Contact

• workRoom B48 The Mathematical Sciences Building
  University Park
  Nottingham
  NG7 2RD
  UK
• work0115 9514939
• fax0115 9514951
• philip.oneill@nottingham.ac.uk
• http://www.maths.nottingham.ac.uk/personal/pdo

Research Summary

Stochastic models, epidemic models, analysis of infectious disease data, Bayesian inference, Computational Statistics, Markov chain Monte Carlo methods

Selected Publications

• ARISTOTELOUS, G., KYPRAIOS, T. and O'NEILL, P. D., 2023. Posterior Predictive Checking for Partially Observed Stochastic Epidemic Models Bayesian Analysis. 18(4), 1283-1310
• SEYMOUR RG, KYPRAIOS T and O'NEILL PD, 2022. Bayesian nonparametric inference for heterogeneously-mixing infectious disease models Proceedings of the National Academy of Sciences. 119(10),
• SEYMOUR, R. G, KYPRAIOS, T., O'NEILL, P. D. and HAGENAARS, T. J., 2021. A Bayesian Nonparametric Analysis of the 2003 Outbreak of Highly Pathogenic Avian Influenza in the Netherlands. Journal of the Royal Statistical Society: Series C.
• STOCKDALE, JESSICA E, KYPRAIOS, THEODORE and O'NEILL, PHILIP D, 2021. Pair-based likelihood approximations for stochastic epidemic models. Biostatistics (Oxford, England). 22(3), 575-597

• View all publications

• ARISTOTELOUS, G., KYPRAIOS, T. and O'NEILL, P. D., 2023. Posterior Predictive Checking for Partially Observed Stochastic Epidemic Models Bayesian Analysis. 18(4), 1283-1310
• SEYMOUR RG, KYPRAIOS T and O'NEILL PD, 2022. Bayesian nonparametric inference for heterogeneously-mixing infectious disease models Proceedings of the National Academy of Sciences. 119(10),
• STOCKDALE, JESSICA E, KYPRAIOS, THEODORE and O'NEILL, PHILIP D, 2021. Pair-based likelihood approximations for stochastic epidemic models. Biostatistics (Oxford, England). 22(3), 575-597
• SEYMOUR, R. G, KYPRAIOS, T., O'NEILL, P. D. and HAGENAARS, T. J., 2021. A Bayesian Nonparametric Analysis of the 2003 Outbreak of Highly Pathogenic Avian Influenza in the Netherlands. Journal of the Royal Statistical Society: Series C.
• CASSIDY, ROSANNA, KYPRAIOS, THEODORE and O'NEILL, PHILIP D., 2020. Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole-genome-sequence data STATISTICS IN MEDICINE. 39(12), 1746-1765
• THOMPSON, R.N. and ET AL., 2020. Key questions for modelling COVID-19 exit strategies Proceedings of the Royal Society B. 287(1932),
• MUTEB ALHARTHI, THEODORE KYPRAIOS and PHILIP D. O'NEILL, 2019. Bayes Factors for Partially Observed Stochastic Epidemic Models Bayesian Analysis. 14(3), 927-956
• KYPRAIOS, T. and O'NEILL, P.D., 2018. Bayesian nonparametrics for stochastic epidemic models Statistical Science. 33(1), 44-56
• WEI, YINGHUI, KYPRAIOS, THEODORE, O'NEILL, PHILIP D., HUANG, SUSAN S., RIFAS-SHIMAN, SHERYL L. and COOPER, BEN S., 2018. Evaluating hospital infection control measures for antimicrobial-resistant pathogens using stochastic transmission models: Application to vancomycin-resistant enterococci in intensive care units STATISTICAL METHODS IN MEDICAL RESEARCH. 27(1), 269-285
• STOCKDALE JE, KYPRAIOS T and O'NEILL PD, 2017. Modelling and Bayesian analysis of the Abakaliki smallpox data. Epidemics. 19, 13-23
• VERYKOUKI, E., KYPRAIOS, T. and O'NEILL, P. D., 2016. Modelling the effect of antimicrobial treatment on carriage of hospital pathogens with application to MRSA. Biostatistics. 17(1), 65-78
• WORBY, C. J., O'NEILL, P. D., KYPRAIOS, T., ROBOTHAM, J. V., DE ANGELIS, D., CARTWRIGHT, E. J. P., PEACOCK, S. J. and COOPER, B. S., 2016. Reconstructing transmission trees for communicable diseases using densely sampled genetic data. Annals of Applied Statistics. 10(1), 395-417
• XIAOGUANG XU, THEODORE KYPRAIOS and PHILIP D O'NEILL, 2016. Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes Biostatistics. 17(4), 619-633
• BALL FG, KNOCK ES and O'NEILL PD, 2015. Stochastic epidemic models featuring contact tracing with delays. Mathematical biosciences. 266, 23-35
• KNOCK ES and O'NEILL PD, 2014. Bayesian model choice for epidemic models with two levels of mixing. Biostatistics (Oxford, England). 15(1), 46-59
• S.E.F. SPENCER and P.D. O'NEILL, 2013. Assessing the Impact of Intervention Delays on Stochastic Epidemics Methodology and Computing in Applied Probability. 15, 803-820
• WORBY CJ, JEYARATNAM D, ROBOTHAM JV, KYPRAIOS T, O'NEILL PD, DE ANGELIS D, FRENCH G and COOPER BS, 2013. Estimating the effectiveness of isolation and decolonization measures in reducing transmission of methicillin-resistant Staphylococcus aureus in hospital general wards. American journal of epidemiology. 177(11), 1306-13
• VAN BOVEN, M, RUIJS, W.L, WALLINGA, J., O'NEILL, P.D. and HAHNE, S., 2013. Estimation of mumps vaccine efficacy and critical vaccination coverage Public Library of Science Computational Biology. 9(5),
• O'NEILL, PD and WEN, CH, 2012. Modelling and inference for epidemic models featuring non-linear infection pressure Mathematical Biosciences. 36-48
• SPENCER, S.E.F. and O'NEILL, P.D., 2011. The probability of containment for multitype branching process models for emerging epidemics Journal of Applied Probability. 48(1), 173-188
• BRITTON, T., KYPRAIOS T. and O'NEILL P.D., 2011. Inference for epidemics with three levels of mixing: methodology and application to a measles outbreak Scandinavian Journal of Statistics, Theory and Applications. 38(3), 578-599
• KYPRAIOS, T, O'NEILL, PD, JONES, DE, WARE, J, BATRA, R, EDGEWORTH, JD and COOPER, BS, 2011. Effect Of Systemic Antibiotics And Topical Chlorhexidine On Meticillin-Resistant Staphylococcus Aureus Carriage In Intensive Care Unit Patients Journal Of Hospital Infection. 79(3), 222-226
• BALL, F.G., KNOCK, E.S. and O'NEILL, P.D., 2011. Threshold behaviour of emerging epidemics featuring contact tracing Advances in Applied Probability. 43(4), 1048-1065
• KYPRAIOS, T., O'NEILL, P.D, HUANG, S.S., RIFAS-SHIMAN, S.L. and COOPER, B.S., 2010. Assessing the role of undetected colonization and isolation precautions in reducing Methicillin-Resistant Staphylococcus aureus transmission in intensive care units BMC Infectious Diseases. 10, 29
• ONEILL, P. D., 2010. Introduction and snapshot review: Relating infectious disease transmission models to data Statistics in Medicine. VOL 29(NUMBER 20), 2069-2077
• VAN BOVEN, M., KRETZSCHMAR, M., WALLINGA, J., O'NEILL, P. D., WICHMANN, O. and HAHNE, S., 2010. Estimation of measles vaccine efficacy and critical vaccination coverage in a highly vaccinated population Journal Of The Royal Society Interface. 7, 1537-1544
• O'NEILL, P.D., 2009. Bayesian inference for stochastic multitype epidemics in structured populations using sample data Biostatistics. 10(4), 779-791
• BALL, F.G, KNOCK, E.S. and O'NEILL, P.D., 2008. Control of emerging infectious diseases using responsive imperfect vaccination and isolation. Mathematical Biosciences. 216(1), 100-113
• CLANCY, D. and O'NEILL, P.D., 2008. Bayesian estimation of the basic reproduction number in stochastic epidemic models Bayesian Analysis. 3(4), 737-758
• BALL, F., O'NEILL, P.D. and PIKE, J., 2007. Stochastic epidemic models in structured populations featuring dynamic vaccination and isolation Journal of Applied Probability. 44(3), 571-585
• CLANCY,D. and O'NEILL,P.D., 2007. Exact Bayesian inference and model selection for stochastic modelsof epidemics among a community of households Scandinavian Journal of Statistics, Theory and Applications. 34, 259-274
• O'NEILL, P.D., 2007. Constructing population processes with specified quasi-stationary distributions. Stochastic Models. 23, 439-449
• BECKER, N.G., BRITTON, T. and O'NEILL, P.D., 2006. Estimating vaccine effects from studies of outbreaks in household pairs Statistics in Medicine. 25(6), 1079-1093
• DEMIRIS, N. and O'NEILL, P.D., 2006. Computation of final outcome probabilities for the generalised stochastic epidemic Statistics and Computing. 16, 309-317
• O'NEILL, P.D. and MARKS, P.J., 2005. Bayesian model choice and infection route modelling in an outbreak of Norovirus Statistics in Medicine. 24(13), 2011-2024
• DEMIRIS, N. and O'NEILL, P.D., 2005. Bayesian inference for stochastic multitype epidemics in structured populations via random graphs Journal of the Royal Statistical Society, Series B: Statistical Methodology. 67(5), 731-745
• DEMIRIS, N. and O'NEILL, P.D., 2005. Bayesian inference for epidemics with two levels of mixing Scandinavian Journal of Statistics, Theory and Applications. VOL 32(NUMBER 2), 265-280
• CLOUGH, H. E, CLANCY, D, FRENCH, N. P and O'NEILL, P. D. AND ROBINSON, S. E., 2005. A Bayesian method for quantifying uncertainty associated with numbers of E. Coli 0157 present in faecal samples Biometrics. 61, 610-616
• HOHLE, M., JORGENSEN, E. and O'NEILL, P. D., 2005. Inference in disease transmission experiments by using stochastic epidemic models Journal of the Royal Statistical Society, Series C (Applied Statistics). VOL 54(NUMBER 2), 349-366
• ONEILL, P. D., 2003. Perfect simulation for Reed-Frost epidemic models Statistics and Computing. VOL 13(NUMBER 1), 37-44
• CLOUGH, H. E., CLANCY, D., O'NEILL, P. D. and FRENCH, N. P., 2003. Bayesian methods for estimating pathogen prevalence within groups of animals from faecal-pat sampling Preventive Veterinary Medicine. VOL 58(NUMBER 3-4), 145-169
• BECKER, N. G., BRITTON, T. and O'NEILL, P. D., 2003. Estimating Vaccine Effects on Transmission of Infection from Household Outbreak Data Biometrics. VOL 59(NUMBER 3), 467-475
• HAYAKAWA, Y., O'NEILL, P. D., UPTON, D. and YIP, P. S., 2003. Bayesian Inference for a Stochastic Epidemic Model with Uncertain Numbers of Susceptibles of Several Types Australian and New Zealand Journal of Statistics. VOL 45(NUMBER 4), 491-502
• BALL, F.G., BRITTON, T. and O'NEILL, P.D., 2002. Empty confidence sets for epidemics, branching processes and Brownian motion Biometrika. VOL 89(PART 1), 211-224
• BRITTON, T. and O'NEILL, P.D., 2002. Bayesian inference for stochastic epidemics in populations with random social structure Scandinavian Journal of Statistics. 29(3), 375-390
• O'NEILL,P.D., 2002. A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo methods. Mathematical Biosciences. 180, 103-114
• O'NEILL,P.D. and BECKER,N.G., 2001. Inference for an epidemic when susceptibility varies. Biostatistics. 2(1), 99-108
• CLANCY,D., O'NEILL,P.D. and POLLETT,P.K., 2001. The long-term behaviour of an open population epidemic model. Methodology and Computing in Applied Probability. 3, 75-95
• O'NEILL,P.D., BALDING,D.J., BECKER,N.G., EEROLA,M. and MOLLISON,D., 2000. Analyses of infectious disease data from household outbreaks by Markov Chain Monte Carlo methods. Journal of Applied Statistics. 49, 517-542
• O'NEILL, P. D. and ROBERTS, G. O., 1999. Bayesian inference for partially observed stochastic epidemics Journal of the Royal Statistical Society, Series A (Statistics in Society). VOL 162(NUMBER 1), 121-130
• BALL, F. and O'NEILL, P., 1999. The distribution of general final state random variables for stochastic epidemic models Journal of Applied Probability. VOL 36(PART 2), 473-491
• O'NEILL,P.D., 1999. On division-within-division cell models IMA Journal of Mathematics Applied in Medicine and Biology. 16, 395-405
• CLANCY, D. and O'NEILL, P., 1998. Approximation of epidemics by inhomogeneous birth-and-death processes Stochastic Processes and Their Applications. VOL 73(ISSUE 2), 233-245
• O'NEILL, P. and BUNDAY, B., 1997. The transient state probabilities of the M/M/1/ì queue via a random walk approach The Mathematical Scientist. VOL 22(NUMBER 1), 32-36
• O'NEILL, P., 1997. An epidemic model with removal-dependent infection rate Annals of Applied Probability. VOL 7(NUMBER 1), 90-109
• O'NEILL, P., 1996. Strong approximations for some open population epidemic models Journal of Applied Probability. VOL 33(NUMBER 2), 448-457