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Merge pull request #1589 from cmu-delphi/release/delphi-epidata-4.1.29
Release Delphi Epidata 4.1.29
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.bumpversion.cfg

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[bumpversion]
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current_version = 4.1.28
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current_version = 4.1.29
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commit = False
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tag = False
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dev/local/setup.cfg

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[metadata]
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name = Delphi Development
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version = 4.1.28
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version = 4.1.29
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[options]
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packages =

docs/api/covidcast-signals/google-symptoms.md

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* **Time type:** day (see [date format docs](../covidcast_times.md))
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* **License:** To download or use the data, you must agree to the Google [Terms of Service](https://policies.google.com/terms)
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<div style="background-color:#ff00001c; padding: 10px 30px;"><strong>Data issue:</strong> Between May 13 2024 and August 6 2024, signals values were 25%-50% lower compared to previous time periods. This affects <i>all</i> signals and symptom sets. Currently there is no explanation for the decrease in search volume, and the issue is under investigation by our data source partners.</div>
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## Overview
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This data source is based on the [COVID-19 Search Trends symptoms
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dataset](https://console.cloud.google.com/marketplace/product/bigquery-public-datasets/covid19-search-trends?hl=en-GB). Using
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this search data, we estimate the volume of searches mapped to symptom sets related
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to COVID-19. The resulting daily dataset for each region shows the average relative frequency of searches for each symptom set. The signals are measured in arbitrary units that are normalized for overall search users in the region and scaled by the maximum value of the normalized popularity within a geographic region across a specific time range. **Values are comparable across signals in the same location but NOT across geographic regions**. For example, within a state, we can compare `s01_smoothed_search` and `s02_smoothed_search`. However, we cannot compare `s01_smoothed_search` between states. Larger numbers represent increased relative popularity of symptom-related searches.
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Between May 13 2024 and August 6 2024, [signal values were much lower](#limitations) compared to previous time periods due to a data outage.
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#### Symptom sets
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* _s01_: Cough, Phlegm, Sputum, Upper respiratory tract infection
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For aggregation purposes only, we assign a value of 0 to source regions that
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have no data provided due to quality or privacy issues for a certain day (see
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Limitations for details). We do not report aggregated regions if none of their
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[Limitations](#limitations) for details). We do not report aggregated regions if none of their
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source regions have data. Because of this censoring behavior, the resulting data
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for aggregated regions does not fully match the _actual_ search volume for these
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regions (which is not provided to us).
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updates every day and provide the most up-to-date data.
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## Limitations
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Between May 13 2024 and August 6 2024, signal values were 25%-50% lower compared to previous time periods.
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This affected _all_ signals and symptom sets.
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The drop does not reflect actual search term popularity during the affected period.
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The apparent decrease in search volume was caused by an outage in the data pipeline on the source side.
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The data was unfortunately not recoverable and the dip can not be repaired, but data outside the listed time period is unaffected.
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When daily volume in a region does not meet quality or privacy thresholds, set
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by Google, no daily value is reported. Weekly data may be available from Google
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in these cases, but we do not yet support importation using weekly data.

docs/api/covidcast-signals/nhsn.md

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---
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title: NHSN ED Visits
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title: NHSN Respiratory Hospitalizations
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parent: Data Sources and Signals
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grand_parent: Main Endpoint (COVIDcast)
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nav_order: 1
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---
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# National Syndromic Surveillance Program Emergency Department Visits
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# National Healthcare Safety Network Respiratory Hospitalizations
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{: .no_toc}
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* **Source name:** `nhsn`

src/acquisition/covidcast/csv_importer.py

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REQUIRED_COLUMNS = {'geo_id', 'val', 'se', 'sample_size'}
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# reasonable time bounds for sanity checking time values
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MIN_YEAR = 2019
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MIN_YEAR = 2017 # `google-symptoms` has 2017 data
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MAX_YEAR = 2030
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# The datatypes expected by pandas.read_csv. Int64 is like float in that it can handle both numbers and nans.

src/client/delphi_epidata.R

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# API base url
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BASE_URL <- getOption('epidata.url', default = 'https://api.delphi.cmu.edu/epidata/')
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client_version <- '4.1.28'
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client_version <- '4.1.29'
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auth <- getOption("epidata.auth", default = NA)
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src/client/delphi_epidata.js

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}
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})(this, function (exports, fetchImpl, jQuery) {
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const BASE_URL = "https://api.delphi.cmu.edu/epidata/";
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const client_version = "4.1.28";
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const client_version = "4.1.29";
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// Helper function to cast values and/or ranges to strings
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function _listitem(value) {

src/client/packaging/npm/package.json

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"name": "delphi_epidata",
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"description": "Delphi Epidata API Client",
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"authors": "Delphi Group",
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"version": "4.1.28",
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"version": "4.1.29",
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"license": "MIT",
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"homepage": "https://github.com/cmu-delphi/delphi-epidata",
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"bugs": {

src/server/_config.py

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load_dotenv()
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VERSION = "4.1.28"
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VERSION = "4.1.29"
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MAX_RESULTS = int(10e6)
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MAX_COMPATIBILITY_RESULTS = int(3650)

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