This document outlines the objectives and progress of a project studying African swine fever (ASF) in East Africa. The project aims to 1) genotype and sequence ASF virus genomes, 2) evaluate rapid diagnosis methods, 3) understand ASF epidemiology in the field, 4) assess the livelihood impact of ASF, 5) identify biosecurity measures, and 6) understand social networks related to ASF transmission. To date, the project has genotyped and sequenced viruses, trained researchers in rapid diagnosis, conducted field studies to examine virus prevalence and transmission pathways, and developed surveys to analyze the economic effects of ASF on smallholder farmers.

1. Epidemiology of African Swine Fever:
A prerequisite to control
Richard Bishop, Edward Okoth,
Jocelyn Davies
10th September 2012

2. 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

3. Global trends in pork production
Half of the world's pork is eaten in China
All of Africa at 1500
U.S. Census Bureau, Statistical Abstract of the United States: 2012

4. Pork production in Africa
Africa’s pig population
estimated at 25 Million

5. ‘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

6. 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.

7. Gender
On smallholder
farms, pigs are
almost always
women's business.

8. Potential income generation
Average 10 piglets x
$ 3 farrowings/year
@ USD 12/piglet
= USD 360/year
= 1 year secondary school fees

9. 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)

10. 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.

11. 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

12. 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.

13. 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

14. ASF global spread
Li
s
Cuba 1971, 1980
b
Dom. Rep 1978
o
Haiti 1978
n
195
7, 6
0
Brasil 1978
Related 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.

15. ASF global risk
ASF spread in
eastern Russia
poses a big food
security risk to
Europe and Asia.

16. Project 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

17. 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

18. Implementation partnerships
Kenya 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

19. 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.

20. 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

21. 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, geographer
Ms Tracey May, GIS and data base expertise
Dr Yiheyis Maru, social-economic systems scientist & veterinarian
Ms Larelle McMillan, communications

22. Project objectives
1. Genotyping and whole genome sequencing
2. Evaluate rapid ASF diagnosis methods
3. Understand ASF epidemiology in the field
4. Assess livelihood impacts of ASF
5. Identify feasible biosecurity measures
6. Understand social networks relevant to ASF

23. 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-phenotype
Automated sequencer correlations
• The overall level of diversity has implications for
the feasibility of developing a vaccine that is
effective in the field.

24. Genomics work flow and outputs
Bioinformatics
Field sampling BecA laboratory analysis
research
Publicly available
Genotyping Annotated virus
Virus Isolation and genotypes
information
genome sequencing of regional isolates

25. 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

26. Senior scientist training at BecA-ILRI
Dr 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)

27. 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

28. Cameroon : Project genomic studies
Dr Abel Wade
from LANAVET
(Cameroon) has
been trained in
CISA-INIA Spain
to analyse
samples from
recent Cameroon
outbreaks.

29. Project objectives
1. Genotyping and whole genome sequencing
2. Evaluate rapid ASF diagnosis methods
3. Understand ASF epidemiology in the field
4. Assess role of pigs in livelihoods & impact of ASF
5. Identify feasible biosecurity measures
6. Understand social networks relevant to ASF

30. 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.

31. Progress: Evaluate rapid diagnosis methods
Here is
the Lab
Field laboratory test run from a basic set-up (i.e. table) or back of a vehicle
BSL-2 lab BSL-3 lab

32. Progress: Evaluate rapid diagnosis methods
Three DNA extraction methods have been tested
Dr Neil LeBlanc,
Swedish Veterinary Institute

33. 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 rapid
remote area testing”
“Applicable to many
health care needs”
Dr Neil LeBlanc
Swedish Veterinary Inst

34. Project 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

35. Why try to understand ASF epidemiology
in the field?
ASF virus can spread to healthy
Swill pigs in many different ways:
Direct • From wild pigs
Feces 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

36. 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?

37. We don’t understand what roles people
play in transmission
Virus
What do people do that causes ASF to
SOURCES TRANSMISSION spread? Why?
PATHWAYS
Carcasses People What would it take for people to behave
Undercooked meat Pigs
differently?
Swill Vehicles
Faeces Scavengers Pig immune Carrier
Wildlife Reservoirs system Pig
Slaughter waste Nutrition
Ticks (Vector)
Co-infection
ENVIRONMENT load
Parasites Immune
Vet services Pig
Susceptible
Infected Pig
Pig
Recovered Pig
Dead
Pig

38. 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.

39. 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

40. 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 * *

41. :
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 km
First round stratified randomised spatial selection

42. 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

43. 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.

44. Project 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

45. 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.

46. 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, illness
First pilot Feb 2012: at Jennifer Swara’s farm

47. Context for ASF livelihood impact
Selected 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

48. 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.

49. Project 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

50. 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

51. 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 are
5
1
presented
|

52. In Kenya (study site)
pigs are tethered
some of the
In Kenya (study site), farmers are time, never housed.
not conscious that ASF virus can Pigs free range after
be spread by people crop harvest
movement/on people’s feet

53. In Kenya (study In Kenya (study
site) , 20% of site) , farmers
farms feed swill say they use swill
from off-farm that does not
sources contain pork

54. Project 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

55. 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 chains
Example (hypothetical) piglet distribution network

56. 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 neighbours
Indicative village pig movement network • Pigs that got sick or died from
over one year ASF were often sold or butchered
at home and eaten

57. 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 these
Indicative village advice network deal with pigs.

58. Epidemiology of African Swine Fever LOCAL GLOBAL
Path 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 control
Development 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

59. Lessons
Integration of social science and biological
science
Working with local and international
partners
Interaction with farmers
Evolution of questionnaire through piloting

60. Thankyou!

61. EXTRAS

62. 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-

63. 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 AFS
Example (hypothetical) piglet distribution network

64. 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 A
Example (hypothetical) piglet distribution network

65. Understanding social networks
Why try to understand social networks?
Meat Purchaser Meat Purchaser
Butcher Butcher
Smallholder Smallholder
Example : pig & pig product market network Example : pig & pig product movement network

66. 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)
– Modeling
Example : pig & pig product movement network

67. 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
– Spatial
Example : piglet distribution spatial network