Since the spread of the coronavirus (COVID-19) in Italy started in February 2020, the number of cases has increased daily. However, the vast majority of people who contracted the virus have recovered. As of January 8, 2025, the number of individuals who recovered from coronavirus in Italy reached over 26.5 million. Conversely, the number of deaths also kept increasing, reaching over 198.6 thousand. When looking at the regional level, the region with the highest number of recoveries was Lombardy. The region, however, registered the highest number of coronavirus cases in the country. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Italy recorded 4081902 Coronavirus Recovered since the epidemic began, according to the World Health Organization (WHO). In addition, Italy reported 135178 Coronavirus Deaths. This dataset includes a chart with historical data for Italy Coronavirus Recovered.

Context

This data was gathered as part of the data mining project for the General Assembly Data Science course. using the API from https://rapidapi.com/astsiatsko/api/coronavirus-monitor .

Covid-19

The Covid-19 is a contagious coronavirus that hailed from Wuhan, China. This new strain of the virus has strike fear in many countries as cities are quarantined and hospitals are overcrowded. This dataset will help us understand how Covid-19 in Italy.

On March 8, 2020 - Italy’s prime minister announced a sweeping coronavirus quarantine early Sunday, restricting the movements of about a quarter of the country’s population in a bid to limit contagions at the epicenter of Europe’s outbreak.

### High Light: - Spread to various overtime in Italy - Try to predict the spread of COVID-19 ahead of time to take preventive measures

Content

• id: id number
• total_cases: the total number of cases have the coronavirus
• new_cases: the number of new cases with coronavirus in this day and time
• active_cases: Number of active cases with coronavirus
• total_deaths: the total deaths numbers by a coronavirus
• new_deaths: the number of new deaths in this day and time
• total_recovered: the number of recovered from the coronavirus
• serious_critical: numbe of the people have the coronavirus in serious critical
• total_cases_per1m: the number of confirmed cases per 1 million people than China
• record_date: Date of notification - YYYY-MM-DD HH:MM:SS

Inspiration

https://www.livescience.com/why-italy-coronavirus-deaths-so-high.html

In a study conducted in Italy in early 2021, almost half of the surveyed Italian wine experts stated that the country's enotourism industry would need approximately two years to recover from the economic impact of the coronavirus (COVID-19) pandemic.

Italy is the member state of the European Union which is set to receive the greatest amount of support funds from the NextGenerationEU economic recovery package. Italy was hit particularly hard by the first wave of the COVID-19 pandemic, with its northern and central regions having some of the highest densities of coronavirus cases of any region on the continent. The pandemic hit an already struggling Italian economy hard, with the country's GDP shrinking by nine percent in 2020.

The NextGenEU package was designed by the EU to support the economic recovery of the hardest hit member states of the bloc, such as Italy, Spain, and Greece. The funds distributed to the member states are to be invested in programs which aim to grow the European economy in ways that are ecologically sustainable, modern, digital, and socially just. Therefore, member states were require to draw up a national recovery & resilience plan in order to receive funds, with each use of funds being attached to two of the six policy pillars which inform the packages. In Italy's case, the Green Transition is by far and away the policy pillar which was chosen as the primary pillar most often when deciding how to use the funds. In terms of the secondary focus of programs chosen by Italy, social & territorial cohesion and smart, sustainable, and inclusive economic growth come out on top as the most common policy pillars.

Context:

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited:

Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Here’s a polished version suitable for a professional Kaggle dataset description:

Dataset Description

This dataset contains time-series and case-level records of the COVID-19 pandemic. The primary file is covid_19_data.csv, with supporting files for earlier records and individual-level line list data.

Files and Columns

1. covid_19_data.csv (Main File)

This is the primary dataset and contains aggregated COVID-19 statistics by location and date.

• Sno – Serial number of the record
• ObservationDate – Date of the observation (MM/DD/YYYY)
• Province/State – Province or state of the observation (may be missing for some entries)
• Country/Region – Country of the observation
• Last Update – Timestamp (UTC) when the record was last updated (not standardized, requires cleaning before use)
• Confirmed – Cumulative number of confirmed cases on that date
• Deaths – Cumulative number of deaths on that date
• Recovered – Cumulative number of recoveries on that date

2. 2019_ncov_data.csv (Legacy File)

This file contains earlier COVID-19 records. It is no longer updated and is provided only for historical reference. For current analysis, please use covid_19_data.csv.

3. COVID_open_line_list_data.csv

This file provides individual-level case information, obtained from an open data source. It includes patient demographics, travel history, and case outcomes.

4. COVID19_line_list_data.csv

Another individual-level case dataset, also obtained from public sources, with detailed patient-level information useful for micro-level epidemiological analysis.

✅ Use covid_19_data.csv for up-to-date aggregated global trends.

✅ Use the line list datasets for detailed, individual-level case analysis.

Country level datasets:

If you are interested in knowing country level data, please refer to the following Kaggle datasets:

India - https://www.kaggle.com/sudalairajkumar/covid19-in-india

South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset

Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil

USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa

Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland

Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

Acknowledgements :

Johns Hopkins University for making the data available for educational and academic research purposes

MoBS lab - https://www.mobs-lab.org/2019ncov.html

World Health Organization (WHO): https://www.who.int/

DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.

BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/

National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml

China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm

Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html

Macau Government: https://www.ssm.gov.mo/portal/

Taiwan CDC: https://sites.google....

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

As of January 1, 2025, the number of active coronavirus (COVID-19) infections in Italy was approximately 218,000. Among these, 42 infected individuals were being treated in intensive care units. Another 1,332 individuals infected with the coronavirus were hospitalized with symptoms, while approximately 217,000 thousand were in isolation at home. The total number of coronavirus cases in Italy reached over 26.9 million (including active cases, individuals who recovered, and individuals who died) as of the same date. The region mostly hit by the spread of the virus was Lombardy, which counted almost 4.4 million cases.For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

This dataset contains daily data about COVID-19 cases that occurred in Italy over the period from Jan. 29, 2020 to October 15, 2021, divided into ten age classes of the population, the first class being 0-9 years, the tenth class being >90 years. The dataset contains eight columns, namely: date (day), age class, number of new cases, number of newly hospitalized patients, number of patients entering intensive care, number of deceased patients, number of recovered patients, number of active infected patients.

Context

The novel coronavirus has taken the world by storm . Italy alone has doubled the no of cases in the last week only . The dataset is aimed to track #no of confirmed cases ,deaths and recovery per day

Content

The data was acquired from github page of John Hopkins university.

Update: As of 23rd March data , recovery cases are not getting updated . Confirmed cases and deaths remain unaffected . Will update on recovery cases in some time

Update: As of 25th March , recovery cases are getting updated . There could possibly some issues as source data is regularly changing as per blog https://github.com/CSSEGISandData/COVID-19/issues/1250

Inspiration

The data will help form interesting insights , trends if any for number of confirmed ,deaths and recovery cases . The data will be updated daily

Italy Coronavirus Cases by Age group and Sex (ICCAS)

This repository contains datasets about the number of Italian Sars-CoV-2 confirmed cases and deaths disaggregated by age group and sex. The data is (automatically) extracted from pdf reports (like this) published by Istituto Superiore di Sanità (ISS) two times a week. A link to the most recent report can be found in this page under section "Documento esteso".

PDF reports are usually published on Tuesday and Friday and contains data updated to the 4 p.m. of the day day before their release.

I wrote a script that is runned periodically in order to automatically update this repository when a new report is published. The code is hosted in a separate repository.

For feedback and issues refers to the GitHub repository.

Data folder structure

The data folder is structured as follows: data ├── by-date │ └── iccas_{date}.csv Dataset with cases/deaths updated to 4 p.m. of {date} └── iccas_full.csv Dataset with data from all reports (by date) The full dataset is obtained by concatenating all datasets in by-date and has an additional date column. If you use pandas, I suggest you to read this dataset using a multi-index on the first two columns: python import pandas as pd df = pd.read_csv('iccas_full.csv', index_col=(0, 1)) # ('date', 'age_group')

NOTE: {date} is the date the data refers to, NOT the release date of the report it was extracted from: as written above, a report is usually released with a day of delay. For example, iccas_2020-03-19.csv contains data relative to 2020-03-19 which was extracted from the report published in 2020-03-20.

Dataset details

Each dataset in the by-date folder contains the same data you can find in "Table 1" of the corresponding ISS report. This table contains the number of confirmed cases, deaths and other derived information disaggregated by age group (0-9, 10-19, ..., 80-89, >=90) and sex.

WARNING: the sum of male and female cases is not equal to the total number of cases, since the sex of some cases is unknown. The same applies to deaths.

Below, {sex} can be male or female.

All columns that can be computed from absolute counts of cases and deaths (bottom half of the table above) were all re-computed to increase precision.

IntroductionItaly is one of the high-income countries hit hardest by Covid-19. During the first months of the pandemic, Italian healthcare workers were praised by media and the public for their efforts to face the emergency, although with limited knowledge and resources. However, healthcare workers soon had to face new challenges at a time when the national health system was working hard to recover. This study focuses on this difficult period to assess the impact of the COVID-19 pandemic on the mental health of Italian healthcare workers.Materials and MethodsHealthcare workers from all Italian regions [n = 5,502] completed an online questionnaire during the reopening phase after the first wave lockdown. We assessed a set of individual-level factors (e.g., stigma and violence against HCWs) and a set of workplace-level factors (e.g., trust in the workplace capacity to handle COVID-19) that were especially relevant in this context. The primary outcomes assessed were score ≥15 on the Patient Health Questionnaire-9 and score ≥4 on the General Health Questionnaire-12, indicators of clinically significant depressive symptoms and psychological distress, respectively. Logistic regression analyses were performed on depressive symptoms and psychological distress for each individual- and workplace-level factor adjusting for gender, age, and profession.ResultsClinically significant depressive symptoms were observed in 7.5% and psychological distress in 37.9% of HCWs. 30.5% of healthcare workers reported having felt stigmatized or discriminated, while 5.7% reported having experienced violence. Feeling stigmatized or discriminated and experiencing violence due to being a healthcare worker were strongly associated with clinically significant depressive symptoms [OR 2.98, 95%CI 2.36–3.77 and OR 4.72 95%CI 3.41–6.54] and psychological distress [OR 2.30, 95%CI 2.01–2.64 and OR 2.85 95%CI 2.16–3.75]. Numerous workplace-level factors, e.g., trust in the workplace capacity to handle COVID-19 [OR 2.43, 95%CI 1.92–3.07] and close contact with a co-worker who died of COVID-19 [OR 2.05, 95%CI 1.56–2.70] were also associated with clinically significant depressive symptoms. Similar results were found for psychological distress.ConclusionsOur study emphasizes the need to address discrimination and violence against healthcare professionals and improve healthcare work environments to strengthen the national health system's capacity to manage future emergencies.

Project Summary This study compares the health behaviors of university students in France and Italy, examining how their choices and lifestyles were affected by the COVID-19 pandemic. The aim of the study is to contribute to the development of adequate public health interventions. The survey methodology employed an online questionnaire administered to French and Italian cohorts of university students. It was found that the pandemic mainly affected the mental health and sense of well-being of young people in both countries. The pandemic also altered dietary habits, alcohol consumption, sleep quality, and physical activity levels, all of which strongly affect overall health. More critical values were generally found among the Italian students. The study underscores the need to recognize the impact that the pandemic has had on the young Italian and French student populations. Data Description and Collection Overview The aim of the study was to survey some aspects of life (secondary effects of the pandemic) related to university student cohorts. Specifically, using a comparative approach, the authors investigated possible differences between the two groups residing in different countries in terms of how those groups coexisted with the pandemic. The target populations were university students enrolled in para-medical health degree programs (Nursing, Physiotherapy, etc.) and degree programs in Physical Education for Health and Prevention. Information was collected in the post-acute phase of the third cycle of the pandemic. Specifically, the French cohort was surveyed between January and February 2022, while the Italian cohort was examined between March and April 2022. Based on investigations carried out in France, a suitable number of behaviors likely to be conditioned by the pandemic were selected from the literature. Data was collected for these behaviors using standardized tools, validated and recovered in full or partial form. The tool used in the Italian context, which is part of a larger French data set, consisted of three specific sections, to which a fourth, dedicated to describing the sociographic picture of the respondents, was added. The first section examined the general experience of the students before and during the pandemic, seeking to provide an initial picture of students’ habitual daily behaviors. The second section focused on eating habits without neglecting possible deviations related to eating disorders (SCOFF questionnaire - Sick Control One Stone Fat Food) and the possible use or abuse of alcohol and cigarettes. The third framework, based on a scale known in the literature as the IFIS questionnaire (International Fitness Scale), was used to assess the level of physical activity that characterized the subjects’ daily lives. In both contexts, data were collected through web surveys using institutional directories of university degree programs. Students were invited to complete the survey by a message explaining the purposes of the initiative. This message was followed a week later by a reminder message, whose aim was to boost the participation rate. The Italian participation rate of 25.7% was much higher than the French rate, which was just over 10%. A total of 567 participant responses were collected. Of those, 70.5% were from the French cohort and the remaining 29.5% from the Italian one, percentages which reflected the general populations in the two contexts under investigation (approximately 73% and 27%). The national cohort a respondent belongs to is reflected with the variable “Nazione” (Nationality) in the data spreadsheet, with “1” denoting French and “2” denoting Italian. Selection and Organization of Shared Data The data file included contains all de-identified questionnaire responses. This file contains both the Italian and French responses to the same survey questions, but with slightly different answer formats. For example, for variable “covid_1” (Sei risultato positivo al COVID (una o più volte)? / Have you tested positive for COVID (one or more times)?), the responses of the Italian students are captured as “Si” (Yes) and “No” (No), while those of the French students are captured as “1” and “2”. The correspondent categories can be found in the full questionnaire, which is shared (in Italian) as a documentation file. In the spreadsheet, lack of answers because of skip pattern is distinguished from answers that the respondent chose not to provide. The other documentation file included is the recruitment email / consent script used (in English, French and Italian).

Context

The novel coronavirus that has infected more than 79,551 people worldwide (as of time of writing this context) is spreading rapidly, and independently, in countries outside of China, including Italy, South Korea, and Iran. The viral illness is being diagnosed among hundreds of people in South Korea, Italy and Iran who have no connection to China.

Content

In the notebook I use the time series data. Time series data columns are described in the column description.

Acknowledgements

Thanks to the Johns Hopkins University for providing this data-set for educational purposes. https://github.com/CSSEGISandData/COVID-19

Inspiration

To visualize COVID-19 spread world wide.

The data are related to Tuscany and its provinces. They covered the period from 24/2/2020 to 15/6/2022 and they were updated daily.

Two tables were created: one with data from the entire Tuscany and the other with data from each province within Tuscany (AR, FI, GR, LI, LU, MS, PI, PO, PT, SI) and each medical district of the region (aslCENTRO,aslNO,aslSE).

Inspiration

You can perform an exploratory data analysis of the data, working with Pandas or Numpy.

Interesting visualizations can be performed too using, for instance, Python libraries to plot the data of the number of deaths, dismissed patients, total and current positives, recoveries etc.

It might be useful to plot the data in time, working with different date formats too and conducting a time series analysis.

Moreover, this dataset is very good to practice queries using SQL or Pandas.

Remember to upvote if you found the dataset useful :).

Collection methodology

The data were fetched from the following link: https://dati.toscana.it/dataset/open-data-covid19.

The rows from provinces were separated from the rows related to Tuscany region and some columns were removed from the catalogue since they didn't contain any data. Furthermore, some columns were transformed from floats to integers, missing values were filled with the integer '0' and the headers were translated to English.

After entering Italy, the coronavirus (COVID-19) spread fast. The strict lockdown implemented by the government during the Spring 2020 helped to slow down the outbreak. However, the country had to face four new harsh waves of contagion. As of January 1, 2025, the total number of cases reported by the authorities reached over 26.9 million. The north of the country was mostly hit, and the region with the highest number of cases was Lombardy, which registered almost 4.4 million of them. The north-eastern region of Veneto and the southern region of Campania followed in the list. When adjusting these figures for the population size of each region, however, the picture changed, with the region of Veneto being the area where the virus had the highest relative incidence. Coronavirus in Italy Italy has been among the countries most impacted by the coronavirus outbreak. Moreover, the number of deaths due to coronavirus recorded in Italy is significantly high, making it one of the countries with the highest fatality rates worldwide, especially in the first stages of the pandemic. In particular, a very high mortality rate was recorded among patients aged 80 years or older. Impact on the economy The lockdown imposed during the Spring 2020, and other measures taken in the following months to contain the pandemic, forced many businesses to shut their doors and caused industrial production to slow down significantly. As a result, consumption fell, with the sectors most severely hit being hospitality and tourism, air transport, and automotive. Several predictions about the evolution of the global economy were published at the beginning of the pandemic, based on different scenarios about the development of the pandemic. According to the official results, it appeared that the coronavirus outbreak had caused Italy’s GDP to shrink by approximately nine percent in 2020.

Context

This dataset is a summary of the document "2021/0168 (NLE) - Allegato RIVEDUTO della DECISIONE DI ESECUZIONE DEL CONSIGLIO relativa all'approvazione della valutazione del piano per la ripresa e la resilienza dell'Italia" issued by the European Council on 2021-07-08.

It is part of a series of datasets that I already released about the Italian side of the European Union initiative known as #NextGenerationEU, specifically the Recovery and Resilience Facility.

The Italian side is called PNRR - Piano Nazionale di Ripresa e Resilience, and has been released in various versions, with and without attachments explaining the projects to be covered, and associated performance/target achievement measures.

The three datasets already released are: - EU recovery and resilience facility current status - PNRR NextGenerationEU in Italy - pres. details - PNRR NextGenerationEU in Italy - presentations

This dataset has been created to support my publications on https://robertolofaro.com.

The Recovery and Resilience Facility is accessible to the current 27 European Union (henceforth, EU) Member States, and contains both a "grants" and a "loans" components.

It was initiated in summer 2020 to support EU Member States in both recovering from the immediate impacts of the COVID-19 crisis, and to improve resilience in the event of future crisis, while also accelerating the dual digital and green transitions.

Content

This dataset contains contains the following information: | Filename | Content | | --- | --- | | Missions_Timeline_TargetReferences.csv | for each of the 527 reference numbers, contains the target deadline for completion and the page within the document where it is outlined | | Missions_Timeline_Coverage.csv | for each of the 527 reference numbers, contains the grant or loan instalment whose funding covers it | | Missions_Timeline_Funding.csv | for each one of the 10 grant and 10 loan instalments, the amount assessed as of 2021-07-08 | | Timeline.csv | for each of the 527 reference numbers, contains the timeline and association with the grant or loan instalment, as 2021-07-08 |

While being used in publications, Jupyter Notebooks will be released whenever an article or publication using the information will need a table or visualization.

Note 2021-10-09: amended in "Mission_Timeline_Coverage.csv" M2C4-22 and M2C4-23, as mistakenly the latter was reported as being covered by Loan6 and Loan10, while instead Loan6 covers M2C4-22 and Loan10 covers M2C4-23.

Acknowledgements

Information extracted manually from the document reference above, in the Italian version, containing a total of 566 pages.

Inspiration

Just a continuation of previous datasets, focused on using open data to seed analysis

Dataset from Tiziana NANIA, Rosario CARUSO, Claudia Aparecida DE MORAIS, Federica DELLAFIORE What is the level of post-traumatic stress disorder experienced by Italian University students during the Covid-19 pandemic? Result of an online survey

10.1016/j.critrevonc.2021.103373

ABSTRACT

OBJECTIVE

To date, level of Post-Traumatic Stress Disorder (PTSD) symptoms experienced from university students during the peak of Covid-19

in Italy is until under investigated. Therefore, this study aims to describe PTDS related to the Covid-19 outbreak among Italian uni-

versity students.

METHODS

A multicentre cross-sectional study was conducted, involving convenience and consecutive sampling of Italians University students.

A self-reported web questionnaire on the on-line platform Qualtrics®, was used to data collect, in March and April 2020.

RESULTS

Overall, 720 Italian University students participated to this study. The sample are major male (80.7%) with an average mean of

23.52 years. The results of data analysis highlighted the important level of PTSD experienced from Italian University students

during the Covid-19 outbreak, especially by female students that presented higher levels of PTSD. Additionally, no diff erences

were found between students in healthcare fi eld and not in healthcare fi eld.

CONCLUSIONS

The results showed, for the fi rst time, the level of PTSD experienced by Italian Universities students, triggered by the psycho-

log-ical consequences of the health emergency Covid-19. This situation requires public health interventions aimed at preventing

the early development of such mental disorders, which negatively aff

ect the growth of future generations.

Context

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited: Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Column Description

Main file in this dataset is covid_19_data.csv and the detailed descriptions are below.

covid_19_data.csv

• Sno - Serial number
• ObservationDate - Date of the observation in MM/DD/YYYY
• Province/State - Province or state of the observation (Could be empty when missing)
• Country/Region - Country of observation
• Last Update - Time in UTC at which the row is updated for the given province or country. (Not standardised and so please clean before using it)
• Confirmed - Cumulative number of confirmed cases till that date
• Deaths - Cumulative number of of deaths till that date
• Recovered - Cumulative number of recovered cases till that date

Apart from that these two files have individual level information

COVID_open_line_list_data.csv This file is originally obtained from this link

COVID19_line_list_data.csv This files is originally obtained from this link

Country level datasets If you are interested in knowing country level data, please refer to the following Kaggle datasets: South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset Italy -
https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Acknowledgements

• Johns Hopkins University for making the data available for educational and academic research purposes
• MoBS lab - https://www.mobs-lab.org/2019ncov.html World Health Organization (WHO): https://www.who.int/
• DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.
• BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
• National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
• China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
• Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
• Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
• US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
• Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
• Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
• European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
• Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
• Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

Inspiration

Some useful insi...

BackgroundThe COVID-19 pandemic has likely affected the most vulnerable groups of patients and those requiring time-critical access to healthcare services, such as patients with cancer. The aim of this study was to use time trend data to assess the impact of COVID-19 on timely diagnosis and treatment of head and neck cancer (HNC) in the Italian Piedmont region.MethodsThis study was based on two different data sources. First, regional hospital discharge register data were used to identify incident HNC in patients ≥18 years old during the period from January 1, 2015, to December 31, 2020. Interrupted time-series analysis was used to model the long-time trends in monthly incident HNC before COVID-19 while accounting for holiday-related seasonal fluctuations in the HNC admissions. Second, in a population of incident HNC patients eligible for recruitment in an ongoing clinical cohort study (HEADSpAcE) that started before the COVID-19 pandemic, we compared the distribution of early-stage and late-stage diagnoses between the pre-COVID-19 and the COVID-19 period.ResultsThere were 4,811 incident HNC admissions in the 5-year period before the COVID-19 outbreak and 832 admissions in 2020, of which 689 occurred after the COVID-19 outbreak in Italy. An initial reduction of 28% in admissions during the first wave of the COVID-19 pandemic (RR 0.72, 95% CI 0.62–0.84) was largely addressed by the end of 2020 (RR 0.96, 95% CI 0.89–1.03) when considering the whole population, although there were some heterogeneities. The gap between observed and expected admissions was particularly evident and had not completely recovered by the end of the year in older (≥75 years) patients (RR: 0.88, 0.76–1.01), patients with a Romano-Charlson comorbidity index below 2 (RR 0.91, 95% CI: 0.84–1.00), and primary surgically treated patients (RR 0.88, 95% CI 0.80–0.97). In the subgroup of patients eligible for the ongoing active recruitment, we observed no evidence of a shift toward a more advanced stage at diagnosis in the periods following the first pandemic wave.ConclusionsThe COVID-19 pandemic has affected differentially the management of certain groups of incident HNC patients, with more pronounced impact on older patients, those treated primarily surgically, and those with less comorbidities. The missed and delayed diagnoses may translate into worser oncological outcomes in these patients.