1. Predicting an Epidemic A Quantitative Assessment of TSE Sampling Data to Predict Outbreak Magnitude Aspen Shackleford HONR299J

2. Variant Creutzfeldt-Jakob Disease (vCJD)  Linked to BSE  Unknown number of individuals who may be infected  Iatrogenic contamination The National CJD Research & Surveillance Unit. (2014). Variant Creutzfeldt-Jakob Disease current data (March 2014) [Data file]. Retrieved from http://www.cjd.ed.ac.uk/documents/worldfigs.pdf

3. How are outbreak predictions made?  Reporting  Statistical Models

4. Dealler & Kent, 1995  Age of onset of clinical BSE symptoms was sampled  Results  Decrease in peak age of onset  Led to further investigation of under-reporting Dealler, S.F. & Kent, J.T. (1995). BSE: an update on the statistical evidence. British Food Journal, 97 (8), 3-18. http://docserver.ingentaconnect.com

5. Under-Reporting  MAFF Requirements  2 visits by a veterinarian  Slaughter and send in tissue sample  Case denial  Dealler and Kent Suspicion  Paul Brown’s predictions  Farmer Initiative

6. Paul Brown’s Prediction Brown, Paul. (2004). Mad Cow Disease in cattle and human beings: Bovine spongiform encephalopathy provides a case study in how to manage risks while still learning facts. American Scientist, 92 (4), 334-341. http://www.jstor.org/stable/27858422

7. Under-Reporting  MAFF Requirements  2 visits by a veterinarian  Slaughter and send in tissue sample  Case denial  Dealler and Kent Suspicion  Paul Brown’s predictions  Farmer Initiative

8. Dealler and Kent Statistical Model (1995)  Based upon data collection year ( i) and bovine age ( j)  Follows a Poisson Distribution  Predicts the expected number of deaths at age j in year i from 1984 to 2001  Reporting effect and parameters compensated for under-reporting

9. Poisson Distribution  Requirements  Used to determine the frequency of an abnormal event  Graphical representation  r-curve closer to zero signifies a rare event The Warring States Project. (2007, August 24). Statistics: The Poisson distribution. Retrieved from http://www.umass.edu/wsp/resources/poisson/index.html

10. Dealler and Kent Statistical Model (1995)  Based upon data collection year and bovine age  Follows a Poisson Distribution  Predicts the expected number of deaths at age j in year i from 1984 to 2001  Reporting effect and parameters compensated for under-reporting

11. Results  Model predictions from 1984 through 2001 (solid line)  Actual reports from 1984 to 1993 (dotted line)  Under-reporting  Peak in 1994  Overlap Dealler, S.F. & Kent, J.T. (1995). BSE: an update on the statistical evidence. British Food Journal, 97 (8), 3-18. http://docserver.ingentaconnect.com

12. Hagenaars et al., 2006  Scrapie  Within-flock model  Sheep age  Genotypes: ARR (0.45), ARQ (0.5), VRQ (0.05)  Between-flock model  Model determines duration and magnitude of an outbreak

13.  N - flock-size  n - geometric mean of the size distribution  c 1, c 2 – constants Hagenaars, T.J, Donnelly, C.A., & Ferguson, N.M. (2006). Epidemiological analysis of data for scrapie in Great Britain. Epidemiology and Infection, 134 (2), 359-367. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2870388/

14. Within Flock Results  Large case rate  Case rate less than 5 per year  Average number of cases: 2.8 per year Hagenaars, T.J, Donnelly, C.A., & Ferguson, N.M. (2006). Epidemiological analysis of data for scrapie in Great Britain. Epidemiology and Infection, 134 (2), 359-367. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2870388/

15.  γ – the rate of recovery of affected farms  λ - the rate per capita that a farm becomes affected  t - time (in years)  a t>0 - the number of sheep flocks that have experienced at least one BSE case since the starting year Hagenaars, T.J, Donnelly, C.A., & Ferguson, N.M. (2006). Epidemiological analysis of data for scrapie in Great Britain. Epidemiology and Infection, 134 (2), 359-367. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2870388/

16. Between Flock Results  Low basic reproduction rate  High basic reproduction rate  Breeding for resistance  ARR/ARR genotype  Changes in management Hagenaars, T.J, Donnelly, C.A., & Ferguson, N.M. (2006). Epidemiological analysis of data for scrapie in Great Britain. Epidemiology and Infection, 134 (2), 359-367. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2870388/

17. Conclusion  Statistical models are widely used  Statistical models offer information that uses parameters and constraints that model real life  The predictions of statistical models can be used to make decisions about how to best prevent an outbreak that threatens human and animal populations