getit_all: get COVID-19 cases and other statistics

In c1au6i0/covid19census_dev: Extracts Covid-19 and other demographic metrics regarding U.S.A and Italy

Description

extracts and translates time series form the git repository of the protezione civile and combines them with other statistics related to italian population.

Usage

getit_all()

Details

Data regarding COVID-19 comes form the repository of the protezione civile and it is updated daily. Age and sex of the population (2019), first aid and medical guard visits (2018), smoking status (2018), prevalence of chronic conditions (2018), annual-household income (2017) household crowding index (2018) and body-mass index were dataset collect by ISTAT. Prevalence of types of cancer patients (2016), influenza-vaccination coverage (2019) and the number of hospital beds per 1000 people (2017) were obtained from Ministero della Salute. Note that cancer patients prevalence was calculated using region population esitmates of 2019. Data of particulate 2.5 (2017) comes from the Istituto Superiore Per La protezione Ambientale.

Value

a dataframe with following 64 variables:

date

date of data

state

state

region_code

region abbreviation

region

full name of region

lat

lat

long

long

imm

influenza vaccination coverage in the general population

imm65

influenza vaccination coverage in people age 65 or older

cmr

case-mortality rate for that region and that date (deaths/total_cases * 100)

total_cases

number of COVID-19 positive cases detected

deaths

number of deaths

tests

number of tests performed

hospitalized_with_symptoms

number of people hospitalized with symptoms, that day

intensive_care_unit

number of people in intensive care units, that day

total_hospitalized

hospitalized_with_symptoms + intensive_care_unit

home_quarantine

number of people COVID-19 positive in home quarantine, that day

total_positives

total currently positives: hospitalized_with_symptoms + intensive_care_unit + home_quarantine

change_positives

change in the number of positive cases: total_positives that day - total_positives preceding day

new_positives

number of new positive cases: total_cases that day - total_cases preceding day

recovered_released

recovered - released from hospital

people_tested

number of people tested

p_house

number of people per squared meter living in the same house

pop_tot

total population

area_km2

household crowding index (number of components of household per square meter)

pop_km2

density of population per squared kilometer

female_65m

percent of females age 65 years old or more

male_65m

percent of males age 65 years old or more

chronic_ type

percent of population with that chronic condistion

cancer_type

percent of population with that type of cancer

bweight_type

percent of people underweight, normalweight, overweight or obese

first_aid

number of peple using first aid in 3 months preceding the survey

medical_guard

number of people using medical guard in 3 months preceding the survey

bed_acute

inpatient hospital beds per 1000 people in acure care

bed_long

inpatient hospital beds per 1000 people in long care

bed_rehab

inpatient hospital beds per 1000 people in rehabilitation

bed_tot

inpatient hospital beds per 1000 people, total

netinc

median net annual households income, in euros

pm2.5

emission of pm2.5 in tons per region, 2017

Source

protezione civile, ISTAT

See Also

for details regarding the methodology of specific datasets check it_bweight, it_cancer, it_chronic, it_dem, it_firstaid, it_fl, it_fl65,it_fl, it_hospbed, it_house, it_pm2.5

c1au6i0/covid19census_dev documentation built on May 8, 2020, 1:01 a.m.