Investigating the benefits of an adaptive management ... · •Radius of vaccination zones around...
Transcript of Investigating the benefits of an adaptive management ... · •Radius of vaccination zones around...
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Investigating the benefits of an adaptive management approach involving emergency vaccination using simulated FMD outbreaks
in New ZealandRobert Sanson, Zhidong Yu, Tom Rawdon, Mary van Andel
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Objectives
• Investigate the strategic use of emergency FMD vaccination via an adaptive management approach• i.e. Only vaccinate if there are sufficient early decision
indicators that a large outbreak is developing
• Assess the predictability of these early decision indicators (EDIs)
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Background• New Zealand has a model of FMD that has been
progressively refined and extended since 1993, using InterSpread Plus (ISP)
• A number of modelling studies in New Zealand and elsewhere have demonstrated the benefits of emergency FMD vaccination, particularly during large outbreaks
• However there are time and cost penalties to restore country disease status to FMD Free Without Vaccination under current OIE rules if vaccination is used
• Therefore investigating the optimal use of vaccination to augment Stamping Out
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Background (contd.)
• Recently collaborated with the QUADs countries to explore the predictability of large outbreaks simulated in our respective countries early in the response (particularly first 3 weeks)• Generated 10,000 random introductions of FMD into
each country
• Assessed a set of Early Decision Indicators (EDIs) • Number of IPs at various time points
• Estimated Dissemination Rates (EDR)
• Number of geographical clusters and extents
• Density of humans and livestock populations around the index farm etc.
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Early Decision Indicators (EDIs)
• From these analyses, selected two EDIs that could be used within the ISP modelling system to act together as a complex trigger to implement vaccination if the defined thresholds were exceeded:• Estimated Dissemination Rates (EDR) calculated daily
• Where EDR = IPs t2 / IPs t1
• Cumulative number of IPs at various time points
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EDI Trigger – defined thresholds
Complex Trigger
Time period of the response
(days)
5-day EDR Cumulative IPs
11 - 14 >= 2.0 >= 20
15 - 21 >= 1.5 >= 25
22 - 28 >= 1.5 >= 29
29 - 35 >= 1.5 >= 32
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Methods
• 5000 random introductions into northern New Zealand
• 4 broad strategies:• Stamping Out only (SO)
• SO + Vaccination triggered by the complex EDI trigger operating between days 11 - 35 of the response (TRV)
• SO + Vaccination randomly started between days 11 – 35 (VAC)
• SO + Vaccination started on Day 21 of the response (VACf)
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Methods (contd.)
• Randomise a number of other parameters for each iteration:• Airborne spread on/off
• Numbers of personnel available for different tasks• 6 personnel types, each varied randomly from 7 ~ 1000
• Radius of vaccination zones around IPs (1 – 5 km)
• Whether lifestyle farms (smallholders) are vaccinated
• Whether all farm types are vaccinated or only those with cattle
• Number of traces investigated per person per shift
• Number of farms per day that a surveillance Vet can visit
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Outputs• Type of Primary Case farm
• Density of livestock around Primary Case
• Time to first detection (days)
• If and when the EDI trigger fired (recorded for all simulations, even though only used by TRV strategy to modify the response)
• # doses of vaccine used
• # IPs
• Length of response (up to a maximum of 365 days)
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Statistical Analyses• Descriptive and uni-variable
• Boxplots / Kruskal Wallis tests
• Multi-variable• Logistic & negative binomial regression
• Machine Learning Tree partitioning approaches• CART / Random Forests / Boosted Regression Trees
• Performance of the two complex EDI triggers via 2x2 contingency tables:• Se / Sp / PPV / NPV
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Results
• 13 / 5000 (0.26%) where disease died out without being detected
• 605 iterations (12.1%) where disease failed to spread off Primary Case (single outbreaks)
• Highly skewed with extreme outliers due to insufficient manpower resources
• Excluding the outliers:• Mean IPs 30.4, Median 10, Range 1 – 751
• Mean duration 27.1 days, Median 19, Range 1 - 217
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Negative Binomial Regression – significant variables
• IPs• EDI Trigger fired
• Strategy (Vaccination protective but TRV best)
• Time to first detection
• Type of Primary Case farm
• Number of Livestock Technicians and Slaughtermen
• Duration• EDI Trigger Fired
• Strategy (Vaccination protective but TRV best)
• Time to first detection
• Type of Primary Case farm
• # Farms that a Vet can visit per day
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Relative Benefits of VaccinationIPs Duration
Kruskal Wallis p=0.049 Kruskal Wallis p<0.0001
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Doses used for Vaccination Strategies
Kruskal Wallis p<0.0001
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Performance of 5d EDI Trigger on IPs
IPs > 32 (lg)
IPs <= 32 (sm)
Totals
Trigger + 142 147 289
Trigger - 1 290 291
Totals 143 437 580
Sensitivity (Se) = 0.993
Specificity (Sp) = 0.664
Positive predictive value (PPV) = 0.491
Negative predictive value (NPV) = 0.997
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Performance of 5d EDI Trigger on Duration
> 40d
(long)
<= 40d
(short)
Totals
Trigger + 130 159 289
Trigger - 6 285 291
Totals 136 444 580
Se = 0.956Sp = 0.642PPV = 0.45NPV = 0.979
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Conclusions• An adaptive management approach to
implementing vaccination only when there was an indication that a large outbreak was developing (TRV) was the most effective strategy
• The EDI trigger was very sensitive to detecting large outbreaks. NPV was also very high – which means that if an outbreak was predicted to be small, generally it turned out to be small.
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Conclusions (contd.)
• Examining the extreme outliers, # Vets available was of crucial importance for managing large outbreaks. If <= 43 Vets available, the disease could become endemic in New Zealand
• Vaccinating only farms with cattle was no better or worse than vaccinating all farm types
• Vaccinating Lifestyle / smallholder farms reduced duration but not the number of IPs amongst the ‘moderate’ (expected) outbreaks
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Acknowledgements
• The Ministry for Primary Industries (MPI) for funding the project
• EpiSoft for allowing use of the InterSpread Plus modelling system
• AsureQuality Limited for providing access to farm and animal data from AgriBase