelihood impactSelected very preliminary findings: Kenya cross-sectional study • Disease is not often mentioned as a constraint on pig-keeping. However disease is seen by farmers as the biggest risk to their investment in pigs. • 10% of sampled farms have experienced ASF.
  • Project objectives1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess livelihood impact of ASF5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
  • Why identify feasible biosecurity measures? Only good biosecurity will prevent spread of ASF. Farmer awareness of ASF biosecurity is a prerequisite for adoption. Smallholder capacity to adopt ASF biosecurity measures is unknown. Farmer Jennifer Swara using a disinfectant foot bath for the first time
  • Progress: feasible biosecurity measures Key messages developed, translated and illustrated Poster calendar produced for Kenya and for Uganda Next: – Distribution during sentinel pig selection (Kenya) – Distribution during cross-sectional study (Uganda) – Assess farmer understanding, discuss feasibility, consider alternatives during longitudinal study and focus groups – Revise messages and how they are51 presented|
  • In Kenya (study site) pigs are tethered some of theIn Kenya (study site), farmers are time, never housed.not conscious that ASF virus can Pigs free range afterbe spread by people crop harvestmovement/on people’s feet
  • In Kenya (study In Kenya (studysite) , 20% of site) , farmersfarms feed swill say they use swillfrom off-farm that does notsources contain pork
  • Project objectives1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess livelihood impact of ASF5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
  • Why try to understand social networks? • ASF virus can be spread along pig Piglet breeder movement networks Development agent • Pig movement networks can also Smallholder reveal the structure of market chains and their spatiality • Network structure has implications for design of effective interventions • Networks are starting points for: – Collective efforts on ASF biosecurity – Collective efforts on other production constraints (eg feed gaps) – Stronger market chainsExample (hypothetical) piglet distribution network
  • Progress: Spatial network structure of pig & pig product movements Very preliminary findings: Kenya cross- sectional study Butcher • Most grown pigs sold to butchers Smallholder in same or nearby village • Kenya/Uganda border makes no difference to this pattern • Occasional sales to butchers from nearby towns that the farmers do not know • Most piglets sold to neighboursIndicative village pig movement network • Pigs that got sick or died fromover one year ASF were often sold or butchered at home and eaten
  • Progress: Advice networks Very preliminary findings: Kenya Adviser cross-sectional study Smallholder • Many farmers seek pig help from the same few people. • Very few farmers know the government vet officers. • Most farmers belong to an organisation/association (or ‘circle’) but none of theseIndicative village advice network deal with pigs.
  • Epidemiology of African Swine Fever LOCAL GLOBALPath to impact Stronger Increased food security ASF risks to smallholder pig Increased pig production global food networks: Increased income for security - procurement smallholders managed -production --marketing ASF risk managed Vaccine? Smallholders adopt biosecurity Effective national & regional action on ASF controlDevelopment outcomes Control strategies: national, regional, Africa wide PatjPath (FAO, AU-IBAR, OIE) Direct science outputs Feasible smallholder ASF Rapid methods Publicly available genotypes of biosecurity epidemiology to confirm ASF regional ASF virus measures model diagnosis isolates Smallholder ASF impact on Spatial network ASF ASF virus advice/trust livelihoods structure of pig epidemiology characteristics networks movements in the field Field study area Field study area Field study area Field study area Field study area ASF Virus samples Household Smallholder Health and growth rates Pig & pig product movements ASF Virus incidence in from outbreaks characteristics pig keeping practices Smallholder pigs (procurement, markets, consumption) Smallholder pigs &economy
  • LessonsIntegration of social science and biological scienceWorking with local and international partnersInteraction with farmersEvolution of questionnaire through piloting
  • Thankyou!
  • EXTRAS
  • ASF Vaccine Development • Experimental live attenuated vaccines induce protection against challenge with homologous strain -proof of concept that a vaccine is possible • Immunity is partially based on T cells and not just antibody-based • Work on second generation vaccines using modern approaches to antigen identification and delivery is beginning • ILRI comparative advantage- Work at Biosecurity level 2-
  • Understanding social networks • Social networks describe how people [or animals] behave collectively Piglet breeder • Something (eg piglets) moves between Development agent nodes(circles) Smallholder • Nodes (circles) are people entities of different types (eg breeder, smallholder) • Arrows are direction of movement (eg of piglets) • Width of arrow is quantity of the thing that is being moved (eg number of piglets) • Bounding the system is critical for analysis • Time period is a key boundary consideration for AFSExample (hypothetical) piglet distribution network
  • Building an understanding of pig movement networks in the study area Farmer A (sampled in longitudinal and/or cross- Piglet breeder sectional field study) told us she sold a weaner pig to Farmer B. She had that young pig for a month. It was Development agent one of three piglets that she got through a livestock Smallholder development project. We aim to also interview Farmer B, to triangulate A B information from Farmer A, and to find out what ? Farmer B did with the weaner pig. If Farmer B not sampled in the cross-sectional study, interview will be in Fieldwork Phase 3: the ‘extended social network’ study. In Phase 3: ‘extended social network study’, we also aim to interview the development agent who supplied the three piglets to Farmer AExample (hypothetical) piglet distribution network
  • Understanding social networks Why try to understand social networks? Meat Purchaser Meat Purchaser Butcher Butcher Smallholder SmallholderExample : pig & pig product market network Example : pig & pig product movement network
  • Why try to understand social networks? Network structure has Meat Purchaser implications for designing Butcher Smallholder interventions to prevent or contain an ASF outbreak. Analysis options: – Qualitative – Quantitative (graph theory) – ModelingExample : pig & pig product movement network
  • Why try to understand social networks? Piglet breeder Network structure has Development agent implications for designing Smallholder interventions to prevent or contain an ASF outbreak. Analysis options: – Qualitative – Quantitative (graph theory) – Modeling – SpatialExample : piglet distribution spatial network