Abstract
The World Health Organization (WHO) states that globally 1 in 10 people will become ill every year from eating a contaminated food product, resulting in 420,000 deaths. Food contamination can occur through several points within the food supply chain, a result of the intentional or accidental presence of biological/chemical contaminants. This has led public health officials to track food borne illness and create various mitigation strategies.
According to the Food Safety Modernization Act, companies are now required to develop Food Defense Plans that incorporate vulnerability assessments and mitigation strategies. However, the most common risk mitigation strategy to reduce illness is a recall. A recall can fall within three main classes to denote severity of risk associated with certain products. However, these classes are unclear to most consumers and delay their response to a contamination event. Therefore, companies need better risk communication practices in order to have full information for consumers to comply effectively with messages. Thirteen percent of Americans use information given by various media sources to look for recalled food products in their home. A survey conducted by Rutgers Food Institute Policy highlighted the common information that consumers feel they need during a recall which were identifying information (1) about the food product, (2) the food product source, and (3) where the food product is sold.
This chapter presents an overview of food supply chain vulnerabilities and the relationship between risk mitigation strategies and consumer behavior with a focus on messaging. We also discuss several approaches to modeling food supply chain contamination events and the associated impacts on consumers that incorporate consumer behavior, messaging, and recalls. We specifically consider consumer purchasing, consumption, and compliance behavior as a function of various risk mitigation strategies implemented in the food supply chain. The goal is to understand the extent of the contamination event, frequency and timing of the messaging, and recall notifications that reduce foodborne illness.
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Akkerman R, Poorya F, Grunow M (2010) Quality, safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges. OR Spectr 32(2):863–904
American Bar Association (2014) The role of the government in regulating food: an overview. https://www.americanbar.org/publications/aba_health_esource/2013-14/february/the_role_of_the_government.html. Accessed 7 Feb 2018
Basak U, Datta B, Ghose P (2015) Mathematical analysis of an HIV/AIDS epidemic model. Am J Math Stat 5(5):253–258
Batz M, Hoffmann S, Morris J (2011) Ranking the risks: the 10 pathogen-food combinations with the greatest burden on public health. https://epi.ufl.edu/blog/keeping-americas-food-supply-safe/
Blaynesh K, Cao Y, Kwon H (2009) Optimal control of vector-borne diseases: treatment and prevention. Discrete Continuous Dyn Syst Ser B 11(3):1–31
Buchanan R, Appel B (2010) Combining analysis tools and mathematical modeling to enhance and harmonize food safety and food defense regulatory requirements. Int J Food Microbiol 139:S48–S56
Caraballo-Martinez K, Burt S (2011) Determining how and why consumer purchasing of grocery and household products varies. Afr J Bus Manag 5(16):6917–6926
Centers for Disease Control and Prevention (2013a) Multistate outbreak of human salmonella Heidelberg infections linked to ground Turkey. http://www.cdc.gov/salmonella/heidelberg/111011/
Centers for Disease Control and Prevention (2013b) CDC estimates of foodborne illness in the United States. http://www.cdc.gov/foodborneburden/2011-foodborne-estimates.html
Centers for Disease Control and Prevention (2013c) Timeline for reporting cases of salmonella infection. http://www.cdc.gov/salmonella/reporting-timeline.html
Centers for Disease Control and Prevention (2014) Tracking and reporting foodborne disease outbreaks. http://www.cdc.gov/features/dsfoodborneoutbreaks/
Centers for Disease Control and Prevention (CDC) and US Department of Health and Human Services (2015) Surveillance for foodborne disease outbreaks United States, 2013, annual report
Chang Y, Zhang Y, Erera AL, White C (2012) Vulnerability assessment of a food supply chain to an intentional attack. In: IEEE Conference on Technologies for Homeland Security, pp 319–324
Chang Y, Erera A, White C (2015a) A leader-follower partially observed, multiobjective Markov game. Ann Oper Res 235(1)
Chang Y, Erera A, White C (2015b) Value of information for a leader-follower partially observed Markov game. Ann Oper Res 235(1):129–153
Chao DL, Longini IM Jr, Halloran ME (2013) The effects of vector movement and distribution in a mathematical model of dengue transmission. PLoS One 8(10):e76044
Chaturvedi A, Armstrong B, Chaturvedi R (2014) Securing the food supply chain: understanding complex interdependence through agent-based simulation. Health Technol 4:159–169
Chebolu-Subramanian V, Gaukler G (2015) Product contamination in a multi-stage food supply chain. Eur J Oper Res 244:164–175
Chen C, Zhang J, Delaurentis T (2013) Quality control in food supply chain management: An analytical model and case study of the adulterated milk incident in china. Int J Prod Econ 152:188–199
Chitnis N, Hyman J, Manore C (2012) Modelling vertical transmission in vector-borne diseases with applications to rift valley fever. J Biol Dyn 7(1):11–40
Crooks A, Hailegiorgis A (2014) An agent-based modeling approach applied to the spread of cholera. Environ Model Simul 62:164–177
Dasakalis T, Pappis C, Rachaniotis N (2012) Epidemics control and logistic operations: A review. J Prod Econ 139:393–410
Department of Homeland Security Risk Steering Committee (2010) Dhs risk lexicon. Department of Homeland Security, Washington, DC
Dewey C (2017) Why the FDA hides the names of grocery stores that sell contaminated food. https://www.washingtonpost.com/news/wonk/wp/2017/03/13/fda-says-soynut-butter-could-make-your-child-sick-who-sold-it-thats-a-trade-secret/?utm_term=.03209f1e0acf. Accessed 6 Jan 2018
Dietz K (1967) Epidemics and rumors: a survey. J R Stat Soc Series A 130(4):505–528
Erongul B (2013) Consumer awareness and perception to food safety: a consumer analysis. Food Control 32:461–471
FDA (2007) Food protection plan
FDA (2017) Where did the FDA come from, and what does it do? https://www.smithsonianmag.com/science-nature/origins-FDA-what-does-it-do-180962054/. Accessed 7 Feb 2018
Fendyur A (2011) Applications of operations research/statistics in infection outbreak management. Int Business Econ Res J 10(2)
Flynn D (2013) Turkish pomegranate seeds spread rare virus across U.S. http://www.foodsafetynews.com/2013/07/turkish-pomengranate-seeds-spread-rare-virus-across-u-s/#.VOQVFS7q9yV
Flynn D (2015) Schwan’s ice cream salmonella outbreak. http://www.foodsafetynews.com/2009/09/meaningful-outbreak-10-schwans-ice-cream-salmonella-outbreak/#.VraK9DyvsB
Foundation GC (2015) Food processing & slaughterhouses. http://www.sustainabletable.org/279/food-processing-slaughterhouses
Freberg K (2012) Intention to comply with crisis messages communicated via social media. Public Relat Rev 38:416–421
Gao SJ, Teng ZD, Xie DH (2008) The effects of pulse vaccination on SEIR model with two time delays. Appl Math Comput 201(1–2):282–292
Gourley S, Liu R, Wu J (2007) Some vector borne diseases with structured host populations: extinction and spatial spread. J Appl Math 67(2):408–432
Government Accountability Office (2012) FDA’s food advisory and recall process needs strengthening
Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V, Giske J, Goss-Custard J, Grand T, Heinz SK, Huse G, Huth A, Jepsen JU, Jorgensen C, Mooij WM, Müller B, Pe’er G, Piou C, Railsback SF, Robbins AM, Robbins MM, Rossmanith E, Rüger N, Strand E, Souissi S, Stillman RA, Vabe R, Visser U, DeAngelis DL (2006) A standard protocol for describing individual-based and agent-based models. Ecol Model 198:115–126
Grunert K (2002) Current issues in understanding of consumer food choice. Trends Food Sci Technol 13:275–285
Hallman W, Cutie C (2009) Food recalls and the American public: improving communications. Technical report. Rutgers University
Hartnett E, Paoli GM, Schaffner DW (2009) Modeling the public health system response to a terrorist event in the food supply. Risk Anal 29(11):1506–1520
Huang S (2008) A new SEIR epidemic model with application to the theory of eradication and control of diseases, and to the eradication of R0. Math Biosci 215:21
Jaine A (2005) A predictive modeling and decision-making tool to facilitate government and industry response to an intentional contamination of the food supply. In: The Institute of Food Technologists’ First Annual Food Protection and Defense Conference
Kirkizlar E, Faissol DM, Griffin PM, Swann JL (2010) Timing of testing and treatment for asymptomatic diseases. Math Biosci 226(1):28–37
Knowles-McPhee S (2015) Growing food safety from the bottom up: an agent based model for food safety inspections. J Artif Soc Social Simul 18(2):1–11
Kramer GJ, Fasone V (2016) Consumer food trends create food safety challenges for the food service industry. https://www.foodsafetymagazine.com/magazine-archive1/junejuly-2017/consumer-food-trends-create-food-safety-challenges-for-the-foodservice-industry/. Accessed 21 Jan 2018
Kuo HS, Chang HJ, Chou P, Teng L, Chen THH (1999) A Markov chain model to assess the efficacy of screening for non-insulin dependent diabetes mellitus (NIDDM). Int J Epidemiol 28(2):233–240
Lee JM, Choi D, Cho G, Kim Y (2012) The effect of public health interventions on the spread of influenza among cities. J Theor Biol 293:131–142
Liu Y, Wein LM (2005) Analyzing a bioterror attack on the food supply: the case of botulinum toxin in milk. Proc Natl Acad Sci U S A 102(8):9984–9989
Liu Y, Wein LM (2008) Mathematically assessing the consequences of food terrorism scenarios. J Food Sci 73(7):M346–M353
Manizini R, Accorsi R (2013) The new conceptual framework for food supply chain assessment. J Food Eng 115:251–263
Marucheck A, Greis N, Mena C, Cai LN (2011) Product safety and security in the global supply chain: Issues, challenges and research opportunities. J Oper Manag 29(7-8):707–720
Mohtadi H, Murshid AP (2009) Risk analysis of chemical, biological, or radionuclear threats: implications for food security. Risk Anal 29(9):1317–1335
National Center for Food Protection and Defense (2014) Overview. https://www.ncfpd.umn.edu/about/overview/
Ozair M, Lashari A, Jung I, Seo Y, Kim B (2013) Stability analysis of a vector-borne disease with variable human population. Abstr Appl Anal 2013:1–12
Pacheo-Cruz G, Esteva L, Vargas C (2014) Vaccination strategies for SIR vector-transmitted diseases. Bull Math Biol 76:2073–2090
Pappas S (2013) Cancer doctor’s alleged poisoning: how can ethylene glycol kill?
Parham PE, Michael E (2011) Outbreak properties of epidemic models: the roles of temporal forcing and stochasticity on pathogen invasion dynamics. J Theor Biol 271(1):1–9
Paul J (2012) Consumer behavior and purchase intention for organic food. J Consum Mark 29(6):412–422
Rong A, Grunow M (2010) A methodology for controlling dispersion in food production and distribution. OR Spectr 32:957–978
Saulo A, Moskowitz H (2011) Uncovering the mind-sets of consumers towards food safety messages. Food Qual Prefer 22:422–432
Shah NH, Gupta J (2013) SEIR model and simulation for vector borne diseases. Appl Math 4:13–17
Sifferlin A (2015) 351,000 people die of food poisoning globally every year. http://time.com/3768003/351000-people-die-of-food-poisoning-globally-every-year/
Souza MO (2014) Multiscale analysis for a vector-borne epidemic model. J Math Biol 68(5):1269–1293
Steelfisher G, Weldon K, Benson J, Blendon R (2010) Public perceptions of food recalls and production safety: two survey’s of the American public. J Food Saf 30:848
Sydsaeter K, Hammond PJ (1995) Mathematics for economic analysis. Prentice Hall, Upper Saddle River
Talley JB (2016) Modeling individual consumer food contamination progression with interventions. Dissertation. North Carolina Agricultural and Technical State University, Greensboro
Tasty S (2015) Your ultimate shelf life guide. stilltasty.com
Teasley R, Bemley J, Davis LB, Erera A, Chang Y (2016) A Markov chain model for quantifying consumer risk in food supply chains. Health Syst 5(2):149–161
Trottier H, Philippe P (2002) Deterministic modeling of infectious diseases: measles cycles and the role of births and vaccination. Internet J Infect Dis 2(2):1–8
Uhry Z, Hedelin G, Colonna M, Asselain B, Arveux P, Exbrayat C, Guldenfelds C, Soler-Michel P, Molinie F, Tretarre B, Rogel A, Courtial I, Danzon A, Guizard AV, Ancelle-Park R, Eilstein D, Duffy S (2011) Modelling the effect of breast cancer screening on related mortality using French data. Cancer Epidemiol 35(3):235–242
USDA (2014) Food safety and inspection service protecting public health and preventing foodborne illness. https://www.fsis.usda.gov/wps/wcm/connect/7a35776b-4717-43b5-b0ce-aeec64489fbd/mission-book.pdf?MOD=AJPERES. Accessed 3 Feb 2018
Viljoen S, Pienaar E, Viljoen H (2011) A state-time epidemiology model of tuberculosis: importance of re-infection. Comput Biol Chem 36:8
Vlajic JV, van der Vorst JG, van der Vorst AJ, Haijema R (2012) A framework for designing robust food supply chains. Int J Prod Econ 137(1):176–189
Ward J, Ward L (2015) Principles of food science, 4th edn. Goodheart-Willcox, Tinley Park
Watkins ME (2015) Modeling consumer behavior for high risk foods. Master’s thesis. North Carolina Agricultural and Technical State University, Greensboro
Wei H, Zhi X, Martchova M (2008) An epidemic model of a vector-borne disease with direct transmission and time delay. J Math Anal Appl 342(2):895–908
Wei D, Zongchen F, Peng Z, Gang G, Xiaogang Q (2015) Mathematical and computational approaches to epidemic modeling: a comprehensive review. Front Comp Sci 9(5):806–826
Yan P, Feng Z (2010) Variability order of the latent and the infectious periods in a deterministic SEIR epidemic model and evaluation of control effectiveness. Math Biosci 224(1):43–52
Zechmann E (2011) Agent-based modeling to simulate contamination events and evaluate threat management strategies in water distribution systems. Risk Anal 31(5):758–772
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Talley, J., Davis, L.B. (2020). Simulation-Based Approach to Evaluate the Effects of Food Supply Chain Mitigation and Compliance Strategies on Consumer Behavior and Risk Communication Methods. In: Smith, A. (eds) Women in Industrial and Systems Engineering. Women in Engineering and Science. Springer, Cham. https://doi.org/10.1007/978-3-030-11866-2_17
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