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Karodia AB, Shaik T, Qekwana DN. Occurrence of Salmonella spp. in animal patients and the hospital environment at a veterinary academic hospital in South Africa. Vet World 2024; 17:922-932. [PMID: 38798288 PMCID: PMC11111710 DOI: 10.14202/vetworld.2024.922-932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/21/2024] [Indexed: 05/29/2024] Open
Abstract
Background and Aims Nosocomial infections caused by Salmonella spp. are common in veterinary facilities. The early identification of high-risk patients and sources of infection is important for mitigating the spread of infections to animal patients and humans. This study investigated the occurrence of Salmonella spp. among patients at a veterinary academic hospital in South Africa. In addition, this study describes the environmental factors that contribute to the spread of Salmonella spp. in the veterinary facility. Materials and Methods This study used a dataset of Salmonella-positive animals and environmental samples submitted to the bacteriology laboratory between 2012 and 2019. The occurrence of Salmonella isolates at the veterinary hospital was described based on source, month, season, year, and location. Proportions and 95% confidence intervals were calculated for each variable. Results A total of 715 Salmonella isolates were recorded, of which 67.6% (483/715) came from animals and the remainder (32.4%, 232/715) came from environmental samples. The highest proportion (29.2%) of Salmonella isolates was recorded in 2016 and most isolates were reported in November (17.4%). The winter season had the lowest (14.6%) proportion of isolates reported compared to spring (31.3%), summer (27.8%), and autumn (26.4%). Salmonella Typhimurium (20.0%) was the most frequently reported serotype among the samples tested, followed by Salmonella Anatum (11.2%). Among the positive animal cases, most (86.3%) came from equine clinics. Most reported isolates differed based on animal species with S. Typhimurium being common in equines and S. Anatum in bovines. Conclusion In this study, S. Typhimurium emerged as the predominant strain in animal and environmental samples. Equines were the most affected animals; however, Salmonella serotypes were also detected in the production animals. Environmental contamination was also a major source of Salmonella species in this study. To reduce the risk of transmission, strict infection prevention and control measures (biosecurity) must be implemented.
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Affiliation(s)
- Ayesha Bibi Karodia
- Department of Paraclinical Sciences, Section Veterinary Public Health, University of Pretoria, Pretoria, Gauteng, South Africa
| | - Tahiyya Shaik
- Department of Paraclinical Sciences, Section Veterinary Public Health, University of Pretoria, Pretoria, Gauteng, South Africa
| | - Daniel Nenene Qekwana
- Department of Paraclinical Sciences, Section Veterinary Public Health, University of Pretoria, Pretoria, Gauteng, South Africa
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Chen Y, Wu C, Zhang Q, Wu D. Review of visual analytics methods for food safety risks. NPJ Sci Food 2023; 7:49. [PMID: 37699926 PMCID: PMC10497676 DOI: 10.1038/s41538-023-00226-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/31/2023] [Indexed: 09/14/2023] Open
Abstract
With the availability of big data for food safety, more and more advanced data analysis methods are being applied to risk analysis and prewarning (RAPW). Visual analytics, which has emerged in recent years, integrates human and machine intelligence into the data analysis process in a visually interactive manner, helping researchers gain insights into large-scale data and providing new solutions for RAPW. This review presents the developments in visual analytics for food safety RAPW in the past decade. Firstly, the data sources, data characteristics, and analysis tasks in the food safety field are summarized. Then, data analysis methods for four types of analysis tasks: association analysis, risk assessment, risk prediction, and fraud identification, are reviewed. After that, the visualization and interaction techniques are reviewed for four types of characteristic data: multidimensional, hierarchical, associative, and spatial-temporal data. Finally, opportunities and challenges in this area are proposed, such as the visual analysis of multimodal food safety data, the application of artificial intelligence techniques in the visual analysis pipeline, etc.
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Affiliation(s)
- Yi Chen
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China.
| | - Caixia Wu
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China
| | - Qinghui Zhang
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China
| | - Di Wu
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, Northern Ireland, UK
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3
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Naumova EN, Simpson RB, Zhou B, Hartwick MA. Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs. Int Stat Rev 2022; 90:S82-S95. [PMID: 38607896 PMCID: PMC9874745 DOI: 10.1111/insr.12529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 09/15/2022] [Accepted: 09/28/2022] [Indexed: 11/05/2022]
Abstract
The confluence of growing analytic capacities and global surveillance systems for seasonal infections has created new opportunities to further develop statistical methodology and advance the understanding of the global disease dynamics. We developed a framework to characterise the seasonality of infectious diseases for publicly available global health surveillance data. Specifically, we aimed to estimate the seasonal characteristics and their uncertainty using mixed effects models with harmonic components and the δ-method and develop multi-panel visualisations to present complex interplay of seasonal peaks across geographic locations. We compiled a set of 2 422 weekly time series of 14 reported outcomes for 173 Member States from the World Health Organization's (WHO) international influenza virological surveillance system, FluNet, from 02 January 1995 through 20 June 2021. We produced an analecta of data visualisations to describe global travelling waves of influenza while addressing issues of data completeness and credibility. Our results offer directions for further improvements in data collection, reporting, analysis and development of statistical methodology and predictive approaches.
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Affiliation(s)
- Elena N. Naumova
- Nutrition Epidemiology and Data Science DivisionTufts University Friedman School of Nutrition Science and Policy150 Harrison AvenueBoston02111MassachusettsUSA
- Initiative for the Forecasting and Modeling of Infectious Diseases (InForMID)Tufts UniversityBoston02111MassachusettsUSA
| | - Ryan B. Simpson
- Nutrition Epidemiology and Data Science DivisionTufts University Friedman School of Nutrition Science and Policy150 Harrison AvenueBoston02111MassachusettsUSA
| | - Bingjie Zhou
- Nutrition Epidemiology and Data Science DivisionTufts University Friedman School of Nutrition Science and Policy150 Harrison AvenueBoston02111MassachusettsUSA
| | - Meghan A. Hartwick
- Initiative for the Forecasting and Modeling of Infectious Diseases (InForMID)Tufts UniversityBoston02111MassachusettsUSA
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4
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Swanson D, Koren C, Hopp P, Jonsson ME, Rø GI, White RA, Grøneng GM. A One Health real-time surveillance system for nowcasting Campylobacter gastrointestinal illness outbreaks, Norway, week 30 2010 to week 11 2022. Euro Surveill 2022; 27:2101121. [PMID: 36305333 PMCID: PMC9615412 DOI: 10.2807/1560-7917.es.2022.27.43.2101121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
BackgroundCampylobacter is a leading cause of food and waterborne illness. Monitoring and modelling Campylobacter at chicken broiler farms, combined with weather pattern surveillance, can aid nowcasting of human gastrointestinal (GI) illness outbreaks. Near real-time sharing of data and model results with health authorities can help increase potential outbreak responsiveness.AimsTo leverage data on weather and Campylobacter on broiler farms to build a risk model for possible human Campylobacter outbreaks and to communicate risk assessments with health authorities.MethodsWe developed a spatio-temporal random effects model for weekly GI illness consultations in Norwegian municipalities with Campylobacter monitoring and weather data from week 30 2010 to 11 2022 to give 1-week nowcasts of GI illness outbreaks. The approach combined a municipality random effects baseline model for seasonally-adjusted GI illness with a second model for peak deviations from that baseline. Model results are communicated to national and local stakeholders through an interactive website: Sykdomspulsen One Health.ResultsLagged temperature and precipitation covariates, as well as 2-week-lagged positive Campylobacter sampling in broilers, were associated with higher levels of GI consultations. Significant inter-municipality variability in outbreak nowcasts were observed.ConclusionsCampylobacter surveillance in broilers can be useful in GI illness outbreak nowcasting. Surveillance of Campylobacter along potential pathways from the environment to illness such as via water system monitoring may improve nowcasting. A One Health system that communicates near real-time surveillance data and nowcast changes in risk to health professionals facilitates the prevention of Campylobacter outbreaks and reduces impact on human health.
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Affiliation(s)
- David Swanson
- Norwegian Institute of Public Health, Oslo, Norway,Department of Biostatistics, University of Oslo, Oslo, Norway
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5
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Zhou B, Liang S, Monahan KM, El-Abbadi N, Cruz MS, Chen Y, DeVane A, Reedy J, Zhang J, Semenova I, Montoliu I, Mozaffarian D, Wang D, Naumova EN. An Open-Access Data Platform: Global Nutrition and Health Atlas (GNHA). Curr Dev Nutr 2022; 6:nzac031. [PMID: 35434472 PMCID: PMC9007240 DOI: 10.1093/cdn/nzac031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/17/2022] [Accepted: 02/23/2022] [Indexed: 11/25/2022] Open
Abstract
The rapid development of nutrition science is embracing digital transformation to generate large amounts of data. Precision nutrition and "Big Data" place increasing demand for data repositories and visualization, which enhances the digital transformation. We defined the need for an integrated nutrition data platform as a web-based platform that can collect, store, track, analyze, monitor, and visually display key metrics in nutrition and health while allowing users to interact with visuals and download data provided in the platform. Interactive dashboards create new opportunities for scholars and practitioners to generate and test hypotheses. We present the development and implementation of the Global Nutrition and Health Atlas (GNHA; https://sites.tufts.edu/gnha/), an open-access online platform covering nutrition and health data with 26 themes and 500+ indicators from 190+ countries up to 30 y. We view GNHA as an interactive tool aiming to share information and perspectives and foster collaborations and innovations.
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Affiliation(s)
- Bingjie Zhou
- Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Shiwei Liang
- Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Kyle M Monahan
- Data Lab, Tufts Technology Services, Tufts University, Medford, MA, USA
| | - Naglaa El-Abbadi
- Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Melissa S Cruz
- Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Yutong Chen
- Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Annie DeVane
- Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Julia Reedy
- Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Jianyi Zhang
- Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Iaroslava Semenova
- Nestlé Institute of Health Sciences, Nestlé Research, Lausanne, Switzerland
| | - Ivan Montoliu
- Nestlé Institute of Health Sciences, Nestlé Research, Lausanne, Switzerland
| | - Dariush Mozaffarian
- Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Dantong Wang
- Nestlé Institute of Health Sciences, Nestlé Research, Lausanne, Switzerland
| | - Elena N Naumova
- Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
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Zhou B, Liang S, Monahan KM, Singh GM, Simpson RB, Reedy J, Zhang J, DeVane A, Cruz MS, Marshak A, Mozaffarian D, Wang D, Semenova I, Montoliu I, Prozorovscaia D, Naumova EN. Food and Nutrition Systems Dashboards: A Systematic Review. Adv Nutr 2022; 13:748-757. [PMID: 35254406 PMCID: PMC9156375 DOI: 10.1093/advances/nmac022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 01/20/2022] [Accepted: 02/28/2022] [Indexed: 11/14/2022] Open
Abstract
The rapid expansion of food and nutrition information requires new ways of data sharing and dissemination. Interactive platforms integrating data portals and visualization dashboards have been effectively utilized to describe, monitor, and track information related to food and nutrition; however, a comprehensive evaluation of emerging interactive systems is lacking. We conducted a systematic review on publicly available dashboards using a set of 48 evaluation metrics for data integrity, completeness, granularity, visualization quality, and interactivity based on 4 major principles: evidence, efficiency, emphasis, and ethics. We evaluated 13 dashboards, summarized their characteristics, strengths, and limitations, and provided guidelines for developing nutrition dashboards. We applied mixed effects models to summarize evaluation results adjusted for interrater variability. The proposed metrics and evaluation principles help to improve data standardization and harmonization, dashboard performance and usability, broaden information and knowledge sharing among researchers, practitioners, and decision makers in the field of food and nutrition, and accelerate data literacy and communication.
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Affiliation(s)
- Bingjie Zhou
- Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Shiwei Liang
- Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Kyle M Monahan
- Data Lab, Tufts Technology Services, Tufts University, Medford, MA, USA
| | - Gitanjali M Singh
- Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Ryan B Simpson
- Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Julia Reedy
- Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Jianyi Zhang
- Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Annie DeVane
- Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Melissa S Cruz
- Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Anastasia Marshak
- Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Dariush Mozaffarian
- Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
| | - Dantong Wang
- Nestlé Institute of Health Sciences, Nestlé Research, Lausanne, Switzerland
| | - Iaroslava Semenova
- Nestlé Institute of Health Sciences, Nestlé Research, Lausanne, Switzerland
| | - Ivan Montoliu
- Nestlé Institute of Health Sciences, Nestlé Research, Lausanne, Switzerland
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Investigating seasonal patterns in enteric infections: a systematic review of time series methods. Epidemiol Infect 2022; 150:e50. [PMID: 35249590 PMCID: PMC8915194 DOI: 10.1017/s0950268822000243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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8
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Xu T, Cui Y. Seasonal Variation Analysis for Weekly Cases, Deaths, and Hospitalizations of COVID-19 in the United States. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022. [DOI: 10.1007/5584_2022_750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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9
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Simpson RB, Babool S, Tarnas MC, Kaminski PM, Hartwick MA, Naumova EN. Signatures of Cholera Outbreak during the Yemeni Civil War, 2016-2019. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 19:ijerph19010378. [PMID: 35010649 PMCID: PMC8744546 DOI: 10.3390/ijerph19010378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/26/2021] [Accepted: 12/27/2021] [Indexed: 11/29/2022]
Abstract
The Global Task Force on Cholera Control (GTFCC) created a strategy for early outbreak detection, hotspot identification, and resource mobilization coordination in response to the Yemeni cholera epidemic. This strategy requires a systematic approach for defining and classifying outbreak signatures, or the profile of an epidemic curve and its features. We used publicly available data to quantify outbreak features of the ongoing cholera epidemic in Yemen and clustered governorates using an adaptive time series methodology. We characterized outbreak signatures and identified clusters using a weekly time series of cholera rates in 20 Yemeni governorates and nationally from 4 September 2016 through 29 December 2019 as reported by the World Health Organization (WHO). We quantified critical points and periods using Kolmogorov–Zurbenko adaptive filter methodology. We assigned governorates into six clusters sharing similar outbreak signatures, according to similarities in critical points, critical periods, and the magnitude of peak rates. We identified four national outbreak waves beginning on 12 September 2016, 6 March 2017, 28 May 2018, and 28 January 2019. Among six identified clusters, we classified a core regional hotspot in Sana’a, Sana’a City, and Al-Hudaydah—the expected origin of the national outbreak. The five additional clusters differed in Wave 2 and Wave 3 peak frequency, timing, magnitude, and geographic location. As of 29 December 2019, no governorates had returned to pre-Wave 1 levels. The detected similarity in outbreak signatures suggests potentially shared environmental and human-made drivers of infection; the heterogeneity in outbreak signatures implies the potential traveling waves outwards from the core regional hotspot that could be governed by factors that deserve further investigation.
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Affiliation(s)
- Ryan B. Simpson
- Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA; (P.M.K.); (M.A.H.)
- Correspondence: (R.B.S.); (E.N.N.); Tel.: +1-978-697-1037 (R.B.S.); +1-617-636-2927 (E.N.N.)
| | - Sofia Babool
- Department of Neuroscience, The University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080, USA;
| | - Maia C. Tarnas
- Department of Community Health, School of Arts and Sciences, Tufts University, 574 Boston Avenue, Medford, MA 02155, USA;
| | - Paulina M. Kaminski
- Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA; (P.M.K.); (M.A.H.)
| | - Meghan A. Hartwick
- Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA; (P.M.K.); (M.A.H.)
| | - Elena N. Naumova
- Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA; (P.M.K.); (M.A.H.)
- Correspondence: (R.B.S.); (E.N.N.); Tel.: +1-978-697-1037 (R.B.S.); +1-617-636-2927 (E.N.N.)
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