30 results
Search Results
2. Ten simple rules to consider regarding preprint submission.
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Bourne, Philip E., Polka, Jessica K., Vale, Ronald D., and Kiley, Robert
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PREPRINTS ,DATA mining ,LICENSES ,COPYRIGHT - Abstract
The article discusses rules for considering regarding preprint submission of scientific work in journals. Topics include analysis of published papers undertaken by cell biologist Stephen Royle for estimating the average time from first submission to publication, data mining of the written content for making it better utilizing the knowledge, and encouraging authors to licenses and formats for facilitating reuse while retaining copyright to their work.
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- 2017
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3. Forecasting Human African Trypanosomiasis Prevalences from Population Screening Data Using Continuous Time Models.
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De Vries, Harwin, Wagelmans, Albert P. M., Hasker, Epco, Lumbala, Crispin, Lutumba, Pascal, De Vlas, Sake J., and Klundert, Joris Van De
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AFRICAN trypanosomiasis ,MEDICAL screening ,DISEASE prevalence ,DISEASE progression ,EPIDEMICS ,DIAGNOSIS - Abstract
To eliminate and eradicate gambiense human African trypanosomiasis (HAT), maximizing the effectiveness of active case finding is of key importance. The progression of the epidemic is largely influenced by the planning of these operations. This paper introduces and analyzes five models for predicting HAT prevalence in a given village based on past observed prevalence levels and past screening activities in that village. Based on the quality of prevalence level predictions in 143 villages in Kwamouth (DRC), and based on the theoretical foundation underlying the models, we consider variants of the Logistic Model—a model inspired by the SIS epidemic model—to be most suitable for predicting HAT prevalence levels. Furthermore, we demonstrate the applicability of this model to predict the effects of planning policies for screening operations. Our analysis yields an analytical expression for the screening frequency required to reach eradication (zero prevalence) and a simple approach for determining the frequency required to reach elimination within a given time frame (one case per 10000). Furthermore, the model predictions suggest that annual screening is only expected to lead to eradication if at least half of the cases are detected during the screening rounds. This paper extends knowledge on control strategies for HAT and serves as a basis for further modeling and optimization studies. [ABSTRACT FROM AUTHOR]
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- 2016
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4. Age density patterns in patients medical conditions: A clustering approach.
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Alhasoun, Fahad, Aleissa, Faisal, Alhazzani, May, Moyano, Luis G., Pinhanez, Claudio, and González, Marta C.
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HEALTH facilities ,POPULATION biology ,POPULATION density ,MEDICAL care ,COMMUNICABLE diseases ,CHICKENPOX - Abstract
This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature. [ABSTRACT FROM AUTHOR]
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- 2018
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5. Eleven quick tips for finding research data.
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Gregory, Kathleen, Khalsa, Siri Jodha, Michener, William K., Psomopoulos, Fotis E., de Waard, Anita, and Wu, Mingfang
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INSTITUTIONAL repositories ,DATA ,INFORMATION retrieval ,SUBJECT headings ,METADATA - Abstract
The article offers suggestions for finding research data to meet research needs. Topics discussed include assess data relevance and suitability and refine data search; look for data services and monitor the latest data; and familiarize with the controlled vocabularies, subject categories, and search fields used in particular repositories.
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- 2018
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6. COVID-19 modeling and non-pharmaceutical interventions in an outpatient dialysis unit
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Hankyu Jang, Philip M. Polgreen, Alberto M. Segre, and Sriram V. Pemmaraju
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Viral Diseases ,business.product_category ,Pulmonology ,Physiology ,Epidemiology ,medicine.medical_treatment ,Attack rate ,030232 urology & nephrology ,Psychological intervention ,Molting ,Ambulatory Care Facilities ,Respirators ,Social Distancing ,Patient Isolation ,0302 clinical medicine ,Medical Conditions ,Health care ,Outpatients ,Medicine and Health Sciences ,Medicine ,030212 general & internal medicine ,Biology (General) ,Respirator ,Ecology ,Social distance ,Simulation and Modeling ,Infectious Diseases ,Computational Theory and Mathematics ,Nephrology ,Modeling and Simulation ,Engineering and Technology ,Kidney Diseases ,Research Article ,Biotechnology ,medicine.medical_specialty ,Isolation (health care) ,Infectious Disease Control ,QH301-705.5 ,education ,Bioengineering ,Disease Surveillance ,Research and Analysis Methods ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,Respiratory Disorders ,Renal Dialysis ,Intervention (counseling) ,Medical Dialysis ,Genetics ,Humans ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Dialysis ,business.industry ,SARS-CoV-2 ,COVID-19 ,Biology and Life Sciences ,Covid 19 ,Infectious Disease Surveillance ,Emergency medicine ,Respiratory Infections ,Medical Devices and Equipment ,business ,Physiological Processes - Abstract
This paper describes a data-driven simulation study that explores the relative impact of several low-cost and practical non-pharmaceutical interventions on the spread of COVID-19 in an outpatient hospital dialysis unit. The interventions considered include: (i) voluntary self-isolation of healthcare personnel (HCPs) with symptoms; (ii) a program of active syndromic surveillance and compulsory isolation of HCPs; (iii) the use of masks or respirators by patients and HCPs; (iv) improved social distancing among HCPs; (v) increased physical separation of dialysis stations; and (vi) patient isolation combined with preemptive isolation of exposed HCPs. Our simulations show that under conditions that existed prior to the COVID-19 outbreak, extremely high rates of COVID-19 infection can result in a dialysis unit. In simulations under worst-case modeling assumptions, a combination of relatively inexpensive interventions such as requiring surgical masks for everyone, encouraging social distancing between healthcare professionals (HCPs), slightly increasing the physical distance between dialysis stations, and—once the first symptomatic patient is detected—isolating that patient, replacing the HCP having had the most exposure to that patient, and relatively short-term use of N95 respirators by other HCPs can lead to a substantial reduction in both the attack rate and the likelihood of any spread beyond patient zero. For example, in a scenario with R0 = 3.0, 60% presymptomatic viral shedding, and a dialysis patient being the infection source, the attack rate falls from 87.8% at baseline to 34.6% with this intervention bundle. Furthermore, the likelihood of having no additional infections increases from 6.2% at baseline to 32.4% with this intervention bundle., Author summary As we write this, the COVID-19 pandemic has essentially taken over the world, with more than 20 million cases spread over 216 countries. A big concern for policy makers all across the world has been the impact of COVID-19 on healthcare systems and whether these systems can cope with the enormous strain placed on them by COVID-19. In this paper, we consider the spread of COVID-19 in a specific healthcare setting: the outpatient dialysis unit. Hemodialysis patients are extremely vulnerable to infections in large part due to multiple immune-system deficiencies associated with renal failure and hemodialysis. Hemodialysis facilities also increase the risk of COVID-19 transmission because each patient is in frequent, close contact with other patients and healthcare personnel. Thus, a dialysis unit can be seen as a microcosm for the worst-case impacts of COVID-19 in a healthcare setting. In this manuscript, we show via high-fidelity modeling and simulations that under pessimistic modeling assumptions, there is a combination of relatively simple, inexpensive, and practical non-pharmaceutical interventions that can substantially lower the impact of COVID-19 in the dialysis unit. Our simulations are based on fine-grained healthcare personnel movement data that we make available for other modelers to use.
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- 2020
7. Fast and near-optimal monitoring for healthcare acquired infection outbreaks.
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Adhikari, Bijaya, Lewis, Bryan, Vullikanti, Anil, Jiménez, José Mauricio, and Prakash, B. Aditya
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MEDICAL personnel ,INFECTION ,PREVENTIVE medicine ,HOSPITAL patients - Abstract
According to the Centers for Disease Control and Prevention (CDC), one in twenty five hospital patients are infected with at least one healthcare acquired infection (HAI) on any given day. Early detection of possible HAI outbreaks help practitioners implement countermeasures before the infection spreads extensively. Here, we develop an efficient data and model driven method to detect outbreaks with high accuracy. We leverage mechanistic modeling of C. difficile infection, a major HAI disease, to simulate its spread in a hospital wing and design efficient near-optimal algorithms to select people and locations to monitor using an optimization formulation. Results show that our strategy detects up to 95% of “future” C. difficile outbreaks. We design our method by incorporating specific hospital practices (like swabbing for infections) as well. As a result, our method outperforms state-of-the-art algorithms for outbreak detection. Finally, a qualitative study of our result shows that the people and locations we select to monitor as sensors are intuitive and meaningful. [ABSTRACT FROM AUTHOR]
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- 2019
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8. Tracking Pseudomonas aeruginosa transmissions due to environmental contamination after discharge in ICUs using mathematical models.
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Pham, Thi Mui, Kretzschmar, Mirjam, Bertrand, Xavier, Bootsma, Martin, and null, null
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MARKOV chain Monte Carlo ,MONTE Carlo method ,MATHEMATICAL models ,PSEUDOMONAS aeruginosa ,INTENSIVE care units ,HEALTH facilities - Abstract
Pseudomonas aeruginosa (P. aeruginosa) is an important cause of healthcare-associated infections, particularly in immunocompromised patients. Understanding how this multi-drug resistant pathogen is transmitted within intensive care units (ICUs) is crucial for devising and evaluating successful control strategies. While it is known that moist environments serve as natural reservoirs for P. aeruginosa, there is little quantitative evidence regarding the contribution of environmental contamination to its transmission within ICUs. Previous studies on other nosocomial pathogens rely on deploying specific values for environmental parameters derived from costly and laborious genotyping. Using solely longitudinal surveillance data, we estimated the relative importance of P. aeruginosa transmission routes by exploiting the fact that different routes cause different pattern of fluctuations in the prevalence. We developed a mathematical model including background transmission, cross-transmission and environmental contamination. Patients contribute to a pool of pathogens by shedding bacteria to the environment. Natural decay and cleaning of the environment lead to a reduction of that pool. By assigning the bacterial load shed during an ICU stay to cross-transmission, we were able to disentangle environmental contamination during and after a patient’s stay. Based on a data-augmented Markov Chain Monte Carlo method the relative importance of the considered acquisition routes is determined for two ICUs of the University hospital in Besançon (France). We used information about the admission and discharge days, screening days and screening results of the ICU patients. Both background and cross-transmission play a significant role in the transmission process in both ICUs. In contrast, only about 1% of the total transmissions were due to environmental contamination after discharge. Based on longitudinal surveillance data, we conclude that cleaning improvement of the environment after discharge might have only a limited impact regarding the prevention of P.A. infections in the two considered ICUs of the University hospital in Besançon. Our model was developed for P. aeruginosa but can be easily applied to other pathogens as well. [ABSTRACT FROM AUTHOR]
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- 2019
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9. Reappraising the utility of Google Flu Trends.
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Kandula, Sasikiran and Shaman, Jeffrey
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BOX-Jenkins forecasting ,INFLUENZA ,REGRESSION analysis ,VIRUS diseases ,PHYSICAL sciences ,RESPIRATORY infections - Abstract
Estimation of influenza-like illness (ILI) using search trends activity was intended to supplement traditional surveillance systems, and was a motivation behind the development of Google Flu Trends (GFT). However, several studies have previously reported large errors in GFT estimates of ILI in the US. Following recent release of time-stamped surveillance data, which better reflects real-time operational scenarios, we reanalyzed GFT errors. Using three data sources—GFT: an archive of weekly ILI estimates from Google Flu Trends; ILIf: fully-observed ILI rates from ILINet; and, ILIp: ILI rates available in real-time based on partial reporting—five influenza seasons were analyzed and mean square errors (MSE) of GFT and ILIp as estimates of ILIf were computed. To correct GFT errors, a random forest regression model was built with ILI and GFT rates from the previous three weeks as predictors. An overall reduction in error of 44% was observed and the errors of the corrected GFT are lower than those of ILIp. An 80% reduction in error during 2012/13, when GFT had large errors, shows that extreme failures of GFT could have been avoided. Using autoregressive integrated moving average (ARIMA) models, one- to four-week ahead forecasts were generated with two separate data streams: ILIp alone, and with both ILIp and corrected GFT. At all forecast targets and seasons, and for all but two regions, inclusion of GFT lowered MSE. Results from two alternative error measures, mean absolute error and mean absolute proportional error, were largely consistent with results from MSE. Taken together these findings provide an error profile of GFT in the US, establish strong evidence for the adoption of search trends based 'nowcasts' in influenza forecast systems, and encourage reevaluation of the utility of this data source in diverse domains. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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10. Close proximity interactions support transmission of ESBL-K. pneumoniae but not ESBL-E. coli in healthcare settings.
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Duval, Audrey, Obadia, Thomas, Boëlle, Pierre-Yves, Fleury, Eric, Herrmann, Jean-Louis, Guillemot, Didier, Temime, Laura, Opatowski, Lulla, and null, null
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ENTEROBACTERIACEAE ,KLEBSIELLA pneumoniae ,DRUG resistance in bacteria ,NOSOCOMIAL infections ,ESCHERICHIA coli ,INFECTION prevention ,INFECTIOUS disease transmission ,ANTIBIOTICS - Abstract
Antibiotic-resistance of hospital-acquired infections is a major public health issue. The worldwide emergence and diffusion of extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae, including Escherichia coli (ESBL-EC) and Klebsiella pneumoniae (ESBL-KP), is of particular concern. Preventing their nosocomial spread requires understanding their transmission. Using Close Proximity Interactions (CPIs), measured by wearable sensors, and weekly ESBL-EC–and ESBL-KP–carriage data, we traced their possible transmission paths among 329 patients in a 200-bed long-term care facility over 4 months. Based on phenotypically defined resistance profiles to 12 antibiotics only, new bacterial acquisitions were tracked. Extending a previously proposed statistical method, the CPI network’s ability to support observed incident-colonization episodes of ESBL-EC and ESBL-KP was tested. Finally, mathematical modeling based on our findings assessed the effect of several infection-control measures. A potential infector was identified in the CPI network for 80% (16/20) of ESBL-KP acquisition episodes. The lengths of CPI paths between ESBL-KP incident cases and their potential infectors were shorter than predicted by chance (P = 0.02), indicating that CPI-network relationships were consistent with dissemination. Potential ESBL-EC infectors were identified for 54% (19/35) of the acquisitions, with longer-than-expected lengths of CPI paths. These contrasting results yielded differing impacts of infection control scenarios, with contact reduction interventions proving less effective for ESBL-EC than for ESBL-KP. These results highlight the widely variable transmission patterns among ESBL-producing Enterobacteriaceae species. CPI networks supported ESBL-KP, but not ESBL-EC spread. These outcomes could help design more specific surveillance and control strategies to prevent in-hospital Enterobacteriaceae dissemination. [ABSTRACT FROM AUTHOR]
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- 2019
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11. Evaluating reproducibility of AI algorithms in digital pathology with DAPPER.
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Bizzego, Andrea, Bussola, Nicole, Chierici, Marco, Maggio, Valerio, Francescatto, Margherita, Cima, Luca, Cristoforetti, Marco, Jurman, Giuseppe, and Furlanello, Cesare
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REPRODUCIBLE research ,ARTIFICIAL intelligence in medicine ,ALGORITHMS ,PATHOLOGY ,DEEP learning ,DATA analysis ,BIOMARKERS ,ARTIFICIAL intelligence ,COMPUTERS in biology - Abstract
Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing results across trials. Deep learning features inferred from digital pathology scans can improve validity and robustness of current clinico-pathological features, up to identifying novel histological patterns, e.g., from tumor infiltrating lymphocytes. In this study, we examine the issue of evaluating accuracy of predictive models from deep learning features in digital pathology, as an hallmark of reproducibility. We introduce the DAPPER framework for validation based on a rigorous Data Analysis Plan derived from the FDA’s MAQC project, designed to analyze causes of variability in predictive biomarkers. We apply the framework on models that identify tissue of origin on 787 Whole Slide Images from the Genotype-Tissue Expression (GTEx) project. We test three different deep learning architectures (VGG, ResNet, Inception) as feature extractors and three classifiers (a fully connected multilayer, Support Vector Machine and Random Forests) and work with four datasets (5, 10, 20 or 30 classes), for a total of 53, 000 tiles at 512 × 512 resolution. We analyze accuracy and feature stability of the machine learning classifiers, also demonstrating the need for diagnostic tests (e.g., random labels) to identify selection bias and risks for reproducibility. Further, we use the deep features from the VGG model from GTEx on the KIMIA24 dataset for identification of slide of origin (24 classes) to train a classifier on 1, 060 annotated tiles and validated on 265 unseen ones. The DAPPER software, including its deep learning pipeline and the Histological Imaging—Newsy Tiles (HINT) benchmark dataset derived from GTEx, is released as a basis for standardization and validation initiatives in AI for digital pathology. [ABSTRACT FROM AUTHOR]
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- 2019
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12. Exploring the impact of inoculum dose on host immunity and morbidity to inform model-based vaccine design.
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Handel, Andreas, Li, Yan, McKay, Brian, Pawelek, Kasia A., Zarnitsyna, Veronika, and Antia, Rustom
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IMMUNE response ,VACCINATION ,VACCINE effectiveness ,COMMUNICABLE disease immunology ,PREVENTION of communicable diseases ,DOSE-effect relationship in pharmacology ,INFLUENZA A virus - Abstract
Vaccination is an effective method to protect against infectious diseases. An important consideration in any vaccine formulation is the inoculum dose, i.e., amount of antigen or live attenuated pathogen that is used. Higher levels generally lead to better stimulation of the immune response but might cause more severe side effects and allow for less population coverage in the presence of vaccine shortages. Determining the optimal amount of inoculum dose is an important component of rational vaccine design. A combination of mathematical models with experimental data can help determine the impact of the inoculum dose. We illustrate the concept of using data and models to inform inoculum dose determination for vaccines, wby fitting a mathematical model to data from influenza A virus (IAV) infection of mice and human parainfluenza virus (HPIV) infection of cotton rats at different inoculum doses. We use the model to map inoculum dose to the level of immune protection and morbidity and to explore how such a framework might be used to determine an optimal inoculum dose. We show how a framework that combines mathematical models with experimental data can be used to study the impact of inoculum dose on important outcomes such as immune protection and morbidity. Our findings illustrate that the impact of inoculum dose on immune protection and morbidity can depend on the specific pathogen and that both protection and morbidity do not necessarily increase monotonically with increasing inoculum dose. Once vaccine design goals are specified with required levels of protection and acceptable levels of morbidity, our proposed framework can help in the rational design of vaccines and determination of the optimal amount of inoculum. [ABSTRACT FROM AUTHOR]
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- 2018
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13. Scabies in residential care homes: Modelling, inference and interventions for well-connected population sub-units.
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Kinyanjui, Timothy, Middleton, Jo, Güttel, Stefan, Cassell, Jackie, Ross, Joshua, and House, Thomas
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SCABIES ,RESIDENTIAL care ,AGING ,INFECTIOUS disease transmission ,MARKOV chain Monte Carlo - Abstract
In the context of an ageing population, understanding the transmission of infectious diseases such as scabies through well-connected sub-units of the population, such as residential care homes, is particularly important for the design of efficient interventions to mitigate against the effects of those diseases. Here, we present a modelling methodology based on the efficient solution of a large-scale system of linear differential equations that allows statistical calibration of individual-based random models to real data on scabies in residential care homes. In particular, we review and benchmark different numerical methods for the integration of the differential equation system, and then select the most appropriate of these methods to perform inference using Markov chain Monte Carlo. We test the goodness-of-fit of this model using posterior predictive intervals and propagate forward the resulting parameter uncertainty in a Bayesian framework to consider the economic cost of delayed interventions against scabies, quantifying the benefits of prompt action in the event of detection. We also revisit the previous methodology used to assess the safety of treatments in small population sub-units—in this context ivermectin—and demonstrate that even a very slight relaxation of the implicit assumption of homogeneous death rates significantly increases the plausibility of the hypothesis that ivermectin does not cause excess mortality based upon the data of Barkwell and Shields [1]. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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14. A model of risk and mental state shifts during social interaction.
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Hula, Andreas, Vilares, Iris, Lohrenz, Terry, Dayan, Peter, and Montague, P. Read
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SOCIAL interaction ,SOCIAL facts ,PARTIALLY observable Markov decision processes ,SOCIAL exchange ,INTERPERSONAL relations - Abstract
Cooperation and competition between human players in repeated microeconomic games offer a window onto social phenomena such as the establishment, breakdown and repair of trust. However, although a suitable starting point for the quantitative analysis of such games exists, namely the Interactive Partially Observable Markov Decision Process (I-POMDP), computational considerations and structural limitations have limited its application, and left unmodelled critical features of behavior in a canonical trust task. Here, we provide the first analysis of two central phenomena: a form of social risk-aversion exhibited by the player who is in control of the interaction in the game; and irritation or anger, potentially exhibited by both players. Irritation arises when partners apparently defect, and it potentially causes a precipitate breakdown in cooperation. Failing to model one’s partner’s propensity for it leads to substantial economic inefficiency. We illustrate these behaviours using evidence drawn from the play of large cohorts of healthy volunteers and patients. We show that for both cohorts, a particular subtype of player is largely responsible for the breakdown of trust, a finding which sheds new light on borderline personality disorder. [ABSTRACT FROM AUTHOR]
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- 2018
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15. SozRank: A new approach for localizing the epileptic seizure onset zone.
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Murin, Yonathan, Kim, Jeremy, Parvizi, Josef, and Goldsmith, Andrea
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PEOPLE with epilepsy ,TREATMENT of epilepsy ,ANTICONVULSANTS ,NEUROLOGICAL disorders ,NEUROLOGISTS - Abstract
Epilepsy is one of the most common neurological disorders affecting about 1% of the world population. For patients with focal seizures that cannot be treated with antiepileptic drugs, the common treatment is a surgical procedure for removal of the seizure onset zone (SOZ). In this work we introduce an algorithm for automatic localization of the seizure onset zone (SOZ) in epileptic patients based on electrocorticography (ECoG) recordings. The proposed algorithm builds upon the hypothesis that the abnormal excessive (or synchronous) neuronal activity in the brain leading to seizures starts in the SOZ and then spreads to other areas in the brain. Thus, when this abnormal activity starts, signals recorded at electrodes close to the SOZ should have a relatively large causal influence on the rest of the recorded signals. The SOZ localization is executed in two steps. First, the algorithm represents the set of electrodes using a directed graph in which nodes correspond to recording electrodes and the edges’ weights quantify the pair-wise causal influence between the recorded signals. Then, the algorithm infers the SOZ from the estimated graph using a variant of the PageRank algorithm followed by a novel post-processing phase. Inference results for 19 patients show a close match between the SOZ inferred by the proposed approach and the SOZ estimated by expert neurologists (success rate of 17 out of 19). [ABSTRACT FROM AUTHOR]
- Published
- 2018
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16. Modeling the genetic relatedness of Plasmodium falciparum parasites following meiotic recombination and cotransmission.
- Author
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Wong, Wesley, Wenger, Edward A., Hartl, Daniel L., and Wirth, Dyann F.
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PLASMODIUM falciparum genetics ,MALARIA transmission ,EPIDEMIOLOGICAL models ,MOSQUITO vectors ,POPULATION genetics - Abstract
Unlike in most pathogens, multiple-strain (polygenomic) infections of P. falciparum are frequently composed of genetic siblings. These genetic siblings are the result of sexual reproduction and can coinfect the same host when cotransmitted by the same mosquito. The degree with which coinfecting strains are related varies among infections and populations. Because sexual recombination occurs within the mosquito, the relatedness of cotransmitted strains could depend on transmission dynamics, but little is actually known of the factors that influence the relatedness of cotransmitted strains. Part of the uncertainty stems from an incomplete understanding of how within-host and within-vector dynamics affect cotransmission. Cotransmission is difficult to examine experimentally but can be explored using a computational model. We developed a malaria transmission model that simulates sexual reproduction in order to understand what determines the relatedness of cotransmitted strains. This study highlights how the relatedness of cotransmitted strains depends on both within-host and within-vector dynamics including the complexity of infection. We also used our transmission model to analyze the genetic relatedness of polygenomic infections following a series of multiple transmission events and examined the effects of superinfection. Understanding the factors that influence the relatedness of cotransmitted strains could lead to a better understanding of the population-genetic correlates of transmission and therefore be important for public health. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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17. Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States.
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Yamana, Teresa K., Kandula, Sasikiran, and Shaman, Jeffrey
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FORECASTING ,SEASONAL influenza ,DISEASE outbreaks ,BAYESIAN analysis - Abstract
Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence for seasonal influenza outbreaks in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecasts to create weighted-average superensemble forecasts. We compared the relative performance of these individual and superensemble forecast methods by geographic location, timing of forecast, and influenza season. We find that, overall, the superensemble forecasts are more accurate than any individual forecast method and less prone to producing a poor forecast. Furthermore, we find that these advantages increase when the superensemble weights are stratified according to the characteristics of the forecast or geographic location. These findings indicate that different competing influenza prediction systems can be combined into a single more accurate forecast product for operational delivery in real time. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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18. Measuring distance through dense weighted networks: The case of hospital-associated pathogens.
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Donker, Tjibbe, Smieszek, Timo, Henderson, Katherine L., Johnson, Alan P., Walker, A. Sarah, and Robotham, Julie V.
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NOSOCOMIAL infections ,PATHOGENIC microorganisms ,MEDICAL care ,INFECTIOUS disease transmission ,PATIENTS - Abstract
Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014–2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen. While the risk of transferring a hospital-associated pathogen between directly neighbouring hospitals is a direct reflection of the number of shared patients, the distance between two hospitals far-away in the network is determined largely by the number of intermediate hospitals in the network. Because the network is dense, most long distance transmission chains in fact involve only few intermediate steps, spreading along the many weak links. The dense connectivity of hospital networks, together with a strong regional structure, causes hospital-associated pathogens to spread from the initial outbreak in a two-step process: first, the directly surrounding hospitals are affected through the strong connections, second all other hospitals receive introductions through the multitude of weaker links. Although the strong connections matter for local spread, weak links in the network can offer ideal routes for hospital-associated pathogens to travel further faster. This hold important implications for infection prevention and control efforts: if a local outbreak is not controlled in time, colonised patients will appear in other regions, irrespective of the distance to the initial outbreak, making import screening ever more difficult. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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19. Assessing computational genomics skills: Our experience in the H3ABioNet African bioinformatics network.
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Jongeneel, C. Victor, Achinike-Oduaran, Ovokeraye, Adebiyi, Ezekiel, Adebiyi, Marion, Adeyemi, Seun, Akanle, Bola, Aron, Shaun, Ashano, Efejiro, Bendou, Hocine, Botha, Gerrit, Chimusa, Emile, Choudhury, Ananyo, Donthu, Ravikiran, Drnevich, Jenny, Falola, Oluwadamila, Fields, Christopher J., Hazelhurst, Scott, Hendry, Liesl, Isewon, Itunuoluwa, and Khetani, Radhika S.
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GENOMICS ,GENETIC databases ,BIOINFORMATICS ,INFORMATION science ,PUBLIC health - Abstract
The H3ABioNet pan-African bioinformatics network, which is funded to support the Human Heredity and Health in Africa (H3Africa) program, has developed node-assessment exercises to gauge the ability of its participating research and service groups to analyze typical genome-wide datasets being generated by H3Africa research groups. We describe a framework for the assessment of computational genomics analysis skills, which includes standard operating procedures, training and test datasets, and a process for administering the exercise. We present the experiences of 3 research groups that have taken the exercise and the impact on their ability to manage complex projects. Finally, we discuss the reasons why many H3ABioNet nodes have declined so far to participate and potential strategies to encourage them to do so. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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20. Ten simple rules for responsible big data research.
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Zook, Matthew, Barocas, Solon, boyd, danah, Crawford, Kate, Keller, Emily, Gangadharan, Seeta Peña, Goodman, Alyssa, Hollander, Rachelle, Koenig, Barbara A., Metcalf, Jacob, Narayanan, Arvind, Nelson, Alondra, and Pasquale, Frank
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RESEARCH ethics ,BIG data ,DATA security failures ,RESEARCH methodology ,PREVENTION - Abstract
The article discusses the results of a two-year project which aimed to provide guidance to the National Science Foundation (NSF) on how to best encourage ethical practices in scientific and engineering research involving the use of big data research methods and infrastructures. The assumption that most data represent or impact people should be used to guide one's analysis. Researchers are also encouraged to situate and contextualize data to anticipate privacy breaches and minimize harm.
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- 2017
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21. THE REAL McCOIL: A method for the concurrent estimation of the complexity of infection and SNP allele frequency for malaria parasites.
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Chang, Hsiao-Han, Worby, Colin J., Yeka, Adoke, Nankabirwa, Joaniter, Kamya, Moses R., Staedke, Sarah G., Dorsey, Grant, Murphy, Maxwell, Neafsey, Daniel E., Jeffreys, Anna E., Hubbart, Christina, Rockett, Kirk A., Amato, Roberto, Kwiatkowski, Dominic P., Buckee, Caroline, and Greenhouse, Bryan
- Subjects
BIOINFORMATICS software ,MALARIA transmission ,SINGLE nucleotide polymorphisms - Abstract
As many malaria-endemic countries move towards elimination of Plasmodium falciparum, the most virulent human malaria parasite, effective tools for monitoring malaria epidemiology are urgent priorities. P. falciparum population genetic approaches offer promising tools for understanding transmission and spread of the disease, but a high prevalence of multi-clone or polygenomic infections can render estimation of even the most basic parameters, such as allele frequencies, challenging. A previous method, COIL, was developed to estimate complexity of infection (COI) from single nucleotide polymorphism (SNP) data, but relies on monogenomic infections to estimate allele frequencies or requires external allele frequency data which may not available. Estimates limited to monogenomic infections may not be representative, however, and when the average COI is high, they can be difficult or impossible to obtain. Therefore, we developed THE REAL McCOIL, Turning HEterozygous SNP data into Robust Estimates of ALelle frequency, via Markov chain Monte Carlo, and Complexity Of Infection using Likelihood, to incorporate polygenomic samples and simultaneously estimate allele frequency and COI. This approach was tested via simulations then applied to SNP data from cross-sectional surveys performed in three Ugandan sites with varying malaria transmission. We show that THE REAL McCOIL consistently outperforms COIL on simulated data, particularly when most infections are polygenomic. Using field data we show that, unlike with COIL, we can distinguish epidemiologically relevant differences in COI between and within these sites. Surprisingly, for example, we estimated high average COI in a peri-urban subregion with lower transmission intensity, suggesting that many of these cases were imported from surrounding regions with higher transmission intensity. THE REAL McCOIL therefore provides a robust tool for understanding the molecular epidemiology of malaria across transmission settings. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
22. Ten Simple Rules to Enable Multi-site Collaborations through Data Sharing.
- Author
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Boland, Mary Regina, Karczewski, Konrad J., and Tatonetti, Nicholas P.
- Subjects
WEBSITES ,RESEARCH methodology ,SOFTWARE productivity ,ALGORITHMS ,SOFTWARE development tools ,OPEN source software - Abstract
The article offers suggestions for development of multi-site collaborations for enhancement of data sharing approaches. Topics discussed include observation of increment in research productivity through enhancement of software services; involvement of several algorithms along with different software tools as research products; enhancement of software as open-source; security of data use agreements; and engagement with different software social platforms.
- Published
- 2017
- Full Text
- View/download PDF
23. Ten Simple Rules for Taking Advantage of Git and GitHub.
- Author
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Perez-Riverol, Yasset, Gatto, Laurent, Wang, Rui, Sachsenberg, Timo, Uszkoreit, Julian, Leprevost, Felipe da Veiga, Fufezan, Christian, Ternent, Tobias, Eglen, Stephen J., Katz, Daniel S., Pollard, Tom J., Konovalov, Alexander, Flight, Robert M., Blin, Kai, and Vizcaíno, Juan Antonio
- Subjects
WEB hosting ,INTERNET content - Abstract
The article evaluates GitHub, a web-based platform that provides a free and integrated environment for hosting the source code, documentation, and project-related web content for open-source.
- Published
- 2016
- Full Text
- View/download PDF
24. A Molecular Clock Infers Heterogeneous Tissue Age Among Patients with Barrett’s Esophagus.
- Author
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Curtius, Kit, Wong, Chao-Jen, Hazelton, William D., Kaz, Andrew M., Chak, Amitabh, Willis, Joseph E., Grady, William M., and Luebeck, E. Georg
- Subjects
BIOMARKERS ,BARRETT'S esophagus ,ESOPHAGEAL cancer ,DNA methylation ,DINUCLEOTIDES ,BAYESIAN analysis - Abstract
Biomarkers that drift differentially with age between normal and premalignant tissues, such as Barrett’s esophagus (BE), have the potential to improve the assessment of a patient’s cancer risk by providing quantitative information about how long a patient has lived with the precursor (i.e., dwell time). In the case of BE, which is a metaplastic precursor to esophageal adenocarcinoma (EAC), such biomarkers would be particularly useful because EAC risk may change with BE dwell time and it is generally not known how long a patient has lived with BE when a patient is first diagnosed with this condition. In this study we first describe a statistical analysis of DNA methylation data (both cross-sectional and longitudinal) derived from tissue samples from 50 BE patients to identify and validate a set of 67 CpG dinucleotides in 51 CpG islands that undergo age-related methylomic drift. Next, we describe how this information can be used to estimate a patient’s BE dwell time. We introduce a Bayesian model that incorporates longitudinal methylomic drift rates, patient age, and methylation data from individually paired BE and normal squamous tissue samples to estimate patient-specific BE onset times. Our application of the model to 30 sporadic BE patients’ methylomic profiles first exposes a wide heterogeneity in patient-specific BE onset times. Furthermore, independent application of this method to a cohort of 22 familial BE (FBE) patients reveals significantly earlier mean BE onset times. Our analysis supports the conjecture that differential methylomic drift occurs in BE (relative to normal squamous tissue) and hence allows quantitative estimation of the time that a BE patient has lived with BE. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
25. The Development of Computational Biology in South Africa: Successes Achieved and Lessons Learnt.
- Author
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Mulder, Nicola J., Christoffels, Alan, de Oliveira, Tulio, Gamieldien, Junaid, Hazelhurst, Scott, Joubert, Fourie, Kumuthini, Judit, Pillay, Ché S., Snoep, Jacky L., Tastan Bishop, Özlem, and Tiffin, Nicki
- Subjects
COMPUTATIONAL biology ,BIOLOGY ,BIOINFORMATICS ,BIOLOGICAL databases ,COMPUTERS in biology - Abstract
Bioinformatics is now a critical skill in many research and commercial environments as biological data are increasing in both size and complexity. South African researchers recognized this need in the mid-1990s and responded by working with the government as well as international bodies to develop initiatives to build bioinformatics capacity in the country. Significant injections of support from these bodies provided a springboard for the establishment of computational biology units at multiple universities throughout the country, which took on teaching, basic research and support roles. Several challenges were encountered, for example with unreliability of funding, lack of skills, and lack of infrastructure. However, the bioinformatics community worked together to overcome these, and South Africa is now arguably the leading country in bioinformatics on the African continent. Here we discuss how the discipline developed in the country, highlighting the challenges, successes, and lessons learnt. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
26. Probabilistic transmission models incorporating sequencing data for healthcare-associated Clostridioides difficile outperform heuristic rules and identify strain-specific differences in transmission
- Author
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David W Eyre, Mirjam Laager, A Sarah Walker, Ben S Cooper, Daniel J Wilson, and CDC Modeling Infectious Diseases in Healthcare Program (MInD-Healthcare)
- Subjects
0301 basic medicine ,Nosocomial Infections ,Epidemiology ,law.invention ,Disease Outbreaks ,0302 clinical medicine ,Medical Conditions ,law ,Microbial Physiology ,Credible interval ,Environmental Microbiology ,Medicine and Health Sciences ,Infection control ,Heuristics ,030212 general & internal medicine ,Bacterial Physiology ,Biology (General) ,Cross Infection ,Ecology ,Simulation and Modeling ,Genomics ,Hospitals ,Transmission (mechanics) ,Infectious Diseases ,Computational Theory and Mathematics ,Modeling and Simulation ,Research Article ,medicine.medical_specialty ,Heuristic (computer science) ,Clostridium Difficile ,QH301-705.5 ,Sequencing data ,Computational biology ,Biology ,Research and Analysis Methods ,Microbiology ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,medicine ,Genetics ,Humans ,Bacterial Spores ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Models, Statistical ,Bacteria ,Clostridioides difficile ,Gut Bacteria ,Probabilistic logic ,Organisms ,Computational Biology ,Biology and Life Sciences ,Bacteriology ,Human Genetics ,United Kingdom ,Health Care ,030104 developmental biology ,Health Care Facilities ,Clostridium Infections - Abstract
Fitting stochastic transmission models to electronic patient data can offer detailed insights into the transmission of healthcare-associated infections and improve infection control. Pathogen whole-genome sequencing may improve the precision of model inferences, but computational constraints have limited modelling applications predominantly to small datasets and specific outbreaks, whereas large-scale sequencing studies have mostly relied on simple rules for identifying/excluding plausible transmission. We present a novel approach for integrating detailed epidemiological data on patient contact networks in hospitals with large-scale pathogen sequencing data. We apply our approach to study Clostridioides difficile transmission using a dataset of 1223 infections in Oxfordshire, UK, 2007–2011. 262 (21% [95% credibility interval 20–22%]) infections were estimated to have been acquired from another known case. There was heterogeneity by sequence type (ST) in the proportion of cases acquired from another case with the highest rates in ST1 (ribotype-027), ST42 (ribotype-106) and ST3 (ribotype-001). These same STs also had higher rates of transmission mediated via environmental contamination/spores persisting after patient discharge/recovery; for ST1 these persisted longer than for most other STs except ST3 and ST42. We also identified variation in transmission between hospitals, medical specialties and over time; by 2011 nearly all transmission from known cases had ceased in our hospitals. Our findings support previous work suggesting only a minority of C. difficile infections are acquired from known cases but highlight a greater role for environmental contamination than previously thought. Our approach is applicable to other healthcare-associated infections. Our findings have important implications for effective control of C. difficile., Author summary Preventing infections spreading in hospitals is a major priority for healthcare systems globally. Mathematical models can be used with electronic hospital records to reconstruct how infections spread, which in turn can help guide infection control interventions. Sequencing the genetic code (DNA) of the bacteria that cause infections can help to follow when transmission is occurring, as bacterial DNA from two patients infected from the same source will likely be very similar. Our paper describes a new statistical method for combining hospital records and sequencing data to track infections. We use our approach to study over 1200 infections of Clostridioides difficile (C. diff.), which is a common cause of diarrhoea in hospitals. We show that only a minority of infections are acquired from other unwell patients, but the amount of spread varies by the subtype of C. diff involved. We also show that different C. diff subtypes survive in the hospital environment for longer than others and may need enhanced control strategies. We also can detect differences in spread at different hospitals and show that by the end of the study we had largely eliminated transmission of C. diff from unwell patients in our hospitals.
- Published
- 2021
27. A Likelihood-Based Approach to Identifying Contaminated Food Products Using Sales Data: Performance and Challenges.
- Author
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Kaufman, James, Lessler, Justin, Harry, April, Edlund, Stefan, Hu, Kun, Douglas, Judith, Thoens, Christian, Appel, Bernd, Käsbohrer, Annemarie, and Filter, Matthias
- Subjects
FOOD contamination ,SALES ,LIKELIHOOD ratio tests ,FOODBORNE diseases ,EPIDEMIOLOGICAL models - Abstract
Foodborne disease outbreaks of recent years demonstrate that due to increasingly interconnected supply chains these type of crisis situations have the potential to affect thousands of people, leading to significant healthcare costs, loss of revenue for food companies, and—in the worst cases—death. When a disease outbreak is detected, identifying the contaminated food quickly is vital to minimize suffering and limit economic losses. Here we present a likelihood-based approach that has the potential to accelerate the time needed to identify possibly contaminated food products, which is based on exploitation of food products sales data and the distribution of foodborne illness case reports. Using a real world food sales data set and artificially generated outbreak scenarios, we show that this method performs very well for contamination scenarios originating from a single “guilty” food product. As it is neither always possible nor necessary to identify the single offending product, the method has been extended such that it can be used as a binary classifier. With this extension it is possible to generate a set of potentially “guilty” products that contains the real outbreak source with very high accuracy. Furthermore we explore the patterns of food distributions that lead to “hard-to-identify” foods, the possibility of identifying these food groups a priori, and the extent to which the likelihood-based method can be used to quantify uncertainty. We find that high spatial correlation of sales data between products may be a useful indicator for “hard-to-identify” products. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
28. Age density patterns in patients medical conditions: A clustering approach
- Author
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Fahad Alhasoun, Marta C. González, Luis Gregorio Moyano, Faisal Aleissa, Claudio S. Pinhanez, May Alhazzani, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Institute for Data, Systems, and Society, Massachusetts Institute of Technology. Program in Computation for Design and Optimization, Program in Media Arts and Sciences (Massachusetts Institute of Technology), Alhasoun, Fahad, Aleissa, Faisal Saad, Alhazzani, May, Pinhanez, Claudio S., and Gonzalez, Marta C.
- Subjects
0301 basic medicine ,Male ,Viral Diseases ,Medical Doctors ,Epidemiology ,Health Care Providers ,purl.org/becyt/ford/1.7 [https] ,Health records ,Geographical locations ,purl.org/becyt/ford/1 [https] ,0302 clinical medicine ,Elderly ,Chickenpox ,Medicine and Health Sciences ,Cluster Analysis ,Disease ,Medical Personnel ,lcsh:QH301-705.5 ,COMPLEX NETWORKS ,Geriatrics ,Heterogeneous sample ,Ecology ,Age Factors ,Professions ,Geography ,Infectious Diseases ,Computational Theory and Mathematics ,Modeling and Simulation ,Female ,HEALTHCARE ,Algorithms ,Brazil ,Research Article ,medicine.medical_specialty ,Large population ,Age and sex ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,DATA ANALYSIS ,Sex Factors ,Age Distribution ,Population Metrics ,International Classification of Diseases ,Diagnostic Medicine ,Genetics ,medicine ,Humans ,In patient ,Cluster analysis ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Population Density ,Population Biology ,Biology and Life Sciences ,South America ,medicine.disease ,Comorbidity ,Health Care ,030104 developmental biology ,lcsh:Biology (General) ,Age Groups ,Population Groupings ,People and places ,COMORBIDITY ,Epidemiologic Methods ,030217 neurology & neurosurgery ,Demography - Abstract
This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature., Author summary Age and sex of a patient can be directly related to susceptibilities to certain medical conditions. We present a method to generate clusters of human phenotype, based on the age of the population. This method helps extract knowledge on age and sex from the data. The age and sex correlations with disease conditions can help in a task of predicting the susceptibility of incoming patients to conditions.
- Published
- 2017
29. Confronting the ethical challenges of big data in public health
- Author
-
Philip E. Bourne
- Subjects
medicine.medical_specialty ,Big data ,Cellular and Molecular Neuroscience ,Political science ,Health care ,Common Rule ,Genetics ,Global health ,medicine ,Electronic Health Records ,Humans ,Social media ,Molecular Biology ,lcsh:QH301-705.5 ,Ecology, Evolution, Behavior and Systematics ,Ecology ,business.industry ,Public health ,International health ,Public relations ,Social research ,Editorial ,Computational Theory and Mathematics ,lcsh:Biology (General) ,Modeling and Simulation ,Public Health ,business ,Confidentiality - Abstract
“Ethical Challenges of Big Data in Public Health” by Vayena et al. is a timely paper for a number of reasons, all of which are concisely embodied within the title. Ethical challenges are not something we consider often enough in this or most biomedical research journals. Yet, such challenges increasingly confront us as private citizens providing health data and as researchers using these data for public health and research purposes. Big data is an ill-defined term, yet there can be no denying that social media data (which are described in this article for use in digital disease detection [DDD]) cover a large and varied area, measured by both volume and velocity (rate of accumulation) and, as the authors describe, have the potential for important public health uses, but also have risks. And that, as described, is only one potential use of these data; there are many other uses barely introduced by the authors. In short the appeal of such data is huge, but so are the risks. The authors do an excellent job of characterizing the pluses and minuses of DDD. I encourage all of us to contemplate this article and the implications it masterfully elucidates. The underlying message from Vayena et al. is clear: use of these data far outpaces the governance and due diligence of the ethical considerations that need to be addressed. Quoting the authors, stigmatization of particular communities which may adversely affect individual members; and even the infringement of individual freedoms, such as the freedom of movement of an individual falsely identified as a carrier of a particular disease.… the same problems associated with current disease detection and public health approaches are likely to be more pronounced with the advent of social media and big data. In fact, DDD will only exacerbate the challenges of preventing misuse of the data. The authors are to be commended for raising the profile of this important issue and getting us thinking. Interestingly, the framework for addressing the issue has been around for a long time. It is embodied in Article 27 of the 1948 Universal Declaration of Human Rights, which declares it a human right “to share in scientific advancement and its benefits” (including to freely engage in responsible scientific inquiry) and at the same time have “the protection of the moral and material interests resulting from any scientific production of which [a person] is the author” [1]. More in a research context, this has recently been considered further by the global community and has been embodied in the Global Alliance for Genomic Health (GA4GH)’s work on a framework for responsible sharing of genomic and health-related data [2]. Governments and organizations worldwide have their own more in-depth versions. In the United States and within the National Institutes of Health (NIH), there is an update to the Common Rule underway [3], and a more extensive genomics data sharing plan was recently announced [4]. Further measures are under consideration for protecting the rights of the individual whilst ensuring that maximum access to patient data for appropriate research purposes is maintained to accelerate much needed cures and improvements to health care in general. As illustrated in this article, whether for purposes of research and/or public health, social media data require that further attention be paid. The authors are to be commended for raising the profile of these ethical issues.
- Published
- 2015
30. A likelihood-based approach to identifying contaminated food products using sales data: performance and challenges
- Author
-
Stefan Edlund, Bernd Appel, James H. Kaufman, Kun Hu, Annemarie Käsbohrer, April Harry, Judith V. Douglas, Justin Lessler, Matthias Filter, and C. Thoens
- Subjects
Food industry ,Epidemiology ,Computer science ,Disease Outbreaks ,Foodborne Diseases ,Food group ,Medicine and Health Sciences ,Cluster Analysis ,Food Industry ,Public and Occupational Health ,lcsh:QH301-705.5 ,Likelihood Functions ,0303 health sciences ,Food poisoning ,Ecology ,Applied Mathematics ,Agriculture ,04 agricultural and veterinary sciences ,040401 food science ,3. Good health ,Product (business) ,Infectious Diseases ,Computational Theory and Mathematics ,Risk analysis (engineering) ,Modeling and Simulation ,Physical Sciences ,Public Health ,Environmental Health ,Algorithms ,Research Article ,Computer and Information Sciences ,Infectious Disease Control ,Supply chain ,Models, Biological ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,Agricultural Production ,0404 agricultural biotechnology ,Genetics ,medicine ,Humans ,Revenue ,Set (psychology) ,Computerized Simulations ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,030306 microbiology ,business.industry ,Computational Biology ,Biology and Life Sciences ,Outbreak ,medicine.disease ,Health Care ,lcsh:Biology (General) ,business ,Mathematics - Abstract
Foodborne disease outbreaks of recent years demonstrate that due to increasingly interconnected supply chains these type of crisis situations have the potential to affect thousands of people, leading to significant healthcare costs, loss of revenue for food companies, and—in the worst cases—death. When a disease outbreak is detected, identifying the contaminated food quickly is vital to minimize suffering and limit economic losses. Here we present a likelihood-based approach that has the potential to accelerate the time needed to identify possibly contaminated food products, which is based on exploitation of food products sales data and the distribution of foodborne illness case reports. Using a real world food sales data set and artificially generated outbreak scenarios, we show that this method performs very well for contamination scenarios originating from a single “guilty” food product. As it is neither always possible nor necessary to identify the single offending product, the method has been extended such that it can be used as a binary classifier. With this extension it is possible to generate a set of potentially “guilty” products that contains the real outbreak source with very high accuracy. Furthermore we explore the patterns of food distributions that lead to “hard-to-identify” foods, the possibility of identifying these food groups a priori, and the extent to which the likelihood-based method can be used to quantify uncertainty. We find that high spatial correlation of sales data between products may be a useful indicator for “hard-to-identify” products., Author Summary Response to foodborne disease outbreaks is complicated by globalization of our food supply chains. Rapid identification of contaminated products is essential to limit the damage caused by foodborne disease. Worldwide, foodborne disease outbreaks are responsible for $9B a year in medical costs and over $75B in economic losses. Yet relevant data required to accelerate the identification of suspicious food already exists as part of the inventory control systems used by retailers and distributors today. Combining this retail data with public health case reports has the potential to hasten outbreak investigations and provide public health investigators with better information on suspected products to test. This paper demonstrates the feasibility of the principle and efficiency of this approach. Based on these findings it can be concluded that in foodborne disease outbreaks retail data could be used to speed and target public health investigations and consequently reduce numbers of sick/dead people as well as reduce economic losses to the industry.
- Published
- 2014
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