to footandmouth disease outbreaks Will Probert Research Fellow Tildesley Lab School of Life Sciences Mathematics Institute University of Warwick UK Acknowledgements Mike Tildesley ID: 1045155
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1. Real-time updating in emergency responseto foot-and-mouth disease outbreaksWill ProbertResearch Fellow, Tildesley LabSchool of Life Sciences, Mathematics InstituteUniversity of Warwick, UK
2. AcknowledgementsMike Tildesley WarwickChris Jewell LancasterMarleen Werkman CVI, NetherlandsMatt Ferrari Penn StateKat Shea Penn StateMatt Keeling WarwickMike Runge USGSChris Fonnesbeck VanderbiltSatoshi Sekiguchi MiyazakiYoshitaka Goto MiyazakiEcology and Evolution of Infectious Disease NSF/NIH/BBSRC 1 R01 GM105247-01
3. In disease control problems, a relationship exists between: • That is, more informed decisions are made with more information • Yet, in emergencies we cannot wait for info to accrue; we must act3InformationaccrualCapitalizing upon information by taking action
4. How does information accrual using near real-time updating impact control recommendations when using epidemiological models?
5. 5Severity• >2000 infected premises over 230,000 km2 (across UK)• 292 infected premises over 8,000 km2 (just Miyazaki prefecture)Distribution at time of confirmation• Multiple foci of infection• Single focus of infectionControl actions applied• Culling only• Vaccination due to limits on carcass disposalUK 2001; Miyazaki 2010Contrasting outbreaksMiyazaki prefecture (Muroga et al., 2001)
6. CasesInfectedCulled24 Dec2001Control AControl BControl CPredictivemodelDataTotal culls26 Feb200126 Feb2001
7. CasesInfectedCulled24 Dec2001Control AControl BControl CPredictivemodelData26 Feb2001Projections are conditional on:• parameter estimates• state of the outbreak
8. CasesInfectedCulledControl AControl BControl CPredictivemodelData26 Feb2001Confirmed InfectedSusceptible
9. CasesControl AControl BControl CData26 Feb2001Confirmed InfectedSusceptibleUndetected infectionI =12.5**62.1We’re also estimatingthe infection times of undetected infections
10. • parameter estimates• state of the outbreak • known infecteds • inferred infectedsCasesInfectedCulled24 Dec2001Control AControl BControl CPredictivemodelData26 Feb2001Projections are conditional on:
11. CasesInfectedCulled24 Dec2001Control AControl BControl CPredictivemodelDataTotal culls26 Feb20015 Mar20015 Mar2001
12. CasesInfectedCulled24 Dec2001Control AControl BControl CPredictivemodelDataTotal culls26 Feb20015 Mar200112 Mar20015 Mar2001
13. CasesInfectedCulled24 Dec2001Total culls26 Feb20015 Mar20013 Sep2001Control AControl BControl CForward projections based upon ‘accrued’ information
14. Update parameters with new data McMC estimation methods (Jewell et al. (2009) Interface) Species-specific, herd-level, infection model Joint distribution of transmission parameters and undetected IPs Conditional on true outbreak until that week Generate forward projections Under several control strategies Based upon confirmed IPs and undetected IPs Keeling et al. (2001) Science; Tildesley et al. (2008) Proc B.For each week:
15. CasesInfectedCulled24 Dec2001Control AControl BControl CPredictivemodelTotal culls26 Feb20015 Mar2001DataForward projections based upon ‘complete’ information
16. UK
17. NoneExpected no. undetect IPs(log10)Week 2Week 3AccruedCompleteAccruedCompleteUK
18. Week 2Week 3AccruedCompleteAccruedCompletePr(undetected IP)Japan
19. Projections of outbreak size (total culls)Rankings of control actionsProportion of simulations in which control was optimalTime
20. Projections of outbreak size (total culls)Rankings of control actionsProportion of simulations in which control was optimalCulling of infected premises (IP)Culling of IP and dangerous contacts (DC)Culling of IP, DC andcontiguous premises
21. Projections of outbreak size (total culls)Rankings of control actionsProportion of simulations in which control was optimalCulling of infected premises (IP)Culling of IP and dangerous contacts (DC)Culling of IP, DC andcontiguous premisesRing culling at 3kmRing culling at 10km
22. Projections of outbreak size (total culls)Rankings of control actionsProportion of simulations in which control was optimalCulling of infected premises (IP)Culling of IP and dangerous contacts (DC)Culling of IP, DC andcontiguous premisesRing culling at 3kmRing culling at 10kmRing vaccination at 3kmRing vaccination at 10km
23. Projections of outbreak sizeRankings of control actionsProportion of simulations in which control was optimalUK
24. Compared with projections based upon all data(‘complete’ information)UK
25. Early-on, projections over-estimate outbreak severityUK
26. However, relative rankings are resolved after 5 weeksUK
27. Week 3 sees a large change in rankings of optimal controlUK
28. Week 2Week 3AccruedCompleteAccruedCompleteUK
29. Japan
30. Japan
31. • Early rankings of control actions may be robust despite poor early projections • focus on relative performance of actions; not absolute size of projections • reassuring our use of models in the early stages of an outbreak• Optimal action strongly influenced by spatial distribution of IPs • Important to re-evaluate performance of actions as state changes • Estimation of both infection parameters and undetected IPs is intertwined• Control needs to adapt to the specific realisation of the outbreak at hand • Reinforcement learning methods can offer solutionsSummary
32. Thank youAcknowledgementsMike Tildesley WarwickChris Jewell LancasterMarleen Werkman CVI, NetherlandsMatt Ferrari Penn StateKat Shea Penn StateMatt Keeling WarwickMike Runge USGSChris Fonnesbeck VanderbiltSatoshi Sekiguchi MiyazakiYoshitaka Goto MiyazakiEcology and Evolution of Infectious Disease NSF/NIH/BBSRC 1 R01 GM105247-01Will ProbertResearch FellowUniversity of Warwick, Coventry, UKEmail: w.probert@warwick.ac.ukWeb: www.probert.co.nz