A strategic model for the simulation of drug resistance in
African Animal Trypanosomiasis
Frank Hansen, Bernard Bett, Erick Ouma Mungube, Tom Randolph
The problem:
African Animal Trypanosomiasis (AAT) is a major constraint for the productivity of African agricultural systems. Tsetse flies of the genus Glossina act as vectors that
transport the parasitic protozoan Trypanosoma spp. between hosts. The strategy most widely used to manage the disease is the application of trypanocidal drugs, but
the emergence of resistance has put into question the long-term viability of their use.
In certain areas of West Africa, drug resistant and drug susceptible strains of trypanosomes co-exist. When in such an area the disease prevalence is successfully
reduced by removal of the majority of the tsetse vectors, the remaining numbers of diseased animals is so small that it becomes difficult to measure the impact of vector
control on the development of drug resistance. Moreover, little is known about how drug resistance is likely to evolve if vector control is subsequently discontinued.
The solution:
Dynamic system models can simulate the processes that drive the dynamics of vector, host and parasite populations. Such models can increase our understanding of
the diseases dynamics even in situations where empirical measurement is problematic.
The model:
Cattle as
individuals
Both vector and host can be in one of 4 states
• Susceptible
• infected with a drug sensitive strain
• infected with a drug resistant strain
• infected with a mix of both strains
Vectors as pools in
different infection states
Treatment:
• every 30 days on animals with visible infection
• removes drug sensitive parasites
• no effect on drug resistant parasites
Infection:
• homogenous mixing
• Tsetse feed every 3 days
• infections mix upon contact
Population:
• 125 cows (25 not treated, serve as wildlife reservoir)
• 1000 tsetse, reduced to 100 by vector control
0
20
40
60
80
100
120
n
u
m
b
e
r
s
healthy sensitive mixed resistant
0
5
10
15
20
25
30
35
1
70
139
208
277
346
415
484
553
622
691
760
829
898
967
1036
n
u
m
b
e
r
s
time [days]
0
20
40
60
80
100
120
n
u
m
b
e
r
s
healthy sensitive mixed resistant
0
5
10
15
20
25
30
35
1
74
147
220
293
366
439
512
585
658
731
804
877
950
1023
n
u
m
b
e
r
s
time [days]
Worst case scenario Best case scenario- Simulations run for 3 years
- vector control applied in 2nd year
- average of 100 replicates-Treatment efficiancy: 60%
- Test sensitivity: 60%
-Treatment efficiancy: 95%
- Test sensitivity: 99%
In both cases purely sensitive
infections die out. There is an
equilibrium between mixed and
purely resistant infections. In
the worst case there are less
purely resistant infections.
Cattle
Tsetse
Cattle
Tsetse
During vector control disease
prevalence is effectively
reduced. With the end of vector
control the same equilibrium
between mixed and resistant
infections is established.
Conclusions:
The ratio of drug sensitive to drug resistant parasites is determined by factors
other than vector abundance. Hence the reduction in vector abundance does not
alter this ratio. It is likely that the presence of hosts that are not treated (wildlife
reservoir or other livestock) stabilizes the ratio of drug resistance.