Michael S Williams

Summary

Affiliation: Agricultural Research Service
Country: USA

Publications

  1. ncbi request reprint Estimating the correlation between concentrations of two species of bacteria with censored microbial testing data
    Michael S Williams
    Risk Assessment and Analytics Staff, Office of Public Health Science, Food Safety and Inspection Service, United States Department of Agriculture, 2150 Centre Ave, Building D, Fort Collins, CO 80526, United States Electronic address
    Int J Food Microbiol 175:1-5. 2014
  2. doi request reprint Sample size guidelines for fitting a lognormal probability distribution to censored most probable number data with a Markov chain Monte Carlo method
    Michael S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety and Inspection Service, USDA, 2150 Centre Avenue, Building D, Fort Collins, CO 80526, USA
    Int J Food Microbiol 165:89-96. 2013
  3. doi request reprint Fitting distributions to microbial contamination data collected with an unequal probability sampling design
    M S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety Inspection Service, USDA, Fort Collins, CO 80526, USA
    J Appl Microbiol 114:152-60. 2013
  4. doi request reprint Methods for fitting a parametric probability distribution to most probable number data
    Michael S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety and Inspection Service, USDA, Fort Collins, CO 80526, USA
    Int J Food Microbiol 157:251-8. 2012
  5. ncbi request reprint Estimating changes in public health following implementation of hazard analysis and critical control point in the United States broiler slaughter industry
    Michael S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety Inspection Service, USDA, Fort Collins, Colorado 80526, USA
    Foodborne Pathog Dis 9:59-67. 2012
  6. doi request reprint Framework for microbial food-safety risk assessments amenable to Bayesian modeling
    Michael S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety and Inspection Service, USDA, CO, USA
    Risk Anal 31:548-65. 2011
  7. doi request reprint Methodology for determining the appropriateness of a linear dose-response function
    Michael S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety Inspection Service, USDA, CO, USA
    Risk Anal 31:345-50. 2011
  8. doi request reprint Determining relationships between the seasonal occurrence of Escherichia coli O157:H7 in live cattle, ground beef, and humans
    Michael S Williams
    Risk Assessment Division, Food Safety and Inspection Service, U S Department of Agriculture, 2150 D Centre Ave, Fort Collins, CO 80526, USA
    Foodborne Pathog Dis 7:1247-54. 2010
  9. doi request reprint Estimating removal rates of bacteria from poultry carcasses using two whole-carcass rinse volumes
    Michael S Williams
    Risk Assessment Division, USDA Food Safety Inspection Service, 2150 D Centre Avenue, Fort Collins, CO 80526, USA
    Int J Food Microbiol 139:140-6. 2010
  10. doi request reprint Poisson sampling: a sampling strategy for concurrently establishing freedom from disease and estimating population characteristics
    Michael S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety Inspection Service USDA, 2150 Centre Avenue, Building D, Fort Collins, CO 80526, USA
    Prev Vet Med 89:34-42. 2009

Detail Information

Publications14

  1. ncbi request reprint Estimating the correlation between concentrations of two species of bacteria with censored microbial testing data
    Michael S Williams
    Risk Assessment and Analytics Staff, Office of Public Health Science, Food Safety and Inspection Service, United States Department of Agriculture, 2150 Centre Ave, Building D, Fort Collins, CO 80526, United States Electronic address
    Int J Food Microbiol 175:1-5. 2014
    ..A weak positive correlation was also observed between concentrations of Campylobacter and Salmonella, but it was not statistically significant. ..
  2. doi request reprint Sample size guidelines for fitting a lognormal probability distribution to censored most probable number data with a Markov chain Monte Carlo method
    Michael S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety and Inspection Service, USDA, 2150 Centre Avenue, Building D, Fort Collins, CO 80526, USA
    Int J Food Microbiol 165:89-96. 2013
    ..The results do, however, demonstrate that simple guidelines for this application, such as the proportion of positive samples, cannot be provided...
  3. doi request reprint Fitting distributions to microbial contamination data collected with an unequal probability sampling design
    M S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety Inspection Service, USDA, Fort Collins, CO 80526, USA
    J Appl Microbiol 114:152-60. 2013
    ..This study develops a weighted maximum likelihood estimation framework that is appropriate for microbiological samples that are collected with unequal probabilities of selection...
  4. doi request reprint Methods for fitting a parametric probability distribution to most probable number data
    Michael S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety and Inspection Service, USDA, Fort Collins, CO 80526, USA
    Int J Food Microbiol 157:251-8. 2012
    ..The Bayesian method provided unbiased estimates of the concentration distribution parameters for all data sets. We provide computer code for the Bayesian fitting method...
  5. ncbi request reprint Estimating changes in public health following implementation of hazard analysis and critical control point in the United States broiler slaughter industry
    Michael S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety Inspection Service, USDA, Fort Collins, Colorado 80526, USA
    Foodborne Pathog Dis 9:59-67. 2012
    ..An analysis relating the necessary magnitude of change in contamination required for detection via human surveillance also is provided...
  6. doi request reprint Framework for microbial food-safety risk assessments amenable to Bayesian modeling
    Michael S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety and Inspection Service, USDA, CO, USA
    Risk Anal 31:548-65. 2011
    ..Empirical validation of the policy effect is also examined by estimating the annual change in the numbers of illnesses observed via disease surveillance systems...
  7. doi request reprint Methodology for determining the appropriateness of a linear dose-response function
    Michael S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety Inspection Service, USDA, CO, USA
    Risk Anal 31:345-50. 2011
    ..Simple examples illustrate how this approximation can be used to inform policy decisions and improve an analyst's understanding of risk...
  8. doi request reprint Determining relationships between the seasonal occurrence of Escherichia coli O157:H7 in live cattle, ground beef, and humans
    Michael S Williams
    Risk Assessment Division, Food Safety and Inspection Service, U S Department of Agriculture, 2150 D Centre Ave, Fort Collins, CO 80526, USA
    Foodborne Pathog Dis 7:1247-54. 2010
    ....
  9. doi request reprint Estimating removal rates of bacteria from poultry carcasses using two whole-carcass rinse volumes
    Michael S Williams
    Risk Assessment Division, USDA Food Safety Inspection Service, 2150 D Centre Avenue, Fort Collins, CO 80526, USA
    Int J Food Microbiol 139:140-6. 2010
    ..5, and 17.0) still indicates a significant difference in the removal rates for the two sampling methods...
  10. doi request reprint Poisson sampling: a sampling strategy for concurrently establishing freedom from disease and estimating population characteristics
    Michael S Williams
    Risk Assessment Division, Office of Public Health Science, Food Safety Inspection Service USDA, 2150 Centre Avenue, Building D, Fort Collins, CO 80526, USA
    Prev Vet Med 89:34-42. 2009
    ..95 when using Poisson sampling was less than half that required when using simple random sampling. The performance of estimators for prevalence of scrapie and distribution of genotypes are also compared...
  11. doi request reprint Population inferences from targeted sampling with uncertain epidemiologic information
    Michael S Williams
    Risk Assessment and Residue Division, Office of Public Health Science, Food Safety Inspection Service USDA, 2150 Centre Avenue, Building D, Fort Collins, CO 80526, USA
    Prev Vet Med 89:25-33. 2009
    ..Results of a simulation study are provided to illustrate the effect of the uncertainty in these parameters...
  12. doi request reprint Estimating herd prevalence of bovine brucellosis in 46 USA states using slaughter surveillance
    Eric D Ebel
    National Surveillance Unit, Veterinary Services, Animal and Plant Health Inspection Service, USDA, 2150 Centre Avenue, Building B, Fort Collins, CO 80526, United States
    Prev Vet Med 85:295-316. 2008
    ..The most influential analytic input was the probability of introducing new infection into a putatively brucellosis-free state or group of states...
  13. doi request reprint Parametric distributions of underdiagnosis parameters used to estimate annual burden of illness for five foodborne pathogens
    Eric D Ebel
    U S Department of Agriculture, Food Safety Inspection Service, Risk Assessment Division, Office of Public Health Science, Fort Collins, CO 80526, USA
    J Food Prot 75:775-8. 2012
    ..Distributions are provided for the five foodborne pathogens deemed most relevant to meat and poultry...
  14. ncbi request reprint Monte Carlo approaches for determining power and sample size in low-prevalence applications
    Michael S Williams
    Animal and Plant Health Inspection Service, USDA 2150 B Centre Avenue, Mail Stop 2E6, Fort Collins, CO 80526, USA
    Prev Vet Med 82:151-8. 2007
    ..We provide a Monte Carlo-based solution and show that in low-prevalence applications this approach can lead to reductions in the total samples size of more than 10,000 samples...