Food Safety Model Virtual Library
Digitally enabled food safety has the potential to advance public health, and contribute to a more resilient and sustainable food supply chain. Reusable and interoperable computational models developed to improve food safety along the food supply chain (farm-to-table) are an essential first step.
This is a virtual library of digital food safety tools. The tools are assembled through a systematic scoping review of peer-reviewed literature of existing food safety digital tools and models published since June 2012.
How were models included in this library identified?
Models included in this virtual library were identified through a rapid systematic review of published peer-reviewed articles in the databases PubMed, Web of Science, AGRICOLA, CAB Abstracts, Medline, and FSTA in the period between June 2012–June 2022. The initial search of these databases identified 6,110 potentially relevant articles. However, after title/abstract screening and full-text review, only 49 studies met the inclusion criteria of which 39 are included here. The most common reason for excluding articles was a lack of open access to computer code or a link to the model. Additional models will be included as they are identified.
How should you cite the library?
To cite: Ruser S, Kalunga L, Luongo N, and Ivanek R. Institute for Food Safety at Cornell (2024). Food Safety Model Virtual Library. Virtual library of digital food safety tools. https://cals.cornell.edu/institute-for-food-safety/resources/model-virtual-library
Contact: Sophia J. Ruser (sjr272 [at] cornell.edu (sjr272[at]cornell[dot]edu)) | Dr. Renata Ivanek (ri25 [at] cornell.edu (ri25[at]cornell[dot]edu))
Salmonella
This virtual library was designed to provide a short and easy-to-navigate resource about food safety models. For each model, listed are: (i) the name of the author with a link to the article, (ii) the name of the model, (iii) the pathogen it applied to, (iv) a link to the code/website and (v) the corresponding author’s name and email for contact purposes.
Salmonella Models
Source Explorer
A Bayesian source attribution model.
- Key Words: Data Visualization; Molecular Epidemiology; Salmonella, Salmonellosis; Source Attribution
- Description: Ahlstrom 2017
- Code: warwick.ac.uk/fac/sci/statistics/staff/academic-research/spencer/software
- Contact: Petra Muellner | petra [at] epi-interactive.com (petra[at]epi-interactive[dot]com)
Risk Assessment for Salmonella in Broiler Chickens
A within-flock model of Salmonella Heidelberg transmission in broiler chickens.
- Key Words: Compartmental Model; SEIR Model; Infection Dynamics; Risk Assessment; Public Health
- Description: Collineau 2020
- Code: doi.org/10.5281/zenodo.3351611
- Contact: Ben A. Smith | ben.smith [at] canada.ca (ben[dot]smith[at]canada[dot]ca)
Predicting the Source and Spread of Food Safety Outbreaks
The use of trade data to predict the source and spread of food safety outbreaks: An innovative mathematical modeling approach.
- Key Words: Mathematical Modeling; Food Safety; Surveillance Systems; Network Theory
- Description: Garre 2019
- Code: sciencedirect.com/science/article/pii/S096399691930362X?via%3Dihub#s0045
- Contact: Alberto Garre | alberto.garre [at] upct.es (alberto[dot]garre[at]upct[dot]es)
Thermal Inactivation Model for Salmonella in Pork Burger Patties
Thermal inactivation of Salmonella spp. in pork burger patties.
- Key Words: Inactivation; Cooking; Pork Burger; Skillet; Salmonella; Predictive Model
- Description: Gurman 2016
- Code: ars.els-cdn.com/content/image/1-s2.0-S016816051530177X-mmc1.txt
- Contact: Phillip M. Gurman | phillip.gurman [at] gmail.com (phillip[dot]gurman[at]gmail[dot]com)
Neural Network Model for Salmonella Behavior in Cold Stored Chicken Meat
Neural network model for survival and growth of Salmonella enterica serotype 8,20:-:z(6) in ground chicken thigh meat during cold storage: extrapolation to other serotypes.
- Key Words: Neural Network Model; Salmonella Enterica; Cold Storage; Ground Chicken
- Description: Oscar 2015
- Code: ars.usda.gov/nea/errc/PoultryFARM
- Contact: Thomas Oscar | thomas.oscar [at] ars.usda.gov (thomas[dot]oscar[at]ars[dot]usda[dot]gov)
Neural Network Model for Thermal Inactivation of Salmonella Typhimurium
Neural network model for thermal inactivation of Salmonella Typhimurium to elimination in ground chicken: acquisition of data by whole sample enrichment, miniature most-probable-number method.
- Key Words: Neural Network Model; Ground Chicken; Predictive Microbiology; Salmonella Typhimurium; Thermal Inactivation; Whole Sample Enrichment, Miniature Most-Probable-Number Method
- Description: Oscar 2017
- Code: ars.usda.gov/nea/errc/PoultryFARM
- Contact: Thomas Oscar | thomas.oscar [at] ars.usda.gov (thomas[dot]oscar[at]ars[dot]usda[dot]gov)
Neural Network Model of Salmonella Growth in Cold Stored Ground Chicken
Neural network models for growth of Salmonella serotypes in ground chicken subjected to temperature abuse during cold storage for application in HACCP and risk assessment.
- Key Words: Ground Chicken; HACCP; Neural Network Model; Risk Assessment; Salmonella; Temperate Abuse
- Description: Oscar 2017
- Code: ars.usda.gov/nea/errc/PoultryFARM
- Contact: Thomas Oscar | thomas.oscar [at] ars.usda.gov (thomas[dot]oscar[at]ars[dot]usda[dot]gov)
Neural Network Model of Salmonella Growth in Diced Tomatoes
Neural network model for predicting growth of Salmonella Newport on diced Roma tomatoes during simulated salad preparation and serving: extrapolation to other serotypes.
- Key Words: Acceptable Prediction Zone Method; Growth; Neural Network Model; Risk Assessment; Roma Tomatoes; Salmonella Newport; Validation
- Description: Oscar 2018
- Code: ars.usda.gov/nea/errc/PoultryFARM
- Contact: Thomas Oscar | thomas.oscar [at] ars.usda.gov (thomas[dot]oscar[at]ars[dot]usda[dot]gov)
Neural Network Model of Salmonella Growth on Cucumber
Neural network model for growth of Salmonella Newport from chicken on cucumber for use in risk assessment.
- Key Words: Neural Network Model; Salmonella Newport; Chicken; Cucumber; Risk Assessment; Predictive Model
- Description: Oscar 2021
- Code: ars.usda.gov/nea/errc/PoultryFARM
- Contact: Thomas Oscar | thomas.oscar [at] ars.usda.gov (thomas[dot]oscar[at]ars[dot]usda[dot]gov)
Risk Assessment for Salmonella in Servings of Chicken Liver
Monte Carlo simulation model for predicting Salmonella contamination of chicken liver as a function of serving size for use in quantitative microbial risk assessment.
- Key Words: Chicken Liver; Contamination; Predictive Microbiology; Predictive Model; Quantitative Microbial Risk Assessment; Salmonella
- Description: Oscar 2021
- Code: ars.usda.gov/nea/errc/PoultryFARM
- Contact: Thomas Oscar | thomas.oscar [at] ars.usda.gov (thomas[dot]oscar[at]ars[dot]usda[dot]gov)
Model of Salmonella Survival in Tree Nuts
Modeling the survival kinetics of Salmonella in tree nuts for use in risk assessment.
- Key Words: Low Moisture; Predictive Microbiology; Fixed Effect Model; Mixed Effect Model; Bayesian Inference
- Description: Santillana Farakos 2016
- Code: ars.els-cdn.com/content/image/1-s2.0-S0168160516301192-mmc1.docx
- Contact: Sofia M. Santillana Farakos | sofia.santillanafarakos [at] fda.hhs.gov (sofia[dot]santillanafarakos[at]fda[dot]hhs[dot]gov)
Heat Inactivation Model for Salmonella and Spoilage Organisms
Bayesian global regression model relating product characteristics of intermediate moisture food products to heat inactivation parameters for Salmonella Napoli and Eurotium herbariorum mould spores.
- Key Words: Modeling; Inactivation; Predictive Microbiology; Spoilage
- Description: Smid 2022
- Code: sciencedirect.com/science/article/abs/pii/S016816052200109X#s0105
- Contact: Joost H. Smid | joost.smid [at] unilever.com (joost[dot]smid[at]unilever[dot]com)
In-Silico Serotyping and Subtyping of Salmonella
The Salmonella In-Silico Typing Resource (SISTR): an open web-accessible tool for rapidly typing and subtyping draft Salmonella genome assemblies.
- Key Words: Salmonella; In-Silico Typing; Genoserotyping; Molecular Epidemiology
- Description: Yoshida 2016
- Code: lfz.corefacility.ca/sistr-app
- Contact: Eduardo Taboada | eduardo.taboada [at] phac-aspc.gc.ca (eduardo[dot]taboada[at]phac-aspc[dot]gc[dot]ca)
Campylobacter
This virtual library was designed to provide a short and easy-to-navigate resource about food safety models. For each model, listed are: (i) the name of the author with a link to the article, (ii) the name of the model, (iii) the pathogen it applied to, (iv) a link to the code/website and (v) the corresponding author’s name and email for contact purposes.
Campylobacter Models
KEDRF – A Comprehensive Dose-Response Model for Campylobacter Jejuni
A comprehensive dose-response model incorporating various key events that occur during Campylobacter jejuni infection.
- Key Words: Bayesian Predictive Model; Foodborne Pathogen; Infection Mechanism; Quantitative Microbial Risk Assessment
- Description: Abe 2021
- Code: github.com/Hiroki-Abe/KEDRF2021
- Contact: Shigenobu Koseki | pj.ca.iadukoh.rga.epb@ikesok
Three Models for Predicting Campylobacteriosis Risk in New Zealand
Comparing and evaluating the effectiveness of three modeling approaches to predicting the risk of campylobacteriosis in New Zealand.
- Key Words: Bayesian Hierarchical Models; Campylobacter; Monte Carlo Simulation; Quantitative Risk Assessment; Time Series
- Description: Al-Sakkaf 2020
- Code: sciencedirect.com/science/article/pii/S2352352218300732#sec0024
- Contact: Ali Al-Sakkaf | ali.al-sakkaf [at] lincolnuni.ac.nz (ali[dot]al-sakkaf[at]lincolnuni[dot]ac[dot]nz)
Risk Assessment for Campylobacteriosis from Raw Beef
Quantitative Microbial Risk Assessment for Campylobacter Foodborne Illness in Raw Beef Offal Consumption in South Korea.
- Key Words: Campylobacter; Microbial Risk Assessment; Predictive Model; Raw Beef Offal
- Description: Jeong 2017
- Code: sciencedirect.com/science/article/pii/S0362028X22099975#ec0010
- Contact: Yohan Yoon | yyoon [at] sookmyung.ac.kr (yyoon[at]sookmyung[dot]ac[dot]kr)
Modeling Source Attribution for Foodborne Diseases - Campylobacter Example
SourceR: Classification and source attribution of infectious agents among heterogeneous populations.
- Key Words: Epidemiology; Islands; Zoonoses; Campylobacteriosis; Campylobacter; Genetic Epidemiology; New Zealand; Infectious Disease Surveillance
- Description: Miller 2017
- Code: fhm-chicas-code.lancs.ac.uk/millerp/sourceR
- Contact: Poppy Miller | p.miller [at] lancaster.ac.uk (p[dot]miller[at]lancaster[dot]ac[dot]uk)
Risk-Based Microbiological Criteria in Broiler Production – Campylobacter Example
A Bayesian approach to the evaluation of risk-based microbiological criteria for Campylobacter in broiler meat.
- Key Words: Bayesian Modeling; Hierarchical Models; Evidence Synthesis; Uncertainty; OpenBUGS; 2D Monte Carlo; Quantitative Microbiological Risk Assessment; Food Safety; Campylobacter
- Description: Ranta 2015
- Code: dx.doi.org/10.1214/15-AOAS845SUPP
- Contact: Jukka Ranta | jukka.ranta [at] evira.fi (jukka[dot]ranta[at]evira[dot]fi)
Listeria
This virtual library was designed to provide a short and easy-to-navigate resource about food safety models. For each model, listed are: (i) the name of the author with a link to the article, (ii) the name of the model, (iii) the pathogen it applied to, (iv) a link to the code/website and (v) the corresponding author’s name and email for contact purposes.
Listeria Models
Agent-Based Model of Corrective Actions for Listeria in a Food Facility
Agent-based models that model Listeria contamination dynamics within food processing facilities.
- Key Words: Listeria; Sanitation; Equipment; Agent-Based Modeling; Listeria Monocytogenes; Reducing Agents; Food Poisoning; Simulation and Modeling
- Description: Barnett-Neefs 2022
- Code: zenodo.org/record/5921808#.Y-PqOOzMJmo
- Contact: Cecil Barnett-Neefs | cwb96 [at] cornell.edu (cwb96[at]cornell[dot]edu)
Model for Assessing Listeria Regulatory and Recall Risks
A modeling tool to assess and reduce regulatory and recall risks for cold-smoked salmon due to Listeria monocytogenes contamination.
- Key Words: Cold-Smoked Salmon; Decision Support Tool; Listeria Monocytogenes; Nisin; Regulatory and Recall Risk
- Description: Chen 2022
- Code: github.com/FSL-MQIP/RegulatoryAndRecallRiskModel_Listeria_ColdSmokedSalmon
- Contact: Martin Wiedmann | mw16 [at] cornell.edu (mw16[at]cornell[dot]edu)
Bayesian Growth Modeling for Multiple Bacterial Populations
Bayesian modeling of bacterial growth for multiple populations.
- Key Words: Bacterial Population Modeling, Growth Functions, Neural Networks, Bayesian Inference
- Description: Palacios 2014
- Code: dx.doi.org/10.1214/14-AOAS720SUPPA
- Contact: A. Paula Palacios | ana.palacios [at] plymouth.ac.uk (ana[dot]palacios[at]plymouth[dot]ac[dot]uk)
Agent-Based Model for Design of Listeria Environmental Monitoring
In-Silico models for design and optimization of science-based Listeria environmental monitoring programs in fresh-cut produce facilities.
- Key Words: Agent-Based Modeling; Listeria; Environmental Monitoring; Produce
- Description: Sullivan 2021
- Code: github.com/IvanekLab/CPS_2019_OpenData
- Contact: Renata Ivanek | ri25 [at] cornell.edu (ri25[at]cornell[dot]edu)
EnABLe – Environmental Monitoring with an Agent-Based Model of Listeria
An agent-based model to understand Listeria dynamics in food processing facilities.
- Key Words: Agent-Based Model, Listeria; Food Processing; Environmental Monitoring; Equipment; Optimization; Cold Smoked-Salmon
- Description: Zoellner 2019
- Code: foodcovidcontrol.com/foodctl
- Contact: Claire Zoellner | cez23 [at] cornell.edu (cez23[at]cornell[dot]edu)
E. Coli
This virtual library was designed to provide a short and easy-to-navigate resource about food safety models. For each model, listed are: (i) the name of the author with a link to the article, (ii) the name of the model, (iii) the pathogen it applied to, (iv) a link to the code/website and (v) the corresponding author’s name and email for contact purposes.
E. Coli Models
Agent-Based Model of Faecal Indicator Organisms in an Agricultural Catchment
An agent-based model that simulates the spatio-temporal dynamics of sources and transfer mechanisms contributing faecal indicator organisms to streams.
- Key Words: Diffuse Pollution; E. coli; EcH2O-iso; Microbial Water Quality; Tracer-Aided Modeling; Water Quality Modeling
- Description: Neill 2020
- MAFIO Source Code: doi.org/10.20392/66f74663-ece3-4a52-8bed-f0cf52d0831a
- EcH2O-iso Source Code: bitbucket.org/sylka/ech2o_iso/src/master_2.0
- Contact: Aaron J. Neill | aaron.neill [at] abdn.ac.uk (aaron[dot]neill[at]abdn[dot]ac[dot]uk)
Tool for Visualizing E. coli Risk on Agricultural Land
A decision support tool for visualizing E. coli risk on agricultural land using a stakeholder-driven approach.
- Key Words: Diffuse Pollution; Environmental Risk; Faecal Indicator Organism; Participatory Research; Stakeholder Engagement
- Description: Oliver 2017
- Code: nercviper.co.uk
- Contact: David M. Oliver | david.oliver [at] stir.ac.uk (david[dot]oliver[at]stir[dot]ac[dot]uk)
Norovirus
This virtual library was designed to provide a short and easy-to-navigate resource about food safety models. For each model, listed are: (i) the name of the author with a link to the article, (ii) the name of the model, (iii) the pathogen it applied to, (iv) a link to the code/website and (v) the corresponding author’s name and email for contact purposes.
Norovirus Models
Model of Norovirus Transport from Irrigation Water to Lettuce
Dynamic transport model for quantification of norovirus internalization in lettuce from irrigation water and associated health risk.
- Key Words: Wastewater Reuse; Subsurface Irrigation; Virus; Microbial Risk Assessment; Dynamic Model; Transport Model
- Description: Chandrasekaran 2018
- Code: github.com/JiangLabUCI/ViralTransport
- Contact: Sunny C. Jiang | sjiang [at] uci.edu (sjiang[at]uci[dot]edu)
Clostridium Perfringens
This virtual library was designed to provide a short and easy-to-navigate resource about food safety models. For each model, listed are: (i) the name of the author with a link to the article, (ii) the name of the model, (iii) the pathogen it applied to, (iv) a link to the code/website and (v) the corresponding author’s name and email for contact purposes.
Clostridium Perfringens Models
Simulation of Clostridium Growth in Cooked Turkey Meat During Cooling
Direct dynamic kinetic analysis and computer simulation of growth of Clostridium perfringens in cooked turkey during cooling.
- Key Words: Bootstrap; C. perfringens; Dynamic Modeling; Monte Carlo Analysis; Numerical Analysis; Optimization
- Description: Huang 2016
- Code: ars.usda.gov/northeast-area/wyndmoor-pa/eastern-regional-research-center/docs/ipmp-dynamic-prediction
- Contact: Lihan Huang | lihan.huang [at] ars.usda.gov (lihan[dot]huang[at]ars[dot]usda[dot]gov)
Multiple Pathogens
This virtual library was designed to provide a short and easy-to-navigate resource about food safety models. For each model, listed are: (i) the name of the author with a link to the article, (ii) the name of the model, (iii) the pathogen it applied to, (iv) a link to the code/website and (v) the corresponding author’s name and email for contact purposes.
Multiple Pathogen Models
APROBA-Plus – Hazard Characterization and Exposure Assessment
A probabilistic tool to evaluate and express uncertainty in hazard characterization and exposure assessment of substances.
- Key Words: Probabilistic Risk Assessment; Communicating Uncertainty; Exposure Assessment
- Description: BokkersBGH 2017
- Code: rivm.nl/en/aproba-plus
- Contact: Bas G. H. Bokkers | bas.bokkers [at] rivm.nl (bas[dot]bokkers[at]rivm[dot]nl)
FDA-iRISK – A Comparative Risk Assessment System
A comparative risk assessment system for evaluating and ranking food-hazard pairs: case studies on microbial hazards.
- Key Words: Risk Assessment; Microbial Hazards; Public Health; Quantitative
- Description: Chen 2013
- Code: irisk.foodrisk.org
- Contact: Dennis B. Sherri | sherri.dennis [at] fda.hhs.gov (sherri[dot]dennis[at]fda[dot]hhs[dot]gov)
Surveillance for Foodborne Illness in Online Restaurant Reviews
Discovering foodborne illness in online restaurant reviews.
- Key Words: Machine Learning; Social Media; Foodborne Diseases; Text Mining; Classification
- Description: Effland 2018
- Code: github.com/teffland/FoodborneNYC/tree/master/jamia_2017
- Contact: Thomas Effland | teffland [at] cs.columbia.edu (teffland[at]cs[dot]columbia[dot]edu)
Bioinactivation FE – Application for Modeling Microbial Inactivation
A free web application for modeling isothermal and dynamic microbial inactivation.
- Key Words: Mathematical Modeling; Bioinactivation; Free Software; Microbial Inactivation; Food Safety
- Description: Garre 2018
- Code: cran.r-project.org/package=bioinactivation
- Contact: Jose A. Egea | josea.egea [at] gmail.com (josea[dot]egea[at]gmail[dot]com)
MicroHibro – Software for Predictive Microbiology Along the Food Chain
A software tool for predictive microbiology and microbial risk assessment in foods.
- Key Words: Sensitivity Analysis; Stochastic Model; Foodborne Pathogens; Software; Probability Distribution; Dose-Response Model
- Description: Gonzalez 2019
- Code: microhibro.com
- Contact: Fernando Pérez-Rodríguez | b42perof [at] uco.es (b42perof[at]uco[dot]es)
ConFerm – A Tool to Predict the Reduction of Pathogens in Sausages
A tool to predict the reduction of pathogens during the production of fermented and matured sausage.
- Key Words: Fermented Sausages; Salmonella; ST E. coli; Listeria Monocytogenes; Predictive Modeling; Reduction
- Description: Gunvig 2016
- Code: dmripredict.dk
- Contact: Annemarie Gunvig | agg [at] dti.dk (agg[at]dti[dot]dk)
FRISBEE Tool – Software for Optimizing Food Cold Chain
A software for optimizing the trade-off between food quality, energy use, and global warming impact of cold chains.
- Key Words: Cold Chain; Sustainability; Modeling; Software; Refrigeration
- Description: Gwanpua 2015
- Code: frisbee-project.eu
- Contact: Annemie H. Geereard | annemie.geeraerd [at] biw.kuleuven.be (annemie[dot]geeraerd[at]biw[dot]kuleuven[dot]be)
Microrisk Lab – Application for Predictive Microbiology
An online freeware for predictive microbiology.
- Key Words: Predictive Microbiology; Nonlinear Regression; Interactive Interface; Nonisothermal Condition; Stochastic Model
- Description: Liu 2021
- Code: microrisklab.shinyapps.io/english
- Contact: Qingling Dong | dongqingli [at] 126.com (dongqingli[at]126[dot]com)
Praedicere Possumus – Application for Predictive Microbiology
An Italian web-based application for predictive microbiology to ensure food safety.
- Key Words: Predictive Microbiology; Modeling; Food Safety; Praedicere Possumus
- Description: Polese 2018
- Code: praedicere.uniud.it
- Contact: Mara Lucia Stecchini | mara.stecchini [at] uniud.it (mara[dot]stecchini[at]uniud[dot]it)
STARTEC Decision Support Tool for Optimizing Ready-to-Eat Food Production
Better tradeoffs between food safety, quality, nutrition, and costs in production of advanced ready-to-eat foods.
- Key Words: Decision Support; Ready-to-Eat; Food Safety; Food Quality; Nutrition; Listeria Monocytogenes
- Description: Skjerdal 2017
- Code: startec-tool.iris.cat/startec/production
- Contact: Taran Skerjdal | taran.skerjdal [at] ventist.no (taran[dot]skerjdal[at]ventist[dot]no)
Water Quality Modeling
Microbial risks associated with manure on pasture and arable land.
- Key Words: C. parvum; Hydrodymanic Modeling; Hydrological Modeling; Pathogens; Salmonella; VTEC
- Description: Sokolova 2018
- Code: sourceforge.net/projects/hype/files
- Contact: Ekaterina Sokolova | ekaterina.sokolova [at] chalmers.se (ekaterina[dot]sokolova[at]chalmers[dot]se)
FRAMES – Risk Analysis in Multimedia Environmental Systems
An integrated environmental modeling framework for performing quantitative microbial risk assessments.
- Key Words: Integrated Environmental Modeling; QMRA; Risk Assessment; Pathogens; Manure; Watershed Modeling
- Description: Whelan 2014
- Code: iemhub.org/resources/133
- Contact: Gene Whelan | whelan.gene [at] epa.gov (whelan[dot]gene[at]epa[dot]gov)