Dataset Information

New methods for analyzing serological data with applications to influenza surveillance.

ABSTRACT: Two important challenges to the use of serological assays for influenza surveillance include the substantial amount of experimental effort involved and the inherent noisiness of serological data.I show that log-transformed serological data exist in an effectively one-dimensional space. I use this result, together with new mechanistic insights into serological assays, to develop computational methods for accurately and efficiently recovering unmeasured serological data from a sample of measured data, for systematically minimizing noise and other types of non-antigenic variation found in the data, and for quantifying and visualizing antigenic variation. The methods can also be applied to data with effective dimensionality greater than one, under certain conditions.Careful application of the methods developed here would enable the collection of better-quality serological data on a greater number of circulating influenza viruses than is currently possible and improve the ability to identify potential epidemic and pandemic viruses before they become widespread. Although the focus here is on influenza surveillance, the described methods are more widely applicable.

SUBMITTER: Ndifon W

PROVIDER: S-EPMC4986581 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

New methods for analyzing serological data with applications to influenza surveillance.

Ndifon Wilfred W

Influenza and other respiratory viruses 20110105 3

<h4>Background</h4>Two important challenges to the use of serological assays for influenza surveillance include the substantial amount of experimental effort involved and the inherent noisiness of serological data.<h4>Results</h4>I show that log-transformed serological data exist in an effectively one-dimensional space. I use this result, together with new mechanistic insights into serological assays, to develop computational methods for accurately and efficiently recovering unmeasured serologic ...[more]

PMID: 21477140

Similar Datasets

Project description:BackgroundThe amount of medical and clinical-related information on the Web is increasing. Among the different types of information available, social media-based data obtained directly from people are particularly valuable and are attracting significant attention. To encourage medical natural language processing (NLP) research exploiting social media data, the 13th NII Testbeds and Community for Information access Research (NTCIR-13) Medical natural language processing for Web document (MedWeb) provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering 3 languages (Japanese, English, and Chinese) and annotated with 8 symptom labels (such as cold, fever, and flu). Then, participants classify each tweet into 1 of the 2 categories: those containing a patient's symptom and those that do not.ObjectiveThis study aimed to present the results of groups participating in a Japanese subtask, English subtask, and Chinese subtask along with discussions, to clarify the issues that need to be resolved in the field of medical NLP.MethodsIn summary, 8 groups (19 systems) participated in the Japanese subtask, 4 groups (12 systems) participated in the English subtask, and 2 groups (6 systems) participated in the Chinese subtask. In total, 2 baseline systems were constructed for each subtask. The performance of the participant and baseline systems was assessed using the exact match accuracy, F-measure based on precision and recall, and Hamming loss.ResultsThe best system achieved exactly 0.880 match accuracy, 0.920 F-measure, and 0.019 Hamming loss. The averages of match accuracy, F-measure, and Hamming loss for the Japanese subtask were 0.720, 0.820, and 0.051; those for the English subtask were 0.770, 0.850, and 0.037; and those for the Chinese subtask were 0.810, 0.880, and 0.032, respectively.ConclusionsThis paper presented and discussed the performance of systems participating in the NTCIR-13 MedWeb task. As the MedWeb task settings can be formalized as the factualization of text, the achievement of this task could be directly applied to practical clinical applications.

Project description:The National Institute for Environmental Health Sciences (NIEHS) is conducting an epidemiologic study (GuLF STUDY) to investigate the health of the workers and volunteers who participated from April to December of 2010 in the response and cleanup of the oil release after the Deepwater Horizon explosion in the Gulf of Mexico. The exposure assessment component of the study involves analyzing thousands of personal monitoring measurements that were collected during this effort. A substantial portion of these data has values reported by the analytic laboratories to be below the limits of detection (LOD). A simulation study was conducted to evaluate three established methods for analyzing data with censored observations to estimate the arithmetic mean (AM), geometric mean (GM), geometric standard deviation (GSD), and the 95th percentile (X0.95) of the exposure distribution: the maximum likelihood (ML) estimation, the β-substitution, and the Kaplan-Meier (K-M) methods. Each method was challenged with computer-generated exposure datasets drawn from lognormal and mixed lognormal distributions with sample sizes (N) varying from 5 to 100, GSDs ranging from 2 to 5, and censoring levels ranging from 10 to 90%, with single and multiple LODs. Using relative bias and relative root mean squared error (rMSE) as the evaluation metrics, the β-substitution method generally performed as well or better than the ML and K-M methods in most simulated lognormal and mixed lognormal distribution conditions. The ML method was suitable for large sample sizes (N ≥ 30) up to 80% censoring for lognormal distributions with small variability (GSD = 2-3). The K-M method generally provided accurate estimates of the AM when the censoring was <50% for lognormal and mixed distributions. The accuracy and precision of all methods decreased under high variability (GSD = 4 and 5) and small to moderate sample sizes (N < 20) but the β-substitution was still the best of the three methods. When using the ML method, practitioners are cautioned to be aware of different ways of estimating the AM as they could lead to biased interpretation. A limitation of the β-substitution method is the absence of a confidence interval for the estimate. More research is needed to develop methods that could improve the estimation accuracy for small sample sizes and high percent censored data and also provide uncertainty intervals.

Dataset Information

New methods for analyzing serological data with applications to influenza surveillance.

Publications

New methods for analyzing serological data with applications to influenza surveillance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets