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Article ; Online: New insights into grocery store visits among east Los Angeles residents using mobility data.

Xu, Mengya / Wilson, John P / Bruine de Bruin, Wändi / Lerner, Leo / Horn, Abigail L / Livings, Michelle Sarah / de la Haye, Kayla

Health & place

2024 Volume 87, Page(s) 103220

Abstract: In this study, we employed spatially aggregated population mobility data, generated from mobile phone locations in 2021, to investigate patterns of grocery store visits among residents east and northeast of Downtown Los Angeles, in which 60% of the ... ...

Abstract	In this study, we employed spatially aggregated population mobility data, generated from mobile phone locations in 2021, to investigate patterns of grocery store visits among residents east and northeast of Downtown Los Angeles, in which 60% of the census tracts had previously been designated as "food deserts". Further, we examined whether the store visits varied with neighborhood sociodemographics and grocery store accessibility. We found that residents averaged 0.4 trips to grocery stores per week, with only 13% of these visits within home census tracts, and 40% within home and neighboring census tracts. The mean distance from home to grocery stores was 2.2 miles. We found that people visited grocery stores more frequently when they lived in neighborhoods with higher percentages of Hispanics/Latinos, renters and foreign-born residents, and a greater number of grocery stores. This research highlights the utility of mobility data in elucidating grocery store use, and factors that may facilitate or be a barrier to store access. The results point to limitations of using geographically constrained metrics of food access like food deserts.
Language	English
Publishing date	2024-03-15
Publishing country	England
Document type	Journal Article
ZDB-ID	1262540-1
ISSN	1873-2054 ; 1353-8292
ISSN (online)	1873-2054
ISSN	1353-8292
DOI	10.1016/j.healthplace.2024.103220
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Article ; Online: Emerging Applications of Machine Learning in Food Safety.

Deng, Xiangyu / Cao, Shuhao / Horn, Abigail L

Annual review of food science and technology

2021 Volume 12, Page(s) 513–538

Abstract: Food safety continues to threaten public health. Machine learning holds potential in leveraging large, emerging data sets to improve the safety of the food supply and mitigate the impact of food safety incidents. Foodborne pathogen genomes and novel data ...

Abstract	Food safety continues to threaten public health. Machine learning holds potential in leveraging large, emerging data sets to improve the safety of the food supply and mitigate the impact of food safety incidents. Foodborne pathogen genomes and novel data streams, including text, transactional, and trade data, have seen emerging applications enabled by a machine learning approach, such as prediction of antibiotic resistance, source attribution of pathogens, and foodborne outbreak detection and risk assessment. In this article, we provide a gentle introduction to machine learning in the context of food safety and an overview of recent developments and applications. With many of these applications still in their nascence, general and domain-specific pitfalls and challenges associated with machine learning have begun to be recognized and addressed, which are critical to prospective use and future deployment of large data sets and their associated machine learning models for food safety applications.
MeSH term(s)	Disease Outbreaks ; Food Safety ; Foodborne Diseases/epidemiology ; Foodborne Diseases/prevention & control ; Humans ; Machine Learning ; Prospective Studies
Language	English
Publishing date	2021-01-20
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2516759-5
ISSN	1941-1421 ; 1941-1413
ISSN (online)	1941-1421
ISSN	1941-1413
DOI	10.1146/annurev-food-071720-024112
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Article ; Online: Locating the source of large-scale outbreaks of foodborne disease.

Horn, Abigail L / Friedrich, Hanno

Journal of the Royal Society, Interface

2019 Volume 16, Issue 151, Page(s) 20180624

Abstract: In today's globally interconnected food system, outbreaks of foodborne disease can spread widely and cause considerable impact on public health. We study the problem of identifying the source of emerging large-scale outbreaks of foodborne disease; a ... ...

Abstract	In today's globally interconnected food system, outbreaks of foodborne disease can spread widely and cause considerable impact on public health. We study the problem of identifying the source of emerging large-scale outbreaks of foodborne disease; a crucial step in mitigating their proliferation. To solve the source identification problem, we formulate a probabilistic model of the contamination diffusion process as a random walk on a network and derive the maximum-likelihood estimator for the source location. By modelling the transmission process as a random walk, we are able to develop a novel, computationally tractable solution that accounts for all possible paths of travel through the network. This is in contrast to existing approaches to network source identification, which assume that the contamination travels along either the shortest or highest probability paths. We demonstrate the benefits of the multiple-paths approach through application to different network topologies, including stylized models of food supply network structure and real data from the 2011 Shiga toxin-producing Escherichia coli outbreak in Germany. We show significant improvements in accuracy and reliability compared with the relevant state-of-the-art approach to source identification. Beyond foodborne disease, these methods should find application in identifying the source of spread in network-based diffusion processes more generally, including in networks not well approximated by tree-like structure.
MeSH term(s)	Disease Outbreaks ; Escherichia coli Infections/epidemiology ; Escherichia coli Infections/microbiology ; Escherichia coli Infections/transmission ; Food Microbiology ; Foodborne Diseases/epidemiology ; Foodborne Diseases/microbiology ; Germany/epidemiology ; Humans ; Models, Biological
Language	English
Publishing date	2019-04-04
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2156283-0
ISSN	1742-5662 ; 1742-5689
ISSN (online)	1742-5662
ISSN	1742-5689
DOI	10.1098/rsif.2018.0624
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Article ; Online: Effect of mobile food environments on fast food visits.

García Bulle Bueno, Bernardo / Horn, Abigail L / Bell, Brooke M / Bahrami, Mohsen / Bozkaya, Burçin / Pentland, Alex / de la Haye, Kayla / Moro, Esteban

Nature communications

2024 Volume 15, Issue 1, Page(s) 2291

Abstract: Poor diets are a leading cause of morbidity and mortality. Exposure to low-quality food environments saturated with fast food outlets is hypothesized to negatively impact diet. However, food environment research has predominantly focused on static food ... ...

Abstract	Poor diets are a leading cause of morbidity and mortality. Exposure to low-quality food environments saturated with fast food outlets is hypothesized to negatively impact diet. However, food environment research has predominantly focused on static food environments around home neighborhoods and generated mixed findings. In this work, we leverage population-scale mobility data in the U.S. to examine 62M people's visits to food outlets and evaluate how food choice is influenced by the food environments people are exposed to as they move through their daily routines. We find that a 10% increase in exposure to fast food outlets in mobile environments increases individuals' odds of visitation by 20%. Using our results, we simulate multiple policy strategies for intervening on food environments to reduce fast-food outlet visits. This analysis suggests that optimal interventions are informed by spatial, temporal, and behavioral features and could have 2x to 4x larger effect than traditional interventions focused on home food environments.
MeSH term(s)	Humans ; Fast Foods/adverse effects ; Diet ; Residence Characteristics
Language	English
Publishing date	2024-03-14
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-024-46425-2
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Article ; Online: The Network Source Location Problem in the Context of Foodborne Disease Outbreaks

Horn, Abigail L. / Friedrich, Hanno

Dynamics On and Of Complex Networks III

Abstract: In today’s globally interconnected food system, outbreaks of foodborne disease can spread widely and cause considerable impact on public health. Food distribution is a complex system that can be seen as a network of trade flows connecting supply chain ... ...

Abstract	In today’s globally interconnected food system, outbreaks of foodborne disease can spread widely and cause considerable impact on public health. Food distribution is a complex system that can be seen as a network of trade flows connecting supply chain actors. Identifying the source of an outbreak of foodborne disease distributed across this network can be solved by considering this network structure and the dimensions of information it contains. The literature on the network source identification problem has grown widely in recent years covering problems in many different contexts, from contagious disease infecting a human population, to computer viruses spreading through the Internet, to rumors or trends diffusing through a social network. Much of this work has focused on studying this problem in analytically tractable frameworks, designing approaches to work on trees and extending to general network structures in an ad hoc manner. These simplified frameworks lack many features of real-world networks and problem contexts that can dramatically impact transmission dynamics, and therefore, backwards inference of the transmission process. Moreover, the features that distinguish foodborne disease in the context of source identification have not previously been studied or identified. In this article we identify these features, then provide a review of existing work on the network source identification problem, categorizing approaches according to these features. We conclude that much of the existing work cannot be implemented in the foodborne disease problem because it makes assumptions about the transmission process that are unrealistic in the context of food supply networks—that is, identifying the source of an epidemic contagion whereas foodborne contamination spreads through a transport network-mediated diffusion process, or because it requires data that is not available—complete observations of the contamination status of all nodes in the network.
Keywords	covid19
Publisher	PMC
Document type	Article ; Online
DOI	10.1007/978-3-030-14683-2_7
Database	COVID19

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Article ; Online: Locating the source of large-scale outbreaks of foodborne disease

Horn, Abigail L. / Friedrich, Hanno

2019

Abstract	In today's globally interconnected food system, outbreaks of foodborne disease can spread widely and cause considerable impact on public health. We study the problem of identifying the source of emerging large-scale outbreaks of foodborne disease; a crucial step in mitigating their proliferation. To solve the source identification problem, we formulate a probabilistic model of the contamination diffusion process as a random walk on a network and derive the maximum-likelihood estimator for the source location. By modelling the transmission process as a random walk, we are able to develop a novel, computationally tractable solution that accounts for all possible paths of travel through the network. This is in contrast to existing approaches to network source identification, which assume that the contamination travels along either the shortest or highest probability paths. We demonstrate the benefits of the multiple-paths approach through application to different network topologies, including stylized models of food supply network structure and real data from the 2011 Shiga toxin-producing Escherichia coli outbreak in Germany. We show significant improvements in accuracy and reliability compared with the relevant state-of-the-art approach to source identification. Beyond foodborne disease, these methods should find application in identifying the source of spread in network-based diffusion processes more generally, including in networks not well approximated by tree-like structure.
Keywords	Text ; ddc:610 ; Epidemic ; Food supply networks ; Foodborne disease ; Network diffusion ; Network source identification ; Spreading
Subject code	410
Language	English
Publishing country	de
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Gravity-Based Food Flow Model to Identify the Source of Foodborne Disease Outbreaks

Tim Schlaich / Abigail L. Horn / Marcel Fuhrmann / Hanno Friedrich

International Journal of Environmental Research and Public Health, Vol 17, Iss 2, p

2020 Volume 444

Abstract: Computational traceback methodologies are important tools for investigations of widespread foodborne disease outbreaks as they assist investigators to determine the causative outbreak location and food item. In modeling the entire food supply chain from ... ...

Abstract	Computational traceback methodologies are important tools for investigations of widespread foodborne disease outbreaks as they assist investigators to determine the causative outbreak location and food item. In modeling the entire food supply chain from farm to fork, however, these methodologies have paid little attention to consumer behavior and mobility, instead making the simplifying assumption that consumers shop in the area adjacent to their home location. This paper aims to fill this gap by introducing a gravity-based approach to model food-flows from supermarkets to consumers and demonstrating how models of consumer shopping behavior can be used to improve computational methodologies to infer the source of an outbreak of foodborne disease. To demonstrate our approach, we develop and calibrate a gravity model of German retail shopping behavior at the postal-code level. Modeling results show that on average about 70 percent of all groceries are sourced from non-home zip codes. The value of considering shopping behavior in computational approaches for inferring the source of an outbreak is illustrated through an application example to identify a retail brand source of an outbreak. We demonstrate a significant increase in the accuracy of a network-theoretic source estimator for the outbreak source when the gravity model is included in the food supply network compared with the baseline case when contaminated individuals are assumed to shop only in their home location. Our approach illustrates how gravity models can enrich computational inference models for identifying the source (retail brand, food item, location) of an outbreak of foodborne disease. More broadly, results show how gravity models can contribute to computational approaches to model consumer shopping interactions relating to retail food environments, nutrition, and public health.
Keywords	gravity model ; food supply network ; food retailing ; network source identification ; epidemic ; foodborne diseases ; Medicine ; R
Subject code	006 ; 360
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Gravity-Based Food Flow Model to Identify the Source of Foodborne Disease Outbreaks.

Schlaich, Tim / Horn, Abigail L / Fuhrmann, Marcel / Friedrich, Hanno

International journal of environmental research and public health

2020 Volume 17, Issue 2

Abstract	Computational traceback methodologies are important tools for investigations of widespread foodborne disease outbreaks as they assist investigators to determine the causative outbreak location and food item. In modeling the entire food supply chain from farm to fork, however, these methodologies have paid little attention to consumer behavior and mobility, instead making the simplifying assumption that consumers shop in the area adjacent to their home location. This paper aims to fill this gap by introducing a gravity-based approach to model food-flows from supermarkets to consumers and demonstrating how models of consumer shopping behavior can be used to improve computational methodologies to infer the source of an outbreak of foodborne disease. To demonstrate our approach, we develop and calibrate a gravity model of German retail shopping behavior at the postal-code level. Modeling results show that on average about 70 percent of all groceries are sourced from non-home zip codes. The value of considering shopping behavior in computational approaches for inferring the source of an outbreak is illustrated through an application example to identify a retail brand source of an outbreak. We demonstrate a significant increase in the accuracy of a network-theoretic source estimator for the outbreak source when the gravity model is included in the food supply network compared with the baseline case when contaminated individuals are assumed to shop only in their home location. Our approach illustrates how gravity models can enrich computational inference models for identifying the source (retail brand, food item, location) of an outbreak of foodborne disease. More broadly, results show how gravity models can contribute to computational approaches to model consumer shopping interactions relating to retail food environments, nutrition, and public health.
MeSH term(s)	Commerce ; Consumer Behavior ; Disease Outbreaks ; Food Contamination/prevention & control ; Foodborne Diseases/epidemiology ; Humans ; Models, Theoretical ; Public Health/methods
Language	English
Publishing date	2020-01-09
Publishing country	Switzerland
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph17020444
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Article ; Online: Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations.

Horn, Abigail L / Bell, Brooke M / Bulle Bueno, Bernardo García / Bahrami, Mohsen / Bozkaya, Burçin / Cui, Yan / Wilson, John P / Pentland, Alex / Moro, Esteban / de la Haye, Kayla

NPJ digital medicine

2023 Volume 6, Issue 1, Page(s) 208

Abstract: The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and ... ...

Abstract	The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people's homes. As smartphone ownership increases, large-scale data on human mobility (i.e., smartphone geolocations) represents a promising resource for studying dynamic food environments that people have access to and visit as they move throughout their day. This study investigates whether mobility data provides meaningful indicators of diet, measured as FF intake, and diet-related disease, evaluating its usefulness for food environment research. Using a mobility dataset consisting of 14.5 million visits to geolocated food outlets in Los Angeles County (LAC) across a representative sample of 243,644 anonymous and opted-in adult smartphone users in LAC, we construct measures of visits to FF outlets aggregated over users living in neighborhood. We find that the aggregated measures strongly and significantly correspond to self-reported FF intake, obesity, and diabetes in a diverse, representative sample of 8,036 LAC adults included in a population health survey carried out by the LAC Department of Public Health. Visits to FF outlets were a better predictor of individuals' obesity and diabetes than their self-reported FF intake, controlling for other known risks. These findings suggest mobility data represents a valid tool to study people's use of dynamic food environments and links to diet and health.
Language	English
Publishing date	2023-11-15
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-023-00949-x
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Article ; Online: Socioeconomic Disparities in Electronic Cigarette Use and Transitions from Smoking.

Friedman, Abigail S / Horn, Samantha J L

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco

2018 Volume 21, Issue 10, Page(s) 1363–1370

Abstract: Introduction: Socioeconomic disparities have been established for conventional cigarette use, but not for electronic cigarettes. This study estimates socioeconomic gradients in exclusive use of conventional cigarettes, electronic cigarettes, and dual ... ...

Abstract	Introduction: Socioeconomic disparities have been established for conventional cigarette use, but not for electronic cigarettes. This study estimates socioeconomic gradients in exclusive use of conventional cigarettes, electronic cigarettes, and dual use (ie, use of both products) among adults in the United States. Methods: Analyses consider nationally representative data on 25- to 54-year-old respondents to the 2014-2016 National Health Interview Surveys (N = 50306). Demographically adjusted seemingly unrelated regression models estimate how two socioeconomic status measures-respondent education and household income-relate to current exclusive use of conventional cigarettes, electronic cigarettes, and dual use. Results: Conventional cigarette use exhibits negative education and income gradients, consistent with existing research: -12.9 percentage points (confidence interval [CI]: -14.0, -11.8) if college educated, and -9.5 percentage points (CI: -10.9, -8.1) if household income exceeds 400% of the federal poverty level. These gradients are flatter for dual use (-1.4 [CI: -1.8, -0.9] and -1.9 [CI: -2.5, -1.2]), and statistically insignificant for electronic cigarette use (-0.03 [CI: -0.5, 0.4] and -0.3 [CI: -0.8, -0.2]). Limiting the sample to ever-smokers, higher education is associated with a 0.9 percentage point increase in likelihood of exclusive electronic cigarette use at interview (CI: 0.0, 1.9). Conclusions: Education and income gradients in exclusive electronic cigarette use are small and statistically insignificant, contrasting with strong negative gradients in exclusive conventional cigarette use. Furthermore, more educated smokers are more likely to switch to exclusive e-cigarette use than less educated smokers. Such differential switching may exacerbate socioeconomic disparities in smoking-related morbidity and mortality, but lower the burden of tobacco-related disease. Implications: Research has not yet established whether socioeconomic disparities in electronic cigarette (e-cigarette) use resemble those observed for conventional cigarettes. This article uses nationally representative data on US adults aged 25-54 to estimate income and education gradients in exclusive use of conventional cigarettes, e-cigarettes, and dual use. Both gradients are steep and negative for conventional cigarette use, but flat and statistically insignificant for e-cigarette use. Repeating the analysis among ever-smokers indicates that more educated smokers are more likely to transition toward exclusive e-cigarette use than less educated smokers. Such differential substitution may exacerbate disparities in smoking-related morbidity and mortality.
MeSH term(s)	Adult ; Health Surveys ; Humans ; Middle Aged ; Smoking/epidemiology ; Socioeconomic Factors ; Vaping/epidemiology
Language	English
Publishing date	2018-06-09
Publishing country	England
Document type	Journal Article
ZDB-ID	1452315-2
ISSN	1469-994X ; 1462-2203
ISSN (online)	1469-994X
ISSN	1462-2203
DOI	10.1093/ntr/nty120
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