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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