All Stories

  1. Effect of variable and low analytical recovery on sensitivity of Kato-Katz, Lutz and Ritchie methods for diagnosis of schistosomiasis
  2. Particulate contaminants and treatment decision-making: maximizing the value of raw water pathogen monitoring for drinking water safety
  3. Drinking water QMRA and decision-making: Sensitivity of risk to common independence assumptions about model inputs
  4. Filter operation effects on plant‐scale microbial risk: Opportunities for enhanced treatment performance
  5. Ensuring That Fundamentals of Quantitative Microbiology Are Reflected in Microbial Diversity Analyses Based on Next-Generation Sequencing
  6. Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities
  7. Ensuring that fundamentals of quantitative microbiology are reflected in microbial diversity analyses based on next-generation sequencing
  8. Reply to Comment on “Describing water treatment process performance: Why average log-reduction can be a misleading statistic” by Schmidt, P.J., Anderson, W.B., and Emelko, M.B. [Water Research 176 (2020), 115702]
  9. To rarefy or not to rarefy: Enhancing microbial community analysis through next-generation sequencing
  10. Evaluation of the 50% Infectious Dose of Human Norovirus Cin-2 in Gnotobiotic Pigs: A Comparison of Classical and Contemporary Methods for Endpoint Estimation
  11. Describing water treatment process performance: Why average log-reduction can be a misleading statistic
  12. Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit
  13. Confirming the need for virus disinfection in municipal subsurface drinking water supplies
  14. Learning Something From Nothing: The Critical Importance of Rethinking Microbial Non-detects
  15. Comment on “Guidelines for Use of the Approximate Beta-Poisson Dose-Response Model”: Previously Published Guidelines Continue to be Ignored
  16. Comment on “CryptosporidiumInfection Risk: Results of New Dose-Response Modeling” - Discussion of Underlying Assumptions and Their Implications
  17. Towards a more accurate quantitative assessment of seasonal Cryptosporidium infection risks in surface waters using species and genotype information
  18. Estimating the burden of acute gastrointestinal illness due toGiardia, Cryptosporidium, Campylobacter, E. coliO157 and norovirus associated with private wells and small water systems in Canada
  19. Norovirus Dose-Response: Are Currently Available Data Informative Enough to Determine How Susceptible Humans Are to Infection from a Single Virus?
  20. Variance decomposition: A tool enabling strategic improvement of the precision of analytical recovery and concentration estimates associated with microorganism enumeration methods
  21. Bacteria, viruses, and parasites in an intermittent stream protected from and exposed to pasturing cattle: Prevalence, densities, and quantitative microbial risk assessment
  22. Using Campylobacter spp. and Escherichia coli data and Bayesian microbial risk assessment to examine public health risks in agricultural watersheds under tile drainage management
  23. Analytical recovery of protozoan enumeration methods: Have drinking water QMRA models corrected or created bias?
  24. Harnessing the Theoretical Foundations of the Exponential and Beta-Poisson Dose-Response Models to Quantify Parameter Uncertainty Using Markov Chain Monte Carlo
  25. QMRA and decision-making: Are we handling measurement errors associated with pathogen concentration data correctly?
  26. Particle and Microorganism Enumeration Data: Enabling Quantitative Rigor and Judicious Interpretation
  27. Quantification of Analytical Recovery in Particle and Microorganism Enumeration Methods
  28. Quantification of uncertainty in microbial data-reporting and regulatory implications