Predicting an Epidemic A Quantitative Assessment of TSE Sampling Data to Predict Outbreak Magnitude Aspen Shackleford HONR299J
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
How are outbreak predictions made? Reporting Statistical Models
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),
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
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),
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
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
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
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
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),
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
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),
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),
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),
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),
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