economist_excess_deaths

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Data license: CC BY 4.0 · Data source: The Economist · About: simonw/covid-19-datasette

40 rows where country = "Brazil"

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Suggested facets: region, region_code, start_date, end_date, month, population, total_deaths, covid_deaths, expected_deaths, excess_deaths, non_covid_deaths, covid_deaths_per_100k, start_date (date), end_date (date)

country

  • Brazil · 40

cadence

Link rowid ▼ country region region_code start_date end_date year month population total_deaths covid_deaths expected_deaths excess_deaths non_covid_deaths covid_deaths_per_100k excess_deaths_per_100k excess_deaths_pct_change cadence week
1 Brazil Fortaleza 2304400 2020-01-01 2020-01-31 2020 1 2669342 1569 0 1400.75 168.25 1569 0.0 6.30305146361912 0.120114224522577 monthly  
2 Brazil Fortaleza 2304400 2020-02-01 2020-02-29 2020 2 2669342 1473 0 1347.25 125.75 1473 0.0 4.71089879078814 0.0933382816849138 monthly  
3 Brazil Fortaleza 2304400 2020-03-01 2020-03-31 2020 3 2669342 1693 22 1550.0 143.0 1671 0.824173148288979 5.35712546387836 0.0922580645161291 monthly  
4 Brazil Fortaleza 2304400 2020-04-01 2020-04-30 2020 4 2669342 2187 629 1593.5 593.5 1558 23.5638595578985 22.2339437958868 0.372450580483213 monthly  
5 Brazil Manaus 1302603 2020-01-01 2020-01-31 2020 1 2182763 1121 0 933.0 188.0 1121 0.0 8.61293690611395 0.201500535905681 monthly  
6 Brazil Manaus 1302603 2020-02-01 2020-02-29 2020 2 2182763 975 0 875.75 99.25 975 0.0 4.54698929750962 0.113331430202683 monthly  
7 Brazil Manaus 1302603 2020-03-01 2020-03-31 2020 3 2182763 1047 1 923.0 124.0 1046 0.0458134941814572 5.68087327850069 0.134344528710726 monthly  
8 Brazil Manaus 1302603 2020-04-01 2020-04-30 2020 4 2182763 2656 410 950.5 1705.5 2246 18.7835326143974 78.1349143264752 1.79431877958969 monthly  
9 Brazil Recife 2611606 2020-01-01 2020-01-31 2020 1 1645727 1330 0 1086.75 243.25 1330 0.0 14.7807017810366 0.223832528180354 monthly  
10 Brazil Recife 2611606 2020-02-01 2020-02-29 2020 2 1645727 1242 0 1030.75 211.25 1242 0.0 12.8362723586597 0.20494785350473 monthly  
11 Brazil Recife 2611606 2020-03-01 2020-03-31 2020 3 1645727 1384 6 1158.25 225.75 1378 0.364580516695661 13.7173419406742 0.194906108353119 monthly  
12 Brazil Recife 2611606 2020-04-01 2020-04-30 2020 4 1645727 1787 216 1042.0 745.0 1571 13.1248986010438 45.2687474897112 0.714971209213052 monthly  
13 Brazil Rio de Janeiro 3304557 2020-01-01 2020-01-31 2020 1 6718903 4731 0 4824.75 -93.75 4731 0.0 -1.39531706291935 -0.0194310586040728 monthly  
14 Brazil Rio de Janeiro 3304557 2020-02-01 2020-02-29 2020 2 6718903 4489 0 4253.75 235.25 4489 0.0 3.50131561655229 0.0553041434028798 monthly  
15 Brazil Rio de Janeiro 3304557 2020-03-01 2020-03-31 2020 3 6718903 4872 43 4617.75 254.25 4829 0.639985426192341 3.78409987463727 0.0550592821179146 monthly  
16 Brazil Rio de Janeiro 3304557 2020-04-01 2020-04-30 2020 4 6718903 6929 1655 4825.5 2103.5 5274 24.6319972174029 31.3071940464091 0.43591337685214 monthly  
17 Brazil São Paulo 3550308 2020-01-01 2020-01-31 2020 1 12252023 8121 0 6531.75 1589.25 8121 0.0 12.9713272657095 0.243311516821679 monthly  
18 Brazil São Paulo 3550308 2020-02-01 2020-02-29 2020 2 12252023 6076 0 5792.75 283.25 6076 0.0 2.31186311027983 0.0488973285572483 monthly  
19 Brazil São Paulo 3550308 2020-03-01 2020-03-31 2020 3 12252023 7639 307 6381.5 1257.5 7332 2.50570864909411 10.2636111603774 0.197053984173 monthly  
20 Brazil São Paulo 3550308 2020-04-01 2020-04-30 2020 4 12252023 9323 1738 6430.0 2893.0 7585 14.1854124824937 23.6124271069357 0.449922239502333 monthly  
5254 Brazil Fortaleza 2304400 2020-01-01 2020-01-31 2020 1 2669342 1569 0 1400.75 168.25 1569       monthly  
5255 Brazil Fortaleza 2304400 2020-02-01 2020-02-29 2020 2 2669342 1473 0 1347.25 125.75 1473       monthly  
5256 Brazil Fortaleza 2304400 2020-03-01 2020-03-31 2020 3 2669342 1693 22 1550.0 143.0 1671       monthly  
5257 Brazil Fortaleza 2304400 2020-04-01 2020-04-30 2020 4 2669342 2187 629 1593.5 593.5 1558       monthly  
5258 Brazil Manaus 1302603 2020-01-01 2020-01-31 2020 1 2182763 1121 0 933.0 188.0 1121       monthly  
5259 Brazil Manaus 1302603 2020-02-01 2020-02-29 2020 2 2182763 975 0 875.75 99.25 975       monthly  
5260 Brazil Manaus 1302603 2020-03-01 2020-03-31 2020 3 2182763 1047 1 923.0 124.0 1046       monthly  
5261 Brazil Manaus 1302603 2020-04-01 2020-04-30 2020 4 2182763 2656 410 950.5 1705.5 2246       monthly  
5262 Brazil Recife 2611606 2020-01-01 2020-01-31 2020 1 1645727 1330 0 1086.75 243.25 1330       monthly  
5263 Brazil Recife 2611606 2020-02-01 2020-02-29 2020 2 1645727 1242 0 1030.75 211.25 1242       monthly  
5264 Brazil Recife 2611606 2020-03-01 2020-03-31 2020 3 1645727 1384 6 1158.25 225.75 1378       monthly  
5265 Brazil Recife 2611606 2020-04-01 2020-04-30 2020 4 1645727 1787 216 1042.0 745.0 1571       monthly  
5266 Brazil Rio de Janeiro 3304557 2020-01-01 2020-01-31 2020 1 6718903 4731 0 4824.75 -93.75 4731       monthly  
5267 Brazil Rio de Janeiro 3304557 2020-02-01 2020-02-29 2020 2 6718903 4489 0 4253.75 235.25 4489       monthly  
5268 Brazil Rio de Janeiro 3304557 2020-03-01 2020-03-31 2020 3 6718903 4872 43 4617.75 254.25 4829       monthly  
5269 Brazil Rio de Janeiro 3304557 2020-04-01 2020-04-30 2020 4 6718903 6929 1655 4825.5 2103.5 5274       monthly  
5270 Brazil São Paulo 3550308 2020-01-01 2020-01-31 2020 1 12252023 8121 0 6531.75 1589.25 8121       monthly  
5271 Brazil São Paulo 3550308 2020-02-01 2020-02-29 2020 2 12252023 6076 0 5792.75 283.25 6076       monthly  
5272 Brazil São Paulo 3550308 2020-03-01 2020-03-31 2020 3 12252023 7639 307 6381.5 1257.5 7332       monthly  
5273 Brazil São Paulo 3550308 2020-04-01 2020-04-30 2020 4 12252023 9323 1738 6430.0 2893.0 7585       monthly  

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CREATE TABLE [economist_excess_deaths] (
   [country] TEXT,
   [region] TEXT,
   [region_code] TEXT,
   [start_date] TEXT,
   [end_date] TEXT,
   [year] INTEGER,
   [month] INTEGER,
   [population] INTEGER,
   [total_deaths] INTEGER,
   [covid_deaths] INTEGER,
   [expected_deaths] FLOAT,
   [excess_deaths] FLOAT,
   [non_covid_deaths] INTEGER,
   [covid_deaths_per_100k] FLOAT,
   [excess_deaths_per_100k] FLOAT,
   [excess_deaths_pct_change] FLOAT,
   [cadence] TEXT
, [week] INTEGER);
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