text:
Jimmy T. Efird
The University of Newcastle, Australia
Marco Brandizi
Rothamsted Research, United Kingdom
Reza M. Salek
Bruker (Germany), Germany
Vimal k. Singh
Amity University Gurgaon, India
PERSPECTIVE article
Front. Public Health, 23 November 2018
Sec. Epidemiology
Volume 6 - 2018 | https://doi.org/10.3389/fpubh.2018.00336
Epidemiological Data Challenges: Planning for a More Robust Future Through Data Standards
Geoffrey Fairchild1*, Byron Tasseff1, Hari Khalsa1, Nicholas Generous1, Ashlynn R. Daughton1, Nileena Velappan2, Reid Priedhorsky3 and Alina Deshpande2
1Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States
2Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
3High Performance Computing Division, Los Alamos National Laboratory, Los Alamos, NM, United States
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.
At the heart of disease surveillance and modeling are epidemiological data. These data are generally presented as a time series of cases, T, for a geographic region, G, and for a demographic, D. The type of cases presented may vary depending on the context. For example, T may be a time series of confirmed or suspected cases, or it might be hospitalizations or deaths; in some circumstances, it may be a summation of some combination of these (e.g., confirmed + suspected cases). G is most commonly a political boundary; it might be a country, state/province, county/district, city, or sub-city region, such as a postal code or United States (U.S.) Census Bureau census tract. Depending on the context, D may simply be the the entire population of G, or it might be stratified by age, sex, race, education, or other relevant factors.
Epidemiological data have a variety of uses. From a public health perspective, they can be used to gain an understanding of population-level disease progression. This understanding can in turn be used to aid in decision-making and allocation of resources. Recent outbreaks like Ebola and Zika have demonstrated the value of accessible epidemiological data for emergency preparedness and the need for better data sharing (1). These data may influence vaccine distribution (2), and hospitals can anticipate surge capacity during an outbreak, allowing them to obtain extra temporary help if necessary (3, 4).
From a modeler's perspective, high quality reference data (also commonly referred to as ground truth data) are needed to enable prediction and forecasting (5). These data can be used to parameterize compartmental models (6) as well as stochastic agent-based models [e.g., (7–11)]. They can also be used to train and validate machine learning and statistical models [e.g., (12–19)].
The internet has become the predominant way to publish, share, and collect epidemiological data. While data standards exist for observational studies (20) and clinical research (21), for example, no such standards exist for the publication of the kind of public health-related epidemiological data described above. Despite the strong need to share and consume data, there are many legal, technical, political, and cultural challenges in implementing a standardized epidemiological data framework (22, 23). As a result, the methods by which data are presented to the public often differ significantly among data-sharing institutions (e.g., public health departments, ministries of health, data collection or aggregation services). Moreover, these problems are not unique to epidemiological data; the issues described in this paper are common across many different disciplines.
First, epidemiological data on the internet are presented to the user through a variety of interfaces. These interfaces vary widely not only in their appearance but also in their functionality. Some data are openly available through clear modern web interfaces, complete with well-documented programmer-friendly application programming interfaces (APIs), while others are displayed as static web pages that require error-prone and brittle web scraping. Still others are offered as machine-readable documents [e.g., comma-separate values (CSV), Microsoft Excel, Extensible Markup Language (XML), Adobe PDF]. Finally, some necessitate contacting a human, who then prepares and sends the requested data manually.
Second, there are many data formats. Data containers [e.g., CSV, JavaScript Object Notation (JSON)] and element formats (e.g., timestamp format, location name format) may differ. Character encodings (24) (e.g., ASCII, UTF-8) and line endings (25) (e.g., \r\n, \n) may also differ. Compounding these issues, formats can change over time (e.g., renaming or reordering spreadsheet columns). More broadly, these challenges are closely tied to schema, data model, and vocabulary standardization.
Finally, there are differences among institutions in their reporting habits; even within a single institution, there are often reporting nuances among diseases. For example, one context may be reported monthly (e.g., Q fever in Australia), while another context is reported weekly (e.g., influenza in the U.S.) or even more finely (e.g., 2014 West African Ebola outbreak). Furthermore, what is meant by "weekly" in one context may be different than another context (e.g., CDC epi weeks vs. irregular reporting intervals in Poland, as described later).
Together, these challenges make large-scale public health data analysis and modeling significantly more difficult and time-consuming. Gathering, cleaning, and eliciting relevant data often require more time than the actual analysis itself. This paper discusses these three key technical challenges involving public health-related epidemiological data, in detail and with examples that were identified through detailed analysis of data deposition practices around the globe. Building from this analysis, we offer a framework of best practices comprised of modern standards that should be adhered to when releasing epidemiological data to the public. Such a framework will enable a more robust future for accurate and high-confidence epidemiological data and analysis.
2.1. Interface Challenges
The interface is the mechanism by which data are presented to a user for consumption.
Epidemiological data repositories implementing current best practices provide an interactive web-based searching and filtering interface that enables users to easily export desired data in a variety of formats. These are generally accompanied by an API that allows users to programmatically acquire desired data. For example, if one wants to download the latest influenza surveillance data weekly, instead of manually navigating an interactive web interface each week to export the data, the process could be automated by writing code that interacts with the API. Such an interface provides the simplest and most powerful method of data acquisition. Examples of this type of interface are the U.S. Centers for Disease Control and Prevention (CDC)1 and the World Health Organization (WHO) Global Health Observatory (GHO)2.
While an interactive web-based interface coupled with an API is a best practice, it can be complex and expensive to implement. Many public health departments are under resource constraints and depend on older websites that tend to release data in one of two ways: 1) data are uploaded in some common format (e.g., CSV, Microsoft Excel, PDF) or 2) data are displayed in Hypertext Markup Language (HTML) tables. An example of the first is seen via Israel's Ministry of Health website, where data are provided weekly in Microsoft Excel formats (26). An example of the second is seen via Australia's Department of Health website, where data are provided within simply-formatted HTML tables (27).
Data uploaded in a common format can often be automatically downloaded and processed, and HTML tables can generally be automatically scraped and processed. While HTML scraping is often straightforward, there are some instances where it can be quite difficult. One example of a difficult-to-scrape data source is the Robert Koch Institute SurvStat 2.0 website (28). Although the service is capable of providing epidemiological data at superior spatial and temporal resolutions (county- and week-level, respectively), the interface is not easily amenable to scraping. First, the HTTP requests formed by the ASP.NET application cannot be easily reverse-engineered; this necessitates the use of browser-automation software like Selenium3, which enables automating website user interaction, such as mouse clicks and keyboard presses, for data scraping. Second, the selection of new filters, attributes, and display options results in a newly-refreshed page for each change; because many options are required to obtain each desired dataset, scraping can take a long time.
Additionally, while there may be no technical barriers to downloading or scraping data, there may be barriers relating to a website's terms of service (TOS). In some instances, the TOS may prevent users from scraping or downloading data en masse; this is sometimes done to prevent unreasonable load on the website, for example. Ignoring the TOS raises ethical issues that are often overlooked in research; after all, the goals of most epidemiological researchers are benevolent, and the data are public and usually funded by taxpayers. Ignoring a website's TOS could also raise logistical issues related to publishing and institutional review board (IRB) approval.
A concern underlying all scraping efforts is that data scraping scripts are brittle. Web scraping relies on patterns in the HTML/CSS source code of a website. If an institution modifies its layouts, even slightly, scrapers may exhibit unexpected behavior.
In some cases, a human must be contacted directly, who then prepares and sends the requested data. However, these manually requested and prepared data are often saddled with many restrictions. For example, when one of the authors contacted a ministry of health for more detailed epidemiological data, the data were offered with a five-page data request form that significantly restricted use and sharing of the data. Furthermore, it stated that it would take "up to 3 months" to be released because of the review and approval from the various data owners (local, state, and territory health departments). These types of restrictions and hurdles to data access prevent the development and adoption of advanced analytics.
Finally, finding epidemiological data interfaces or data within an interface is often a time-consuming and error-prone task. For example, the Zika virus epidemic has resulted in increased global attention for Brazil, but it has not resulted in a single easy-to-understand machine-readable interface (29). Until just recently, Brazil's Ministry of Health maintained two separate lists of mosquito-borne illness epidemiological bulletins (30, 31). Although these lists pointed to the exact same bulletins, (31) is consistently more up-to-date than (30) (see Figures 1, 2). Having multiple interfaces increases the likelihood of human error when collecting epidemiological data. For instance, if one assumes that there is only one official source for Zika, the most current information may be overlooked.
Figure 1. Screenshot showing part of the mosquito-borne illness epidemiological bulletin list available at (31). This is the most current and complete list, with data available through the 38th week of 2016.
Figure 2. Screenshot showing part of the mosquito-borne illness epidemiological bulletin list available at (30). This list only goes through week 21 of 2016 and is missing a number of weeks when compared to the list in Figure 1. This screenshot was taken at the same time as the one in Figure 1.
2.2. Data Format Challenges
The data format specifies how the data are read and written. There are two layers: 1) the data container and 2) the element format. The data container specifies how individual elements should be agglomerated; CSV is an example of a data container. The element format specifies how each individual element should be arranged; the ISO 8601 date and time specification is an example of an element formatting standard.
Data format challenges often provide the biggest obstacles that users must overcome. In order for an analyst to use data from multiple sources, they must first be merged. In practice, however, data from one institution are seldom available in a format that can be directly compared to data from another institution.
2.2.1. Data Containers
First, data container issues must be addressed. For example, CSV files are among the simplest file types to parse; they are plain text files with a simple structure (i.e., columns are separated by a comma, rows are separated by a newline). Figure 3 demonstrates how epidemiological data might be provided in a CSV file. Any spreadsheet software can open CSV files natively, and most programming languages require no third-party libraries to read and write CSV files.
Figure 3. Sample epidemiological case count data in CSV format. CSV files are plain text files that allow tabular data to be laid out as rows separated by newlines and columns separated by commas. This time series does not contain real data and only exists for demonstration purposes.
A conceptually similar file type to CSV is Microsoft Excel's XLSX. XLSX is a spreadsheet format developed by Microsoft and is a part of the Office Open XML (OOXML) specification. OOXML is a complex specification comprised of zipped XML files and other embedded data (e.g., images) (32). This format is common among public health practitioners due to the ubiquity of Microsoft Excel. For the programmer, however, this format presents a variety of challenges not present with CSV files. Due to the file type's complexity, a third-party library will be necessary in virtually all circumstances for reading/writing XLSX files (e.g., xlrd4 and xlwt5 for Python, Apache POI6 for Java). Depending on the maturity of the library used, formulas, pivot tables, and other complex features should be handled with varying degrees of trust.
JSON is another common data container used on the internet (see Figure 4 for an example). For instance, JSON data are commonly returned when querying an API endpoint. JSON is easy and fast to use; many programming languages offer built-in JSON read/write support (e.g., Python and Java). Additionally, similar to CSV, JSON is a plain text format that is human-readable. Unlike CSV, however, JSON is not limited to tabular data. JSON can represent more complex relationships between data and is conceptually more similar to XML. Due to its ubiquity and structure, a number of application-specific JSON standards are available. For example, GeoJSON (33) and TopoJSON (34) enable sharing geographic data. In 2016, Finnie et al. proposed EpiJSON, which offers a standardized way to encode epidemiological data (35). Although EpiJSON shows promise, it is young and has yet to be broadly embraced. To make adoption simpler, open-source EpiJSON libraries could be developed for common programming languages; currently, the only such library exists for the programming language R (36), but additional libraries should be developed for Python, Java, and other languages commonly used for epidemiological data analysis.
Figure 4. Sample epidemiological case count data in a simple JSON format. Compared to CSV (demonstrated in Figure 3), JSON contains more structure that can more rigorously specify data relationships (including hierarchical relationships). Note that this is not EpiJSON; EpiJSON can be quite verbose (due to, for example, metadata specifications and GeoJSON-specified locations), and the authors felt a complete EpiJSON example would take up an unreasonable amount of space in this paper. As in Figure 3, this time series does not contain real data and only exists for demonstration purposes.
PDF files provide a number of unique challenges in addition to complexity. Extraction of data is the biggest challenge, as epidemiological information is often provided in mixed formats: textual (e.g., paragraphs of descriptive text in a report), graphical (e.g., bar and line charts), and tabular. Simply extracting the text of a PDF correctly and in the right order can prove to be a non-trivial challenge. Named-entity recognition and extraction, a natural language processing task, can be used to elicit case counts from unstructured text (19), but this supervised machine learning task requires knowledge of the language in which the document is published, as well as epidemiological subject matter expertise. Graphical data are intended for the human eye. While graphical data can potentially be digitized using software like WebPlotDigitizer7, this cannot always be reliably automated. Even tabular data, which visually appear structured, are typically difficult to extract due to the variety of ways a table can be presented in a PDF document (37).
Furthermore, PDF files need not even contain text. In a number of circumstances, the PDF files that institutions provide simply contain scanned images of documents. The resulting PDF simply contains the image, rather than the raw text that comprised the original document. For example, many of the weekly reports available through the Department of Health website for the Philippines (38) are PDFs of scanned documents [e.g., (39, 40)]. Text can potentially be elicited with optical character recognition (OCR) software, but the quality of the resulting textual data will vary significantly depending on the quality of the scanned images.
Finally, one must be aware of the character encoding when reading text. Since, at the basic level, computers represent all data using binary bits, there must be some binary representation of each character or symbol in an alphabet or language; the character encoding specifies how the raw bits stored in a file should be converted to readable text and vice versa (24). While there are a number of possible encodings, ASCII, ISO-8859-1, and UTF-8 are among the most common encodings encountered in practice. In 2012, UTF-8 surpassed 60% adoption across the web (41) and is currently approaching the 90% mark (42). Encoding differences are important; for example, while reading ASCII text as UTF-8 yields correct results, the converse does not.
2.2.2. Element Format
2.2.2.1. Date and Time
Beyond data container challenges, there are a number of element formatting differences that must be addressed. First and foremost are date and time formatting discrepancies. While the ISO 8601 date/time standard has existed since 1988, it is often bypassed in favor of locale-dependent formats. For example, much of Europe follows the day-month-year convention, the U.S. follows the month-day-year convention, and China follows the year-month-day convention. Depending on the locale, 03-09-2005 may refer to March 9, 2005 or September 3, 2005. Additionally, not all locales use the Gregorian calendar. Thailand, for example, uses the Buddhist calendar. The current year, as represented by the Gregorian calendar, is 2018; a Thai timestamp would instead specify 2561. Finally, some locales use 24-h time, while others use 12-h time.
In addition to these timestamp parsing differences, there are significant implicit timestamp differences that must be understood. To understand these, one must first recognize that a timestamp on a typical disease curve usually implicitly refers to an interval of time (i.e., actual event-level epidemiological data are rare). To illustrate this, consider the time series in Table 1. Each timestamp can be interpreted using one of three possible interval types:
Table 1. Sample historical weekly epidemiological time series consisting of timestamps and case counts.
Leading: The timestamp starts the interval, and the interval ends the "instant" before the next specified timestamp. Table 2 shows how the time series in Table 1 would be transformed to an interval series with an interval type of leading.
Trailing exclusive: The timestamp ends the interval but is not included in the interval; Table 3 demonstrates this transformation.
Trailing inclusive: The timestamp ends the interval and is included in the interval; Table 4 shows this transformation.
Note that we do not currently feel it is necessary to include a leading exclusive option; leading will always be inclusive.
Table 2. Explicit transformation of Table 1 into a leading interval series.
Table 3. Explicit transformation of Table 1 into a trailing exclusive interval series.
Table 4. Explicit transformation of Table 1 into a trailing inclusive interval series.
As an example, the CDC standardizes reporting dates in the U.S. using the notion of an "MMWR week" or "epi week" (43). MMWR weeks always begin on Sundays and end on Saturdays. Weeks can be numbered 1–53, and, as a result, many institutions choose to report them as such (e.g., the interval [2016-05-15 00:00, 2016-05-22 00:00) is reported as "2016, week 20"). However, while most U.S.-based health departments respect the weekly MMWR aggregation standard, many continue to report timestamps based on the MMWR week concept. For example, Figure 5 shows how Texas identifies its weekly influenza surveillance PDF reports by trailing inclusive timestamps rather than by MMWR week.
Figure 5. Screenshot taken from Texas' Department of State Health Services 2014–2015 weekly influenza reports web page (44). Texas identifies its weekly influenza surveillance PDF reports by trailing inclusive timestamps rather than by MMWR week (e.g., "10/3/15" instead of "2015, week 39"). Interestingly, much of the data in each PDF uses MMWR week numbers rather than timestamps.
Outside of the U.S., a variety of reporting date standards exist. In Japan, for example, the epi week starts on Monday and ends on Sunday (45). In Poland, the reporting is even more different. Poland reports influenza cases four "weeks" a month, regardless of the length of the month. As a result, the intervals between reports are not regular. For example, the four influenza reports in May 2016 are shown in Table 5.
Table 5. Influenza reporting intervals in Poland in May 2016.
One remaining concern related to date and time is time zone. With the increasing use of internet data streams in disease forecasting and surveillance, it is important to be able to precisely place reference epidemiological data since associated internet data streams might be timestamped down to the second. Many data sources fail to report a time zone, so local time is often assumed. An incorrect time zone may impact analysis of high resolution data. For example, norovirus data are sometimes provided hourly [e.g., (50, 51)], and time zone errors could have a potentially drastic negative effect on model results or analysis.
2.2.2.2. Geography
Political boundaries and names must be carefully managed. Subtle differences in names (e.g., Zurich vs. Zürich) may lead to incorrect results during an analysis. The ISO 3166 standard defines country and principle subdivision (e.g., state or province) names, but it does not handle finer-than-subdivision regions, such as counties, districts, or cities.
Moreover, political boundaries (and thus populations and demographics) change over time. For example, South Sudan's split from Sudan in 2011 decreased Sudan's population by more than ten million people and dramatically changed its political boundary. Computing the historical attack rate for a disease (e.g., influenza incidence per 100,000 people), for instance, must take into account these changes.
2.3. Data Reporting Challenges
Beyond interface and data format challenges, there are challenges that lie within the bureaucratic reporting process for an epidemiological institution. Modern disease surveillance systems rely on complex reporting hierarchies; raw data are initially captured at each provider, who then anonymizes and aggregates data as necessary before sending it to the next level in the hierarchy (perhaps a local or state public health department) (52). This hierarchy can have many levels. Even in many of the most developed regions of the world, much of this process continues to be done by hand, although the push to electronic medical records is gaining traction. As a result, most disease surveillance systems across the world experience reporting lags of at least one to two weeks.
This reporting lag can, in some cases, affect both an intuitive understanding of the situation as well as computational forecasting models. In an effort to combat surveillance system reporting lag, a number of attempts have been made to "fill in" the gaps using internet data [e.g., (13–15, 17)], but these studies require moderate to high levels of internet usage in the locales of interest, which are often not guaranteed.
Another issue is heterogeneous case definitions across jurisdictions. Many times, the case definitions used in epidemiological data are not clearly defined, and it is often difficult to navigate websites to identify the definitions. For example, many of the influenza surveillance systems in Europe use common, but not identical, case definitions (53). Contextual differences in case definitions for Ebola (54) could make interpreting data for the 2014 West African Ebola outbreak difficult.
One must also be concerned about language issues. Data are often provided in the native language of the region of the world in which they originate. For example, Thailand's Bureau of Epidemiology website (55) is natively displayed in Thai but also offers an English version (56). While online language translation services do exist (e.g., Google Translate8), these are not always reliable, and they cannot easily translate text in images (e.g., a website header comprised of images). To assist with language issues, formal disease- and epidemiological-focused ontologies [e.g., (57)] can help; translations, abbreviations, and alternate names can be encoded in an ontology to help automatically map different records to the same concept.
Furthermore, even within a single institution, there are often reporting nuances among diseases. For example, one context may be reported monthly, while another context may be reported weekly or daily. Some contexts may not be regularly reported; irregular reporting can lead to questions like, "Is the value for a missing timestamp zero or unknown?"
Finally, case count data are often retroactively updated as new data are made available. In other words, historical data are not fixed the first time they are published. For example, a case count data point published today may be updated next week or the following week, as new data appear. This problem, often called "backfill," is due to the number and variety of members that comprise the complex reporting hierarchy that modern disease surveillance systems rely on; if a surveillance member's computer system goes down temporarily, for example, it may not be able to submit its data until the following week. Backfill can in some cases drastically affect analyses, so analysts and modelers must be aware of this potential issue (58, 59).
We have identified three key challenges involving epidemiological data: 1) interface challenges, 2) data format challenges, and 3) data reporting challenges. Each of these challenges can be addressed to simplify the efforts of analysts and modelers. Here, we propose a framework of best practices comprised of modern standards that should be adhered to when releasing epidemiological data to the public:
1. Present the user with an interactive web interface to search and filter data. This interface should allow users to export data in common open formats (e.g., JSON using the EpiJSON standard, CSV).
2. Provide a web-based API to allow automated data retrieval.
3. Always use ISO 8601 dates, times, timestamps, and durations. Timestamps should either explicitly provide the local time zone or be adjusted for UTC, as specified by the ISO 8601 standard.
4. When providing time series, clearly define the interval type so that timestamps can be interpreted properly.
5. When possible, use ISO 3166 location names.
6. Ensure all data are encoded using UTF-8.
7. Ensure website can be run through an online language translation service (e.g., do not place important text in an image).
8. When reporting case counts, the case definitions should be made explicit and clear.
9. Clearly distinguish between unknown and zero values.
These suggestions are not prescribing a single format or process; instead, these items provide a means for clearly defining and presenting epidemiological data to the public.
We implore the members of the global public health community to work together to create and follow standards for publishing data. Many institutions attempt to publish similar types of data using similar interfaces. In general, a user selects locations, diseases, optional time periods, and optional demographics in order to retrieve the desired data. Because many analysts and modelers have similar data desires, we feel this provides an opportunity for a generic shared epidemiological data access platform. Currently used by the CDC, one possibility might be Socrata9, a platform that allows governments to share data openly. Socrata provides not only a modern interactive web interface but also an extensive API. Another option may be for public health institutions to collaboratively develop a free and open source solution that each could use. Such a platform may be more easily implemented by resource-constrained public health departments that have neither the time nor the money to develop their own solutions. Additionally, as web standards evolve, a shared data access platform could be updated in order to propagate these changes to each institution.
If a standard platform could be employed by institutions worldwide, then one could envision a future where global data could be easily collected without the challenges we currently face. This would in turn streamline epidemiological and public health analysis, modeling, and informatics, resulting in better public health decision-making capabilities.
Additionally, while this paper focuses on the public health and epidemiological communities, many of the challenges and solutions discussed here are not unique to them. Many of these same challenges are present whenever data of the same type are published globally by separate institutions that do not have an a priori agreed-upon set of standards. For example, weather and economic data have many of the same features as epidemiological data (e.g., locations, time intervals) and should also adhere to the ISO 8601 and 3166 standards, be encoded in UTF-8, and clearly distinguish between unknown and zero values.
Finally, it is important to recognize that this paper focuses on capable public health institutions with enough funding to collect and disseminate their epidemiological data. It should be noted, however, that a number of regions worldwide do not meet this criterion and are struggling even to monitor and care for their constituent populations, let alone publish reliable data. Unfortunately, it is precisely in these underserved regions that the public health community often desires data. Until worldwide public health infrastructure improves significantly, the suggestions here will remain peripheral for many; thus, the problems and suggested solutions put forth in this paper are likely only to be relevant to well-funded institutions. While the solutions presented in this paper may not be as effective at present time due to the lack of coverage, we offer them in preparation for the expanding global coverage that is continuously occurring, and it is only a matter of time until 100% global internet coverage becomes a reality.
GF and BT compiled the initial manuscript. All authors identified and described challenges and standards, and provided critical revisions of the manuscript.
This work was supported by the Defense Threat Reduction Agency's Joint Science and Technology Office for Chemical and Biological Defense under project numbers CB3656 and CB10007.
This work was supported by the U.S. Department of Energy through the Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218NCA000001).
1. ^https://data.cdc.gov/
2. ^http://www.who.int/gho/en/
3. ^http://www.seleniumhq.org/
4. ^https://github.com/python-excel/xlrd
5. ^https://github.com/python-excel/xlwt
6. ^https://poi.apache.org/
7. ^http://arohatgi.info/WebPlotDigitizer/
8. ^https://translate.google.com/
9. ^https://socrata.com/
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18. Hickmann KS, Fairchild G, Priedhorsky R, Generous N, Hyman JM, Deshpande A, et al. Forecasting the 2013-2014 influenza Season Using wikipedia. PLoS Comput Biol. (2015) 11:e1004239. doi: 10.1371/journal.pcbi.1004239
19. Fairchild G, De Silva L, Del Valle SY, Segre AM. Eliciting disease data from wikipedia articles. in Ninth International AAAI Conference on Weblogs and Social Media - Wikipedia Workshop. Oxford, UK (2015). p. 26–33.
20. STROBE Initiative. STROBE Statement (2018). Available online at: https://www.strobe-statement.org/ (Accessed October 01,2018).
21. CDISC. CDISC | Strength Through Collaboration (2018). Available online at: https://www.cdisc.org/ (Accessed October 01,2018).
22. Pisani E, AbouZahr C. Sharing health data: good intentions are not enough. Bull. World Health Organ. (2010) 88:462–6. doi: 10.2471/BLT.09.074393
23. Sane J, Edelstein M. Overcoming Barriers to Data Sharing in Public Health: A Global Perspective. London: Chatham House: The Royal Institute of International Affairs (2015). Available online at: https://www.chathamhouse.org/publication/overcoming-barriers-data-sharing-public-health-global-perspective
24. Zentgraf DC. What Every Programmer Absolutely, Positively Needs to Know About Encodings and Character Sets to Work With Text (2015). Available online at: http://kunststube.net/encoding/ (Accessed August 23, 2016).
25. Atwood J. The Great Newline Schism (2010). Available online at: https://blog.codinghorror.com/the-great-newline-schism/ (Accessed October 01, 2016).
26. Israeli Ministry of Health. Epidemiological Reports Archive (2016). Available online at: http://www.health.gov.il/UnitsOffice/HD/PH/epidemiology/Pages/epidemiology_report.aspx?WPID=WPQ7&PN=1 (Accessed September 04, 2016).
27. Australian Government Department of Health. National Notifiable Disease Surveillance System (2016). Available online at: http://www9.health.gov.au/cda/source/rpt_1_sel.cfm (Accessed September 04, 2016).
28. Robert Koch Institute. SurvStat@RKI 2.0 (2016). Available online at: https://survstat.rki.de (Accessed September 04, 2016).
29. Coelho FC, Codeço CT, Cruz OG, Camargo S, Bliman PA. Epidemiological data accessibility in Brazil. Lancet Infect Dis. (2016) 16:524–5. doi: 10.1016/S1473-3099(16)30007-X
30. Federal Government of Brazil. Epidemiological Situation (2017). Available online at: http://www.combateaedes.saude.gov.br/en/epidemiological-situation (Accessed January 19, 2017).
31. Secretaria de Vigilância em Saúde – Ministério da Saúde. Boletim Epidemiológico (2017). Available online at: http://portalsaude.saude.gov.br/index.php/o-ministerio/principal/leia-mais-o-ministerio/197-secretaria-svs/11955-boletins-epidemiologicos-arquivos (Accessed January 19, 2017).
32. Apache OpenOffice. Office Open XML (2016). Available online at: https://wiki.openoffice.org/wiki/Office_Open_XML (Accessed August 18, 2016).
33. GeoJSON. GeoJSON (2016). Available online at: http://geojson.org/ (Accessed August 22, 2016).
34. TopoJSON. TopoJSON (2016). Available online at: https://github.com/topojson/topojson (Accessed August 22, 2016).
35. Finnie TJR, South A, Bento A, Sherrard-Smith E, Jombart T. EpiJSON: a unified data-format for epidemiology. Epidemics (2016) 15:20–6. doi: 10.1016/j.epidem.2015.12.002
36. Finnie T. Repijson (2015). Available online at: https://github.com/Hackout2/repijson (Accessed October 23, 2018).
37. Khusro S, Latif A, Ullah I. On methods and tools of table detection, extraction and annotation in PDF documents. J Inform Sci. (2015) 41:41–57. doi: 10.1177/0165551514551903
38. Republic of the Philippines Department of Health,. Statistics. Department of Health (2016). Available online at: http://www.doh.gov.ph/statistics (Accessed December 13, 2016).
39. Republic of the Philippines Department of Health. 2015 Dengue Morbidity Week 26 (2016). Available online at: http://www.doh.gov.ph/sites/default/files/statistics/dengueMw26.compressed.pdf (Accessed December 13, 2016).
40. Republic of the Philippines Department of Health. 2016 Diphtheria Morbidity Week 12 (2016). Available online at: http://www.doh.gov.ph/sites/default/files/statistics/DIPHTHERIA%20MW12.pdf (Accessed December 13, 2016).
41. Davis M. Unicode Over 60 Percent of the Web (2016). Avialable online at: https://googleblog.blogspot.com/2012/02/unicode-over-60-percent-of-web.html (Accessed August 23, 2016).
42. W3Techs. Usage of Character Encodings for Websites. (2016). Available online at: https://w3techs.com/technologies/overview/character_encoding/all (Accessed August 23, 2016).
43. Centers for Disease Control and Prevention. MMWR Weeks (2016). Available online at: https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf (Accessed August 22, 2016).
44. Texas Department of State Health Services. 2014–2015 Texas Influenza Surveillance Activity Report (2016). Available online at: http://www.dshs.texas.gov/idcu/disease/influenza/surveillance/2015/ (Accessed December 20, 2016).
45. National Institute of Infectious Diseases Japan. Weeks Ending Log (2016). Available online at: http://www.nih.go.jp/niid/en/calendar-e.html (Accessed Decemeber 13, 2016).
46. National Institute of Public Health Poland. Influenza and influenza-like illness in Poland, 5A (2016). Available online at: http://wwwold.pzh.gov.pl/oldpage/epimeld/grypa/2016/I_16_05B.pdf (Accessed Decemeber 13, 2016).
47. National Institute of Public Health Poland. Influenza and influenza-Like Illness in Poland, 5B (2016). Available online at: http://wwwold.pzh.gov.pl/oldpage/epimeld/grypa/2016/I_16_05A.pdf (Accessed Decemeber 13, 2016).
48. National Institute of Public Health Poland. Influenza and influenza-like illness in Poland, 5C (2016) Available online at: http://wwwold.pzh.gov.pl/oldpage/epimeld/grypa/2016/I_16_05C.pdf (Accessed Decemeber 13, 2016).
49. National Institute of Public Health Poland. Influenza and influenza-like illness in Poland, 5D (2016). Available online at: http://wwwold.pzh.gov.pl/oldpage/epimeld/grypa/2016/I_16_05D.pdf (Accessed Decemeber 13, 2016).
50. Guzman-Herrador B, Heier BT, Osborg EJ, Nguyen VH, Vold L. Outbreak of norovirus infection in a hotel in Oslo, Norway, January 2011. Euro Surveill. (2011) 16:19928. Available online at: https://www.eurosurveillance.org/content/10.2807/ese.16.30.19928-en
51. Mayet A, Andréo V, Bédubourg G, Victorion S, Plantec JY, Soullié B, et al. Food-borne outbreak of norovirus infection in a French military parachuting unit, April 2011. Euro Surveill. (2011) 16:19930. Available online at: https://www.eurosurveillance.org/content/10.2807/ese.16.30.19930-en
52. Fairchild G. Improving Disease Surveillance: Sentinel Surveillance Network Design and Novel Uses of Wikipedia. Ph.D. Dissertation. University of Iowa (2014).
53. Aguilera JF, Paget WJ, Mosnier A, Heijnen ML, Uphoff H, van der Velden J, et al. Heterogeneous case definitions used for the surveillance of influenza in Europe. Eur J Epidemiol. (2003) 18:751–4. doi: 10.1023/A:1025337616327.
54. World Health Organization. Case Definition Recommendations for Ebola or Marburg Virus Diseases (2017). Available online at: http://www.who.int/csr/resources/publications/ebola/ebola-case-definition-contact-en.pdf (Accessed January 10, 2017).
55. Thailand Bureau of Epidemiology. Thailand Bureau of Epidemiology (2016). Available online at: http://203.157.15.110/boe/ (Accessed August 23, 2016).
56. Thailand Bureau of Epidemiology. Thailand Bureau of Epidemiology (2016). Available online at: http://203.157.15.110/boeeng/ (Accessed August 23, 2016).
57. Schriml L. Disease Ontology - Institute for Genome Sciences @ University of Maryland (2011). Available online at: http://disease-ontology.org/ (Accessed October 01, 2018).
58. Farrow DC, Brooks LC, Hyun S, Tibshirani RJ, Burke DS, Rosenfeld R. A human judgment approach to epidemiological forecasting. PLoS Comput Biol. (2017) 13:e1005248. doi: 10.1371/journal.pcbi.1005248
59. Osthus D, Gattiker J, Priedhorsky R, Del Valle SY. Dynamic Bayesian influenza forecasting in the United States with hierarchical discrepancy. arXiv:1708.09481 arXiv pre-print (2017). doi: 10.1214/18-BA1117
Keywords: data, computational epidemiology, public health, disease modeling, informatics, disease surveillance
Citation: Fairchild G, Tasseff B, Khalsa H, Generous N, Daughton AR, Velappan N, Priedhorsky R and Deshpande A (2018) Epidemiological Data Challenges: Planning for a More Robust Future Through Data Standards. Front. Public Health 6:336. doi: 10.3389/fpubh.2018.00336
Received: 17 July 2018; Accepted: 01 November 2018;
Published: 23 November 2018.
Jimmy Thomas Efird, University of Newcastle, Australia
Vimal kishor Singh, Amity University Gurgaon, India
Marco Brandizi, Rothamsted Research (BBSRC), United Kingdom
Reza M. Salek, International Agency For Research On Cancer (IARC), France
Copyright © 2018 Fairchild, Tasseff, Khalsa, Generous, Daughton, Velappan, Priedhorsky and Deshpande. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Geoffrey Fairchild, gfairchild@lanl.gov


text:
Russia says it won't start a war…
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South Africa wins 3rd test to clinch series against India
Cricket Latest Stories Sports
By Special Correspondent January 15, 2022 January 15, 2022
South Africa won the decisive final test against top-ranked India by seven wickets Friday to come from behind and clinch the series 2-1.
It denied India its first test series win in South Africa and was an unexpected result considering the strength of the Indian team under captain Virat Kohli, and the relative inexperience of the South African side.
"That people expect us to beat South Africa in South Africa is testimony to how far we've come," said Kohli. "We haven't done it, that's the reality. Accept it and come back as better cricketers. You've got to credit the opposition where it's due."
India was a convincing winner of the first test in Centurion by 113 runs and that seemed to be a forecast for the series ahead. But the Proteas leveled in Johannesburg with a gutsy fourth-innings run chase and did the same again when it chased down 212 in the decider at Newlands in Cape Town on Friday.
South Africa got home surprisingly comfortably and finished on 212-3 soon after lunch on Day 4.
Temba Bavuma won it with a four through square leg and then pumped his fists and let out a roar. He was 32 not out to see his team home and Rassie van der Dussen was 41 not out. The South African players in the dressing room celebrated exuberantly after their biggest win under the leadership of captain Dean Elgar, who was appointed last year, and coach Mark Boucher.
"I threw down the challenge after the first game and the guys responded brilliantly," Elgar said, adding he'd had some "tough chats" with his players following the big loss in the first test.
"If you want to operate at a high-performance level, have to have tough chats. If guys don't like it, it's up to them to deal with. I've got old-school mentality."
India has now had eight attempts to win a test series in South Africa dating back to when the Proteas were readmitted into international cricket in 1992. South Africa has won seven of the contests in South Africa, with one drawn series.
South Africa's success in chasing down a tricky target on a wearing Newlands pitch was largely down to No. 3 Keegan Petersen, who made 82 in the second innings to anchor South Africa's series-clinching innings. He also contributed 72 in the first innings in his fifth test and first home series.
The series was low-scoring and tight throughout, partly down to the fast bowler-friendly nature of the South African pitches and also the strength of both teams' bowling attacks.
Ultimately, South Africa's perceived weakness — its batting — prevailed against the strong Indian bowling attack led by Jasprit Bumrah, who was a threat throughout but ended with just 1-54 when it counted in the final innings of the series.
South Africa started Day 4 on 101-2 and with the match in the balance.
But Petersen shared a partnership of 54 with van der Dussen following a stand of 78 with Elgar the day before. When Petersen fell by cutting a ball onto his own stumps to leave South Africa 155-3, India had a glimpse of victory but van der Dussen and Bavuma shut the tourists out.
Credit : Associated Press (AP) | Photo Credit : (AP)
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redpajama_set_name: RedPajamaCommonCrawl


text:
Home > Latest Hyundai News > New CEO named at Hyundai Motor America
New CEO named at Hyundai Motor America
Hyundai Motor America names new president and CEO!
Hyundai Motor America has a new Chief Executive Officer. It was announced yesterday that John Krafcik, who has joined Hyundai Motor America (HMC) in April 2004 and has, until recently, worked as a Vice President of Product Development and Strategic Planning at HMC, has been named the new acting president and CEO of Hyundai Motor America.
He will be responsible for implementation of strategic direction and management of the company's operations.
He has taken the position from Jong Eun Kim, who has served as President and Chief Executive Officer of Hyundai Motor America for the past 15 months and has now returned to work at Hyundai Motor Company headquarters in South Korea.
When asked to give a statement regarding his promotion, John Krafcik said he is honored by Hyundai Motor Company decision to name him as a CEO and very excited to help improve Hyundais position in the U.S. market
Hyundai Motor America also announced it has already begun the search to fill Krafcik's prior position. [Source: Hyundai]
Congrats to John Krafcik! Hyundai Blog wishes him good luck!
Brilliant dream: Brilliant Imagination With Keone & Mariel Madrid
Hyundai Owners Satisfied With Fuel Economy Reimbursement Program


If you are a native New Yorker like me, you probably think the Big Apple is the center of the universe. If you are Jewish New Yorker, you know that when someone talks about "the second holy city," they of course mean Brooklyn.
Previous Previous post: If You Really Think You Need Protection From Your Own Government……..


Looks like Snoop has another hobby… Painting. Actually they don't look that bad!
This is why you play the game until its over! Why didn't he just take a knee?!
Well looks like he'll never box again! The crazy part is that it wasn't even a professional fight! Watch below.
Well that didn't take long! Check out the remix to one of the hottest songs out right now below!


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text:
Q: How do I specify the file location for style.css in config.rb? I'm using Sass and Compass and despite viewing some tutorials I can not specify the location where I want my style.css output to. This is my config.rb
http_path = "/"
css_dir = "css"
sass_dir = "sass"
images_dir = "images"
javascripts_dir = "js"

# You can select your preferred output style here (can be overridden via the command line):
# output_style = :expanded or :nested or :compact or :compressed

output_style = :expanded # After dev :compressed
relative_assets = true
preferred_syntax = :scss

The style.css is being placed in the root instead of the css path.
My file structure is:
Root folder
 - css
 - images
 - js
 - sass
 - config.rb

redpajama_set_name: RedPajamaStackExchange


When I began performing on a street corner in the late 90's, I had no idea that my idea of performative mythology would grow to be what The Cabiri is now. To see your project grow through infancy, to adolescence and then to approach adulthood, is a stupendous thing. Now, after two years of searching, we are approaching opening our own rehearsal training space, and in that next step there is time for reflection of what happens when you carry through with your dreams.
It all began on a street corner in 1997. My family had all grown up dancing for Pacific Northwest Ballet and I too had spent my childhood either backstage or onstage. But at some odd point that all came to an end, and as things turn out I ended up instead as a somewhat miserable biochemist. This did not sit well, and to put to rest the weeks crazy thoughts, I let myself fall into the street art scene that was bustling in the then exploding Fremont community. I was pulled into a somewhat mad Halloween performance by Maque Davis, the presidente aeternus of the Fremont Arts Council… and the work just stuck. Three months later, I was street performing in Pioneer Square on New Years Eve and the most moving thing happened to me.
And well, it got me a little teary eyed as he tossed three quarters in my hat. You know, if you can't play to the richesse, then you can perform to those they live over, So those quarters made me return to that park a week later and I made something like $15. A year and a half later I had made enough busking to invest in creating some real street theatre and I had gotten enough charisma my friend John Mulally got sold on it.
We started doing some craziness with Fremont fire circus Cirque de Flambe, but in the process I realized that there was an undercurrent that was calling me. All of my stories had an undercurrent of myth, whether they were straight up telling of tales or allegorical myth like the Cohen brothers dish out, it was all there. But unlike most allegorical tales that rehash Greek classics, I was lost in Sumer, or the Ch'u dynasty or some other forgotten people.
I'd grown up lost in Bullfinch and other classic collections of myth, and my time at the ballet and being around Kent Stowell had really impressed upon me how effectively a story could be told without words. Keyed in to what I had been doing with street theatre, I created the idea of sort of an animate diorama that would tell the tales of the ancient gods with images and movement. This had been done before, in Greece and before that in ancient Anatolia, and with that realization I decided that this had to be reborn. Taking on the moniker of The Cabiri, we paid homage to the first mystery plays in the southeastern Aegean and decided to form a company.
Right around then, Seattle was really exploding with street art, to the point that Paul Allen created a legendary festival called ArtsEdge (that he proceeded to abandon the following year at a great cost to the Seattle artistic community). This was exciting; it was using Seattle Center to be a giant multi performer stage. So I was excited, the time was now. But I needed a company.
The calling together of the original five members of the Cabiri is a huge tale in itself, but let's just move through this and say that we came together. One night I pulled together the usual suspects (JP, Jeanine, Kirsten and Charly) at my live/work hovel at the Fremont Fine Arts Foundry and we cast the little circle that was the formal declaration of Cabiria. A few weeks later, on May 1st we gathered together for a maypole celebration at a local witches' coven that Charly was a part of and formally performed for the first time under the name The Cabiri.
Five weeks later, we were at Seattle Center for ArtsEdge performing in front of the Flag Pavillion to hundreds of people and… they got it! They actually saw the story, a tale of Hekate loosely based on the Greek Magical Papyri and were able to discuss the idea of it.
The idea had been sowed, and with love and care we brought it to fruition and it was accepted by audiences. We started to move forward setting up flaming pillars and altars in parks anytime there was a local festival. A regular at the Fremont celebrations and the original Ballard Art Walk that started in the end of the 90's, we placed a little ritual theatre everywhere we could at every opportunity offered.


You've just received your monthly SIR Report and your tank has not passed. The SIR data results say the tank is losing fuel.
If you've been using a dipstick to measure your wet stock, the best case scenario is that the data is incorrect because reconciliation procedures haven't been followed.
The worst case scenario is that the data is correct, and your tank is leaking fuel.
But how can you decide on the next course of action if you don't have confidence in the accuracy of your data?
What are the risks of inaccurate SIR data?
Tank reconciliation data shouldn't be taken lightly – and for good reason. Inaccurate data can make a tank appear to be gaining or losing fuel and can mask potential issues with your tank, including potential leaks.
If your dipstick data is incorrect, the SIR Report may not even be able detect a leak.
Fuel leaks that go undetected cause extensive contamination to the surrounding environment and lose you thousands of dollars in revenue. If your leak gets reported to the EPA, your site is at risk of being shut down and, depending on the damage your leaking tank has caused, you could be facing fines in excess of $100,000.00.
Of course, your tank may not be leaking, and your SIR data may just be inaccurate. But why take the risk? There are measures you can take to ensure your SIR data is accurate.
At EMS, we recommend actioning these five tips to get your data accurate every time you measure your tank.
Timing is everything. Record the sales and dip readings at the same time every day. Ideally no fuel should be sold between recording sales and dipping the tank.
Be consistent. Our recommendation is to dip the tank 3 times and take the average, rounding to the nearest increment of the dipstick. Diesel can be tricky, so dip slower to get a more accurate reading.
Cover the ins and outs. Have you included all your deliveries, meter tests or tank swaps? Are you recording the right delivery volume? Check that your sales data is collating properly.
Revise your data. If you see something odd, dip again.
Ask the experts. EMS is always available for advice via phone, email and the data entry portal.
If you'd like to know more about how to ensure accurate SIR data, or other ways you can improve your SIR Reporting – get in touch with us today!


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Contour21 Splash WCs designed with the latest water saving technology and incorporating rimless flushing to help improve hygiene.


text:
On the prowl
By Mark Janzen / Special to the Star-Advertiser
MARK JANZEN / SPECIAL TO THE STAR-ADVERTISER
Former UH standout Solomon Elimimian is leading the CFL in tackles this season with 54 through the first seven games.
Vancouver, BRITISH COLUMBIA » If Solomon Elimimian had his way, he'd be known in football circles as The Lion. And it has nothing to do with his current team, the B.C. Lions of the Canadian Football League.
The former University of Hawaii linebacker, who holds the school record for most career tackles, has found himself a home — or den, if you will — in Vancouver, and for the past year and half has become one of the CFL's top-prowling big cats.
The former WAC co-defensive player of the year was last season's CFL most outstanding rookie and this year is leading the league in tackles with 54 through his team's first eight games.
So it all makes sense that this 6-foot, 225-pound middle linebacker and fan of the National Geographic Channel is lobbying his teammates to call him The Lion.
"Even before B.C. I envisioned myself as a lion on the prowl in the Sahara going hunting," said Elimimian, who played his last season with the Warriors in 2008.
"I like watching National Geographic and watching the hunting. When you play linebacker, you have to envision yourself as a predator and whoever has the ball is the prey. My thing is The Lion," Elimimian said.
The nickname is a work in progress with his teammates. Some of the training staff started calling him Simba recently after he brushed them off, saying, "Have you ever seen a lion get treatment?" But since arriving in B.C., his hunting capabilities have more than qualified him to don such a moniker.
"I don't remember him hitting quite as hard in college as he's hitting guys now," said Lions roommate Adam Leonard, who also roomed with Elimimian for three years in Hawaii. "Even though in college he was a beast on the field, he's become even more physical here."
AFTER GETTING RELEASED by the Buffalo Bills in 2009, Elimimian, who graduated from Crenshaw High School and still calls Los Angeles home, took the year off to train, all the while hoping for a call from another NFL club.
That call never came.
What did come was a shout out from his brother Abraham, a former Warriors defensive back, to consider the CFL.
Elimimian attended a Lions free-agent camp in Los Angeles in the spring of 2010 and then, after being told by the B.C. brass to work himself into better shape, went to a second camp — this time in Las Vegas — and, while there, impressed Lions coach/GM Wally Buono enough to earn a contract.
"We had looked at tape on him before and (former Hawaii head coach) June Jones had recommended both Solomon and Adam," Buono said "When (Solomon) came to the workouts we liked his athleticism. You can see that he was a good football player.
"He's a tremendously instinctive linebacker who has a character trait that few guys do. He's very explosive on contact. That's what separates the good linebackers from the rest."
Shortly after he got into the Lions' training camp, Elimimian suffered a hamstring injury, which set him back a bit, but ultimately didn't stop him from having what turned into a phenomenal first year playing football of the three-down variety.
It took him until about the sixth week of last season before he truly grasped the CFL's nuances — notably the fact that receivers can hit the line of scrimmage at full speed at the snap of the ball — and he took over the starting role in the middle of the Lions defensive unit. Once he did, the Lions and The Lion were a perfect match.
Elimimian got six tackles against Calgary and went on to record 65 tackles in the final 11 games of the season.
And that was just his first year.
THIS YEAR, while his team hasn't gotten off to such a slick start — the Lions are 2-6 — Elimimian has. He's notched seven or more tackles in four of the Lions' eight games and on Aug. 5 had a CFL defensive player of the week performance in which he had a career-best 12 tackles against Saskatchewan, plus a sack, to lead B.C. to its first win of the year.
"My whole life has been a blessing," Elimimian said. "Without God I definitely wouldn't be here. That time after I got cut by Buffalo strengthened my faith. It helped me know that He's going to see me through tough times and He has something for me. I couldn't have imagined how things worked out for me.
"God had the right time at the right moment and on the right team."
Elimimian has led the Lions in tackles in five of eight games this year, and it's plain to see he's got a pretty good grip on what's going on north of the 49th parallel.
"At the end of the day it's football," said Elimimian, who plans on returning to Oahu after the season for the first time since graduating. "I always knew I could play anywhere given the right opportunity."
Rainbows take great memories from China
League a bright spot for Seattle


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Q: EXCHANGE and MAPI : domain admins not allowed I can't configure Outlook 2013 with Exchange 2016 as domain admin, it asks me for password, i nter it and reask as well.
No problem with users, only with Administrators (Builtin, domain admins...)
Is it a normal behavior?
Is it possible to bypass this issue? If yes, How? is it secure to let admins have a mailbox?
Thank you.


Analogue clock in wooden steering wheel design with automatic shift metal stand, ideal for driving-lovers. 1 cell battery included.
Elegant pen set including a twist blue ink ball pen and a black ink roller pen, all decorated with a crocodile pattern and presented in an attractive case.
Wooden beach chair. Polyester and cotton clothing.
Reversible safety coach jacket. With foldable hood. Jacket can be folded into the in-built pouch. Outside shell polyester. Sizes M/L or XL/XXL.
Ceramic tea set for 2 including one tea pot (500ml), 2 cups (100ml) and 2 saucers.
Backgammon, chess, checkers, dices, cards and dominos.
Easy, lightweight and portable to sit at your favourite place and work anywhere around your home or office. Plastic pad with soft cushion filled with micro-beads for extra comfort when using your laptop. Built-in cup holder.
Relaxing set with black stone plate, ceramic yoga figurine, ceramic diffuser with 6 diffuser sticks, bottle of soft vanilla aroma (30ml) and a pack of white stones. Tealight included.
Steering wheel shaped desk clock. 1 cell battery included.
Trendy beach set. Includes a 600D polyester beach bag with zipper and a contrasting colour beach towel in 100% terry cotton. 280 Gr/m2.
Analogue wall clock in aluminium casing. Includes a digital thermometer and a hygrometer. 3 AA not included.
15 inch laptop shoulder bag with compartment. 600D polyester.
Computer backpack made in high quality microfibre with TPE backing and aluminium handle. Main compartment includes multi function organizer and 13 inch screen laptop pocket. Item is PVC free.
BBQ Gourmet gift set including mitten, 4 stainless steel skewers, basting brush, bottle of Italian 100% extra virgin olive oil (50ml) and 2 x 20g seasoning pots with cumin seeds and chili flakes.
Wine set in wooden box including 4 essential wine accessories.
A bicycle shopping basket. But it's not just any basket. It's your own personal basket. Easy to attach and detach from handlebars. No fiddly assembly as no front bracket support is required. Bike light does not need to be altered. Can hold just as much as a supermarket basket. Forget about expensive, environmentally-unfriendly plastic bags.
These two aluminium fire extinguishers will mist your salads with the perfect amount of oil and vinegar. The set is packed in a high quality gift box.
Large volume foldable cooler trolley made in 70D polyester with PVC backing. Cooler lining in white PVA and isolation foam 5 mm thick. Detachable foot.
Tool set presented in a plastic window box containing 1 plier, 1 claw hammer, 1 socket screwdriver, 1 adjustable wrench and 11 piece bit sets.
Wireless slim card size laser pointer and PowerPoint slide presenter in gift box. 1 cell battery included. Safety Class II.


Textile finishing machine operators operate, supervise, monitor and maintain the production of textiles finishing machines.
Operate textile washing machines keeping efficiency and productivity at high levels.
Operate textile finishing machines keeping efficiency and productivity at high levels.
Operate textile drying machines keeping efficiency and productivity at high levels.


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