Advertisement

A Gravity-Based Approach to Connect Food Retailers with Consumers for Traceback Models of Food-Borne Diseases

  • Tim SchlaichEmail author
  • Hanno Friedrich
  • Abigail Horn
Conference paper
First Online:
Part of the Studies in Computational Intelligence book series (SCI, volume 882)

Abstract

Computational traceback models are important tools for investigations of widespread food-borne disease outbreaks as they help to determine the causative outbreak location and food item. In an attempt to understand the entire food supply chain from farm to fork, however, these models have paid little attention to consumer behavior and mobility, instead making the simplifying assumption that consumers shop in their home location. This paper aims to fill this gap by modelling food-flows from supermarkets to consumers in a large-scale gravity model for Hesse, Germany. Modelling results show that on average, groceries are sourced from two to four postal zones with half of all goods originating from non-home postal zones. The results contribute to a better understanding of the last link in the food supply chain. In practice, this allows investigators to relate reported outbreak cases with sourcing zones and respective food-retailers. The inclusion of this information into existing models is expected to improve their performance.

Keywords

Food supply network Supply-chain network Gravity model Grocery shopping Food-borne diseases 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgement

The project this report is based on was supported with funds from the German Federal Ministry for Education and Research (BMBF) in the context of the call “Civil Security - Critical structures and processes in production and logistics” under project number 13N15072.

Open image in new window

References

  1. 1.
    World Health Organization: Foodborne Disease Outbreaks: Guidelines for Investigation and Control. WHO Library Cataloguing-in-Publication Data. World Health Organization, Geneva (2008)Google Scholar
  2. 2.
    Tinga, C., Todd, E., Cassidy, M., Pollari, F., Marshall, B., Greig, J., et al.: Exploring historical Canadian foodborne outbreak data sets for human illness attribution. J. Food Prot. 72(9), 1963–1976 (2016)Google Scholar
  3. 3.
    Marvin, H.J.P., Janssen, E.M., Bouzembrak, Y., Hendriksen, P.J.M., Staats, M.: Big data in food safety: an overview. Crit. Rev. Food Sci. Nutr. 57(11), 2286–2295 (2017)CrossRefGoogle Scholar
  4. 4.
    Horn, A.L., Friedrich, H.: Locating the source of large-scale diffusion of foodborne contamination. J. R. Soc. Interface 16(151), 1–11 (2019)CrossRefGoogle Scholar
  5. 5.
    Manitz, J., Kneib, T., Schlather, M., Helbing, D., Brockmann, D.: Origin detection during food-borne disease outbreaks - a case study of the 2011 EHEC/HUS outbreak in Germany. PLoS Curr. (2014)Google Scholar
  6. 6.
    Norström, M., Kristoffersen, A.B., Görlach, F.S., Nygård, K., Hopp, P.: An adjusted likelihood ratio approach analysing distribution of food products to assist the investigation of foodborne outbreaks. PLoS ONE 10(8), 1–13 (2015)CrossRefGoogle Scholar
  7. 7.
    Kaufman, J., Lessler, J., Harry, A., Edlund, S., Hu, K., Douglas, J., et al.: A likelihood-based approach to identifying contaminated food products using sales data: performance and challenges. PLoS Comput. Biol. 10(7), 1–10 (2014)CrossRefGoogle Scholar
  8. 8.
    Infas: Mobilität in Deutschland - Ergebnisbericht (2017)Google Scholar
  9. 9.
    Veenstra, S.A., Thomas, T., Tutert, S.I.A.: Trip distribution for limited destinations: a case study for grocery shopping trips in the Netherlands. Transportation (Amst) 37(4), 663–676 (2010)CrossRefGoogle Scholar
  10. 10.
    Jonker, N.J., Venter, C.J.: Modeling trip-length distribution of shopping center trips from GPS data. J. Transp. Eng. Part A Syst. 145(1), 04018079 (2019)CrossRefGoogle Scholar
  11. 11.
    McFadden, D.: Disaggregate behavioral travel demand’s RUM side a 30-year retrospective (2000)Google Scholar
  12. 12.
    Suhara, Y., Bahrami, M., Bozkaya, B., Pentland, A.(S.), Suhara, Y., et al.: Validating gravity-based market share models using large-scale transactional data (2019)Google Scholar
  13. 13.
    Cascetta, E., Pagliara, F., Papola, A.: Alternative approaches to trip distribution modelling: a retrospective review and suggestions for combining different approaches. Pap. Reg. Sci. 86(4), 597–620 (2007)CrossRefGoogle Scholar
  14. 14.
    Drezner, T.: Derived attractiveness of shopping malls. IMA J. Manag. Math. 17, 349–358 (2006)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Hyman, G.M.: The calibration of trip distribution models. Environ. Plan. 1, 105–112 (1969)CrossRefGoogle Scholar
  16. 16.
    Furness, K.P.: Time function iteration. Traffic Eng. Control 77, 458–460 (1965)Google Scholar
  17. 17.
    Suel, E., Polak, J.W.: Development of joint models for channel, store, and travel mode choice: grocery shopping in London. Transp. Res. Part A Policy Pract. 99, 147–162 (2017)CrossRefGoogle Scholar
  18. 18.
    Viegas, J.M., Martinez, L.M., Silva, E.A.: Effects of the modifiable areal unit problem on the delineation of traffic analysis zones. Environ. Plan. B Plan. Des. 36(4), 625–643 (2009)CrossRefGoogle Scholar
  19. 19.
    de Dios Ortúzar, D., Willumsen, L.G.: Modelling Transport. Wiley, Chichester (2011)CrossRefGoogle Scholar
  20. 20.
    Martin, W., McGuckin, N.: Report 365: Travel Estimation Techniques for Urban Planning. Washington, DC (1998)Google Scholar
  21. 21.
    Huff, D.: Calibrating the huff model using ArcGIS business analyst (2008)Google Scholar
  22. 22.
    Open Street Map: OpenStreetMap Deutschland: Die freie Wiki-Weltkarte (2019). https://www.openstreetmap.de/
  23. 23.
    Statistische Ämter des Bundes und der Länder: ZENSUS2011 - Homepage (2018). https://www.zensus2011.de/EN/Home/home_node.html;jsessionid=8A55DF20B6CB474A1DB6DEFDD94B4949.1_cid389
  24. 24.
    Khatib, Z., Ou, Y., Chang, K.: Session #10 GIS and Transportation Planning (1999)Google Scholar
  25. 25.
    Kordi, M., Kaiser, C., Fotheringham, A.S.: A possible solution for the centroid-to-centroid and intra-zonal trip length problems. In: Gense, J., Josselin, D., Vandenbroucke, D. (es.) Multidisciplinary Research on Geographical Information in Europe and Beyond, Avignon, pp. 147–152 (2012)Google Scholar
  26. 26.
    Bhatta, B.P., Larsen, O.I.: Are intrazonal trips ignorable? Transp. Policy 18, 13–22 (2010)CrossRefGoogle Scholar
  27. 27.
    Manout, O., Bonnel, P.: The impact of ignoring intrazonal trips in assignment models: a stochastic approach. Transportation (Amst), 1–21 (2018)Google Scholar
  28. 28.
    CZuber, E.: Geometrische Wahrscheinlichkeiten und Mittelwerte. T.B. Teubner, Leipzig (1884)Google Scholar
  29. 29.
    Larson, R., Odoni, A.: Urban Operations Research. Prentice Hall, New Jersey (1981)Google Scholar
  30. 30.
    Lebensmittel Zeitung: Ranking: Top 30 Lebensmittelhandel Deutschland 2018 (2018). https://www.lebensmittelzeitung.net/handel/Ranking-Top-30-Lebensmittelhandel-Deutschland-2018-134606
  31. 31.
    Edeka: Edeka Einzelhandel (2019)Google Scholar
  32. 32.
    Infas: Mobilität in Deutschland - Wissenschaftlicher Hintergrund (2019). http://www.mobilitaet-in-deutschland.de/
  33. 33.
    Mekky, A.: A direct method for speeding up the convergence of the furness biproportional method. Transp. Res. Part B 17B(1), 1–11 (1983)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Cesario, F.J.: Parameter estimation in spatial interaction modeling. Environ. Plan. A Econ. Sp. 5(4), 503–518 (1973)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Kuehne Logistics UniversityHamburgGermany
  2. 2.Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA

Personalised recommendations

Cite paper

Buy options