dynamic drivers of disease emergence in africa: from hypothetical frameworks to the field
DESCRIPTION
Presented by Johanna Lindahl at a Centre of Global Animal Diseases (CGD) seminar, Uppsala, Sweden, 13 December 2013.TRANSCRIPT
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Dynamic drivers of disease emergence in Africa: From hypothetical frameworks to the field
Johanna Lindahl
Presented at a Centre of Global Animal Diseases (CGD) seminar
Uppsala, Sweden
13 December 2013
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What to talk about today?
1. Disease drivers
2. Ecosystem services
3. Proposed frameworks
4. Dynamic drivers of disease in Africa- case studies
5. Discussion
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The world today
• Humans are affecting every part of this planet
– Directly or indirectly
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2 billion hidden hunger
The world today
7 billion people
One billion hungry
1.7 billion overweight/obese
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Livestock is important
• 24 billion livestock
After rice, second most important source of food
19 billion in developing countries
1 billion poor people depend on livestock
600 million in South Asia
300 million in Sub-Saharan Africa
25% urban
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Infectious diseases- historically important
• In 1918-1920 Spanish flu:
• 50- 100 million humans
• Late 19th century Rinderpest:
• Death of 2/3 of Maasai population in Tanzania and Kenya
• Early 19th century Potato blight:
• 25% of Irish population starved or migrated
• 1967: "…war against infectious diseases has been won“
• US Surgeon General William H. Steward
FAO/G.R. Thomson
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US Surgeon General William H. Steward was wrong
Infectious diseases as a cause of death has been decreasing
• But not everywhere, and not constant
• Increases due to HIV, drug resistance
In 2011 55 million died
18 million from infection
7 million deaths in under fives (2/3 infectious)
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HIV, TB, malaria
Other infectious
Mat//peri/nutritional
Cardiovascular diseases
Cancers
Other NCD
Road traffic accidents
Other unintentional
Intentional injuries
0
5
10
15
20
25
30
2004 2015 2030 2004 2015 2030 2004 2015 2030
Dea
ths
(mill
ion
s)
High-income countries
Middle-income countries
Low-income countries
Mortality: global projection, 2004-2030
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Top Zoonoses (multiple burdens)
• Assessed 56 zoonoses from 6 listings: responsible 2.7 billion cases, 2.5 million deaths
• Top 13 responsible for 2.2 billion illnesses and most deaths
– Wildlife interface
– 9 have a major impact on livestock- affect 1 out of 7
– All 13 amenable to on-farm intervention
World bank (2010) estimates for last century :
• direct costs of zoonotic outbreaks >20 billion USD
• indirect costs 200 billion USD
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Zoonoses
0
20000
40000
60000
80000
100000
120000
140000
Deaths - annual
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Animal health today
5 billion livestock die each year (~20%)
Young Adult
Cattle 22% 6%
Shoat 28% 11%
Poultry 70% 30%
Otte & Chilonda, IAEA
Annual mortality of African
livestock (About half due to preventable or
curable diseases)
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Disease emergence?
• Definition: – a disease which incidence in humans have been increasing
– a disease which has a tendency to spread geographically, cause an increased incidence or infect a new species or new populations
– a disease spreading within any host population
• Which diseases? • EID twice as likely to be zoonotic than non-zoonotic (zoonotic viruses
and protozoa had highest proportion)
• Quick mutations
• Where? • rapid intensification, increasing interactions between animals, humans
and ecosystems, often rapidly changing habits and practices
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Livestock
Wildlife Humans
Ecosystem
LH WL
WH
X
Disease transmission Spillover event
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Type of wildlife-
livestock-human
interface
Level of Biodiversity Characteristics of
Livestock Population
Connectedness
between populations
Main interface Examples of zoonotic
disease with altered
dynamics
‘Pristine’ ecosystem
with human incursion
to harvest wildlife and
other resources
High No livestock Very low, small
populations and limited
contact
Ignorable WL interface,
large WH interface
Ebola
HIV
SARS
Nipah virus in
Bangladesh and India
Ecotones and
fragmentation of
natural ecosystems -
farming edges, human
incursion to harvest
natural resources
High but decreasing Few livestock, multiple
species, mostly
extensive systems
Increasing contact
between people,
livestock and wild
animals
WH and WL interface
dominating, increasing
LH
Kyasanur Forest disease
Bat rabies
E. coli interspecies
transmission in Uganda
Nipah virus in Malaysia
Evolving landscape -
rapid intensification of
agriculture and
livestock, alongside
extensive and backyard
farming
Low, but increasing
peri-domestic wildlife
Many, both intensive
and genetically
homogenous, as well as
extensive and
genetically diverse
High contacts between
intensive and extensive
livestock, people and
peri-domestic wildlife.
Less with endangered
wildlife.
Patchwise large LH
interface, decreasing
WH and WL
Avian influenza
Japanese encephalitis
virus in Asia
Managed landscape -
islands of intensive
farming, highly
regulated. Farm land
converted to
recreational and
conservancy
Low, but increased
number of certain peri-
domestic wildlife
species
Many, mainly intensive,
genetically
homogeneous,
biosecure
Fewer contacts
between livestock and
people; increasing
contacts with wildlife.
Small but increasing WL
and WH, decreasing LH
Bat-associated viruses
in Australia
WNV in USA
Lyme disease in USA
Urban landscape- high
densities of humans,
with peri-urban intense
farming and urban
lower intense farming,
close to people. Habitat
fragmentation of
wildlife
Low High value animals ,
mainly small ruminants
or pigs, and poultry in
the urban centres
High densities yield
high connectedness
Patchwise increasing LH
and WH, especially
poor areas
Plague outbreaks
Leptospirosis
Dog rabies
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Ecosystem services – and disease emergence
Ecosystem service Importance Effect of decrease
Provisioning Economics, livelihoods Increased poverty
Regulating Health, environment Increased disease
Cultural Well-being, recreation Increased stress?
Supporting Basis for the other services Increase in all above
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Self-actualization
Self-esteem and respect
Love and sense of belonging
Safety and security
Physiological needs: food, rest, water
Hierarchy of needs according to Maslow.
Provisioning
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Regulating services
Cultural services
Provisioning services
Health
Infections
Physical and
chemical
Stress
Nutrition
Vectors
Wildlife Livestock
Lack of nutrients
Lack of energy
Too much energy
Climate
Pollution
Land use changes
Land degradation
Biodiversity
Socio-economics
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Basic epidemiological principles
• For an outbreak to occur: R0 > 1
• SIR model
Susceptibles Infectious Removed
Susceptibles Exposed Infectious Removed
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Increased number of susceptible
New population at risk
Global trade and travelling
Increased contact with wildlife
Close contact between different species
Transfer or recruitment of new vectors New habits,
new cultures
Migration of people or animals to new areas
New species at risk / host transfer
Decreased immunization and immunity
Markets
Urbanization
Environmental land degradation
Poverty
Undernutrition, starvation
Governmental finances and priorities
Ageing population
Civil unrest
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Increased risk of exposure
Habitat fragmentation
Decreased biodiversity
Increased number of vectors
High density
Lack of knowledge
Less dilution from alternate hosts
Reduced food safety
Water scarcity
Disrupted social systems
Poverty
Urbanization
Markets
Industrialization of animal production Littering
Irrigation
Fertilisers
Deforestation
Agricultural intensification and development
Climate changes
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Destroyed agricultural land, soil degradation
Increased infectivity
Pollution
Disrupted social systems
Excess, incorrect use of antibiotics and antivirals
Resistant pathogens
Inadequate health systems
Compromised immune system
Starvation, malnutrition
Lack of fundings
Increased incidence of HIV
War, migration
Pathogen evolution
Remote areas
Poverty
Water scarcity
Ageing population
Lack of knowledge
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Removed/recovered
Access to medicines
Urbanization
Improved infrastructure
Immunization programs
Adequate health systems
Improved nutrition
Global trade
Increased animal production
Irrigation Education
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Anthropogenic action:
Increased irrigation
Effect on ecosystem:
Creates more larval habitats
•This step requires the presence of a vector-borne pathogen and the presence of competent vectors
Possible consequence:
More infected vectors
Epidemiologic consequence:
More individuals exposed
Increased
disease
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One action- multiple results
Deforestation
Decreased biodiversity
Increased disease transmission (where
biodiversity would cause a dilution effect)
Example: Some vector-borne diseases, such as
Lyme disease
Reduced disease transmission (where
biodiversity would cause an amplification effect)
Example: Parasites in greater apes which are
favoured by host richness
Changed vector habitats
Increased vector populations
Example: Deforestation give more agricultural land, more
irrigation and more Japanese encephalitis virus
increase
Decreased vector populations
Example: Malaria decrease after deforestation in
Thailand
Habitat fragmentation
Increased edge effects and interfaces between humans,
domestic animals and wildlife
Example: Habitat destruction and forrest
encroachment were drivers between Nipah virus
otbreaks
Increased animal densities and contact rates
Example: Increased parasite burdens in wildlife
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One action- multiple results
Agricultural industrialization
Improved veterinary care
Increase use of antibiotics
Decrease of bacterial diseases in animals
Increase risk of drug resistant pathogens
Eradication of animal diseases
Eradication of rinderpest- better cattle production
Eradication of Samonella pullorum- better poultry production but increased
Salmonella enterica
Intensification
Higher animal densities
High propagation of infectious diseases, such
as avian influnza
Higher biosecurity
Decreased risk of introduction of disease
Extensification
Trends of ecologic production with outdoor
animals
More natural behaviour could give less stress and increase animal welfare
Increased infectious diseases such as
Toxoplasma gondii
Backyard poultry
Low biosecurity and low animal density
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Dynamic drivers of disease in Africa
How does changes in land use and anthropogenic
changes affect diseases?
And how do we study it?
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DDDAC
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Case study: Zambia/ Zimbabwe
• Trypanosomiasis/tse-tse
• Land use changes
– Protected area
– Area where livestock has been increasing
– Former large-scale farms with low biodiversity
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Case study: Ghana
• Henipa virus/ bats
• Urban –rural migration
• Livelihoods, poverty, ecology and the association with disease
– How do humans interact with bats and what perceptions do they have of the risks
– Protected/sacred area
– Urban area
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Case study: Sierra Leone
• Lassa fever/ multimammate rats
• Land use changes and rodent ecology
– Urban-rural
– Irrigation and precipitation
– Human-rat interaction and risk perceptions
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Case study: Kenya
• Rift valley fever/ mosquitoes
• Land use changes
– Protected area vs irrigated area
– Pastoralist areas
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Case study: Kenya
• Rift valley fever/ mosquitoes
• Land use changes
– Protected area vs irrigated area
– Pastoralist areas
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Case study: Kenya
• Making changes in a highly diverse landscape
• Increased number of scavengers
• Increased numbers of mosquitoes
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Case study: Kenya
• Participatory rural appraisals indicated a concern about rodents
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Case study: Kenya
• What to study:
– Can we trust hospital data?
– Screen all febrile patients
– Too many differentials: Malaria, RVF, Dengue, YF, Brucella, Leptospira, Chikungunya, CCHF
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Case study: Kenya
• Who to study:
– Humans and livestock
– Mosquitoes
– Rodents
– Ticks?
– Baboons?
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Cross-cutting issues
• Participatory rural appraisals
• The economic burden of disease
• The association between poverty and zoonoses- the vicious circle
• Climate change and predictive modelling
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The perfect model?
Ecosystem health
Human health
Animal health
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Far from perfect
• Assessing biodiversity
• Assessing poverty
• Assessing human- animal interactions
• Assessing impact
• Finding mitigations
Elephants or
mosquitoes?
Assets or knowledge?
Food or animal
contact?
Compared to
everything else?
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Not the end…. ….but the beginning
Open to questions
Open to discussion
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Agriculture Associated Diseases
http://aghealth.wordpress.com/
This work, Dynamic Drivers of Disease in Africa Consortium, NERC project no. NE-J001570-1, was
funded with support from the Ecosystem Services for Poverty Alleviation (ESPA) programme. The
ESPA programme is funded by the Department for International Development (DFID), the
Economic and Social Research Council (ESRC) and the Natural Environment Research Council
(NERC).
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The presentation has a Creative Commons licence. You are free to re-use or distribute this work, provided credit is given to ILRI.
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