Modeling to reduce risks of Salmonella in alfalfa sprouts

We developed a risk assessment of human salmonellosis associated with consumption of alfalfa sprouts in the United States to evaluate the public health impact of applying treatments to seeds (0–5-log10 reduction in Salmonella) and testing spent irrigation water (SIW) during production.

The risk model considered variability and uncertainty in Salmonella contamination in seeds, Salmonella growth and spread during sprout production, sprout consumption, and Salmonella dose response.

Based on an estimated prevalence of 2.35% for 6.8 kg seed batches and without interventions, the model predicted 76,600 (95% confidence interval (CI) 15,400–248,000) cases/year. Risk reduction (by 5- to 7-fold) predicted from a 1-log10 seed treatment alone was comparable to SIW testing alone, and each additional 1-log10 seed treatment was predicted to provide a greater risk reduction than SIW testing. A 3-log10 or a 5-log10 seed treatment reduced the predicted cases/year to 139 (95% CI 33–448) or 1.4 (95% CI <1–4.5), respectively. Combined with SIW testing, a 3-log10 or 5-log10 seed treatment reduced the cases/year to 45 (95% CI 10–146) or <1 (95% CI <1–1.5), respectively. If the SIW coverage was less complete (i.e., less representative), a smaller risk reduction was predicted, e.g., a combined 3-log10 seed treatment and SIW testing with 20% coverage resulted in an estimated 92 (95% CI 22–298) cases/year.

Analysis of alternative scenarios using different assumptions for key model inputs showed that the predicted relative risk reductions are robust. This risk assessment provides a comprehensive approach for evaluating the public health impact of various interventions in a sprout production system.

Risk assessment of salmonellosis from consumption of alfalfa sprouts and evaluation of the public health impact of sprout seed treatment and spent irrigation water testing

January 2018, Risk Analysis

Yuhuan Chen, Regis Pouillot, Sofia Farakos, Steven Duret, Judith Spungen, Tong-Jen Fu, Fazila Shakir, Patricia Homola, Sherri Dennis, Jane Van Doren

DOI: 10.1111/risa.12964

Possums, birds and tank water in Queensland: A microbial risk

As Australians begin the workweek with a hung parliament after yet another federal election, I aptly turn my attention to the politicians of the rodent world: possums.

rainwater.brisbane.feb.14The Australian climate can be harsh, in a No-Country-for-Old-Men sorta way, with temperature extremes, flooding, followed by five years of drought.

So we have new-fangled rain barrels that my grandparents used to have in Ontario (ours, right, exactly as shown and I know there’s possums wandering around there at night because possum poop accumulates).

The rainwater is supposed to be used for toilets, dishes, laundry and other non-potable uses, but is there a risk (no drinking from the garden hose here)?

Here’s the most recent from researchers:

Avian and possum fecal droppings may negatively impact roof-harvested rainwater (RHRW) water quality due to the presence of zoonotic pathogens. This study was aimed at evaluating the performance characteristics of a possum feces-associated (PSM) marker by screening 210 fecal and wastewater samples from possums (n = 20) and a range of nonpossum hosts (n = 190) in Southeast Queensland, Australia.

The host sensitivity and specificity of the PSM marker were 0.90 and 0.95 (maximum value, 1.00), respectively. The mean concentrations of the GFD marker in possum fecal DNA samples (8.8 × 107 gene copies per g of feces) were two orders of magnitude higher than those in the nonpossum fecal DNA samples (5.0 × 105 gene copies per g of feces). The host sensitivity, specificity, and concentrations of the avian feces-associated GFD marker were reported in our recent study (W. Ahmed, V. J. Harwood, K. Nguyen, S. Young, K. Hamilton, and S. Toze, Water Res 88:613–622, 2016, The utility of the GFD and PSM markers was evaluated by testing a large number of tank water samples (n = 134) from the Brisbane and Currumbin areas. GFD and PSM markers were detected in 39 of 134 (29%) and 11 of 134 (8%) tank water samples, respectively. The GFD marker concentrations in PCR-positive samples ranged from 3.7 × 102 to 8.5 × 105 gene copies per liter, whereas the concentrations of the PSM marker ranged from 2.0 × 103 to 6.8 × 103 gene copies per liter of water. The results of this study suggest the presence of fecal contamination in tank water samples from avian and possum hosts. study has established an association between the degradation of microbial tank water quality and avian and possum feces. Based on the results, we recommend disinfection of tank water, especially for tanks designated for potable use.


The use of roof-harvested rainwater (RHRW) for domestic purposes is a globally accepted practice. The presence of pathogens in rainwater tanks has been reported by several studies, supporting the necessity for the management of potential health risks. The sources of fecal pollution in rainwater tanks are unknown. However, the application of microbial source tracking (MST) markers has the potential to identify the sources of fecal contamination in a rainwater tank. In this study, we provide evidence of avian and possum fecal contamination in tank water samples using molecular markers. This study established a potential link between the degradation of the microbial quality of tank water and avian and possum feces.

Evidence of avian and possum fecal contamination in rainwater tanks as determined by microbial source tracking approaches

Ahmed a, K. A. Hamilton a,b, P. Gyawali a,c, S. Toze a,c and C. N. Haas b

A CSIRO Land and Water, Ecosciences Precinct, Brisbane, Queensland, Australia

B Drexel University, Philadelphia, Pennsylvania, USA

C School of Public Health, University of Queensland, Herston, Queensland, Australia

Applied and Environmental Microbiology, Volume 82, Number 14, Pages 4379-4386, doi:10.1128/AEM.00892-16

Modeling toxo in meat

Toxoplasma gondii is a protozoan parasite that is responsible for approximately 24% of deaths attributed to foodborne pathogens in the United States.

doug.cats.jun.14It is thought that a substantial portion of human T. gondii infections is acquired through the consumption of meats. The dose-response relationship for human exposures to T. gondii-infected meat is unknown because no human data are available. The goal of this study was to develop and validate dose-response models based on animal studies, and to compute scaling factors so that animal-derived models can predict T. gondii infection in humans. Relevant studies in literature were collected and appropriate studies were selected based on animal species, stage, genotype of T. gondii, and route of infection. Data were pooled and fitted to four sigmoidal-shaped mathematical models, and model parameters were estimated using maximum likelihood estimation. Data from a mouse study were selected to develop the dose-response relationship.Exponential and beta-Poisson models, which predicted similar responses, were selected as reasonable dose-response models based on their simplicity, biological plausibility, and goodness fit. A confidence interval of the parameter was determined by constructing 10,000 bootstrap samples. Scaling factors were computed by matching the predicted infection cases with the epidemiological data. Mouse-derived models were validated against data for the dose-infection relationship in rats. A human dose-response model was developed as P (d) = 1–exp (–0.0015 × 0.005 × d) or P (d) = 1–(1 + d × 0.003 / 582.414)−1.479. Both models predict the human response after consuming T. gondii-infected meats, and provide an enhanced risk characterization in a quantitative microbial risk assessment model for this pathogen.

 Development of Dose-Response Models to Predict the Relationship for Human Toxoplasma gondii Infection Associated with Meat Consumption

Risk Analysis, 19 October 2015

M Guo, A Mishra, R Buchanan, J Dubey, D Hill, H Gamble, J Jones, X Du, and A Pradhan


Food safety modeling and real life

Friend of the blog, Don Schaffner (right, not exactly as shown), of Rutgers University writes:

 stuart.smalleyWhen I started my professional career 25 years ago there was a perception amongst many academics that those with an extension mission were somehow second-class citizens. I can see why, because many extension specialists spent all their time working with their clientele helping them to solve problems. They didn’t do research, they didn’t publish papers, and they certainly didn’t get competitive national grants. Even 25 years ago, peer-reviewed publications and grant dollars were the yardstick against which academic success was measured.

I’m delighted to report that the profession has changed. When I look around the country at my extension peers, I see we are some of the most accomplished, hard working, articulate, and well-funded academics in the game. I think there’s a reason for this. First we are all awesome, but more importantly we are engaged both with our clientele as well as the science. Understanding the needs of the industry is fundamental to doing relevant and high quality science. I did some work for Jetro/Restaurant Depot that was published in the Journal of Food Protection in 2013. That work came from blend of industry need and my skill in the practical applications of predictive food microbiology.

Like all good academics, when I find an interesting problem, I milk it for all it’s worth. That’s a joke. I think the proper academic speak for what I’m talking about is “examining the topic in-depth.” I knew that the 2013 JFP article was a good start, but it needed additional support that could only be found in the laboratory. Fortunately, I had a talented graduate student at the time that needed a good project. Jennifer McConnell and I published a paper this year, also in JFP (Vol. 77, 7:1110–1115) in which we took the modeling framework that I had proposed in the 2013 paper and applied it specifically to the growth of Salmonella in ground beef in situations where there was a loss of temperature control. When we started the project I had in mind the scenario outlined in the 2013 article: An individual transporting food from one location to another without temperature control.  By the time Jenn finished her project we had discovered another potentially even more useful application. We can blame hurricane Sandy for that.

The widespread power outages and their impacts on retail food establishments made the deliberations of the Conference for Food Protection committee updating the “Emergency Action Plan for Retail Food Establishments” document very interested in understanding the food safety implications of foods that experience a loss of temperature control due to power failure and which start to rise slowly in temperature. With my 2013 article published, and Jenn’s 2014 article well underway, I felt confident advising the committee on the utility of computer models for the growth of pathogenic bacteria in foods experiencing a gradual loss of temperature control.

How does the story end?   Jenn successfully defended her MS degree and she’s living her dream as a lab manager with Cornell University in New York City running a lab that studies tuberculosis. The CFP committee submitted their document, which was approved by Council III, and which is sitting somewhere in a repository waiting for the next hurricane. And me? I’m living my dream. Doing research, writing the occasional blog post, and trying to make the world just a little bit safer.

Don Schaffner: modeling listeria growth on melons: in science (and other things), faster isn’t always better

Friend of the Don Schaffner of Rutgers University writes in this guest post:

It’s every scientist’s nightmare to get scooped.  You have an idea, find the funding, do the research and write it up, scan the current literature hoping that no one else has that same idea and beats you to publication.  I don’t worry about this too much, as there aren’t many people that do what I do, and even fewer that do it rock.melon.may.12the way I do. It’s like Jerry Garcia said: “You do not merely want to be considered just the best of the best of the best, you want to be considered the only ones that do what you do.”

But getting scooped on this particular project was on my mind.  Jensen farms had just made a bunch of people sick with listeriosis from cantaloupe, so when Larry Goodridge sent the word out to a bunch of us that we should all submit interlocking proposals to our respective state Departments of Agriculture through state administered Special Crops Research Initiative (SCRI) money for research on Listeria in cantaloupe, I was in.  Larry’s been a good friend, and his nose for grant dollars is second to none.  Alas, this time is was not to be, and only a few of the states funded the interlocking proposals; New Jersey was not one of them. The good news was that Michelle Danyluk at the University of Florida was one who did get funding, and I’m Michelle’s go-to collaborator for all things mathematical.

Prior to the Jensen Farms outbreak, Listeria in cantaloupe was a theoretical risk, but the outbreak underscored the point that L. monocytogenes was a real risk in these foods. Michelle proposed that her lab would collect data on L. monocytogenes growth in cut melons, and that my lab would build the models.  As we suspected from research with Salmonella in cut melons, the organism would multiply rapidly at elevated temperatures but leave the visual appearance of the melons largely unchanged. Listeria’s ability to grow in the fridge made the potential risk even greater.

At this point, I know it’s a race against time, because some other modeler somewhere is thinking the same thing.  Uber-technician Lorrie Friedrich got to work collecting data, and I sharpened my MelonTrucksspreadsheets (or what ever it is that modelers do when waiting for data).  Lorrie soon had the data, and I began making the models, and Michelle started the hard work of writing the manuscript first draft.  All three of us work fast in general. In this particular case, we worked even faster.  We had a rough draft together when I saw the bad news.  My modeling colleagues at the U.S. Department of Agriculture, Agricultural Research Service, in Wyndmoor, PA, near Philadelphia, led by Dr. Lihan Huang published a paper entitled “Growth kinetics of Listeria monocytogenes and spoilage microorganisms in fresh-cut cantaloupe” in Food Microbiology.  A quick read of the paper didn’t leave much doubt; we’d been scooped.

Undaunted, we pressed on.  The first step was to check their model against ours.  It was then that I notice something weird.  Their growth curves showed extraordinarily high bacterial concentrations.  A quick check showed they were reporting bacterial counts in some cases as ln CFU/g instead of log CFU/g.  This explained the high counts in their figures, but even after correcting for this, their model was still giving strange predictions.  I couldn’t even get their model to match their data.  It was then that I noticed that one parameter in their model was about an order of magnitude different from the same parameter in our model.  It might be that slightly different datasets will give model parameters that vary by 25% in some case, but an order of magnitude difference means an error.  Once I corrected their parameter (multiplied by 10), their model and our model fell almost on top of one another (See Figure 2 in Danyluk et al.)

In the end, the reviewers were kind to us, and since we not only corrected, then corroborated Fang et al, (2013), we also validated both models against original data for L. monocytogenes growth in watermelon, and honeydew as well as predictions from ComBase jerry.garciaPredictor for various assumed water activities and pH values.  A contour plot of the model (Figure 3 in the paper) also shows that extended storage of contaminated cut melon slices at even slightly elevated temperatures (in home refrigerators for example) would result in significant risk amplification.

Modeling the growth of Listeria monocytogenes on cut cantaloupe, honeydew and watermelon

Food Microbiology Volume 38, April 2014, Pages 52–55


A recent outbreak linked to whole cantaloupes underscores the importance of understanding growth kinetics of Listeria monocytogenes in cut melons at different temperatures. Whole cantaloupe, watermelon, and honeydew purchased from a local supermarket were cut into 10, 1 g cubes. A four-strain cocktail of L. monocytogenes from food related outbreaks was used to inoculate fruit, resulting in ~10^3 CFU/10 g. Samples were stored at 4, 10, 15, 20, or 25 °C and L. monocytogenes were enumerated at appropriate time intervals. The square root model was used to describe L. monocytogenes growth rate as a function of temperature. The model was compared to prior models for Salmonella and Escherichia coli O157:H7 growth on cut melon, as well as models for L. monocytogenes on cantaloupe and L. monocytogenes ComBase models. The current model predicts faster growth of L. monocytogenes vs. Salmonella and E. coli O157:H7 at temperatures below 20 °C, and agrees with estimates from ComBase Predictor, and a corrected published model for L. monocytogenes on cut cantaloupe. The model predicts ~4 log CFU increase following 15 days at 5 °C, and ~1 log CFU increase following 6 days at 4 °C. The model can also be used in subsequent quantitative microbial risk assessments.