Associate Professor Kathryn Glass

kathryn glass

Contacts

Senior Fellow

Research

Publications

  • Varrone, L, Glass, K, Stafford, R et al 2020, 'Validation of questions designed for investigation of gastroenteritis', Food Control, vol. 108, no. 106871, pp. 1-6.
  • Stone, J, Glass, K, Munn, Z et al 2020, 'Comparison of bias adjustment methods in meta-analysis suggests that quality effects modeling may have less limitations than other approaches', Journal of Clinical Epidemiology, vol. 117, pp. 36-45.
  • Wangdi, K, XU, S, Suwannatrai, A et al 2020, 'A spatio-temporal analysis to identify the drivers of malaria transmission in Bhutan', Scientific Reports, vol. 2020, pp. 1-10.
  • Varrone, L, Glass, K, Stafford, R et al. 2020, 'A meta-analysis of case-control studies examining sporadic campylobacteriosis in Australia and New Zealand from 1990 to 2016', Australian and New Zealand Journal of Public Health, vol. 44, no. 4, pp. -.
  • Sheel, M, Tippins , A, Glass, K et al. 2020, 'Electronic immunization registers � A tool for mitigating outbreaks of vaccine-preventable diseases in the Pacific', Vaccine, vol. 38, no. 28, pp. 4395-4398.
  • McLure, A & Glass, K 2020, 'Some simple rules for estimating reproduction numbers in the presence of reservoir exposure or imported cases', Theoretical Population Biology, vol. 134, pp. 182-194.
  • Franklin, N, Hope, K, Glasgow, K et al. 2020, 'Describing the Epidemiology of Foodborne Outbreaks in New South Wales from 2000 to 2017', Foodborne Pathogens and Disease, vol. Online .
  • Zhang, Y, Joshy, G, Glass, K et al. 2020, 'Physical functional limitations and psychological distress in people with and without colorectal cancer: findings from a large Australian study', Journal of Cancer Survivorship, vol. 14, pp. 894-905.
  • Parisi, A, Phuong, T, Mather, A et al. 2020, 'Differential antimicrobial susceptibility profiles between symptomatic and asymptomatic non-typhoidal Salmonella infections in Vietnamese children
    ', Epidemiology and Infection, vol. 148, no. e144, pp. 1-3.
  • Moss, R, Wood, J, Brown, D et al. 2020, 'Coronavirus Disease Model to Inform Transmission-Reducing Measures and Health System Preparedness, Australia
    ', Emerging Infectious Diseases, vol. 26, no. 12, pp. 2844-2853.
  • Qian, W, Viennet, E, Glass, K et al. 2020, 'Epidemiological models for predicting ross river virus in australia: A systematic review', PLoS Neglected Tropical Diseases, vol. 14, no. 9, pp. 1-17.
  • Wallace, R, Bulach, D, Jennison, A et al. 2020, 'Molecular characterization of Campylobacter spp. recovered from beef, chicken, lamb and pork products at retail in Australia', PLOS ONE (Public Library of Science), vol. 15, no. 7, pp. 1-18.
  • Parisi, A, Phuong, T, Mather, A et al. 2020, 'The role of animals as a source of antimicrobial resistant nontyphoidal Salmonella causing invasive and non-invasive human disease in Vietnam', Infection, Genetics and Evolution, vol. 85, no. -, pp. 1-7.
  • Pane, M, Yin Mei Kong, F, Purnama, T et al 2019, 'Indonesian Hajj Cohorts and Mortality in Saudi Arabia from 2004 to 2011', Journal of Epidemiology and Global Health, vol. 9, no. 1, pp. 11-18.
  • Stone, J, Gurunathan, U, Glass, K et al 2019, 'Letter re: stratification by quality is not recommended in meta-analysis', Journal of Clinical Epidemiology, vol. 115, pp. 172-174.
  • Munck, N, Smith, J, Bates, J et al 2019, 'Source Attribution of Salmonella in Macadamia Nuts to Animal and Environmental Reservoirs in Queensland, Australia', Foodborne Pathogens and Disease, vol. 17, no. 5, pp. 357-364.
  • Ford, L, Ingle, D, Glass, K et al 2019, 'Whole-genome sequencing of Salmonella Mississippi and Typhimurium Definitive Type 160, Australia and New Zealand', Emerging Infectious Diseases, vol. 25, no. 9, pp. 1690-1697.
  • Ford, L, Haywood, P, Kirk, M et al 2019, 'Cost of Salmonella Infections in Australia, 2015', Journal of Food Protection, vol. 82, no. 9, pp. 1607-1614.
  • Maguire, J, Glasgow, K, Glass, K et al. 2019, 'Rotavirus Epidemiology and Monovalent Rotavirus Vaccine Effectiveness in Australia: 2010-2017', Pediatrics (English edition), vol. 144, no. 4, pp. 1-12.
  • Stone, J, Glass, K, Clark, J et al 2019, 'A unified framework for bias assessment in clinical research', International Journal of Evidence-based Healthcare, vol. 17, no. 2, pp. 106-120.
  • Davies, J, Lokuge, K & Glass, K 2019, 'Routine and pulse vaccination for Lassa virus could reduce high levels of endemic disease: A mathematical modelling study', Vaccine, vol. 37, pp. 3451-3456.
  • Walker, E, Wallace, R, Smith, J et al 2019, 'Prevalence of Campylobacter coli and Campylobacter jejuni in Retail Chicken, Beef, Lamb, and Pork Products in Three Australian States', Journal of Food Protection, vol. 82, no. 12, pp. 2126-2134.
  • McLure, A, Clements, A, Kirk, M et al 2019, 'Modelling diverse sources of Clostridium difficile in the community: importance of animals, infants and asymptomatic carriers', Epidemiology and Infection, vol. 147, no. e152, pp. 1-9.
  • Parisi, A, Crump, J, Stafford, R et al 2019, 'Increasing incidence of nontyphoidal Salmonella infections in Queensland, Australia, 2007-2016', PLoS Neglected Tropical Diseases, vol. 13, no. 3, pp. 15pp.
  • McLure, A, Furuya Kanamori, L, Clements, A et al 2019, 'Seasonality and community interventions in a mathematical model of Clostridium difficile transmission', Journal of Hospital Infection, vol. 102, no. 2, pp. 157-164.
  • Stone, J, Gurunathan, U, Glass, K et al 2019, 'Stratification by quality-induced selection bias in a meta-analysis of clinical trials', Journal of Clinical Epidemiology, vol. 107, pp. 51-59.
  • Ford, L, McEwen, S & Glass, K 2018, 'Trends of viral gastroenteritis in the Australian Capital Territory', Communicable Diseases Intelligence, vol. 42.
  • XU, S, Graves, P, Lau, C et al 2018, 'GEOFIL: A spatially-explicit agent-based modelling framework for predicting the long-term transmission dynamics of lymphatic filariasis in American Samoa', Epidemics: the journal of infectious disease dynamics, vol. 27, pp. 19-27.
  • Glass, K, Barnes, B, Scott, A et al 2018, 'Modelling the impact of biosecurity practices on the risk of high pathogenic avian influenza outbreaks in Australian commercial chicken farms', Preventive Veterinary Medicine, vol. 165, pp. 8-14.
  • Ford, L, Moffatt, C, Fearnley, E et al 2018, 'The epidemiology of Salmonella enterica outbreaks in Australia, 2001-2016', Frontiers in Sustainable Food Systems, vol. 2, no. 86, pp. -.
  • Sharmin, S, Glass, K, Viennet, E et al 2018, 'A Bayesian approach for estimating under-reported dengue incidence with a focus on non-linear associations between climate and dengue in Dhaka, Bangladesh', Statistical Methods in Medical Research, vol. 27, no. 4, pp. 991-1000.
  • McLure, A, Clements, A, Kirk, M et al 2018, 'Clostridium difficile classification overestimates hospital acquired infections', Journal of Hospital Infection, vol. 99, no. 4, pp. 453-460pp.
  • Ford, L, Wang, Q, Stafford, R et al 2018, 'Seven Salmonella Typhimurium Outbreaks in Australia Linked by Trace-Back and Whole Genome Sequencing', Foodborne Pathogens and Disease, vol. 15, no. 5, pp. 285-292pp.
  • Scott, A, Toribio, J, Singh, M et al 2018, 'Low pathogenic avian influenza exposure risk assessment in Australian commercial chicken farms', Frontiers in Veterinary Science, vol. 5, pp. 1-25.
  • Ford, L, Carter, G, Wang, Q et al 2018, 'Incorporating Whole-Genome Sequencing into Public Health Surveillance: Lessons from Prospective Sequencing of Salmonella Typhimurium in Australia', Foodborne Pathogens and Disease, vol. 15, no. 3, pp. 161-167pp.
  • Singh, M, Toribio, J, Scott, A et al 2018, 'Assessing the probability of introduction and spread of avian influenza (AI) virus in commercial Australian poultry operations using an expert opinion elicitation', PLOS ONE (Public Library of Science), vol. 13, no. 3, pp. 1-19.
  • Barnes, B & Glass, K 2018, 'Modelling low pathogenic avian influenza introduction into the commercial poultry industry', Mathematical Biosciences, vol. 300, pp. 112-121pp.
  • Parisi, A, Crump, J, Glass, K et al 2018, 'Health Outcomes from Multidrug-Resistant Salmonella Infections in High-Income Countries: A Systematic Review and Meta-Analysis', Foodborne Pathogens and Disease, vol. 15, no. 7, pp. 1-9pp.
  • Scott, A, Singh, M, Groves, P et al 2018, 'Biosecurity practices on Australian commercial layer and meat chicken farms: Performance and perceptions of farmers', PLOS ONE (Public Library of Science), vol. 13, no. 4, pp. 17pp.
  • Bullanday Scott, A, Toribio, J, Singh, M et al 2018, 'Low- and high-pathogenic avian influenza H5 and H7 spread risk assessment within and between Australian commercial chicken farms', Frontiers in Veterinary Science, vol. 5, pp. 23pp.
  • Fearnley, E, Lal, A, Bates, J et al 2018, 'Salmonella source attribution in a subtropical state of Australia: capturing environmental reservoirs of infection', Epidemiology and Infection, vol. 146, no. 15, pp. 1903-1908pp.
  • Sharmin, S, Glass, K, Viennet, E et al 2018, 'Geostatistical mapping of the seasonal spread of under-reported dengue cases in Bangladesh', PLoS Neglected Tropical Diseases, vol. 12, no. 11, pp. 1-13.
  • Varrone, L, Stafford, R, Lilly, K et al 2018, 'Investigating locally relevant risk factors for Campylobacter infection in Australia: Protocol for a case-control study and genomic analysis', BMJ Open, vol. 8, no. 12, pp. 1-7pp.
  • Barnes, B, Scott, A, Hernandez-Jover, M et al 2018, 'Modelling high pathogenic avian influenza outbreaks in the commercial poultry industry', Theoretical Population Biology, vol. 126, pp. 59-71.
  • Scott, A, Singh, M, Toribio, J et al 2017, 'Comparisons of Management Practices and Farm Design on Australian Commercial Layer and Meat Chicken farms: Cage, Barn and Free Range', PLOS ONE (Public Library of Science), vol. 12, no. 11, pp. 17pp.
  • XU, S, Glass, K, Lau, C et al 2017, 'A synthetic population for modelling the dynamics of infectious disease transmission in American Samoa Scientific Reports', Scientific Reports, vol. 7, pp. 16725 (9pp).
  • Mohamed, A, Nowak, G, Bajuk, B et al 2017, 'Variation in hospital mortality in an Australian neonatal intensive care unit network', Archives of Disease in Childhood Fetal and Neonatal Edition, pp. F331-F336.
  • Hogan, A, Campbell, P, Blyth, C et al 2017, 'Potential impact of a maternal vaccine for RSV: a mathematical modelling study', Vaccine, vol. 35, no. 45, pp. 6172-6179.
  • Moffatt, C, Glass, K, Stafford, R et al 2017, 'The campylobacteriosis conundrum - examining the incidence of infection with Campylobacter sp. in Australia, 1998 - 2013', Epidemiology and Infection, vol. 145, no. 4, pp. 839-847.
  • Chen, Y, Glass, K, Liu, B et al 2017, 'Burden of Clostridium difficile infection: Associated hospitalization in a cohort of middle-aged and older adults', American Journal of Infection Control, vol. 45, no. 5, pp. 508-511pp.
  • Hogan, A, Glass, K & Anderssen, R 2017, 'complex demodulation: a novel time series methods for analysing seasonal infectious diseases', ANZIAM Journal, vol. 59, no. 1, pp. 51-60.
  • McLure, A, Clements, A, Kirk, M et al 2017, 'Healthcare-Associated Clostridium difficile Infections are Sustained by Disease from the Community', Bulletin of Mathematical Biology, vol. 79, no. 10, pp. 2242-2257.
  • Ford, L, Glass, K, Veitch, M et al 2016, 'Increasing Incidence of Salmonella in Australia, 2000-2013', PLOS ONE (Public Library of Science), vol. 11, no. 10, pp. 1-11pp.
  • Chen, Y, Liu, B, Glass, K et al 2016, 'Use of Proton Pump Inhibitors and the Risk of Hospitalization for Infectious Gastroenteritis', PLOS ONE (Public Library of Science), vol. 11, no. 12, pp. e0168618-e0168618.
  • Hogan, A, Anderssen, R, Davis, S et al 2016, 'Time series analysis of RSV and bronchiolitis seasonality in temperate and tropical Western Australia', Epidemics: the journal of infectious disease dynamics, vol. 16, pp. 49-55.
  • Ndii, M, Allingham, D, Hickson, R et al 2016, 'The effect of Wolbachia on dengue outbreaks when dengue is repeatedly introduced', Theoretical Population Biology, vol. 111, pp. 9-15.
  • Chen, Y, Glass, K, LIU, B et al 2017, 'A population-based longitudinal study of Clostridium difficile infection-related hospitalization in mid-age and older Australians', Epidemiology and Infection, vol. 145, no. 3, pp. 575-582.
  • Jacoby, P, Glass, K & Moore, H 2017, 'Characterizing the risk of respiratory syncytial virus in infants with older siblings: a population-based birth cohort study', Epidemiology and Infection, vol. 145, no. 2, pp. 266-271.
  • Chen, Y, Glass, K, Liu, B et al 2016, 'Salmonella Infection in Middle-Aged and Older Adults: Incidence and Risk Factors from the 45 and Up Study', Foodborne Pathogens and Disease, vol. 13, no. 12, pp. 689-694.
  • Lokuge, K, Caleo, G, Grieg, J et al 2016, 'Successful Control of Ebola Virus Disease: Analysis of Service Based Data from Rural Sierra Leone', PLoS Neglected Tropical Diseases, vol. 10, no. 3, pp. 13 pp.
  • Ndii, M, Allingham, D, Hickson, R et al 2016, 'The effect of Wolbachia on dengue dynamics in the presence of two serotypes of dengue: symmetric and asymmetric epidemiological characteristics', Epidemiology and Infection, vol. 144, no. 13, pp. 2874-2882.
  • Vally, H, Glass, K, Ford, L et al 2016, 'Evaluation of a structured expert elicitation estimating the proportion of illness acquired by foodborne transmission for nine enteric pathogens in Australia', Epidemiology and Infection, vol. 144, no. 5, pp. 897-906.
  • Glass, K, Fearnley, E, Hocking, H et al 2016, 'Bayesian Source Attribution of Salmonellosis in South Australia', Risk Analysis, vol. 36, no. 3, pp. 561-570.
  • Geard, N, Glass, K, McCaw, J et al 2015, 'The effects of demographic change on disease transmission and vaccine impact in a household structured population', Epidemics: the journal of infectious disease dynamics, vol. 13, pp. 56-64.
  • Glass, K, Tait, P, Hanna, E et al 2015, 'Estimating Risks of Heat Strain by Age and Sex: A Population-Level Simulation Model', International Journal of Environmental Research and Public Health, vol. 12, no. 5, pp. 5241-5255.
  • Vally, H, Glass, K, Ford, L et al 2016, 'Evaluation of a structured expert elicitation estimating the proportion of illness acquired by foodborne transmission for nine enteric pathogens in Australia', Epidemiology and Infection, vol. 144, no. 5, pp. 897-906.
  • Ndii, M, Allingham, D, Hickson, R et al 2016, 'The effect of Wolbachia on dengue dynamics in the presence of two serotypes of dengue: symmetric and asymmetric epidemiological characteristics', Epidemiology and Infection, vol. 144, no. 13, pp. 2874-2882.
  • Sharmin, S, Glass, K, Viennet, E et al 2015, 'Interaction of mean temperature and daily fluctuation influences dengue incidence in Dhaka, Bangladesh', PLoS Neglected Tropical Diseases, vol. 9, no. 7, pp. 1-13.
  • Hogan, A, Glass, K, Moore, H et al 2015, 'Age structures in mathematical models for infectious diseases, with a case study of respiratory syncytial virus', Proceedings of the Forum for Mathematics for Industry 2014, ed. R.S. Anderssen, Springer Japan KK, Tokyo, Japan, pp. 105-116.
  • Chen, Y, Liu, B, Glass, K et al 2015, 'High incidence of hospitalisation due to infectious gastroenteritis in older people associated with poor self-rated health', BMJ Open, vol. 5, no. 12, pp. e010161-e010161.
  • Lal, A, Cornish, L, Fearnley, E et al 2015, 'Cryptosporidiosis: A Disease of Tropical and Remote Areas in Australia', PLoS Neglected Tropical Diseases, vol. 9, no. 9, pp. -.
  • Fielding, J, Kelly, H & Glass, K 2015, 'Transmission of the First Influenza A(H1N1) pdm09 Pandemic Wave in Australia Was Driven by Undetected Infections: Pandemic Response Implications', PLOS ONE (Public Library of Science), vol. 10, no. 12.
  • Sharmin, S, Viennet, E, Glass, K and Harley D 2015, 'The emergence of dengue in Bangladesh: Epidemiology, challenges and future disease risk', Transactions of the Royal Society of Tropical Medicine and Hygiene, vol. 109, no. 10, pp. 619-627.
  • Ford, L, Kirk, M, Glass, K et al 2014, 'Sequelae of foodborne Illness caused by 5 pathogens, Australia, Circa 2010', Emerging Infectious Diseases, vol. 20, no. 11, pp. 1865-1871.
  • Kirk, M, Ford, L, Glass, K et al 2014, 'Foodborne illness, Australia, Circa 2000 and Circa 2010', Emerging Infectious Diseases, vol. 20, no. 11, pp. 1857-1864.
  • Vally, H, Glass, K, Ford, L et al 2014, 'Proportion of illness acquired by foodborne transmission for nine enteric pathogens in Australia: An expert elicitation', Foodborne Pathogens and Disease, vol. 11, no. 9, pp. 727-733.
  • Glass, K, Ford, L & Kirk, M 2014, 'Drivers of uncertainty in estimates of foodborne gastroenteritis incidence', Foodborne Pathogens and Disease, vol. 11, no. 12, pp. 938-944.
  • Fielding, J, Kelly, H, Mercer, G et al 2014, 'Systematic review of influenza A(H1N1)pdm09 virus shedding: duration is affected by severity, but not age', Influenza and Other Respiratory Viruses, vol. 8, no. 2, pp. 142-150.
  • Hogan, A, Mercer, G, Glass, K et al 2013, 'Modelling the seasonality of respiratory syncytial virus in young children', International Congress on Modelling and Simulation MODSIM 2013, ed. Piantadosi, J., Anderssen, R.S. and Boland J., Modelling and Simulation Society of Australia and New Zealand Inc., Australia, pp. 338-344.
  • Klarkowski, D, Glass, K, O'Brien, D et al 2013, 'Variation in specificity of HIV rapid diagnostic tests over place and time: An analysis of discordancy data using a bayesian approach', PLOS ONE (Public Library of Science), vol. 8, no. 11, pp. e81656-e81656.
  • McCaw, J, Glass, K, Mercer, G et al 2014, 'Pandemic controllability: a concept to guide a proportionate and flexible operational response to future influenza pandemics', Journal of Public Health, vol. 36, no. 1, pp. 5-12.
  • Glass K, Barnes B. 2013, 'Eliminating infectious diseases of livestock: a metapopulation model of infection control', Theoretical Population Biology, vol. 85, no. 1, pp. 63-72.
  • Hogan AB, Mercer GN, Glass K, Moore HC. 2013 Modelling the seasonality of respiratory syncytial virus in young children. 20th International Congress on Modelling and Simulation (MODSIM 2013), Modelling and Simulation Society of Australia and New Zealand Inc., Australia.
  • Aditamaa, T, Samaan, G, Kusriastuti, R et al 2012, 'Avian influenza H5N1 transmission in households, Indonesia', PLOS ONE (Public Library of Science), vol. 7, no. 1, pp. e29971-e29971.
  • Glass, K, Kelly, H & Mercer, G 2012, 'Pandemic influenza H1N1: Reconciling serosurvey data with estimates of the reproduction number', Epidemiology, vol. 23, no. 1, pp. 86-94.
  • Glass, K, Mercer, G, Nishiura, H et al 2011, 'Estimating reproduction numbers for adults and children from case data', Journal of the Royal Society Interface, vol. 8, no. 62, pp. 1248-1259.
  • Glass, K, McCaw, J & McVernon, J 2011, 'Incorporating population dynamics into household models of infectious disease transmission', Epidemics: the journal of infectious disease dynamics, vol. 3, pp. 152-158.
  • Mercer, G, Glass, K & Becker, N 2011, 'Effective reproduction numbers are commonly overestimated early in a disease outbreak', Statistics in Medicine, vol. 30, no. 9, pp. 984-997. doi:10.1002/sim.4174
  • Kelly, P, Mercer, G, Glass, K et al 2010, 'Author's reply: Estimation of the reproduction number for 2009 pandemic influenza a(H1N1) in the presence of imported cases', Eurosurveillance (Print), vol. 15, no. 29, pp. 1-2.
  • Paine, S, Mercer, G, Kelly, P et al 2010, 'Transmissibility of 2009 pandemic influenza A(H1N1) in New Zealand: effective reproduction number and influence of age, ethnicity and importations', Eurosurveillance (Online Edition), vol. 15, no. 24, pp. 9-17.
  • Kelly, H, Mercer, G, Fielding, J et al 2010, 'Pandemic (H1N1) 2009 Influenza Community Transmission Was Established in One Australian State When the Virus Was First Identified in North America', PLOS ONE (Public Library of Science), vol. 5, no. 6, pp. e11341-e11341.
  • Glass, K & Becker, N 2009, 'Estimating antiviral effectiveness against pandemic influenza using household data', Journal of the Royal Society Interface, vol. 6, pp. 695 703.
  • Glass, K & Barnes, B 2007, 'How much would closing schools reduce transmission during an influenza pandemic?', Epidemiology, vol. 18, no. 5, pp. 623-628.
  • Glass, K, Becker, N & Clements, M 2007, 'Predicting case numbers during infectious diseaseoutbreaks when some cases are undiagnosed', Statistics in Medicine, vol. 26, no. 1, pp. 171-183.
  • Barnes, B, Glass, K & Becker, N 2007, 'The role of health care workers and antiviral drugs in the control of pandemic influenza', Mathematical Biosciences, vol. 209, no. 2, pp. 403-416.
  • Park, A & Glass, K 2007, 'Dynamic patterns of avian and human influenza in east and southeast Asia', The Lancet Infectious Diseases, vol. 7, no. 8, pp. 543-548.
  • Glass, K & Becker, N 2006, 'Evaluation of measures to reduce international spread of SARS', Epidemiology and Infection, vol. 134, pp. 1092-1101.
  • Wang, J, McMichael, A, Meng, B et al 2006, 'Spatial dynamics of an epidemic of severe acute respiratory syndrome in an urban area', Bulletin of the World Health Organization, vol. 84, pp. 965-968.
  • Becker, N & Glass, K 2006, 'The impact of imported infection', in Adrian C. Sleigh, Chee Heng Leng, Brenda SA Yeoh (ed.), Population dynamics and infectious diseases in Asia, World Scientific Publishing Company, Singapore, pp. 73-96.
  • Becker, N, Glass, K, Li, Z et al 2005, 'Controlling emerging infectious diseases like SARS', Mathematical Biosciences, vol. 193, pp. 205-221.
  • Glass, K 2005, 'Ecological mechanisms that promote arbovirus survival: a mathematical model of Ross River virus transmission', Transactions of the Royal Society of Tropical Medicine and Hygiene, vol. 99, no. 4, pp. 252-260.
  • Glass, K & Grenfell, B 2004, 'Waning immunity and subclinical measles infections in England', Vaccine, vol. 22, pp. 4110-4116.
  • Park, A, Wood, J, Daly, J et al 2004, 'The effects of strain heterology on the epidemiology and equine influenza in a vaccinated population', Proceedings of the Royal Society of London Series B: Biological Sciences, vol. 271, pp. 1547-1555.
  • Glass, K, Kappey, J & Grenfell, B 2004, 'The effect of heterogeneity in measles vaccination on population immunity', Epidemiology and Infection, vol. 132, pp. 675-683.
  • Glass, K, Xia, Y & Grenfell, B 2003, 'Interpreting time-series analyses for continuous-time biological models - Measles as a case study.', Journal of Theoretical Biology, vol. 223, pp. 19-25.
  • Glass, K & Grenfell, B 2003, 'Antibody dynamics in childhood diseases: waning and boosting of immunity and the impact of vaccination', Journal of Theoretical Biology, vol. 221, pp. 121-131.
  • Glass, K, Wood, J, Mumford, J et al 2002, 'Modelling Equine Influenza 1: A stochastic model of within-yard epidemics', Epidemiology and Infection, vol. 128, pp. 491-502.

Teaching

Supervised students