publication . Other literature type . Article . 2018

Identifying Methods for Monitoring Foodborne Illness: Review of Existing Public Health Surveillance Techniques

Oldroyd, Rachel A; Morris, Michelle A; Birkin, Mark;
  • Published: 06 Jun 2018
  • Publisher: JMIR Publications Inc.
  • Country: United Kingdom
Abstract
Background: Traditional methods of monitoring foodborne illness are associated with problems of untimeliness and underreporting. In recent years, alternative data sources such as social media data have been used to monitor the incidence of disease in the population (infodemiology and infoveillance). These data sources prove timelier than traditional general practitioner data, they can help to fill the gaps in the reporting process, and they often include additional metadata that is useful for supplementary research. Objective: The aim of the study was to identify and formally analyze research papers using consumer-generated data, such as social media data or res...
Subjects
free text keywords: Infoveillance, Public health, medicine.medical_specialty, medicine, Disease surveillance, Thematic analysis, Data science, Public health surveillance, Infodemiology, Population, education.field_of_study, education, Computer science, Primary research, Review, disease, social media, foodborne diseases, digital disease detection
78 references, page 1 of 6

Tam, C, Larose, T, O'Brien, S, Adak, B, Cowden, J, Evans, M, Jackson, K, Smyth, B. Food.gov.uk. 2014

World Health Organisation. 2015

Centers for Disease Control and Prevention. 2004

Food.gov.uk.

Achrekar, H, Gandhe, A, Lazarus, R, Yu, S, Liu, B. Twitter Improves Seasonal Influenza Predictions. 2012 [OpenAIRE]

Zhao, L, Chen, J, Chen, F, Wang, W, Lu, CT, Ramakrishnan, N. SimNest: social media nested epidemic simulation via online semi-supervised deep learning. Proc IEEE Int Conf Data Min. 2015; 2015: 639-648 [OpenAIRE] [] [PubMed] [DOI]

Culotta, A. Towards detecting influenza epidemics by analyzing Twitter messages. 2010: 115-122 [OpenAIRE] [DOI]

Aramaki, E, Maskawa, S, Morita, M. Twitter catches the flu: detecting influenza epidemics using Twitter. 2011: 1568-1576

Heaivilin, N, Gerbert, B, Page, JE, Gibbs, JL. Public health surveillance of dental pain via Twitter. J Dent Res. 2011; 90 (9): 1047-51 [OpenAIRE] [] [PubMed] [DOI]

Bernardo, TM, Rajic, A, Young, I, Robiadek, K, Pham, MT, Funk, JA. Scoping review on search queries and social media for disease surveillance: a chronology of innovation. J Med Internet Res. 2013; 15 (7): e147 [OpenAIRE] [] [PubMed] [DOI]

Nsoesie, EO, Kluberg, SA, Brownstein, JS. Online reports of foodborne illness capture foods implicated in official foodborne outbreak reports. Prev Med. 2014; 67: 264-9 [OpenAIRE] [] [PubMed] [DOI]

Harrison, C, Jorder, M, Stern, H, Stavinsky, F, Reddy, V, Hanson, H, Waechter, H, Lowe, L, Gravano, L, Balter, S. CDC. 2014

Kang, JS, Kuznetsova, P, Choi, Y, Luca, M. Harvard Business School. 2013

Kate, K, Negi, S, Kalagnanam, J. Monitoring food safety violation reports from internet forums. Stud Health Technol Inform. 2014; 205: 1090-4 [] [PubMed]

Witten, IH, Frank, E, Hall, MA. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems). 2005

78 references, page 1 of 6
Abstract
Background: Traditional methods of monitoring foodborne illness are associated with problems of untimeliness and underreporting. In recent years, alternative data sources such as social media data have been used to monitor the incidence of disease in the population (infodemiology and infoveillance). These data sources prove timelier than traditional general practitioner data, they can help to fill the gaps in the reporting process, and they often include additional metadata that is useful for supplementary research. Objective: The aim of the study was to identify and formally analyze research papers using consumer-generated data, such as social media data or res...
Subjects
free text keywords: Infoveillance, Public health, medicine.medical_specialty, medicine, Disease surveillance, Thematic analysis, Data science, Public health surveillance, Infodemiology, Population, education.field_of_study, education, Computer science, Primary research, Review, disease, social media, foodborne diseases, digital disease detection
78 references, page 1 of 6

Tam, C, Larose, T, O'Brien, S, Adak, B, Cowden, J, Evans, M, Jackson, K, Smyth, B. Food.gov.uk. 2014

World Health Organisation. 2015

Centers for Disease Control and Prevention. 2004

Food.gov.uk.

Achrekar, H, Gandhe, A, Lazarus, R, Yu, S, Liu, B. Twitter Improves Seasonal Influenza Predictions. 2012 [OpenAIRE]

Zhao, L, Chen, J, Chen, F, Wang, W, Lu, CT, Ramakrishnan, N. SimNest: social media nested epidemic simulation via online semi-supervised deep learning. Proc IEEE Int Conf Data Min. 2015; 2015: 639-648 [OpenAIRE] [] [PubMed] [DOI]

Culotta, A. Towards detecting influenza epidemics by analyzing Twitter messages. 2010: 115-122 [OpenAIRE] [DOI]

Aramaki, E, Maskawa, S, Morita, M. Twitter catches the flu: detecting influenza epidemics using Twitter. 2011: 1568-1576

Heaivilin, N, Gerbert, B, Page, JE, Gibbs, JL. Public health surveillance of dental pain via Twitter. J Dent Res. 2011; 90 (9): 1047-51 [OpenAIRE] [] [PubMed] [DOI]

Bernardo, TM, Rajic, A, Young, I, Robiadek, K, Pham, MT, Funk, JA. Scoping review on search queries and social media for disease surveillance: a chronology of innovation. J Med Internet Res. 2013; 15 (7): e147 [OpenAIRE] [] [PubMed] [DOI]

Nsoesie, EO, Kluberg, SA, Brownstein, JS. Online reports of foodborne illness capture foods implicated in official foodborne outbreak reports. Prev Med. 2014; 67: 264-9 [OpenAIRE] [] [PubMed] [DOI]

Harrison, C, Jorder, M, Stern, H, Stavinsky, F, Reddy, V, Hanson, H, Waechter, H, Lowe, L, Gravano, L, Balter, S. CDC. 2014

Kang, JS, Kuznetsova, P, Choi, Y, Luca, M. Harvard Business School. 2013

Kate, K, Negi, S, Kalagnanam, J. Monitoring food safety violation reports from internet forums. Stud Health Technol Inform. 2014; 205: 1090-4 [] [PubMed]

Witten, IH, Frank, E, Hall, MA. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems). 2005

78 references, page 1 of 6
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publication . Other literature type . Article . 2018

Identifying Methods for Monitoring Foodborne Illness: Review of Existing Public Health Surveillance Techniques

Oldroyd, Rachel A; Morris, Michelle A; Birkin, Mark;