Epidemiology of African Swine Fever: A prerequisite to control
Richard Bishop, Edward Okoth, Jocelyn Davies10th September 2012
• Background• Project objectives & partnerships• Progress on objectives
1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess livelihood impact of ASF 5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
• Path to impact • Lessons
Outline
Global trends in pork production
U.S. Census Bureau, Statistical Abstract of the United States: 2012
Half of the world's pork is eaten in ChinaAll of Africa at 1500
Pork production in Africa
Africa’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
Developing Region
38 41
39 81
1 million tonnes
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.
Pigs in smallholder production systems
Gender
On smallholder farms, pigs are almost always women's business.
Potential income generation
Average 10 piglets x3 farrowings/year@ USD 12/piglet = USD 360/year = 1 year secondary school fees
$
e.g.Average net annual income for butchers in western Kenya - USD 887Profit per pig - USD 3.80(Kagira et al.2010)
Value chain livelihoods
Pork is easy for village households to access regularly, compared to beef.
Pork consumption in villages
One pig provides a manageable quantity of meat for a day’s trade in a village market.
Constraints to smallholder pig production
African
Swine
Fever
Feed
Breed
Housing
Constraints to pig
production
Communication
Roads
Other pig
health problem
s
Traders
Customers
• 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.
Why research African Swine Fever?
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
Related ASF-West Africa viruses
Lisbon
1957, 60
Cuba 1971, 1980
Dom. Rep 1978
Haiti 1978
Brasil 1978
1957 from Angola: genotype I to Lisbon, now spreading in Europe and central & south America.
GeorgiaJune 2007
2007 from Eastern Africa: genotype II to Caucasus Region, now spreading in Ukraine.
ASF spread in eastern Russia poses a big food security risk to Europe and Asia.
ASF global risk
Project objectives
1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess livelihood impact of ASF 5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
Collaborations and partnerships
Collaborations• FAO, AU-IBAR, CISA-INIA, Makerere University,
University of Pretoria, Royal Veterinary College London, University of Nairobi, Swedish Veterinary Institute, University of Edinburgh
Implementation partners (National Institutions)• DVOs, MAAIF-Uganda, MLD-Kenya, LANAVET-
Cameroon
Implementation partnerships: DVS Kenya & MAAIF UgandaJennifer 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
Implementation partnershipsKenya MLD & Uganda MAAIF
Multi-disciplinary multi-lingual team
Animal health, virology, veterinary epidemiology, mathematics, modeling, livestock economics, social science, systems science, geography, animal handling.
At ILRI Nairobi, training and team building, May 2012
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 support
Dr Jocelyn Davies, geographerMs Tracey May, GIS and data base expertiseDr Yiheyis Maru, social-economic systems scientist & veterinarianMs Larelle McMillan, communications
Project objectives
1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess livelihood impacts of ASF 5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
• 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-phenotype correlations
• The overall level of diversity has implications for the feasibility of developing a vaccine that is effective in the field.
Why do genotyping and whole genome sequencing?
Automated sequencer
Genomics work flow and outputs
Virus IsolationGenotyping
and genome sequencing
Annotated virus information
Publicly available genotypes
of regional isolates
Bioinformatics analysisBecA laboratory
researchField sampling
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 virus relationships
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)
Senior scientist training at BecA-ILRIDr Charles Masembe, Makerere University
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.
Kenya outbreaks: Project genomic studies
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 objectives
1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess role of pigs in livelihoods & impact of ASF 5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
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.
Why evaluate rapid ASF diagnosis methods?
Here isthe Lab
Field 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 methods
Field lab tests have screened for ASF virus and prevalence of other pathogens.Results replicated in ILRI conventional labs in Busia & Nairobi .
“Best practice for rapidremote area testing”“Applicable to many health care needs”Dr Neil LeBlancSwedish Veterinary Inst
Progress: Evaluate rapid diagnosis methods
Project objectives
1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess livelihood impact of ASF 5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
ASF virus can spread to healthy pigs in many different ways:
• From wild pigs • From ticks• From infected pork fed to pigs• From contact with sick pigs or
their fecesWe don’t understand what pathways are most important.
Swill
Why try to understand ASF epidemiology in the field?
FecesDirect contact
Ticks
BushpigsWarthog
s
In Homa Bay, many pigs carry genotype X ASF virus but there are no ASF outbreaks (Okoth 2012)
Busia (100km away) has frequent ASF outbreaks caused by genotype IX.
• Are there also carrier pigs in Busia?
• What triggers outbreaks?
Virus prevalence is variable and role of carrier pigs is poorly understood
Busia
Homabay
We don’t understand what roles people play in transmission
Susceptible Pig
Infected Pig
Recovered Pig
ImmunePig
DeadPig
CarrierPig
Virus
Slaughter waste
SwillFaeces
Undercooked meat
Ticks (Vector)
TRANSMISSIONPATHWAYS
People
Pigs
Vehicles
Wildlife ReservoirsScavengers
SOURCES
ENVIRONMENT
Carcasses
What do people do that causes ASF to spread? Why?What would it take for people to behave differently?
Pig immune systemNutrition
Co-infection load
ParasitesVet services
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.
Field study will inform modeling
Cross-border study area: Uganda-Kenya
Facilitates:• Understanding trans-boundary ASF risks• Comparative analysis of laws, policies and customs relevant to ASF transmission and control
Africa agro-ecological zones
Field study designData from Pigs People When?
1 Cross-sectional survey (c.600 HH)
*Blood serum feces
*Structured survey
Kenya: July–Aug 12
Uganda: Sept -Nov 12
2 Longitudinal “sentinel pig” study (100 pigs & HH, 6 mths)
*Blood serum feces
*Inc.
semi-structure
d interview
s
Kenya: Sept 12- Mar 13
Uganda: Jan to June 13
3 Extended social network survey (pig trades, trust/advice networks)
*Tissue at slaughter slabs
*Inc.
semi-structure
d interview
s
Jan -June 13
4 Focus groups * Mar -June 13
5 Outbreaks * *
Progress: sampling strategyStratified randomised design used to select study villages.Pig keeping households identified in selected villages, with help from district vet officers & local leaders.
:
Busia (fieldwork base)
0 20 km
First 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
In Homa Bay, many pigs carry genotype X ASF Virus but there are no ASF outbreaks (Okoth 2012).
Busia (100km away) has frequent ASF outbreaks.
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.
Progress: virus prevalence in study area
Busia
Homabay
Project objectives
1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess livelihood impact of ASF 5. 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 formsHousehold 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, illness
First 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 impact Selected 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 objectives
1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess livelihood impact of ASF 5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
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.
Why identify feasible biosecurity measures?
Farmer Jennifer Swara using a disinfectant foot bath for the first time
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 are presented5
1
|
Progress: feasible biosecurity measures
In Kenya (study site), farmers are not conscious that ASF virus can be spread by people movement/on people’s feet
In Kenya (study site) pigs are tethered some of the time, never housed. Pigs free range after crop harvest
In Kenya (study site) , 20% of farms feed swill from off-farm sources
In Kenya (study site) , farmers say they use swill that does not contain pork
Project objectives
1. Genotyping and whole genome sequencing2. Evaluate rapid ASF diagnosis methods3. Understand ASF epidemiology in the field4. Assess livelihood impact of ASF 5. Identify feasible biosecurity measures6. Understand social networks relevant to ASF
Why try to understand social networks?
• ASF virus can be spread along pig movement networks
• Pig movement networks can also 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 chains
Piglet breederDevelopment agent
Smallholder
Example (hypothetical) piglet distribution network
Progress: Spatial network structure of pig
& pig product movementsVery preliminary findings: Kenya cross-
sectional study • Most grown pigs sold to butchers
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 neighbours• Pigs that got sick or died from
ASF were often sold or butchered at home and eaten
Butcher
Smallholder
Indicative village pig movement networkover one year
Progress: Advice networks
Very preliminary findings: Kenya cross-sectional study
• 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 these deal with pigs.
Adviser
Smallholder
Indicative village advice network
PatjPath
Stronger smallholder pig
networks:- procurement- production- -marketing
Development outcomes
Field study areaPig & pig product movements
(procurement, markets, consumption)
ASF risks to global food
security managed
LOCAL
ASF risk managed Vaccine?
Field study areaSmallholder
pig keeping practices
GLOBAL
Feasible smallholderbiosecurity measures
Direct science outputs
Field study area ASF Virus incidence in
Smallholder pigs
ASF Virus samples from outbreaks
Field study areaHousehold
characteristics &economy
Effective national & regional action on ASF control
Smallholders adopt biosecurity
Rapid methods to confirm ASF
diagnosis
Epidemiology of African Swine Fever
Path to impact
Field study area Health and growth rates
Smallholder pigs
ASFepidemiology
model
ASF impact on livelihoods
ASF virus characteristics
Spatial network structure of pig
movements
ASF epidemiology
in the field
Smallholderadvice/trust
networks
Control strategies: national, regional, Africa wide (FAO, AU-IBAR, OIE)
Increased food securityIncreased pig
productionIncreased income for
smallholders
Publicly available genotypes of
regional ASF virus isolates
Integration of social science and biological science
Working with local and international partners
Interaction with farmersEvolution of questionnaire through piloting
Lessons
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-cheaper Use local African pig breeds
Understanding social networks
• Social networks describe how people [or animals] behave collectively
• Something (eg piglets) moves between nodes(circles)
• 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 AFS
Piglet breederDevelopment agent
Smallholder
Example (hypothetical) piglet distribution network
Building an understanding of pig movement networks in the study area
Piglet breederDevelopment agent
Smallholder
A B
Example (hypothetical) piglet distribution network
Farmer A (sampled in longitudinal and/or cross-sectional field study) told us she sold a weaner pig to Farmer B. She had that young pig for a month. It was one of three piglets that she got through a livestock development project.
In Phase 3: ‘extended social network study’, we also aim to interview the development agent who supplied the three piglets to Farmer A
We aim to also interview Farmer B, to triangulate 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.
?
Why try to understand social networks?Meat Purchaser
Butcher
Smallholder
Example : pig & pig product market network
Meat PurchaserButcher
Smallholder
Example : pig & pig product movement network
Understanding social networks
Network structure has implications for designing interventions to prevent or contain an ASF outbreak.Analysis options:– Qualitative – Quantitative (graph theory)– Modeling
Meat PurchaserButcher
Smallholder
Example : pig & pig product movement network
Why try to understand social networks?
Network structure has implications for designing interventions to prevent or contain an ASF outbreak.Analysis options:– Qualitative – Quantitative (graph theory)– Modeling– Spatial
Piglet breederDevelopment agent
Smallholder
Example : piglet distribution spatial network
Why try to understand social networks?