Machine-learned epidemiology: real-time detection of foodborne illness at scale

Sadilek, Adam; Caty, Stephanie N.; DiPrete, Lauren; Mansour, Raed; Schenk, Tom; Bergtholdt, Mark; Jha, Ashish K.; Ramaswami, Prem; Gabrilovich, Evgeniy

doi:10.1038/s41746-018-0045-1

Cited by 72 publications

(47 citation statements)

References 23 publications

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“…In order for policies, guidelines, and mandates, that may be updated on a weekly or even daily basis, to reach and be adopted by the general public it is important for relevant, vetted information sources to be clearly identified and potentially pointed to in response to misleading posts. In recent years there have been many exciting efforts to combine natural language processing (NLP), machine learning, and social media scraping to monitor clinical outcomes of interest such as foodborne illnesses [20]. There may be an opportunity to work towards adapting such informatics approaches to monitor and perhaps even combat the dissemination of 'bad' information through automated responses that redirect individuals to sources identified as reliable within the scientific community.…”

Section: Systems For Disseminating Accurate Information Related To Comentioning

confidence: 99%

Ideas for how informaticians can get involved with COVID-19 research

et al. 2020

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The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on population health and wellbeing. Biomedical informatics is central to COVID-19 research efforts and for the delivery of healthcare for COVID-19 patients. Critical to this effort is the participation of informaticians who typically work on other basic science or clinical problems. The goal of this editorial is to highlight some examples of COVID-19 research areas that could benefit from informatics expertise. Each research idea summarizes the COVID-19 application area, followed by an informatics methodology, approach, or technology that could make a contribution. It is our hope that this piece will motivate and make it easy for some informaticians to adopt COVID-19 research projects.

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Section: Systems For Disseminating Accurate Information Related To Comentioning

confidence: 99%

Ideas for how informaticians can get involved with COVID-19 research

et al. 2020

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“…9 Consequently, many studies used web search data as a proxy for health concerns experienced by a population. [10][11][12][13][14] Our method, called Lymelight, counts the number of users searching about the disease, and infers in which US county the disease is likely to have occurred. Lymelight starts with the absolute number of cases that it classifies as positive, and uses it to estimate the relative incidence rate for a given geographical area by dividing by the total number of users active on Google search in that area in the same time frame (2014 and 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Devinney et al 17 combined tweets and individual surveys to detect outbreaks. Sadilek et al 14 used deidentified web search and location data to identify foodborne illness incidents in restaurants. Paparizzos et al 18 used web search logs to assess individual searchers' risk of pancreatic adenocarcinoma.…”

Section: Introductionmentioning

confidence: 99%

“…Our eventual goal is to advance the state of the art in epidemiology to a point where issues of public health significance can be quantified in a timely and actionable way using readily available online data. We call this general approach "machinelearned epidemiology", 14 and in this paper we report the results of applying our methodology to Lyme disease. Evaluation of Lymelight shows significant potential to improve Lyme disease monitoring methods to mitigate the spread of Lyme disease across the United States.…”

Section: Introductionmentioning

confidence: 99%

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Lymelight: forecasting Lyme disease risk using web search data

et al. 2020

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Lyme disease is the most common tick-borne disease in the Northern Hemisphere. Existing estimates of Lyme disease spread are delayed a year or more. We introduce Lymelight-a new method for monitoring the incidence of Lyme disease in real-time. We use a machine-learned classifier of web search sessions to estimate the number of individuals who search for possible Lyme disease symptoms in a given geographical area for two years, 2014 and 2015. We evaluate Lymelight using the official case count data from CDC and find a 92% correlation (p < 0.001) at county level. Importantly, using web search data allows us not only to assess the incidence of the disease, but also to examine the appropriateness of treatments subsequently searched for by the users. Public health implications of our work include monitoring the spread of vector-borne diseases in a timely and scalable manner, complementing existing approaches through real-time detection, which can enable more timely interventions. Our analysis of treatment searches may also help reduce misdiagnosis of the disease.

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“…ML has been increasingly utilized for solving complex real-world problems, its application in public health arguably needs more attention. In this context, the ML methods have been successfully applied to in public health problems such as the real-time detection of foodborne illness 10 , and syndromic surveillance that depends on the reporting symptoms of the patients 11,12 . Tessmer et al 13 proposed various ML techniques such as artificial neural networks (ANN), convolutional neural network (CNN), and long-short term memory (LSTM) to determine the parameter of basic reproduction number.…”

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confidence: 99%

Prediction of the Vaccine-derived Poliovirus Outbreak Incidence: A Hybrid Machine Learning Approach

Hemedan

Elaziz

Jiao

et al. 2020

Sci Rep

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Recently, significant attention has been devoted to vaccine-derived poliovirus (VDPV) surveillance due to its severe consequences. Prediction of the outbreak incidence of VDPF requires an accurate analysis of the alarming data. the overarching aim to this study is to develop a novel hybrid machine learning approach to identify the key parameters that dominate the outbreak incidence of VDPV. The proposed method is based on the integration of random vector functional link (RVFL) networks with a robust optimization algorithm called whale optimization algorithm (WOA). WOA is applied to improve the accuracy of the RVFL network by finding the suitable parameter configurations for the algorithm. The classification performance of the WOA-RVFL method is successfully validated using a number of datasets from the UCI machine learning repository. Thereafter, the method is implemented to track the VDPV outbreak incidences recently occurred in several provinces in Lao People's Democratic Republic. The results demonstrate the accuracy and efficiency of the WOA-RVFL algorithm in detecting the VDPV outbreak incidences, as well as its superior performance to the traditional RVFL method.Poliovirus (PV) surveillance is considered as one of the most challenging issues in countries with suboptimal vaccination coverage levels due to the repetitive silent circulation of the vaccine derived poliovirus (VDPV). Despite its durable intestinal and humoral immunity, VDPV is genetically instable that might revert to wild-type virulence. According to a number of studies 1 , vaccines can cause vaccine-associated flaccid paralysis. Noteworthy, it can replicate for a prolonged time coinciding with the suboptimal vaccination 1 . Interaction of PV and CD155 receptors facilitates its entry 2 . Thereafter, the viral RNA is released. The genome enclosed in the viral particle is used as mRNA and translated by the host cell. The virus hijacks the cell's translation, leading to inhibition of protein synthesis during viral protein production. Ribosome entry site directs the viral RNA translation and synthesis of (+) RNA occurs. Some of the (+) RNA are used as templates for (−) RNA synthesis, some function as mRNA, and some are destined to be the genomes of virus progeny 1 .Globally, the concerted surveillance with the continuous integration and interpretation of health-related data are required to keep the prevention and elimination programs updated. PV surveillance is considered as one of the most important element of the Global Polio Eradication Initiative (GPEI) endgame strategy, which is also useful in detecting VDPV. There are mainly two types of PV surveillance, including (1) the environmental surveillance (ES) that analyzes wastewater to detect if the current collected samples carry PV 3,4 , and (2) the acute flaccid paralysis surveillance (AFPS) that depends on clinical presentation. However, the traditional PV surveillance methods are resource intensive to maintain the system of AFPS for the long term 2 . Still, combatting disease outbreaks signif...

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Machine-learned epidemiology: real-time detection of foodborne illness at scale

Cited by 72 publications

References 23 publications

Ideas for how informaticians can get involved with COVID-19 research

Ideas for how informaticians can get involved with COVID-19 research

Lymelight: forecasting Lyme disease risk using web search data

Prediction of the Vaccine-derived Poliovirus Outbreak Incidence: A Hybrid Machine Learning Approach

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