|
1 | | -# USA Facts Cases and Deaths |
| 1 | +# Google Symptoms |
2 | 2 |
|
3 | | -We import the confirmed case and deaths data from USA Facts website and export |
4 | | -the county-level data as-is. We also aggregate the data to the MSA, HRR, and |
5 | | -State levels. |
6 | | - |
7 | | -In order to avoid confusing public consumers of the data, we maintain |
8 | | -consistency how USA Facts reports the data, please refer to [Exceptions](#Exceptions). |
| 3 | +We import the confirmed case and deaths data from the Google Research's |
| 4 | +Open COVID-19 Data project and export the county-level and state-level data |
| 5 | +as-is. |
9 | 6 |
|
10 | 7 | ## Geographical Levels (`geo`) |
11 | | -* `county`: reported using zero-padded FIPS codes. There are some exceptions |
12 | | - that lead to inconsistency with the other COVIDcast data (but are necessary |
13 | | - for internal consistency), noted below. |
14 | | -* `msa`: reported using cbsa (consistent with all other COVIDcast sensors) |
15 | | -* `hrr`: reported using HRR number (consistent with all other COVIDcast sensors) |
16 | | -* `state`: reported using two-letter postal code |
| 8 | +* `county`: reported using zero-padded FIPS codes. The county level data is derived |
| 9 | +from `/subregions/state/2020_US_state_daily_symptoms_dataset.csv`. |
| 10 | +* `state`: reported using two-letter postal code. The state level data is derived from |
| 11 | +`2020_US_daily_symptoms_dataset.csv` which includes data for District of Columbia. |
17 | 12 |
|
18 | 13 | ## Metrics, Level 1 (`m1`) |
19 | | -* `confirmed`: Confirmed cases |
20 | | -* `deaths` |
| 14 | +* `Anosmia`: Google search volume for Anosmia-related searches |
| 15 | +* `Ageusia`: Google search volume for Ageusia-related searches |
21 | 16 |
|
22 | 17 | Recoveries are _not_ reported. |
23 | 18 |
|
24 | 19 | ## Metrics, Level 2 (`m2`) |
25 | | -* `new_counts`: number of new {confirmed cases, deaths} on a given day |
26 | | -* `cumulative_counts`: total number of {confirmed cases, deaths} up until the |
27 | | - first day of data (January 22nd) |
28 | | -* `incidence`: `new_counts` / population * 100000 |
29 | | - |
30 | | -All three `m2` are ultimately derived from `cumulative_counts`, which is first |
31 | | -available on January 22nd. In constructing `new_counts`, we take the first |
32 | | -discrete difference of `cumulative_counts`, and assume that the |
33 | | -`cumulative_counts` for January 21st is uniformly zero. This should not be a |
34 | | -problem, because there there is only one county with a nonzero |
35 | | -`cumulative_count` on January 22nd, with a value of 1. |
36 | | - |
37 | | -For deriving `incidence`, we use the estimated 2019 county population values |
38 | | -from the US Census Bureau. https://www.census.gov/data/tables/time-series/demo/popest/2010s-counties-total.html |
39 | | - |
40 | | -## Exceptions |
41 | | - |
42 | | -At the County (FIPS) level, we report the data _exactly_ as USA Facts reports their |
43 | | -data, to prevent confusing public consumers of the data. |
44 | | -The visualization and modeling teams should take note of these exceptions. |
45 | | - |
46 | | -### New York City |
47 | | - |
48 | | -New York City comprises of five boroughs: |
49 | | - |
50 | | -|Borough Name |County Name |FIPS Code | |
51 | | -|-------------------|-------------------|---------------| |
52 | | -|Manhattan |New York County |36061 | |
53 | | -|The Bronx |Bronx County |36005 | |
54 | | -|Brooklyn |Kings County |36047 | |
55 | | -|Queens |Queens County |36081 | |
56 | | -|Staten Island |Richmond County |36085 | |
57 | | - |
58 | | -**New York City Unallocated cases/deaths are reported by USA Facts independently.** We split them evenly among the five NYC FIPS, which results in float numbers. |
59 | | - |
60 | | -All NYC counts are mapped to the MSA with CBSA ID 35620, which encompasses |
61 | | -all five boroughs. All NYC counts are mapped to HRR 303, which intersects |
62 | | -all five boroughs (297 also intersects the Bronx, 301 also intersects |
63 | | -Brooklyn and Queens, but absent additional information, We are leaving all |
64 | | -counts in 303). |
65 | | - |
66 | | - |
67 | | -### Mismatched FIPS Codes |
68 | | - |
69 | | -There are two FIPS codes that were changed in 2015, leading to |
70 | | -mismatch between us and USA Facts. We report the data using the FIPS code used |
71 | | -by USA Facts, again to promote consistency and avoid confusion by external users |
72 | | -of the dataset. For the mapping to MSA, HRR, these two counties are |
73 | | -included properly. |
74 | | - |
75 | | -|County Name |State |"Our" FIPS |USA Facts FIPS | |
76 | | -|-------------------|---------------|-------------------|---------------| |
77 | | -|Oglala Lakota |South Dakota |46113 |46102 | |
78 | | -|Kusilvak |Alaska |02270 |02158 \& 02270 | |
79 | | - |
80 | | -Documentation for the changes made by the US Census Bureau in 2015: |
81 | | -https://www.census.gov/programs-surveys/geography/technical-documentation/county-changes.html |
82 | | - |
83 | | -Besides, Wade Hampton Census Area and Kusilvak Census Area are reported by USA Facts with FIPS 02270 and 02158 respectively, though there is always 0 cases/deaths reported for Wade Hampton Census Area (02270). According to US Census Bureau, Wade Hampton Census Area has changed name and code from Wade Hampton Census Area, Alaska (02270) to Kusilvak Census Area, Alaska (02158) effective July 1, 2015. |
84 | | -https://www.census.gov/quickfacts/kusilvakcensusareaalaska |
85 | | - |
86 | | -### Grand Princess Cruise Ship |
87 | | -Data from Grand Princess Cruise Ship is given its own dedicated line, with FIPS code 6000. We just ignore these cases/deaths. |
88 | | - |
89 | | - |
90 | | - |
91 | | - |
92 | | -## Negative incidence |
93 | | - |
94 | | -Negative incidence is possible because figures are sometimes revised |
95 | | -downwards, e.g., when a public health authority moves cases from County X |
96 | | -to County Y, County X may have negative incidence. |
97 | | - |
98 | | -## Non-integral counts |
99 | | - |
100 | | -Because the MSA and HRR numbers are computed by taking population-weighted |
101 | | -averages, the count data at those geographical levels may be non-integral. |
102 | | - |
103 | | -## Counties not in our canonical dataset |
104 | | - |
105 | | -Some FIPS codes do not appear as the primary FIPS for any ZIP code in our |
106 | | -canonical `02_20_uszips.csv`; they appear in the `county` exported files, but |
107 | | -for the MSA/HRR mapping, we disburse them equally to the counties with whom |
108 | | -they appear as a secondary FIPS code. The identification of such "secondary" |
109 | | -FIPS codes are documented in `notebooks/create-mappings.ipynb`. The full list |
110 | | -of `secondary, [mapped]` is: |
| 20 | +* `raw_search`: Google search volume reported as-is |
| 21 | +* `smoothed_search`: Google search volume using 7-day moving average |
111 | 22 |
|
112 | | -``` |
113 | | -SECONDARY_FIPS = [ # generated by notebooks/create-mappings.ipynb |
114 | | - ('51620', ['51093', '51175']), |
115 | | - ('51685', ['51153']), |
116 | | - ('28039', ['28059', '28041', '28131', '28045', '28059', '28109', |
117 | | - '28047']), |
118 | | - ('51690', ['51089', '51067']), |
119 | | - ('51595', ['51081', '51025', '51175', '51183']), |
120 | | - ('51600', ['51059', '51059', '51059']), |
121 | | - ('51580', ['51005']), |
122 | | - ('51678', ['51163']), |
123 | | - ] |
124 | | -``` |
| 23 | +This data reflects the volume of Google searches mapped to symptoms such Anosmia |
| 24 | +and Ageusia. The resulting daily dataset for each region showing the relative frequency |
| 25 | +of searches for each symptom. This signal is measured in arbitrary units that are normalized |
| 26 | +for population. Larger numbers represent higher numbers of symptom-related searches. |
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