{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:09:09Z","timestamp":1750306149913,"version":"3.41.0"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2016,6,21]],"date-time":"2016-06-21T00:00:00Z","timestamp":1466467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGSPATIAL Special"],"published-print":{"date-parts":[[2016,6,21]]},"abstract":"<jats:p>Foodborne disease is a global public health problem that affects millions of people every year. During a foodborne illness outbreak, rapid identification of contaminated food is vital to minimize illness, loss and impact on society. Public health officials face a significant challenge and long delays in obtaining critical information to help identify a contaminated product using traditional methods such as surveys and questionnaires. We propose a novel approach mapping geo-coded sales data against geo-coded confirmed case reports, which has the potential to reduce the time required for foodborne illness investigation. Using real grocery retail scanner data with spatial information from Germany, we have implemented a likelihood-based framework to study how such spatial data can be used to accelerate the investigation during the early stages of an outbreak. Our analysis shows that after receiving as few as 10 laboratory confirmed case reports it is possible to narrow the investigation to approximately 12 suspect products with the contaminated product included in this subset 90% of the time for approximately 80% of food products studied.<\/jats:p>","DOI":"10.1145\/2961028.2961031","type":"journal-article","created":{"date-parts":[[2016,6,23]],"date-time":"2016-06-23T13:02:27Z","timestamp":1466686947000},"page":"3-11","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["From farm to fork"],"prefix":"10.1145","volume":"8","author":[{"given":"Kun","family":"Hu","sequence":"first","affiliation":[{"name":"Accelerate Discovery Lab, IBM Almaden Research Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Edlund","sequence":"additional","affiliation":[{"name":"Accelerate Discovery Lab, IBM Almaden Research Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Davis","sequence":"additional","affiliation":[{"name":"Accelerate Discovery Lab, IBM Almaden Research Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Kaufman","sequence":"additional","affiliation":[{"name":"Accelerate Discovery Lab, IBM Almaden Research Center"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,6,21]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Centers for Disease Control and Prevention (CDC): http:\/\/www.cdc.gov\/mmwr\/preview\/mmwrhtml\/rr5002a1.htm  Centers for Disease Control and Prevention (CDC): http:\/\/www.cdc.gov\/mmwr\/preview\/mmwrhtml\/rr5002a1.htm"},{"key":"e_1_2_1_2_1","unstructured":"World Health Organization (WHO): http:\/\/www.afro.who.int\/en\/clusters-a-programmes\/hpr\/food-safety-and-nutrition-fan.html  World Health Organization (WHO): http:\/\/www.afro.who.int\/en\/clusters-a-programmes\/hpr\/food-safety-and-nutrition-fan.html"},{"key":"e_1_2_1_3_1","unstructured":"CDC MMWR: http:\/\/www.cdc.gov\/mmwr\/preview\/mmwrhtml\/mm6250a3.htm  CDC MMWR: http:\/\/www.cdc.gov\/mmwr\/preview\/mmwrhtml\/mm6250a3.htm"},{"key":"e_1_2_1_4_1","volume-title":"A likelihood-based approach to identifying contaminated food products using sales data: performance and challenge. PLoS Comp. Bio. 10(11): e1003999. doi:10.1371\/journal.pcbi.103999","author":"Kaufman J.","year":"2014","unstructured":"J. Kaufman , J. Lessler , A. Harry , S. Edlund , K. Hu , J. Douglas , C. Thoens , B. Appel , A. Kasbohrer , M. Filter . A likelihood-based approach to identifying contaminated food products using sales data: performance and challenge. PLoS Comp. Bio. 10(11): e1003999. doi:10.1371\/journal.pcbi.103999 , 2014 . 10.1371\/journal.pcbi.103999 J. Kaufman, J. Lessler, A. Harry, S. Edlund, K. Hu, J. Douglas, C. Thoens, B. Appel, A. Kasbohrer, M. Filter. A likelihood-based approach to identifying contaminated food products using sales data: performance and challenge. PLoS Comp. Bio. 10(11): e1003999. doi:10.1371\/journal.pcbi.103999, 2014."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodcont.2015.05.017"},{"key":"e_1_2_1_6_1","unstructured":"LandScan Data: http:\/\/web.ornl.gov\/sci\/landscan\/landscan_data_avail.shtml  LandScan Data: http:\/\/web.ornl.gov\/sci\/landscan\/landscan_data_avail.shtml"},{"key":"e_1_2_1_7_1","unstructured":"Google Place: https:\/\/developers.google.com\/places\/  Google Place: https:\/\/developers.google.com\/places\/"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.286.5439.509"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.2307\/3144521"},{"key":"e_1_2_1_10_1","volume-title":"An adjusted likelihood ratio approach analyzing distribution of food products to assist the investigation of foodborne outbreaks. PLoS ONE 10(8): e0134344. doi:10.1371\/journal.pone.0134344","author":"Norstr\u00f6m M.","year":"2015","unstructured":"M. Norstr\u00f6m , A. B. Kristoffersen , F. S. G\u00f6rlach , K. Nyg\u00e5rd , P. Hopp . An adjusted likelihood ratio approach analyzing distribution of food products to assist the investigation of foodborne outbreaks. PLoS ONE 10(8): e0134344. doi:10.1371\/journal.pone.0134344 , 2015 . 10.1371\/journal.pone.0134344 M. Norstr\u00f6m, A. B. Kristoffersen, F. S. G\u00f6rlach, K. Nyg\u00e5rd, P. Hopp. An adjusted likelihood ratio approach analyzing distribution of food products to assist the investigation of foodborne outbreaks. PLoS ONE 10(8): e0134344. doi:10.1371\/journal.pone.0134344, 2015."}],"container-title":["SIGSPATIAL Special"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2961028.2961031","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2961028.2961031","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:39:34Z","timestamp":1750217974000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2961028.2961031"}},"subtitle":["how spatial-temporal data can accelerate foodborne illness investigation in a global food supply chain"],"short-title":[],"issued":{"date-parts":[[2016,6,21]]},"references-count":10,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2016,6,21]]}},"alternative-id":["10.1145\/2961028.2961031"],"URL":"https:\/\/doi.org\/10.1145\/2961028.2961031","relation":{},"ISSN":["1946-7729"],"issn-type":[{"type":"electronic","value":"1946-7729"}],"subject":[],"published":{"date-parts":[[2016,6,21]]},"assertion":[{"value":"2016-06-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}