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description = "Developing the Theory and Practice of Epidemiological Forecasting"
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mission = "To develop the theory and practice of epidemic detection, tracking and forecasting, and their use in decision making, both public and private. Our vision is to make this technology as useful as weather forecasting is today."
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***September 2022.** We launched [Epidata v4](https://delphi.cmu.edu/blog/2022/12/14/introducing-epidata-v4/), prioritizing fast access to the most up-to-date data while retaining the deep data revision history needed by researchers.
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***June 2022.** We ended data collection for the [COVID-19 Trends and Impact Survey (CTIS)](https://delphi.cmu.edu/covid19/ctis/) after more than two years, during which we received 29.5 million survey responses in the US (and [well over 100m globally](https://covidmap.umd.edu/)).
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***June 2022.** We ended data collection for the [COVID-19 Trends and Impact Survey (CTIS)](https://delphi.cmu.edu/epidemic-signals/ctis/) after more than two years, during which we received 29.5 million survey responses in the US (and [well over 100m globally](https://covidmap.umd.edu/)).
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***April 2022.** We received the [Allen Newell Award for Research Excellence in SCS at CMU](https://www.cs.cmu.edu/events/newell-award), which is awarded annually and recognizes an outstanding body of work that epitomizes Allen Newell's research style.
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***April 2022.** We received [the Policy Impact Award and the Warren J. Mitofsky Innovators Award from the the American Association of Public Opinion Research (AAPOR)](https://www.cs.cmu.edu/news/2022/delphi-aapor-awards), along with our partners from the University of Maryland Social Data Science Center and Meta, for our work on the [COVID-19 Trends and Impact Survey](https://delphi.cmu.edu/covid19/ctis/) (CTIS).
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***April 2022.** We received [the Policy Impact Award and the Warren J. Mitofsky Innovators Award from the the American Association of Public Opinion Research (AAPOR)](https://www.cs.cmu.edu/news/2022/delphi-aapor-awards), along with our partners from the University of Maryland Social Data Science Center and Meta, for our work on the [COVID-19 Trends and Impact Survey](https://delphi.cmu.edu/epidemic-signals/ctis/) (CTIS).
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***January 2022.** We added selected signals from the White House COVID-19 Data Strategy and Execution Workgroup’s [Community Profile Report (CPR)](https://healthdata.gov/Health/COVID-19-Community-Profile-Report/gqxm-d9w9), including hospital admission and vaccination rates. This data source was discontinued in February 2023.
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***May 2020.** We added signals for COVID-19 Cases and Deaths, mirrored from the [Center for Systems Science and Engineering](https://systems.jhu.edu/research/public-health/ncov/) at Johns Hopkins University.
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***April 2020.** We partnered with Meta to launch the [COVID-19 Trends and Impact Survey (CTIS)](https://delphi.cmu.edu/covid19/ctis/) to monitor in real-time the spread and impact of the COVID-19 pandemic in the United States.
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***April 2020.** We partnered with Meta to launch the [COVID-19 Trends and Impact Survey (CTIS)](https://delphi.cmu.edu/epidemic-signals/ctis/) to monitor in real-time the spread and impact of the COVID-19 pandemic in the United States.
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***April 2020.** We added signals from two new data sources:
In addition to the Delphi Epidata API server code, we have a number of user-oriented code packages, such as API clients and time-series modeling for epidemiology.
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All source code is freely available on [GitHub](https://github.com/cmu-delphi/).
A collection of data structures and methods for handling epidemiological data.
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The major features are:
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- The epi_df structure, which allows you track epidemiological data measured over location and time (and other potential keys).
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- The epi_archive structure, which allows you to store epi_df objects in a versioned archive and to query the archive for the state of the data at any point in time.
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- Support for applying generic functions across a sliding time window, building on tools like [slider](https://slider.r-lib.org/). Comes with support for
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- handling gaps in time
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- automatically handling grouping of keys
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- Support for version aware data transformation and forecasting, which allows for simple and accurate backtesting of forecasting models (using only the actual data that was available at the time of the forecast and not the revised versions).
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- Growth rate estimation, as estimated using relative rates of change, linear regression, smooth splines, or polynomial trend filtering.
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- Outlier detection and correction, using rolling median or LOESS trend decomposition.
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- Signal correlation over space, time and other keys. It also supports lagged correlations, automatically handles grouping by the specified keys, and handles time gaps.
A framework for building epidata pipelines involving transformation and forecasting. It is designed to work smoothly with epiprocess and provides a simple interface for defining and running forecasting workflows. It supports:
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- A simple interface for defining a sequence of data pre-processing, forecasting, and post-processing steps.
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- A collection of data processing steps common to epidata.
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- A collection of pre-packaged forecasting models commonly used by Delphi's forecasting team.
R package that implements several methods for epidemiological forecasting empirical bayes (EB), basis regression (BR), and time-weighted kernel density estimation (twkde).
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### [utils](https://github.com/cmu-delphi/utils)
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Python utility package for handling dates and locations.
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