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Evaluating the potential burden of zoonotic mycobacteria in Africa: Can modelling disease in wildlife populations help?. Claire Geoghegan & Wayne Getz . Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, South Africa &
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Claire Geoghegan & Wayne Getz
Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, South Africa
Department of Environmental Science, Policy & Management, University of California – Berkeley, USA
James Morris , World Food Programme’s Executive Director, July 2002,
Disease in animals and humans – why should we care?
Of allhuman pathogens, 62% are zoonotic and attributed to animals
Livestock pathogens that can infect wildlife
Human pathogens that can infect wildlife
If a pathogen can infect wildlife, > 2x likely to cause an emerging human disease
Important to understand the temporal and spatial dynamics of pathogens in human and animal reservoirs and populations
M. E. J Woolhouse et al, 2005
It is imperative to understand the fundamental dynamics of infectious diseases in order to mitigate the impacts on public health, wildlife and livestock economies
8 million new cases / year
~3 million deaths / year
1/3 of people are infected and have latent or active tuberculosis
Over 90% of people in Africa have been exposed to the TB bacilli
HIV and TB
80% of global case load in developing countries
TB is an ancient contagious disease, discovered in 5000 B.C
L. Blanc et al, 2002
The Real World
W. Y Ayele et al, 2004
Listed as a categoryBdisease by the OIE
Chronic disease that has an effect on animal populations and productivity
Annual worldwide losses ~$3 billion (trade)
Wide host range, including;
ruminants, predators, scavengers, small mammals
Difficult to eradicate due to the large disease reservoir apparent in wildlife
F Biet et al, 2005
Infected cattle may present with progressive emaciation, capricious appetite and a fluctuating fever.
However, many infected animals do not show any clinical abnormalities.
1 Oral; 2 Aerosol; 3 Passive; 4 Derivative Product; 5 Vertical; 6 Horizontal; 7 Predation
Why should we be concerned?
Thoen and Steele (1995)
Links South Africa, Mozambique and Zimbabwe
No co-evolution of host and pathogen
BTB was first noted in the 1990’sbut probably entered the park in the South East in the1960’s
Incorrect temporal scale used for prediction
Thought to only infect buffalo
Found in lions, kudu, warthog, baboons, small antelope
Not the top priority
Anthrax, rabies and FMD more threatening!Why was the prediction so wrong?
Followed herds to get visual data on individuals
Branded ~500 buffalo (roughly 2% of population)
Mass captures to test for BTB
Marked additional buffalo with ID collars
Removed infected buffalo for pathology analysisStudy design
How the network of connections between individuals and the interactions of group size, movement and recovery affect the probability of BTB infection in structured populations.
Traditional animal disease models assume random mixing of individuals, not the individual connections
Spatial disease models assume limited dispersal between fixed groups
Why was this approach unique?
BUT: individuals risk of infection depends on the global state of the population
Important in determining the probability of disease infection and invasion
Balls represent individual buffalo and lines show all non-zero association values. Individuals are distributed vertically according to herd membership
These were used to simulate disease dynamics along with other factors including scale and behaviour (females move!)
Cluster analysis indicated that buffalo were less tightly clustered in 2003 compared to 2002
Thus, increased host mixing during this time (dry year) would help facilitate disease invasion spread
Climate may play a role in herd movements and in BTB spread
Cross et al. 2004
1. What defines a contact for airborne diseases?
2. What are the appropriate time and spatial scales to sample an animal network?
3. How do you confidently scale up a sample to represent an entire population?
4. How to allow for birth and deaths and changes of association patterns while maintaining the overall properties of the network?
5. Is there a difference in behaviour between susceptible and infected individuals?
Is variance in connection strengths and frequency of contact in individuals important?
How does the duration of infectiousness affect the degree of disease experienced by the population structure?
Why are some hosts affected more than others?
How does incorporation of non-random association data affect predictions about the speed and intensity of a disease outbreak?
How do we get more empirical data and projects to run that require that data?
Beissinger and Westphal,1998
Complex web of socio-economic factors pertinent to controlling disease for feasible, affordable and effective public health policies to be devised and implemented
Host-pathogen interactions in ecological and socio-economic settings are complex, non-linear systems which required detailed maths and statistical analysis
Need experience of biological systems and technical knowledge
Need improved health care systems and information systems about health in order to generate reliable statistics that can be used to monitor progress
Practical Risk Factors
Social, Cultural, Economic Factors of Disease Dynamics
Model and Map for Predictions
Capacity Building / Retention of Ideas
Willing is not enough, we must do."
The project is thankful for the support of the
DIMACS / SACEMA and AIMS
Mammal Research Institute, and the Department of Zoology and Entomology at the University of Pretoria, South Africa
Division of Ecosystem Sciences, Department of Environmental Science, Policy and Management at the University of California – Berkeley, USA.