Merge pull request #1 from cmu-delphi/main

Sync fork

Author: chinandrew

deploy.json

@@ -210,8 +210,22 @@
     "// acquisition - covid_hosp",
     {
       "type": "move",
-      "src": "src/acquisition/covid_hosp/",
-      "dst": "[[package]]/acquisition/covid_hosp/",
+      "src": "src/acquisition/covid_hosp/common/",
+      "dst": "[[package]]/acquisition/covid_hosp/common/",
+      "match": "^.*\\.(py)$",
+      "add-header-comment": true
+    },
+    {
+      "type": "move",
+      "src": "src/acquisition/covid_hosp/facility/",
+      "dst": "[[package]]/acquisition/covid_hosp/facility/",
+      "match": "^.*\\.(py)$",
+      "add-header-comment": true
+    },
+    {
+      "type": "move",
+      "src": "src/acquisition/covid_hosp/state_timeseries/",
+      "dst": "[[package]]/acquisition/covid_hosp/state_timeseries/",
       "match": "^.*\\.(py)$",
       "add-header-comment": true
     },

docs/api/covidcast-signals/chng.md

@@ -26,6 +26,8 @@ commercial purposes.
 | --- | --- |
 | `smoothed_outpatient_covid` | Estimated percentage of outpatient doctor visits with confirmed COVID-19, based on Change Healthcare claims data that has been de-identified in accordance with HIPAA privacy regulations, smoothed in time using a Gaussian linear smoother |
 | `smoothed_adj_outpatient_covid` | Same, but with systematic day-of-week effects removed; see [details below](#day-of-week-adjustment) |
+| `smoothed_outpatient_cli` | Estimated percentage of outpatient doctor visits primarily about COVID-related symptoms, based on Change Healthcare claims data that has been de-identified in accordance with HIPAA privacy regulations, smoothed in time using a Gaussian linear smoother |
+| `smoothed_adj_outpatient_cli` | Same, but with systematic day-of-week effects removed; see [details below](#day-of-week-adjustment) |
 
 ## Table of contents
 {: .no_toc .text-delta}
@@ -64,15 +66,38 @@ not necessarily indicative of a true increase of COVID-19 in a location.
 
 ## Qualifying Conditions
 
-We receive data on the following two categories of counts:
+We receive data on the following six categories of counts:
 
 - Denominator: Daily count of all unique outpatient visits.
 - Covid: Daily count of all unique visits with primary ICD-10 code in any of:
 {U07.1, B97.21, or B97.29}.
+- COVID-like: Daily count of all unique outpatient visits with primary ICD-10 code
+	of any of: {U07.1, U07.2, B97.29, J12.81, Z03.818, B34.2, J12.89}.
+- Flu-like: Daily count of all unique outpatient visits with primary ICD-10 code
+	of any of: {J22, B34.9}. The occurrence of these codes in an area is
+	correlated with that area's historical influenza activity, but are
+	diagnostic codes not specific to influenza and can appear in COVID-19 cases.
+- Mixed: Daily count of all unique outpatient visits with primary ICD-10 code of
+	any of: {Z20.828, J12.9}. The occurance of these codes in an area is
+	correlated to a blend of that area's COVID-19 confirmed case counts and
+	influenza behavior, and are not diagnostic codes specific to either disease.
+- Flu: Daily count of all unique outpatient visits with primary ICD-10 code of
+	any of: {J09\*, J10\*, J11\*}. The asterisk `*` indicates inclusion of all
+	subcodes. This set of codes are assigned to influenza viruses.
+
+For the COVID signal, we consider only the *Denominator* and *Covid* counts.
+
+For the CLI signal, if a patient has multiple visits on the same date (and hence
+multiple primary ICD-10 codes), then we will only count one of and in descending
+order: *Flu*, *COVID-like*, *Flu-like*, *Mixed*. This ordering tries to account for
+the most definitive confirmation, e.g. the codes assigned to *Flu* are only used
+for confirmed influenza cases, which are unrelated to the COVID-19 coronavirus.
 
 ## Estimation
 
-### COVID-Like Illness
+### COVID Illness
+
+The following estimation method is used for the `*_outpatient_covid` signals.
 
 For a fixed location $$i$$ and time $$t$$, let $$Y_{it}$$
 denote the Covid counts and let $$N_{it}$$ be the
@@ -83,6 +108,22 @@ $$
 \hat p_{it} = 100 \cdot  \frac{Y_{it}}{N_{it}}
 $$
 
+### COVID-Like Illness
+
+The following estimation method is used for the `*_outpatient_cli` signals.
+
+For a fixed location $$i$$ and time $$t$$, let $$Y_{it}^{\text{Covid-like}}$$,
+$$Y_{it}^{\text{Flu-like}}$$, $$Y_{it}^{\text{Mixed}}$$, $$Y_{it}^{\text{Flu}}$$
+denote the correspondingly named ICD-filtered counts and let $$N_{it}$$ be the
+total count of visits (the *Denominator*). Our estimate of the CLI percentage is
+given by
+
+$$
+\hat p_{it} = 100 \cdot  \frac{Y_{it}^{\text{Covid-like}} +
+	\left((Y_{it}^{\text{Flu-like}} + Y_{it}^{\text{Mixed}}) -
+	Y_{it}^{\text{Flu}}\right)}{N_{it}}
+$$
+
 ### Day-of-Week Adjustment
 
 The fraction of visits due to COVID-19 is dependent on the day of the week. On
@@ -131,6 +172,10 @@ $$\dot{Y}_{it} = Y_{it} / \alpha_{wd(t)}.$$
 We then use these adjusted counts to estimate the COVID-19 percentage as described
 above.
 
+For the CLI indicator, we apply the same method to the numerator $$Y_{it} =
+Y_{it}^{\text{Covid-like}} + \left((Y_{it}^{\text{Flu-like}} +
+Y_{it}^{\text{Mixed}}) - Y_{it}^{\text{Flu}}\right).$$
+
 ### Backwards Padding
 
 To help with the reporting delay, we perform the following simple

docs/api/covidcast-signals/fb-survey.md

@@ -30,6 +30,8 @@ described in the sections below:
    traveling, and activities outside the home
 3. [Testing indicators](#testing-indicators) based on respondent reporting of
    their COVID test results
+4. [Mental health indicators](#mental-health-indicators), based on self-reports
+   of anxiety, depression, isolation, and worry about COVID
 
 ## Table of contents
 {: .no_toc .text-delta}
@@ -277,12 +279,17 @@ data in the estimation procedures described above.
 
 ## Behavior Indicators
 
-| Signal | Description | Survey Item |
-| --- | --- | --- |
-| `smoothed_wearing_mask` | Estimated percentage of people who wore a mask most or all of the time while in public in the past 5 days; those not in public in the past 5 days are not counted. | C14 |
-
-These indicators are based on questions in Wave 4 of the survey, introduced on
-September 8, 2020.
+| Signal | Description | Survey Item | Introduced |
+| --- | --- | --- | --- |
+| `smoothed_wearing_mask` | Estimated percentage of people who wore a mask for most or all of the time while in public in the past 5 days; those not in public in the past 5 days are not counted. | C14 | Wave 4, Sept 8, 2020 |
+| `smoothed_others_masked` | Estimated percentage of respondents who say that most or all *other* people wear masks, when they are in public and social distancing is not possible | C16 | Wave 5, Nov 24, 2020 |
+| `smoothed_travel_outside_state_5d` | Estimated percentage of respondents who report traveling outside their state in the past 5 days | C6 | Wave 1 |
+| `smoothed_work_outside_home_1d` | Estimated percentage of respondents who worked or went to school outside their home in the past 24 hours | C13 | Wave 4, Sept 8, 2020 |
+| `smoothed_shop_1d` | Estimated percentage of respondents who went to a "market, grocery store, or pharmacy" in the past 24 hours | C13 | Wave 4, Sept 8, 2020 |
+| `smoothed_restaurant_1d` | Estimated percentage of respondents who went to a "bar, restaurant, or cafe" in the past 24 hours | C13 | Wave 4, Sept 8, 2020 |
+| `smoothed_spent_time_1d` | Estimated percentage of respondents who "spent time with someone who isn't currently staying with you" in the past 24 hours | C13 | Wave 4, Sept 8, 2020 |
+| `smoothed_large_event_1d` | Estimated percentage of respondents who "attended an event with more than 10 people" in the past 24 hours | C13 | Wave 4, Sept 8, 2020 |
+| `smoothed_public_transit_1d` | Estimated percentage of respondents who "used public transit" in the past 24 hours | C13 | Wave 4, Sept 8, 2020 |
 
 Weighted versions of these signals, using the [survey weighting described
 below](#survey-weighting) to be more representative of state demographics, are
@@ -307,6 +314,27 @@ also available. These have names beginning `smoothed_w`, such as
 `smoothed_wtested_14d`.
 
 
+## Mental Health Indicators
+
+| Signal | Description | Survey Item |
+| --- | --- | --- |
+| `smoothed_anxious_5d` | Estimated percentage of respondents who reported feeling "nervous, anxious, or on edge" for most or all of the past 5 days | C8 |
+| `smoothed_depressed_5d` | Estimated percentage of respondents who reported feeling depressed for most or all of the past 5 days | C8 |
+| `smoothed_felt_isolated_5d` | Estimated percentage of respondents who reported feeling "isolated from others" for most or all of the past 5 days | C8 |
+| `smoothed_worried_become_ill` | Estimated percentage of respondents who reported feeling very or somewhat worried that "you or someone in your immediate family might become seriously ill from COVID-19" | C9 |
+| `smoothed_worried_finances` | Estimated percentage of respondents who report being very or somewhat worried about their "household's finances for the next month" | C15 |
+
+Some of these questions were present in the earliest waves of the survey, but
+only in Wave 4 did respondents consent to our use of aggregate data to
+study other impacts of COVID, such as mental health. Hence, these aggregates only
+include respondents to Wave 4 and later waves, beginning September 8, 2020.
+
+Weighted versions of these signals, using the [survey weighting described
+below](#survey-weighting) to be more representative of state demographics, are
+also available. These have names beginning `smoothed_w`, such as
+`smoothed_wdepressed_14d`.
+
+
 ## Survey Weighting
 
 Notice that the estimates defined in the previous sections are calculated with

docs/api/covidcast-signals/hospital-admissions.md

@@ -21,10 +21,10 @@ COVID-associated diagnosis code in a given location, on a given day.
 
 | Signal | Description |
 | --- | --- |
-| `smoothed_covid19` | Estimated percentage of new hospital admissions with COVID-associated diagnoses, based on electronic medical record and claims data from health system partners, smoothed in time using a Gaussian linear smoother. _This signal is no longer updated as of 1 October, 2020._  |
-| `smoothed_adj_covid19` | Same as `smoothed_covid19`, but with systematic day-of-week effects removed using [the same mechanism as in `doctor-visits`](doctor-visits.md#day-of-week-adjustment). _This signal is no longer updated as of 1 October, 2020._ |
 | `smoothed_covid19_from_claims` | Estimated percentage of new hospital admissions with COVID-associated diagnoses, based on claims data from health system partners, smoothed in time using a Gaussian linear smoother |
 | `smoothed_adj_covid19_from_claims` | Same as `smoothed_covid19_from_claims`, but with systematic day-of-week effects removed using [the same mechanism as in `doctor-visits`](doctor-visits.md#day-of-week-adjustment) |
+| `smoothed_covid19` | Estimated percentage of new hospital admissions with COVID-associated diagnoses, based on electronic medical record and claims data from health system partners, smoothed in time using a Gaussian linear smoother. _This signal is no longer updated as of 1 October, 2020._  |
+| `smoothed_adj_covid19` | Same as `smoothed_covid19`, but with systematic day-of-week effects removed using [the same mechanism as in `doctor-visits`](doctor-visits.md#day-of-week-adjustment). _This signal is no longer updated as of 1 October, 2020._ |
 
 ## Table of contents
 {: .no_toc .text-delta}

docs/api/covidcast-signals/nchs-mortality.md

@@ -0,0 +1,101 @@
+---
+title: NCHS Mortality Data
+parent: Data Sources and Signals
+grand_parent: COVIDcast Epidata API
+---
+
+# NCHS Mortality Data
+{: .no_toc}
+
+* **Source name:** `nchs-mortality`
+* **First issued:** Epiweek 50 2020 (6-12 December 2020)
+* **Number of data revisions since 19 May 2020:** 0
+* **Date of last change:** Never
+* **Available for:** state (see [geography coding docs](../covidcast_geography.md))
+* **License:** [NCHS Data Use Agreement](https://www.cdc.gov/nchs/data_access/restrictions.htm)
+
+This data source of national provisional death counts is based on death
+certificate data received and coded by the National Center for Health Statistics
+[(NCHS)](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm).
+
+| Signal | Description |
+| --- | --- |
+| `deaths_covid_incidence_num` | Number of weekly new deaths with confirmed or presumed COVID-19 |
+| `deaths_covid_incidence_prop` | Number of weekly new deaths with confirmed or presumed COVID-19, per 100,000 population |
+| `deaths_allcause_incidence_num` | Number of weekly new deaths from all causes |
+| `deaths_allcause_incidence_prop` | Number of weekly new deaths from all causes, per 100,000 population |
+| `deaths_flu_incidence_num` | Number of weekly new deaths involving Influenza and at least one of (Pneumonia, COVID-19)|
+| `deaths_flu_incidence_prop` | Number of weekly new deaths involving Influenza and at least one of (Pneumonia, COVID-19), per 100,000 population |
+| `deaths_pneumonia_notflu_incidence_num` | Number of weekly new deaths involving Pneumonia, excluding Influenza deaths |
+| `deaths_pneumonia_notflu_incidence_prop` | Number of weekly new deaths involving Pneumonia, excluding Influenza deaths, per 100,000 population |
+| `deaths_covid_and_pneumonia_notflu_incidence_num`| Number of weekly new deaths involving COVID-19 and Pneumonia, excluding Influenza |
+| `deaths_covid_and_pneumonia_notflu_incidence_prop`| Number of weekly new deaths involving COVID-19 and Pneumonia, excluding Influenza, per 100,000 population |
+|`deaths_pneumonia_or_flu_or_covid_incidence_num`| Number of weekly new deaths involving Pneumonia, Influenza, or COVID-19|
+|`deaths_pneumonia_or_flu_or_covid_incidence_prop`| Number of weekly new deaths involving Pneumonia, Influenza, or COVID-19, per 100,000 population|
+|`deaths_percent_of_expected`| Number of weekly new deaths for all causes in 2020 compared to the average number across the same week in 2017–2019|
+
+These signals are taken directly from [Table
+1](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm) without
+changes. National provisional death counts include deaths occurring within the
+50 states and the District of Columbia that have been received and coded as of
+the date specified during a given time period. The deaths are classified based
+on a new ICD-10 code. (Note that the classification is based on all the codes on
+the death certificate, not just the primary cause of death). The codes that are
+considered for each signals are described in detail
+[here](https://github.com/cmu-delphi/covidcast-indicators/blob/main/nchs_mortality/DETAILS.md#metrics-level-1-m1). We
+export the state-level data as-is in a weekly format.
+
+## Table of contents
+{: .no_toc .text-delta}
+
+1. TOC
+{:toc}
+
+## Geographical Exceptions
+
+New York City is listed as its own region in the NCHS Mortality data, but
+we don't consider NYC separately. The death counts for NYC are included in New
+York State in our reports.
+
+## Report Using Epiweeks
+
+We report the NCHS Mortality data in a weekly format (`time_type=week` \&
+`time_value=\{YYYYWW\}`, where `YYYYWW` refers to an epiweek). The CDC defines
+the [epiweek](https://wwwn.cdc.gov/nndss/document/MMWR_Week_overview.pdf) as
+seven days, from Sunday to Saturday. We check the week-ending dates provided in
+the NCHS morality data and use Python package
+[epiweeks](https://pypi.org/project/epiweeks/) to convert them into epiweek
+format.
+
+## Missingness
+
+NCHS suppresses some data to protect individual privacy and avoid publishing
+low-confidence figures. This includes data for jurisdictions where counts are
+between 1 and 9, and data for weeks where the counts are less than 50% of the
+expected number, since these provisional counts are highly incomplete and
+potentially misleading.
+
+## Lag and Backfill
+
+There is a lag in time between when the death occurred and when the death
+certificate is completed, submitted to NCHS, and processed for reporting
+purposes. The death counts for earlier weeks are continually revised and may
+increase or decrease as new and updated death certificate data are received from
+the states by NCHS. This delay can range from 1 to 8 weeks or even more.
+Some states report deaths on a daily basis, while other states report deaths weekly
+or monthly. State vital record reporting may also be affected or delayed by
+COVID-19 related response activities which make death counts not comparable
+across states. We check for updates reported by NCHS every weekday but will
+report the signals weekly (on Monday).
+
+## Source and Licensing
+
+This data was originally published by the National Center for Health Statistics,
+and is made available here as a convenience to the forecasting community under
+the terms of the original license. The NCHS places restrictions on how this
+dataset may be used: you may not attempt to identify any individual included in
+the data, whether by itself or through linking to other
+individually=identifiable data; you may only use the dataset for statistical
+reporting and analysis. The full text of the [NCHS Data Use
+Agreement](https://www.cdc.gov/nchs/data_access/restrictions.htm) is available
+from their website.

docs/index.md

@@ -16,7 +16,7 @@ group](https://delphi.cmu.edu/). The Epidata API includes:
   other epidemics tracked by Delphi through a variety of data streams.
 
 The Delphi group is extremely grateful for Pedrito Maynard-Zhang for all his
-help with the Epidata API [documentation](api/index.md).
+help with the Epidata API [documentation](api/README.md).
 
 Developers interested in modifying or extending this project are directed to
 the [Epidata API Development Guide](epidata_development.md).

docs/symptom-survey/collaboration-revision.md

@@ -12,7 +12,22 @@ pandemic. We conduct revisions in collaboration with data users, fellow
 researchers, and public health officials, to ensure the survey data best serves
 public health and research goals.
 
-## Survey Revisions
+### Current Data Use Survey
+
+To allow us to better collaborate with our academic and non-profit research
+partners, we ask that you complete [this short
+checklist](http://cmu.ca1.qualtrics.com/jfe/form/SV_dnSQYuQZDkQhJ3f) of the
+questions currently on the CMU Delphi US symptom survey. This inquiry only needs
+to be completed by one member of your project team. You can complete it again at
+any time there are variables from this survey you'd like to add to your project.
+
+This will allow us to update you of any upcoming plans for revisions or change
+to the current variables, prioritize the questions that are currently in use,
+alleviate some response burden by eliminating unused questions, and allow us to
+connect with current data users regarding their research interests and areas of
+expertise.
+
+### Proposing Revisions
 
 If there is a revision or question you would like us to consider, please fill
 out [this form requesting details about your