modelling to support rinderpest outbreaks preparedness
TRANSCRIPT
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Name Benjamin McMahonName Benjamin McMahon
Title Scientist at Los Alamos National LaboratoryTitle Scientist at Los Alamos National Laboratory
Country USACountry USA
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Modeling tool to support rinderpest outbreak preparedness
Benjamin McMahon, Paul Fenimore, Judy Mourant, Nick Hengartner, Carrie Manore, Mira
Dimitrijevic, Paul Rossiter, Samia MetwallyLos Alamos National Laboratory, FAO
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Past rinderpest outbreaks
• Ethiopia, 1890, 90% cattle mortality• Zimbabwe 1896, 90% mortality• South Africa 1895-1896, 66.6% of 1.6 million cattle died or
slaughtered• Nigeria and Chad basin 1982-84, 2 million deaths?• Tanzania and Kenya, 1964-1968. Wildebeest population
increases from 250,000 to >1million after eradication of rinderpest from cattle
• Pakistan, 1992, 40,000-50,000 cattle
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Our motivation for modeling tool
- Quantify potential impact and motivate virus destruction & sequestration
- Significant stocks of rinderpest virus are maintained under laboratory conditions for responding to contingencies (outbreaks) and research.
- These stocks create a potential risk for re-initiation of rinderpest infections. - Historical outbreaks in cattle need to be considered carefully when
predicting the course of potential future outbreaks because of changes in:- The level of pre-existing immunity- The density and type (dairy, range, transported) of cattle- The availability of surveillance and mitigations, such as:
- Vaccination- Short range movement controls and hygiene- Long range movement controls- Culling
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A rinderpest outbreak could be devastating: Simulated spread of rinderpest in 101 days after point introduction to USA
Manore, McMahon, Fair, Hyman, Brown, & Labute, “Disease properties, geography, and mitigation strategies in a simulation spread of rinderpest across the United States” Vet. Res., 42:1 (2011).
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What modeling can and cannot do
• Modeling can:– Translate historical events to contemporary situations, using best
available understanding of how diseases progress– Provide specific numbers and their dependencies, to guide planning
• Modeling cannot:– Predict the future. The actual course of the epidemic depends on
preparedness measures, responses, the particular viral strain, and random events
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Determinants of disease spread Frequency of long and short range cattle movement
IS
SS
S
S
FAO modeled cattle density
5 km spatial resolution, from http://www.fao.org/3/a-a1259e.pdf
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Comparing types of mitigation
Num
ber of Counties Infected
General impact of three types of mitigation
Time (days)
No Control
Movement Control
Vaccination
Culling
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Disease progression rates determine required timescale of intervention (a couple of weeks).
S = SusceptibleE = Exposed (incubating)I = InfectiousH = Seriously diseasedD = DeadR = Recovered (Immune)VS= Vaccinated, still susceptibleV = Vaccinated, immune
S E I H D
RVVS
Disease progression scheme
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The planning tool http://bsvepi.lanl.gov/rinderpest (password protected)
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Phases of epidemic1. Exponential growth (R0=5)2. Short-range movement
controls and hygiene measures
3. Plus vaccination4. Long-range spread5. Control6. Eradication
Eradication
Control
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MitigationsInfectivity
( * 5 days = R0 ~ 5)Number of sick cattle when epidemic is identified
(25 cows)Further delay and effectiveness of short-range movement restrictions & hygiene
(7 days, cut in half)Further delay and rate of vaccination
(4 weeks, 10,000 cattle / day)Extent of long-range cattle trade occurring, and screening by illness
(0.15% cattle not seriously ill moved per day)
Total dead cattle = 885 cowsVaccine doses given = 315,000 doses
Area affected by epidemic = 17,000 km2
Time to epidemic peak = 10 weeksTime to eradication = 17 weeks
Consequence metrics
An example epidemic:
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Short-range movement controls are immediate
upon epidemic identification
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Further observations• Long-range transport of animals has been added to model, with user-selected
destination and number of healthy animals moved. Infectious animals can be selected from Incubating, Ill, and seriously ill fractions.
• Mixing lengths in model can account for differences between, for example, dairy and range cattle.
• Virulent strains of rinderpest have a mortality rate of 80% and R0 ~ 5.• Less virulent strains of rinderpest have lower mortality rate and R0.• It is possible that naïve populations of cattle select for virulent strains as an
epidemic progresses.
• Studies of the 2001 FMD epidemic in Britain suggest short range movement controls and hygiene measures can decrease R0 by a factor two.
• The calculations here were for cattle densities of ~150 cattle / km2.
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How to use this tool for planning
• Account for local variations in transmissibility, vaccination rate, distance range of spread, long-range transmission, escape from laboratory, movement controls.
• Convert estimates of dead and vaccinated cattle, duration and geographic extent of epidemic and nature of control measures into costs.
• Estimate required attributes of surveillance system to rapidly identify epidemic, and the corresponding needed size of vaccine stockpile.
• Balance cost of preparedness against acceptance of risk.
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Conclusions & Acknowledgement• We have provided a planning tool which provides
considerable flexibility in simulating a rinderpest outbreak and mitigations
• It needs to be developed and validated in a country-specific manner, in a collaboration between each country and FAO
• The earlier an outbreak is halted, the better.
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Questions & Answers