72 research outputs found
Global disease monitoring and forecasting with Wikipedia
Infectious disease is a leading threat to public health, economic stability,
and other key social structures. Efforts to mitigate these impacts depend on
accurate and timely monitoring to measure the risk and progress of disease.
Traditional, biologically-focused monitoring techniques are accurate but costly
and slow; in response, new techniques based on social internet data such as
social media and search queries are emerging. These efforts are promising, but
important challenges in the areas of scientific peer review, breadth of
diseases and countries, and forecasting hamper their operational usefulness.
We examine a freely available, open data source for this use: access logs
from the online encyclopedia Wikipedia. Using linear models, language as a
proxy for location, and a systematic yet simple article selection procedure, we
tested 14 location-disease combinations and demonstrate that these data
feasibly support an approach that overcomes these challenges. Specifically, our
proof-of-concept yields models with up to 0.92, forecasting value up to
the 28 days tested, and several pairs of models similar enough to suggest that
transferring models from one location to another without re-training is
feasible.
Based on these preliminary results, we close with a research agenda designed
to overcome these challenges and produce a disease monitoring and forecasting
system that is significantly more effective, robust, and globally comprehensive
than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein
and adjust novelty claims accordingly; revise title; various revisions for
clarit
Estimating influenza incidence using search query deceptiveness and generalized ridge regression
Seasonal influenza is a sometimes surprisingly impactful disease, causing
thousands of deaths per year along with much additional morbidity. Timely
knowledge of the outbreak state is valuable for managing an effective response.
The current state of the art is to gather this knowledge using in-person
patient contact. While accurate, this is time-consuming and expensive. This has
motivated inquiry into new approaches using internet activity traces, based on
the theory that lay observations of health status lead to informative features
in internet data.
These approaches risk being deceived by activity traces having a
coincidental, rather than informative, relationship to disease incidence; to
our knowledge, this risk has not yet been quantitatively explored. We evaluated
both simulated and real activity traces of varying deceptiveness for influenza
incidence estimation using linear regression.
We found that deceptiveness knowledge does reduce error in such estimates,
that it may help automatically-selected features perform as well or better than
features that require human curation, and that a semantic distance measure
derived from the Wikipedia article category tree serves as a useful proxy for
deceptiveness. This suggests that disease incidence estimation models should
incorporate not only data about how internet features map to incidence but also
additional data to estimate feature deceptiveness. By doing so, we may gain one
more step along the path to accurate, reliable disease incidence estimation
using internet data. This capability would improve public health by decreasing
the cost and increasing the timeliness of such estimates.Comment: 27 pages, 8 figure
Epidemiological data challenges: planning for a more robust future through data standards
Accessible epidemiological data are of great value for emergency preparedness
and response, understanding disease progression through a population, and
building statistical and mechanistic disease models that enable forecasting.
The status quo, however, renders acquiring and using such data difficult in
practice. In many cases, a primary way of obtaining epidemiological data is
through the internet, but the methods by which the data are presented to the
public often differ drastically among institutions. As a result, there is a
strong need for better data sharing practices. This paper identifies, in detail
and with examples, the three key challenges one encounters when attempting to
acquire and use epidemiological data: 1) interfaces, 2) data formatting, and 3)
reporting. These challenges are used to provide suggestions and guidance for
improvement as these systems evolve in the future. If these suggested data and
interface recommendations were adhered to, epidemiological and public health
analysis, modeling, and informatics work would be significantly streamlined,
which can in turn yield better public health decision-making capabilities.Comment: v2 includes several typo fixes; v3 adds a paragraph on backfill; v4
adds 2 new paragraphs to the conclusion that address Frontiers reviewer
comments; v5 adds some minor modifications that address additional reviewer
comment
Charliecloud's layer-free, Git-based container build cache
A popular approach to deploying scientific applications in high performance
computing (HPC) is Linux containers, which package an application and all its
dependencies as a single unit. This image is built by interpreting instructions
in a machine-readable recipe, which is faster with a build cache that stores
instruction results for re-use. The standard approach (used e.g. by Docker and
Podman) is a many-layered union filesystem, encoding differences between layers
as tar archives.
Our experiments show this performs similarly to layered caches on both build
time and disk usage, with a considerable advantage for many-instruction
recipes. Our approach also has structural advantages: better diff format, lower
cache overhead, and better file de-duplication. These results show that a
Git-based cache for layer-free container implementations is not only possible
but may outperform the layered approach on important dimensions.Comment: 12 pages, 12 figure
Forecasting the 2013--2014 Influenza Season using Wikipedia
Infectious diseases are one of the leading causes of morbidity and mortality
around the world; thus, forecasting their impact is crucial for planning an
effective response strategy. According to the Centers for Disease Control and
Prevention (CDC), seasonal influenza affects between 5% to 20% of the U.S.
population and causes major economic impacts resulting from hospitalization and
absenteeism. Understanding influenza dynamics and forecasting its impact is
fundamental for developing prevention and mitigation strategies.
We combine modern data assimilation methods with Wikipedia access logs and
CDC influenza like illness (ILI) reports to create a weekly forecast for
seasonal influenza. The methods are applied to the 2013--2014 influenza season
but are sufficiently general to forecast any disease outbreak, given incidence
or case count data. We adjust the initialization and parametrization of a
disease model and show that this allows us to determine systematic model bias.
In addition, we provide a way to determine where the model diverges from
observation and evaluate forecast accuracy.
Wikipedia article access logs are shown to be highly correlated with
historical ILI records and allow for accurate prediction of ILI data several
weeks before it becomes available. The results show that prior to the peak of
the flu season, our forecasting method projected the actual outcome with a high
probability. However, since our model does not account for re-infection or
multiple strains of influenza, the tail of the epidemic is not predicted well
after the peak of flu season has past.Comment: Second version. In previous version 2 figure references were
compiling wrong due to error in latex sourc
A Practical Guide for the Effective Evaluation of Twitter User Geolocation
Geolocating Twitter users---the task of identifying their home
locations---serves a wide range of community and business applications such as
managing natural crises, journalism, and public health. Many approaches have
been proposed for automatically geolocating users based on their tweets; at the
same time, various evaluation metrics have been proposed to measure the
effectiveness of these approaches, making it challenging to understand which of
these metrics is the most suitable for this task. In this paper, we propose a
guide for a standardized evaluation of Twitter user geolocation by analyzing
fifteen models and two baselines in a controlled experimental setting. Models
are evaluated using ten metrics over four geographic granularities. We use rank
correlations to assess the effectiveness of these metrics.
Our results demonstrate that the choice of effectiveness metric can have a
substantial impact on the conclusions drawn from a geolocation system
experiment, potentially leading experimenters to contradictory results about
relative effectiveness. We show that for general evaluations, a range of
performance metrics should be reported, to ensure that a complete picture of
system effectiveness is conveyed. Given the global geographic coverage of this
task, we specifically recommend evaluation at micro versus macro levels to
measure the impact of the bias in distribution over locations. Although a lot
of complex geolocation algorithms have been applied in recent years, a majority
class baseline is still competitive at coarse geographic granularity. We
propose a suite of statistical analysis tests, based on the employed metric, to
ensure that the results are not coincidental.Comment: Accepted in the journal of ACM Transactions on Social Computing
(TSC). Extended version of the ASONAM 2018 short paper. Please cite the
TSC/ASONAM version and not the arxiv versio
Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge
Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)
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