Willeberg, Preben

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Downloaded from orbit.dtu.dk on: Dec 20, 2017A simple decision tool to help optimize the control strategy 2 weeks into a Danish FMDepidemicWilleberg, PrebenPublication date:2012Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Willeberg, P. (2012). A simple decision tool to help optimize the control strategy 2 weeks into a Danish FMDepidemic [Sound/Visual production (digital)]. Closing seminar for Optimizing the control of foot-and-mouthdisease in Denmark by simulation, Copenhagen, Denmark, 09/05/2012A simple decision tool to help optimize the control strategy 2 weeks into a Danish FMD epidemic  Preben Willeberg    DVM, PhD, Dr.med.vet., Dr.med.vet.h.c. Senior Veterinary Global Health Specialist Center for Animal Disease Modelling and Surveillance School of Veterinary Medicine University of California, Davis  Adjunct Professor  Adjunct Professor  “Blind belief in authority is the greatest enemy of truth”  Albert Einstein Transparency and documentation to substantiate implementation of FMD control measures are prerequisites in achieving political and public acceptance Modelling may provide part of the answer to those needs Few of us have any practical experience with FMD control  • The choice of whether or not to apply emergency vaccination is probably the most difficult decision facing the authorities when disease breaks out in an erstwhile FMD free country.  • Effective computational models should be actively financed for a range of outbreak scenarios to assist objective decision-making and minimise bureaucratic delays in vaccine application. • Although the option of emergency vaccination is included in the EU contingency plans, the qualifying conditions for vaccination have not been finally determined.  • Contingency planning should include provision for emergency vaccination and must address the complex decisions of not only when, where, and how to apply vaccine but also its economic consequences.  • Computer modelling may be a useful aid to cost benefit and decision support systems in this context. Planning must be detailed and regularly reviewed. Comparative Immunology, Microbiology & Infectious Diseases 25 (2002) 345–364 Council Directive 2003/85/EC Original publication: First-fortnight incidence (FFI) is a modelling parameter that can be used to predict both the prevalence and duration of a foot-and-mouth disease (FMD) epidemic at regional and national levels. With an indication of how long an epidemic might last by the end of week two, it becomes possible to estimate whether vaccination would be economically viable from the start of an epidemic, ABSTRACT: Argentina 2001 FMD epidemic Number of detected herds by 17 provinces Vaccination strategies varied throughout the epidemic  Use of FFI with DTU-DADS model simulations of FMD in Denmark  y = 2,1298x - 0,913 R² = 0,346 0 100 200 300 400 500 600 700 0 50 100 150 200 250 No. of herds  Day 14 detected herds  Herds detected - day 14 Total - day 14 Linear (Total - day 14) y = 0,8335x + 29,49 R² = 0,1439 0 50 100 150 200 250 300 350 0 50 100 150 200 250 Days Day 14 detected herds  Duration - 14 EDP - 14 Linear (EDP - 14) Use of FFI with DTU-DADS model simulations of FMD in Denmark  y = 3E+07x + 4E+09 R² = 0,2649 0 2E+09 4E+09 6E+09 8E+09 1E+10 1,2E+10 1,4E+10 1,6E+10 0 50 100 150 200 250 Danish kroner Day 14 detected herds Costs Costs Linear (Costs) Use of FFI with DTU-DADS model simulations  of FMD in Denmark  Decision tool step 1 0 10 20 30 40 50 60 0 1 2 3 4 5 6 7 8 9 11 16 21 27 Number of epidemics Column # Basic scenario Basic scenario Histogram column # Detected herds Number of epidemics 0 < 10 52 1 10 - 19 26 2 20 - 29 18 3 30 - 39 11 4 40 - 49 10 5 50 - 59 3 6 60 - 69 2 7 70 - 79 5 8 80 - 89 4 9 90 - 99 1 11 110 - 119 3 16 160 - 169 2 21 210 - 219 1 27 270 - 279 1 Grand total 139 • At day 14 during an outbreak the actual number of detected herds is known, e.g. 15 herds  • Among the simulated epidemics under the basic control scenario we select the ones with 15 detected herds at day 14, i.e. 139 epidemics out of 4,458 simulations  • The distribution of the number of detected herds at the end minus the number at day 14:  Suppressive vaccination scenario Histogram column # Detected herds Number of epidemics 0 < 10 61 1 10 - 19 28 2 20 - 29 18 3 30 - 39 13 4 40 - 49 5 5 50 - 59 6 6 60 - 69 6 7 70 - 79 1 13 130 - 139 1 Grand total 139 0 10 20 30 40 50 60 70 0 1 2 3 4 5 6 7 13 Number of epidemics Column # Suppressive vaccination scenario   • Repeat step 1 with the vaccination scenario for the same 139 epidemics • Compare with effect of vaccination at day 14: Decision tool step 2 Decision scenario A Scenario A Subsequent period <100 herds =>100 herds Total Day 14 =<20 herds 1879 110 1989 >20 herds 1697 772 2469 Total 3576 882 4458 Sensitivity: 0.88 Specificity: 0.53 Neg. p.v.: 0.94  Pos. p.v.:                          0.31   Predictive values (p.v.): Decision scenario D Scenario D Subsequent period <100 herds =>100 herds Total Day 14 =<50 herds 3377 526 3903 >50 herds 199 356 555 Total 3576 882 4458 Sensitivity: 0.40 Specificity: 0.94 Neg. p.v.: 0.87 Pos. p.v.:  0.64 Predictive values (p.v.): DTU-DADS Model results from Denmark: Epidemic outcome parameters Control scenarios Basic scenario Pre-emptive depopulation in 500 m zones Suppressive emergency vaccination in 1,000 m zones Cumulative number of infected herds 57 (11-273) 48 (8-174) 47 (9-182) Epidemic duration (days) 56 (19-151) 44 (15-105) 44 (15-97) Epidemic size  (km) 386 (80-698) 363 (63-646) 363 (67-646) Epidemic costs  (× €106) 547 (411-947) 511 (395-770) 513 (395-751) The median, 5th and 95th percentiles of epidemic outcome parameters in the three alternative control strategies  for the 4,458 out of 5,000 simulated epidemics that lasted > 14 days following detection of the first infected herd.    CONCLUSIONS (1) • Each country should use modelling continuously adapted to suit their national situation with regard to:  o Input parameters such as: • Population sizes and densities • Location of farms • Movement patterns • Available resources o Output relevance and priorities: • Economic losses • Number of animals killed • Number of herds infected o Strategic priorities: • Importing or exporting • Duration of basic measures phase • Vaccination ”to live” or ”to kill” and/or zonal culling     CONCLUSIONS (2) • Contingency planning should be adapted through application and support of continuous national modelling activities:  • To cater for national and international developments  • To modify input parameters as needed and update output estimates • To update and prioritize strategies using feed-back loop with model data • To ensure consistency between mandated activities and available resources • To support the exercising of decision making during outbreaks • To estimate effectiveness and efficiency of alternative preventive measures • To estimate effectiveness and efficiency of surveillance and early warning activities     CONCLUSIONS (3) • Models are just one tool for providing scientific advice, and their results should be evaluated in conjunction with experience from experimental studies, field studies and scientific wisdom.  • International collaborations such as those supported by the World Organisation for Animal Health (OIE) and the European Commission for the Control of Foot-and-Mouth Disease (EUFMD) can help address validation issues and improve the utility of models for emergency disease management.  We have learned a lot from modelling of FMD –   now it is time to put that knowledge into practical use We need not only accept and be willing to use models –  all countries should start modelling activities to support their contingency planning and emergency preparedness 

A simple decision tool to help optimize the control strategy 2 weeks into a Danish FMD epidemic

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A simple decision tool to help optimize the control strategy 2 weeks into a Danish FMD epidemic

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