The within-pond epidemiology of amphibian ranavirus

1. • The within-pond epidemiology of an
amphibian ranavirus
A synthetic modeling approach
Jesse Brunner
Washington State University

2. Questions
• What which feature(s) of host or pathogen biology are
required to reproduce the broad features of ranaviral
outbreaks in pond-breeding amphibians (i.e., Wood frogs,
Rana sylvatica)?
• Initially low prevalence
• Sudden onset of mortality event in mid-to-late summer (even though Rv
introduced at the beginning of or early in the larval period)
• Some metamorphs leave the pond infected
• How important is water-borne transmission vs direct
transmission?
• How important is the heterogeneity in susceptibility we have
seen in laboratory experiments?

3. Model assumptions / conditions
• Large population (40–400
tadpoles / m2)
• Medium-sized pond (600 m2
x 1m deep)
• Only one species: Rana• Only one species: Rana
sylvatica
• Epidemic starts from a
single infected tadpole
• Transmission is (quickly)
saturating function of
density
(Brunner et al. in prep)

4. Initial model
• SIS model with recovery to susceptible state (no immunity)
• No exposed class (immediately infectious)
• Epidemic is far too early

5. Initial model
• SIS model with recovery to susceptible state (no immunity)
• No exposed class (immediately infectious)
• Epidemic is far too early

6. Initial model
• Does a lower rate of transmission help?
• Only if we lower transmission rate by an order of magnitude!
• Then the epidemic is too slow

7. water-borne transmission
• Add term for concentration-specific transmission from water
• Probability of infection from LD50 study in Warne et al. 2011
• Add terms for accumulation and loss of virus in water
• Viral shedding: rough estimates range from 102 to 104 pfu/day in lab
experiments with Ambystoma nebulosum (Storfer et al. in prep, Brunner
unpublished data)
Half-life of ranaviruses ranges from• Half-life of ranaviruses ranges from
• 9.65 days in “unsterile” pond water at 20°C (Nazir et al. 2011)
• 0.57 days in pond water at 20–24°C (Johnson & Brunner in prep; see
poster)

8. water-borne transmission
• Very few tadpoles infected from the water (even with lower
transmission)

9. water-borne transmission
• Does a longer half-life of Rv in water help?
• Even with very long persistence times, water-borne
transmission contributes very few infections

10. water-borne transmission
• What about a greater shedding rate?
Even with a
• low rate of direct transmission,
• long persistence time, &
• high shedding rate
water-borne transmission is still minor
source of infection compared to direct
contacts

11. Metamorphs & Developmental stages
• Add terms for
metamorphosing tadpoles
(susceptible & infected)
• Rate of metamorphosis is
1/duration of larval period (60-
80d)
• Explicitly model
development from Gosner
(1960) stages 20 – 40
• Rate of development is #
stages / duration of larval period

12. Metamorphs & Developmental stages
• Does not change the timing or shape of epidemics

13. • Probability of ranavirus infection and
death changes dramatically with stage
• Modified the transmission terms by
multiplying by the stage-specific odds-
ratio as predicted logistic-regression
Warne et al. 2011
STAGE-SPECIFIC SUSCEPTIBILITY
ratio as predicted logistic-regression

14. STAGE-SPECIFIC SUSCEPTIBILITY
• Timing of the epidemic is right with estimated transmission rate
• See the sharp increase in cases

15. Conclusions
• Water-borne transmission is minor relative to direct
transmission (and negligible under more realistic
assumptions)
• Environmental heterogeneity may slow transmission• Environmental heterogeneity may slow transmission
• Epidemics appear later
• More gradual onset of mortality
• Smaller epidemic
• Stage-specific susceptibility may be key in timing,
dynamics, and outcome of ranaviral outbreaks in Wood frogs

16. Open questions
• How important is transmission from carcasses?
• An important role for scavengers and decomposers?
• Does the stage-specific susceptibility hypothesis hold for
other species?
• Are multihost communities radically different?
• Can these models match real epidemics?