Investigating the benefits of an adaptive management approach involving emergency vaccination using simulated FMD outbreaks in New Zealand

Transcription

1 Investigating the benefits of an adaptive management approach involving emergency vaccination using simulated FMD outbreaks in New Zealand Robert Sanson, Zhidong Yu, Tom Rawdon, Mary van Andel

2 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)

3 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

4 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.

5 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 t 2 / IPs t 1 Cumulative number of IPs at various time points

6 EDI Trigger defined thresholds Time period of the response (days) Complex Trigger 5-day EDR Cumulative IPs >= 2.0 >= >= 1.5 >= >= 1.5 >= >= 1.5 >= 32

7 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 of the response (TRV) SO + Vaccination randomly started between days (VAC) SO + Vaccination started on Day 21 of the response (VACf)

8 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

9 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)

10 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

11 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 Mean duration 27.1 days, Median 19, Range 1-217

12 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

13 Relative Benefits of Vaccination IPs Duration Kruskal Wallis p=0.049 Kruskal Wallis p<0.0001

14 Doses used for Vaccination Strategies Kruskal Wallis p<0.0001

15 Performance of 5d EDI Trigger on IPs IPs > 32 (lg) IPs <= 32 (sm) Totals Trigger Trigger Totals Sensitivity (Se) = Specificity (Sp) = Positive predictive value (PPV) = Negative predictive value (NPV) = 0.997

16 Performance of 5d EDI Trigger on Duration > 40d <= 40d Totals (long) (short) Trigger Trigger Totals Se = Sp = PPV = 0.45 NPV = 0.979

17 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.

18 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

19 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